# Research on the Social Security and Elderly Care System under the Background of Big Data

Authors: Jinyan Li
Journal: Mobile Information Systems
 (2023)

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## Abstract

In order to solve some of the problems of old-age care in the current society, combined with data mining technology, in-depth research on the current social security field of the elderly has been carried out. At present, the process of urbanization is accelerating. With the rapid increase of the urban population, the aging problem is becoming more and more serious. The pension model has attracted attention from all parties. Elderly care is facing serious aging problems and low quality and efficiency of elderly care services. One of the problems encountered in the development of elderly care is how to alleviate the problems caused by the aging of the population and improve the quality and level of elderly care services. The application of the smart pension model provides new ideas for solving this problem.


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## Body

## 1. Introduction

This section briefly introduces the research purpose and research background of the article. In recent years, big data has set off a huge development wave in the world, which has attracted great attention from academia, industry, and governments of various countries. People do not care about the amount of data. Data are only used to realize their value and to improve business capabilities. Social insurance for the elderly in the country through a series of economic, health, and social service measures aimed at exiting the labor sector or the inability to work. In particular, warranty protection is a part of ensuring the basic life protection of the government and the people of the government. This security system is developed according to the needs of the elderly, which is a new phenomenon in human history. The 21st century is faced with risks caused by negative population growth [1]. The problem of population aging is becoming more and more serious. Retired elderly and empty-nest elderly continue to grow substantially. The elderly facing various difficulties in the elderly care, and the demand for personalized elderly care is increasingly urgent [2]. “Family pension” has always been the preferred method of pension. However, in recent years, with the intensification of population aging and the implementation of the family planning policy, the number of children has decreased, leading to the gradual weakening of the family’s pension function, resulting in “community pension,” “institutional pension,” and other pension models. The traditional social elderly care model lacks personalized elderly care services, and the elderly cannot get a high-quality life in their later years [3]. The hospital ward-style, single, mechanized management mode and closed environment bring the negative life experience of no fun to the elderly, making the elderly afraid of social pensions. Based on the background of big data, this article studies the social security and old-age care system. Therefore, traditional elderly care services cannot meet the new requirements of the elderly for elderly care services. Enriching and innovating traditional elderly care services is also a task of smart elderly care. Therefore, a new type of IoT smart pension was chosen. The application of data mining technology in social security for the elderly is shown in Figure 1. On this basis, this article studies the development process, current situation, and problems of smart old-age care and puts forward countermeasures and suggestions. It analyzes real cases, summarizes the successful experience and difficulties of smart old-age care, analyzes and studies the current problems of smart old-age care, and puts forward targeted opinions and suggestions, which have strong practical guiding significance and provide theoretical support for related smart old-age care projects. This article provides some references for smart pension project managers to promote the application of smart pensions in residents’ lives, promote smart pensions to solve pension problems, ease the burden of aging on the elderly, children, and society, and provide high efficiency for the elderly, high-quality, high-security new elderly care services, thereby promoting family harmony and maintaining social stability. Under the new situation and requirements, it is necessary to review the history, sort out the policies, analyze the current situation, better address the problems, study the countermeasures, and look forward to the future. Knowledge of the status and role in the service system: first of all, in the theoretical basis and analysis framework of Section 1, out of the problems and deficiencies existing in the supply link of China’s existing elderly care service model, the relationship between the main body of supply and the main body of demand, and the risks in the optimization and upgrading of the elderly care service system, thinking about control and the ideal vision of the old-age service system, knowledge, and methods of gradual theory and cooperative governance theory, we arrive at the analytical framework of this article. Second, based on the research framework of this article, the second section systematically sorts out the practical basis and theoretical logic of using smart old-age care to promote the optimization of the old-age service system. Third, based on identifying the ideal picture theoretically and logically, this article summarizes the existing progress of smart elderly care in promoting the optimization of China’s elderly care service system from the perspective of practical exploration and also tests, presents, and details the theoretical analysis framework of this article.Figure 1
The application of data mining technology in social security for the elderly.Faced with the severe aging situation, the original contribution and significance of this study are based on the research and analysis of the existing domestic pension models, the differentiation characteristics of elderly groups, and the supply of elderly care services and on the proposal to build an information technology-based, accessible high-quality elderly care service system. A soft environment platform is used to solve a series of existing problems in the social pension service system. By combining information technology, it expounds on the necessity and effectiveness of using the big data platform and explores the framework of the urban residential elderly care service system based on the big data platform. In this way, various types of elderly care service resources can be reasonably allocated, the operating costs of social elderly care can be shared, the integration of the elderly care service industry will be promoted, and the social elderly care service system will tend to develop sustainably.


## 2. Literature Review

This section is a literature review of acting in this field. Jayalakshmi et al. believe that big data mining and sharing technology have been successfully applied in many fields, including elderly care [4]. Park et al. believe that the needs of the elderly are different. The empty-nest and solitary elderly prefer daily care and emotional companionship. In addition, the elderly also need medical care and hospice care, while the disabled elderly need mainly professional medical and nursing care [5]. Khan et al. believe that big data technology can provide the elderly with multi-level and all-round elderly care options based on the individual needs of the elderly. It can also provide high-quality, personalized, and customized elderly care services for the elderly [6]. Animasahun et al. stated that it is possible to collect, filter, classify, store and track the preferences of the elderly on the data generated by the elderly on trading platforms, social media, and other platforms. Through the analysis of the results, the regularity of the behavior of the elderly is found, and suitable elderly care models and customized elderly care services are recommended [7, 8]. Song and Shin stated that big data technology has enriched the old life of the elderly to a certain extent, provided them with a social platform for communication, and fundamentally promoted the development of social security for the elderly [9]. Chang et al. believe that people often mistakenly believe that the elderly lack the willingness or ability to use the Internet, but in fact, more and more elderly people are using Internet-related technologies more and more frequently and deeply [10]. Mehta Charchit et al. said that through the daily browsing of the elderly, they provide the elderly with intelligent recommendations from hobbies to recuperation and health care. According to the different needs of the elderly in life services, physical and mental health, and hobbies, organizing various online and offline activities can not only help the elderly find like-minded hobbyists but also enrich their daily life of the elderly [11]. Chang et al. believe that as the elderly’s ability to use new technologies improves, it will also be more conducive to collecting available data. Big data technology can build an interest exchange platform for the elderly with the same interests, provide targeted personalized services and needs, improve the sense of belonging to the social group of the elderly, and formulate personalized humanity for the actual needs of each elderly care [10]. Dushi et al. believe that this not only fills up the poverty and emptiness of the elderly in spiritual life but also gives them self-esteem and a certain sense of belonging [12].


## 3. Methods

This section provides a detailed description of the overall research method.


### 3.1. The Background and Significance of Social Security and Nursing for the Elderly

Social insurance for the elderly is one of the main elements of the social security system, namely, social insurance, social security, and social security for the elderly who are not able to leave their jobs or work [13]. The elements of a higher social security system include, but are not limited to, income security, health insurance, improved standard of living (benefits), security, and insurance. The social criteria for old age are generally based on the physiological and social functions of the human being. Measures aimed at improving the social security system for the elderly: social security, i.e., the redistribution and redistribution by the state and society of national income, in particular the income of the population. Social workers, especially the fundamental right to life, find it difficult to guarantee social security. Social insurance is essentially a means of maintaining social justice and promoting the stable development of society, which generally includes social insurance, social assistance, social protection, and order; social security is at the heart of social insurance. Therefore, efforts should be made to expand the coverage of social security, enrich the structure and content of social security, and actively promote the process of social security. We can improve social security and financing through various channels and raise the reasonable retirement age to ensure the sustainable development of pensions [14]. Emphasis has been placed on integrating the security and protection of services as a foundation for the quality of services provided to older people, which ultimately results in consumer behaviors that increase financial security, in particular by enabling the elderly to have access to retirement services [15]. The phenomenon of aging is becoming more and more serious, and the number of elderly people is increasing day by day. The existing number of elderly care services cannot meet the needs of the elderly, as shown in Figures 2 and 3.Figure 2
Statistics and forecast of the number of people aged 65 and over (ten thousand people).Figure 3
Statistics of the old-age dependency ratio.The second is to put forward new requirements for the quality of elderly care services. Nowadays, the demand for elderly care services covers medical care, housekeeping, guardianship, security, distribution, etc., covering a wide range [16]. However, traditional elderly care services are single and inadequate. People’s access to knowledge and information is more convenient and more extensive, which is a technological leap. The Internet of Things uses the technology of implanted chips to enable people to perceive those ordinary “things” more acutely, realize free control, and bring great convenience to life [17]. Great efforts have also been made to promote the development of the Internet of Things. A number of policies have been issued and a number of plans have been formulated. Under this premise, the Internet of Things will continue to develop at a high speed and the development speed will increase year by year. The Internet of Things industry will open up more room for development, and the Internet of Things industry will also bring considerable economic benefits [18, 19], as shown in Figure 4.Figure 4
Growth rate and forecast of the Internet of Things industry.


### 3.2. Using Big Data Algorithms to Build a Social Security Platform

The overall framework of the big data analysis platform is shown in Figure5.Figure 5
The overall framework of the big data analysis platform.According to the age characteristics of the elderly, the elderly can be divided into three stages, as shown in Table1.Table 1
Classification of age characteristics of the elderly.
AgeFeature60–69Young elderly70–85Mid-aged elderlyOver 85ElderlyIn order to better understand and design different caching strategies, four commonly used data access modes are summarized from the application. As shown in Table2, different data access modes have different access characteristics [20].(1)
Recency-friendly data access mode: the basic form is(1)Q1,Q2,…,Qk−1,Qk,Qk,Qk−1,…,Q2,Q1N.Among them,k represents the number of data blocks, and N represents the number of iterative visits. This data access mode has good data locality, that is, the currently accessed data has a high probability of being accessed again shortly. This access mode is very common in big data applications.(2)
Frequency-friendly data access mode: the basic form is(2)Q1,Q2,…,Qk−1,QkAPεQ1,Q2,…,QmN.In this access mode, the data blocks in the system are accessed at an uneven frequency [21]. Caching data blocks that are accessed more frequently will bring greater access performance improvements.(3)
Loop data access mode: the basic form is(3)Q1,Q2,…,Qk−1,QkN.Table 2
Common data access modes.
Data access modesExpressions of formulaRecency friendlyQ1,Q2,…,Qk−1,Qk,Qk,Qk−1,…,Q2,Q1NFrequency friendlyQ1,Q2,…,Qk−1,QkAPεQ1,Q2,…,QmNLoopQ1,Q2,…,Qk−1,QkNMixedQ1,Q2,…,Qk,Qk,…,Q2,Q1N1Q1,Q2,…,QK−1,QKAPεH1,H2,…,HmN2NAmong them,k represents the number of data blocks that are cyclically accessed. The data access mode indicates that the data block is continuously accessed in a cyclic manner. This access mode is more common in big data iterative computing applications, such as k-means and PageRank.Mixed data access mode: the basic form is(4)Q1,Q2,…,Qk,Qk,…,Q2,Q1N1Q1,Q2,…,QK−1,QKAPεH1,H2,…,HmN2N.The data access mode is usually a mixture of multiple access modes.The study found that the most basic LRU caching strategy is only effective for the recency-friendly data access mode; the LRFU strategy can achieve stable performance for recency-friendly, frequency-friendly, and loop access modes; the LRS strategy for the frequency-friendly and loop access mode can achieve relatively high performance; the ARC strategy can achieve better results for mixed, recency-friendly, frequency-friendly access modes. These efficient caching strategies have the potential to accelerate big data applications such as upper-layer Spark and Hadoop.Table2 lists the mixture of the recency-friendly access mode and frequency-friendly access mode. This access mode is also relatively common because the actual big data application generally contains a variety of different access modes [22]. Corresponding to the above-mentioned different data access modes, corresponding cache scheduling strategies can be adopted in actual applications to achieve the most efficient read and write access performance as much as possible. Conditional Random Field (CRF) is the basic model of natural language processing, which is widely used in Chinese word segmentation, named entity recognition, part-of-speech tagging, and other tagging scenarios; LRU is the abbreviation of Least Recently Used, which is the least recently used. It is a commonly used page replacement algorithm, and selects the pages that have not been used for the longest time to be eliminated; LFU (Least Frequently Used (LFU) page replacement algorithm). That is, the page replacement algorithm is used the least frequently, and the page with the smallest reference count is required to be replaced during page replacement because the frequently used page should have a larger reference count. However, some pages are used a lot at the beginning but are not used anymore. Such pages will remain in memory for a long time, so the reference count register can be periodically shifted one bit to the right to form an exponentially decaying average number of uses; The algorithm combines the use time and the number of times (Least Recently Used Least Frequently Used LRFU: each page in the buffer saves a CRF (Cambined Recency and Frequency value) to indicate the possibility of the block being continued to be accessed; each page Also use LAST to record the last time it was accessed. The study found that the most basic LRU (Least Recently Used) caching strategy is only effective for recency-friendly data access modes; the LRFU (Least Recently/Frequently Used) strategy can achieve stable performance for recency-friendly, frequency-friendly, and loop access modes; the LRS (Lese Rechtschreib Schwache) strategy for the frequency-friendly and loop access mode can achieve relatively high performance; the ARC (Arc Readable Configuration) strategy can achieve better results for mixed, recency-friendly, frequency-friendly access modes. These efficient caching strategies have the potential to accelerate big data applications such as upper-layer Spark and Hadoop. LRFU combines the LRU strategy and the LFU (Least Frequently Used) strategy. When replacement occurs, LRFU replaces the data block with the smallest CRF (Conditional Random Field). CRF is an attribute value associated with each data block, indicating the probability that the data block will be accessed in the near future. CRF can be calculated by the following formula:(5)CRFtbaseb=∑i=1KFtbase−tb1.In the abovementioned formula, CRF integrates the contribution value of each access to a data block.F(t) is defined in formula (6). F(t) represents the contribution value brought by a data block access, and the contribution value gradually decreases with the passage of time:(6)Ft=1attenuationstep×t.Among them, the step is a weight adjustment parameter. If its value is equal to 0, then LRFU degenerates into LFU; if step = 1, then LRFU evolves into LRU. Therefore, the step controls whether the behavior of LRFU is closer to LRU or LFU.In the implementation, two adjustable parameter attributes are set in the Alluxio system: worker.evictor.lrfu.step.factor and worker.evictor.lrfu.step.attenuation.factor, corresponding to step and attenuation, respectively. Therefore, the user can control whether LRFU is closer to LRU or closer to LFU by adjusting these two parameters. In addition, in the implementation process, the overhead caused by updating the CRF is also reduced. If all the CRFs must be updated for each time stamp change, it will bring a huge overhead. Only the size of the CRF needs to be updated in the following two cases:(1)
The data block is accessed or submitted. The latest CRF can be updated according to the size of the original CRF, that is,(7)CRF=CRFlast×h−tlast+Θ.Among them,CRFlast is the size of the last updated CRF value and tlast is the last update time of the data block.(2)
When a replacement operation occurs: in this case, the CRF values of all data blocks will be updated. All data blocks must be sorted according to the latest CRF to select the data block with the smallest CRF. The latest CRF can be obtained by the following formula:(8)CRF=CRFlast×Ft−tlast.LRFU can be used as either LRU or LFU. In theory, LRFU can achieve better results in both recency-friendly and frequency-friendly data access modes. LRFU (step = 0) does not consider the sequence information of any data block access, so when all data blocks have the same access frequency, LRFU will randomly replace the data blocks. For the loop access mode, LRFU (step = 0) can always ensure that some data blocks are not replaced in the memory, thereby achieving a certain percentage of hit rates. Therefore, LRFU (step = 0) can achieve better performance results in the loop access mode. HAMA is an early work of distributed parallel dense matrix multiplication on the Hadoop MapReduce platform. However, its algorithm design contains a lot of data reading and writing with the underlying HBasse, and the segmentation of the matrix is relatively simple. Therefore, the performance of HAMA is relatively inefficient, and the maximum matrix dimension of the experiment is only 5000 × 5000. Aiming at the problem of distributed matrix multiplication on a data parallel platform, two distributed matrix segmentation strategies are proposed, which are called RMM (Replication-based Matrix Multiplication) and CPMM (CrossProduct-based Matrix Multiplication). For the convenience of the following description, the two input multiplication matrices and their splitting methods are agreed as follows: the number of sub-blocks in matrixA is Mb×Kb, and the number of sub-blocks in matrix B is Kb×Nb. The matrix multiplication C=A∗B can be written as(9)Ci,j=∑kAi,kBk,j,i<Mb<k,Kb<j.For RMM, there is only one shuffle step in the entire execution process. In the process of calculating the result matrixC, the input sub-matrix blocks Ai,k and Bk,j will generate multiple copies. Specifically, there are a total of Nb copies of sub-blocks of matrix A and Mbcopies of sub-blocks of matrix B, and the amount of data in the shuffle is NbA+MbB. CPMM has two shuffle stages, similar to the amount of intermediate process data that can be analyzed. In terms of concurrency, the concurrency of CPMM sub-block matrix multiplication is Kb, and the concurrency of RMM is Mb×Nb. The strategy also includes two shuffle stages, the first shuffle execution process is very similar to RMM, and the second shuffle execution process is similar to CPMM. In this way, CRMM can make a better trade-off between concurrency and shuffle data volume. When the input matrix dimension k is much smaller than m and n, the implementation of CRMM will be equivalent to RMM, and when the dimension k is much larger than the dimensions m and n, the implementation of CRMM will be equivalent to CPMM. In addition, for the case of multiplying a large matrix by a small matrix, in order to avoid the data of the two matrices from being shuffled through the network, a matrix multiplication on the Map side is proposed, called MapMM (Map-side Matrix Multiplication).For the three execution strategies CPMM, RMM, and CRMM, the theoretical analysis in Figure6 shows that CRMM can better choose between concurrency and the amount of shuffle data.Figure 6
The relationship between the concurrency of the three matrix multiplication execution strategies and the amount of data transmitted by the shuffle.


## 3.1. The Background and Significance of Social Security and Nursing for the Elderly

Social insurance for the elderly is one of the main elements of the social security system, namely, social insurance, social security, and social security for the elderly who are not able to leave their jobs or work [13]. The elements of a higher social security system include, but are not limited to, income security, health insurance, improved standard of living (benefits), security, and insurance. The social criteria for old age are generally based on the physiological and social functions of the human being. Measures aimed at improving the social security system for the elderly: social security, i.e., the redistribution and redistribution by the state and society of national income, in particular the income of the population. Social workers, especially the fundamental right to life, find it difficult to guarantee social security. Social insurance is essentially a means of maintaining social justice and promoting the stable development of society, which generally includes social insurance, social assistance, social protection, and order; social security is at the heart of social insurance. Therefore, efforts should be made to expand the coverage of social security, enrich the structure and content of social security, and actively promote the process of social security. We can improve social security and financing through various channels and raise the reasonable retirement age to ensure the sustainable development of pensions [14]. Emphasis has been placed on integrating the security and protection of services as a foundation for the quality of services provided to older people, which ultimately results in consumer behaviors that increase financial security, in particular by enabling the elderly to have access to retirement services [15]. The phenomenon of aging is becoming more and more serious, and the number of elderly people is increasing day by day. The existing number of elderly care services cannot meet the needs of the elderly, as shown in Figures 2 and 3.Figure 2
Statistics and forecast of the number of people aged 65 and over (ten thousand people).Figure 3
Statistics of the old-age dependency ratio.The second is to put forward new requirements for the quality of elderly care services. Nowadays, the demand for elderly care services covers medical care, housekeeping, guardianship, security, distribution, etc., covering a wide range [16]. However, traditional elderly care services are single and inadequate. People’s access to knowledge and information is more convenient and more extensive, which is a technological leap. The Internet of Things uses the technology of implanted chips to enable people to perceive those ordinary “things” more acutely, realize free control, and bring great convenience to life [17]. Great efforts have also been made to promote the development of the Internet of Things. A number of policies have been issued and a number of plans have been formulated. Under this premise, the Internet of Things will continue to develop at a high speed and the development speed will increase year by year. The Internet of Things industry will open up more room for development, and the Internet of Things industry will also bring considerable economic benefits [18, 19], as shown in Figure 4.Figure 4
Growth rate and forecast of the Internet of Things industry.


## 3.2. Using Big Data Algorithms to Build a Social Security Platform

The overall framework of the big data analysis platform is shown in Figure5.Figure 5
The overall framework of the big data analysis platform.According to the age characteristics of the elderly, the elderly can be divided into three stages, as shown in Table1.Table 1
Classification of age characteristics of the elderly.
AgeFeature60–69Young elderly70–85Mid-aged elderlyOver 85ElderlyIn order to better understand and design different caching strategies, four commonly used data access modes are summarized from the application. As shown in Table2, different data access modes have different access characteristics [20].(1)
Recency-friendly data access mode: the basic form is(1)Q1,Q2,…,Qk−1,Qk,Qk,Qk−1,…,Q2,Q1N.Among them,k represents the number of data blocks, and N represents the number of iterative visits. This data access mode has good data locality, that is, the currently accessed data has a high probability of being accessed again shortly. This access mode is very common in big data applications.(2)
Frequency-friendly data access mode: the basic form is(2)Q1,Q2,…,Qk−1,QkAPεQ1,Q2,…,QmN.In this access mode, the data blocks in the system are accessed at an uneven frequency [21]. Caching data blocks that are accessed more frequently will bring greater access performance improvements.(3)
Loop data access mode: the basic form is(3)Q1,Q2,…,Qk−1,QkN.Table 2
Common data access modes.
Data access modesExpressions of formulaRecency friendlyQ1,Q2,…,Qk−1,Qk,Qk,Qk−1,…,Q2,Q1NFrequency friendlyQ1,Q2,…,Qk−1,QkAPεQ1,Q2,…,QmNLoopQ1,Q2,…,Qk−1,QkNMixedQ1,Q2,…,Qk,Qk,…,Q2,Q1N1Q1,Q2,…,QK−1,QKAPεH1,H2,…,HmN2NAmong them,k represents the number of data blocks that are cyclically accessed. The data access mode indicates that the data block is continuously accessed in a cyclic manner. This access mode is more common in big data iterative computing applications, such as k-means and PageRank.Mixed data access mode: the basic form is(4)Q1,Q2,…,Qk,Qk,…,Q2,Q1N1Q1,Q2,…,QK−1,QKAPεH1,H2,…,HmN2N.The data access mode is usually a mixture of multiple access modes.The study found that the most basic LRU caching strategy is only effective for the recency-friendly data access mode; the LRFU strategy can achieve stable performance for recency-friendly, frequency-friendly, and loop access modes; the LRS strategy for the frequency-friendly and loop access mode can achieve relatively high performance; the ARC strategy can achieve better results for mixed, recency-friendly, frequency-friendly access modes. These efficient caching strategies have the potential to accelerate big data applications such as upper-layer Spark and Hadoop.Table2 lists the mixture of the recency-friendly access mode and frequency-friendly access mode. This access mode is also relatively common because the actual big data application generally contains a variety of different access modes [22]. Corresponding to the above-mentioned different data access modes, corresponding cache scheduling strategies can be adopted in actual applications to achieve the most efficient read and write access performance as much as possible. Conditional Random Field (CRF) is the basic model of natural language processing, which is widely used in Chinese word segmentation, named entity recognition, part-of-speech tagging, and other tagging scenarios; LRU is the abbreviation of Least Recently Used, which is the least recently used. It is a commonly used page replacement algorithm, and selects the pages that have not been used for the longest time to be eliminated; LFU (Least Frequently Used (LFU) page replacement algorithm). That is, the page replacement algorithm is used the least frequently, and the page with the smallest reference count is required to be replaced during page replacement because the frequently used page should have a larger reference count. However, some pages are used a lot at the beginning but are not used anymore. Such pages will remain in memory for a long time, so the reference count register can be periodically shifted one bit to the right to form an exponentially decaying average number of uses; The algorithm combines the use time and the number of times (Least Recently Used Least Frequently Used LRFU: each page in the buffer saves a CRF (Cambined Recency and Frequency value) to indicate the possibility of the block being continued to be accessed; each page Also use LAST to record the last time it was accessed. The study found that the most basic LRU (Least Recently Used) caching strategy is only effective for recency-friendly data access modes; the LRFU (Least Recently/Frequently Used) strategy can achieve stable performance for recency-friendly, frequency-friendly, and loop access modes; the LRS (Lese Rechtschreib Schwache) strategy for the frequency-friendly and loop access mode can achieve relatively high performance; the ARC (Arc Readable Configuration) strategy can achieve better results for mixed, recency-friendly, frequency-friendly access modes. These efficient caching strategies have the potential to accelerate big data applications such as upper-layer Spark and Hadoop. LRFU combines the LRU strategy and the LFU (Least Frequently Used) strategy. When replacement occurs, LRFU replaces the data block with the smallest CRF (Conditional Random Field). CRF is an attribute value associated with each data block, indicating the probability that the data block will be accessed in the near future. CRF can be calculated by the following formula:(5)CRFtbaseb=∑i=1KFtbase−tb1.In the abovementioned formula, CRF integrates the contribution value of each access to a data block.F(t) is defined in formula (6). F(t) represents the contribution value brought by a data block access, and the contribution value gradually decreases with the passage of time:(6)Ft=1attenuationstep×t.Among them, the step is a weight adjustment parameter. If its value is equal to 0, then LRFU degenerates into LFU; if step = 1, then LRFU evolves into LRU. Therefore, the step controls whether the behavior of LRFU is closer to LRU or LFU.In the implementation, two adjustable parameter attributes are set in the Alluxio system: worker.evictor.lrfu.step.factor and worker.evictor.lrfu.step.attenuation.factor, corresponding to step and attenuation, respectively. Therefore, the user can control whether LRFU is closer to LRU or closer to LFU by adjusting these two parameters. In addition, in the implementation process, the overhead caused by updating the CRF is also reduced. If all the CRFs must be updated for each time stamp change, it will bring a huge overhead. Only the size of the CRF needs to be updated in the following two cases:(1)
The data block is accessed or submitted. The latest CRF can be updated according to the size of the original CRF, that is,(7)CRF=CRFlast×h−tlast+Θ.Among them,CRFlast is the size of the last updated CRF value and tlast is the last update time of the data block.(2)
When a replacement operation occurs: in this case, the CRF values of all data blocks will be updated. All data blocks must be sorted according to the latest CRF to select the data block with the smallest CRF. The latest CRF can be obtained by the following formula:(8)CRF=CRFlast×Ft−tlast.LRFU can be used as either LRU or LFU. In theory, LRFU can achieve better results in both recency-friendly and frequency-friendly data access modes. LRFU (step = 0) does not consider the sequence information of any data block access, so when all data blocks have the same access frequency, LRFU will randomly replace the data blocks. For the loop access mode, LRFU (step = 0) can always ensure that some data blocks are not replaced in the memory, thereby achieving a certain percentage of hit rates. Therefore, LRFU (step = 0) can achieve better performance results in the loop access mode. HAMA is an early work of distributed parallel dense matrix multiplication on the Hadoop MapReduce platform. However, its algorithm design contains a lot of data reading and writing with the underlying HBasse, and the segmentation of the matrix is relatively simple. Therefore, the performance of HAMA is relatively inefficient, and the maximum matrix dimension of the experiment is only 5000 × 5000. Aiming at the problem of distributed matrix multiplication on a data parallel platform, two distributed matrix segmentation strategies are proposed, which are called RMM (Replication-based Matrix Multiplication) and CPMM (CrossProduct-based Matrix Multiplication). For the convenience of the following description, the two input multiplication matrices and their splitting methods are agreed as follows: the number of sub-blocks in matrixA is Mb×Kb, and the number of sub-blocks in matrix B is Kb×Nb. The matrix multiplication C=A∗B can be written as(9)Ci,j=∑kAi,kBk,j,i<Mb<k,Kb<j.For RMM, there is only one shuffle step in the entire execution process. In the process of calculating the result matrixC, the input sub-matrix blocks Ai,k and Bk,j will generate multiple copies. Specifically, there are a total of Nb copies of sub-blocks of matrix A and Mbcopies of sub-blocks of matrix B, and the amount of data in the shuffle is NbA+MbB. CPMM has two shuffle stages, similar to the amount of intermediate process data that can be analyzed. In terms of concurrency, the concurrency of CPMM sub-block matrix multiplication is Kb, and the concurrency of RMM is Mb×Nb. The strategy also includes two shuffle stages, the first shuffle execution process is very similar to RMM, and the second shuffle execution process is similar to CPMM. In this way, CRMM can make a better trade-off between concurrency and shuffle data volume. When the input matrix dimension k is much smaller than m and n, the implementation of CRMM will be equivalent to RMM, and when the dimension k is much larger than the dimensions m and n, the implementation of CRMM will be equivalent to CPMM. In addition, for the case of multiplying a large matrix by a small matrix, in order to avoid the data of the two matrices from being shuffled through the network, a matrix multiplication on the Map side is proposed, called MapMM (Map-side Matrix Multiplication).For the three execution strategies CPMM, RMM, and CRMM, the theoretical analysis in Figure6 shows that CRMM can better choose between concurrency and the amount of shuffle data.Figure 6
The relationship between the concurrency of the three matrix multiplication execution strategies and the amount of data transmitted by the shuffle.


## 4. Results and Analysis

This section conducts experiments on the methods proposed in Section 3 and analyzes the experimental results.


### 4.1. Analysis of Establishment of the Social Security Platform

Table3 shows the application of big data technology in the field of social security for the elderly.Table 3
Various applications.
Technical nameLatest technologiesCloud computingCloud storage of elderly care data, cloud computing of elderly data, cloud application of elderly care system, etc.Intelligent technologySmart call terminal, smart sensor equipment, access control radio frequency identification, swipe card sign-in, etc.Mobile InternetSmartphone management, smart call terminal, wearable product terminal, pension APPIntelligent newsletter technologyCall center applications, smart call terminals, affection calls for the elderlyGPS technologyIntelligent positioning terminalStreaming media transmission technologyVideo surveillance, remote video consultation, video chatSmart terminal equipmentSmart call terminal, smart security monitoring terminal, smart wearable terminal, smart rehabilitation nursing terminal, smart cultural entertainment terminalA new type of old-age care information platform driven by big data relies on the “end-network-cloud” model for construction. The massive data generated at the “end” of the information is aggregated through the “network” and transmitted to the “cloud” to become the elderly care module subsystem in the “integrated cloud service platform” of the smart life. “End” deadline for the most basic information. Smart terminals such as smart homes and wearable devices, elderly care apps, information surveys, and other ports are the main information sources. Multi-terminal information has diversified characteristics and presents smaller information granularity. By extracting the semantic structure of multi-terminal pension information, it can be accurately matched with the pension knowledge graph model established by big data and artificial intelligence technology. “Net” means to classify and preprocess network pension information data, and import pension data from the end platform database to the distributed network platform to improve the stability of the big data pension system. “End” and “web” are the foundation and premise of “cloud.” Data preprocessing is carried out through the “web” link, and relies on migration learning technology. The available features are extracted from the massive data collected from multiple terminals and the attributes are analyzed at the same time to prepare for data modeling analysis and visualization of elderly care data. The essence of “cloud” is a service recommendation system. On the one hand, the “cloud” link selects naive Bayes, decision tree, support vector machine, recurrent neural network, reinforcement learning, and other deep learning algorithms and technologies to open up multiple terminals for the “network” link the data features are clustered, and the visualization results of part of the data are output. On the other hand, the “cloud” also contains collaborative filtering algorithms and association rule algorithms, which perform a multi-dimensional comprehensive evaluation of elderly care subjects to achieve “people grouping” and output the matching relationship between multiple sets of services and users. The “cloud” link continuously self-corrects model parameters through unsupervised learning. Multi-end pension data is divided into several clusters without category attributes. After a lot of calculations and feedback, the machine has achieved multiple cuts, increasing its data dissimilarity. Under the new big data-driven old-age care model, the “end-network-cloud” has achieved deep integration, and the integrated cloud service platform is the command core. Through the intelligent monitoring and calculation functions of the platform, the relevant data of each participant is received, identified, and processed. In this way, service instructions are formed to direct the reasonable and efficient allocation of resources such as data, funds, and services among the participating entities. Finally, the coordination and unification of data flow-driven service flow and capital flow will be realized. It is not difficult to find that all data are sent out with the elderly as the starting point, and the integrated cloud service platform is the summary end. All services are service-oriented to the elderly, and all funds come from the elderly, the government, and social security agencies [23]. The elderly, their children, and their families, as the main body of old-age care, are responsible for producing big data for old-age care. Institutions and organizations act as transmitters of big data. The integrated cloud service platform plays the role of a big data processor, serving the elderly and various organizational departments by outputting service demand content and other methods to achieve a sustainable operation state of multi-party win-win.


#### 4.1.1. Construction of the Pension Insurance Correlation Analysis System

According to the design requirements of the correlation analysis system, we establish an indicator system, deal with the policies and macroeconomic factors that affect the social insurance fund from a multi-dimensional and structured perspective, and lay a solid foundation for further excavating the deep relationship between events and social security funds as follows.(1) The Index System of Pension Fund Participation in Insurance Collection and Payment. Fund collection is the main source of pension funds. Therefore, strengthening fund collection and preventing risks will help ensure the overall stability of pension funds. The main factors that affect pension fund collection are Number of people/number of people covered by the pension system × 100%); pension fund collection rate (actual number of people paying/number of people who should pay × 100%); payment base; pension insurance payment rate; pension supplement/interruption; pension account; interest rate Wait.(2) Pension Fund Payment Indicator System. Pension fund payment is mainly affected by the following factors: dependency ratio (number of pensioners/number of on-the-job workers × 100%); hidden debt ratio (hidden debt/total pension fund × 100%); pension insurance replacement rate (in the overall planning area, the number of pensions received per capita in the same period/the per capita wages of on-the-job employees × 100%).(3) The Index System of Violation of Laws and Regulations of Pension Funds. Fund fraudulent claim ratio (total fund fraudulent claim/total pension fund receivable); pension fund fraudulent payment rate (amount of fraudulent payment/total pension fund receivable).


#### 4.1.2. Platform Technical Architecture

The platform technology architecture adopts the industry’s most mature SOA (service-oriented architecture) framework, follows a unified technical route, focuses on loose coupling between layers and high cohesion within layers, and realizes the componentization of business objects through business abstraction and mapping and unified service invocation; it fully considers the scalability, reusability, and configurability of the system, reduces development and maintenance costs, enables the system to change as needed, and quickly and flexibly meets the needs of business changes.(1) Data Source. We make full use of the relational data resources in the existing financial security engineering business system, combine the concept of big data technology, collect data resources on the Internet captured by file processing and web crawler tools, and synchronize them to the data storage layer through cleaning and integration.(2) Data Storage Layer. We adopt advanced mashup technology architecture and use Hadoop software to meet the storage, processing, and parallel computing of massive data of human society; Hive of Hadoop aggregates and integrates data from various sources, is used for the fast query of social security detailed data, and provides information of high concurrent query; we use the column-stored MPP database to build a human society data mart to meet the performance requirements of applications such as associated computing, analytical computing, and mining computing of various data resources and support data analysis and data services.(3) Data Service Layer. By building a unified data service platform, the heterogeneous data storage and computing capabilities are encapsulated as a whole to realize the combination of data and business, meet data analysis, mining, and special services, and improve the value mining capabilities of big data.(4) Data Application Layer. It provides various service interfaces and public components, connects to the existing ESB bus system for unified scheduling management, connects with various front-end application systems or public services (website, mobile terminal) in real time, and provides a higher level for social security business agencies and service objects of customized services.(5) Access Platform Layer. The platform adopts a dual portal (PC portal + mobile portal) design. The PC portal is mainly oriented to business agencies and solves the daily business management statistical analysis needs of business departments. On the one hand, the mobile portal is oriented to social security department and decision-making department, and it solves the real-time query needs of mobile terminals for summary statistics and decision analysis; on the other hand, it is oriented to service objects to provide personalized customized services. Both portals can implement functions such as jumping, slicing/dicing, drilling, and rotating reports on demand.The application on the PC side should be functionally convenient and quick to view professional reports and histograms, pie charts, trend charts, point charts, area charts, etc. It is equipped with dashboards, etc., which can freely combine various reports and charts to display on the same interface. Various dimension changes, data drilling, slicing, etc. can be performed through simple clicks on various graphical interfaces. Connect with other application systems through the development interface to realize data sharing. The mobile portal can realize real-time and dynamic grasp of various statistical query data through mobile applications, and support dimensional changes of data and drilling up and down. The mobile application mainly provides the functions of visual display and early warning. The current business situation needs to be evaluated from multiple dimensions (multi-index items), and different types of alerts should be carried out according to thresholds.


### 4.2. Comparison and Analysis of the Results of Various Algorithms

First, the matrix segmentation method and performance changes on 12 computing nodes (192 logic cores in total) were verified. The input matrix size given in the experiment is 30000 × 30000 × 30000. The experimental results are shown in Figures7–9. Compared with RMM and CPMM, CRMM can make better use of cluster computing resources and weigh the overall performance and the concurrency when calculating sub-block matrix multiplication. Matrix operations with different operations and scales have different computing performances on different platforms. Therefore, when there are multiple big data computing platforms at the bottom of the system, the most suitable computing platform can be selected for the matrix operation of each node in RMM during execution, so that the execution time of the entire RMM is minimized.Figure 7
RMM strategy.Figure 8
CPMM strategy.Figure 9
CRMM strategy.Then, the performance of the three strategies of RMM, CPMM, and CRMM on a cluster of 20 nodes was evaluated. In particular, when the dimensionk is much larger than the dimensions m and n, the CRMM strategy is equivalent to the CPMM strategy; when the dimensions m and n are much larger than the dimension k, the CRMM strategy is equivalent to the RMM strategy. The performance of the torch matrix multiplication execution strategy under different matrix sizes is shown in Figure 10.Figure 10
The performance of torch matrix multiplication execution strategy under different matrix sizes.Finally, the proposed optimization measures for efficiently calling the native library (Batch Calling Native Library, BCNL), efficient distributed row-block matrix transformation optimization measures (Slicing Matrix Transformation, SMT), and optimization measures to reduce shuffle data read and write were evaluated (Shuffle-Light sub-Matrix co-Grouping, SLMG). The experimental results are shown in Tables4 and 5. The performance of matrix multiplication can be improved after the optimization measures are adopted, which proves that the proposed optimization measures are effective.Table 4
Performance of batch calling local native libraries.
Matrix sizeMapMMMapMM with BCNLSpeedup ratio500k × 1k × 1k1191.17500k × 10k × 1k85213.711m × 1k × 1k15101.561m × 10k × 1k151384.225m × 1k × 1k49232.845m × 10k × 1k7261943.33Table 5
Performance of SMT and SLMG optimization measures.
Matrix sizeCRMMCRMM with SMTCRMM with SMT and SLMTSpark MLlib20k × 20k × 20k131827539725k × 25k × 25k31413812990430k × 30k × 30k580202217175135k × 35k × 35k931352331NA40k × 40k × 40k1795671468NAExperimental data unit: second; NA means failure to complete the calculation within 2500 seconds.Here, we use an uncertain sample to partition the data, and create an expression to represent the data collection, the purpose is to use HDFS as the uncertain sample to analyze and estimate the data collection, and we use a more reasonable way to ensure that each data is a random number with uncertainty:(1)
Range partition: based on a given continuous interval range, the data are allocated to different partitions; the interval should be continuous and different overlapping, and the VALUES LESS THAN operation is used to define.(2)
List partition: similar to range partition, the difference is that list partition is based on the enumerated value list partition, and RANGE partition is based on a given continuous interval range; the PARTITION BY LIST clause is used to achieve.(3)
Column partition: it can only be used after MySQL5.5. In fact, this partition is mainly generated to solve the problem that RANGE and LIST partitions cannot be partitioned using non-integer data columns, but this function is not only that because it also supports more column partitions.(4)
Hash partition: it is mainly used to disperse hot reads to ensure that data are distributed as evenly as possible in a predetermined number of partitions. When we partition a data table, MySQL will apply a hash function to the partition key. This determines in which partition of the N partitions the data should be placed. MySQL supports two hash partitions, regular partitions and linear partitions. Regular partitions use the modulo algorithm, and linear partitions use a linear power-of-2 algorithm.(5)
Key partition: similar to the HASH partition, the difference is that the HASH partition can use its expression, while the key partition can only use the hash function provided by the mysql server, and the hash partition only supports integer types, but key partition supports all field types except blob and text types.(6)
Sub-partition: it is a partition on the basis of partition, also known as compound partition.First, the mathematical definition of partition is a non-empty subset of the complete set, and each partition has no intersection with other partitions, and the union of all partitions is the complete set. The following two definitions are given according to the nature of the random sample partition data expression model.Definition 1.
Data set division: suppose a data setD contains N data samples, namely, D=x1,x2,…,xN, and T is regarded as a division of the overall data of data set D, namely, T=D1,D2,…,Dk, and meets the following conditions:(10)U1KEk=E,Ei∩Ey≠φ,i,y∈1,2,…,h,i≠y.
Therefore,T is called a partition operation of dataset D, and each Dkk=1,2,..,K is called a block of dataset D. HDFS files are partitions of dataset D, where data block D1,D2,…,DK is generated by sequentially slicing big data. Typically, these data blocks do not have similar big data distribution characteristics in HDFS files. (The partition operation of the dataset is proposed after formula (10), so please explain how to perform the partition operation.) From the abovementioned explanation, it is impossible to use uncertain samples when analyzing larger data. However, under certain conditions, uncertain sample data can also be used to define data blocks to synthesize huge uncertainty data samples.Definition 2.
Random sample division: dataset 1 is taken as a random sample of the big data as a whole. We assumeF(X) as a function to represent the distribution samples in the collection, and we assume t as a division function to divide the sum of all data, namely, T=E1,E2,…,EK. This T is called a random sample partition of D and satisfies the conditions:(11)DR˜ky=Ryk=1,2,…,K.
Among them,R˜ky represents the sample distribution function of DK, which represents the corresponding expectation. Therefore, each DK is called a random sample division block of data set D, and T is called a random sample division operation on data set D. Through the above processing, the random sample division of big data is completed, and the relevant random sample division data expression model is constructed.


## 4.1. Analysis of Establishment of the Social Security Platform

Table3 shows the application of big data technology in the field of social security for the elderly.Table 3
Various applications.
Technical nameLatest technologiesCloud computingCloud storage of elderly care data, cloud computing of elderly data, cloud application of elderly care system, etc.Intelligent technologySmart call terminal, smart sensor equipment, access control radio frequency identification, swipe card sign-in, etc.Mobile InternetSmartphone management, smart call terminal, wearable product terminal, pension APPIntelligent newsletter technologyCall center applications, smart call terminals, affection calls for the elderlyGPS technologyIntelligent positioning terminalStreaming media transmission technologyVideo surveillance, remote video consultation, video chatSmart terminal equipmentSmart call terminal, smart security monitoring terminal, smart wearable terminal, smart rehabilitation nursing terminal, smart cultural entertainment terminalA new type of old-age care information platform driven by big data relies on the “end-network-cloud” model for construction. The massive data generated at the “end” of the information is aggregated through the “network” and transmitted to the “cloud” to become the elderly care module subsystem in the “integrated cloud service platform” of the smart life. “End” deadline for the most basic information. Smart terminals such as smart homes and wearable devices, elderly care apps, information surveys, and other ports are the main information sources. Multi-terminal information has diversified characteristics and presents smaller information granularity. By extracting the semantic structure of multi-terminal pension information, it can be accurately matched with the pension knowledge graph model established by big data and artificial intelligence technology. “Net” means to classify and preprocess network pension information data, and import pension data from the end platform database to the distributed network platform to improve the stability of the big data pension system. “End” and “web” are the foundation and premise of “cloud.” Data preprocessing is carried out through the “web” link, and relies on migration learning technology. The available features are extracted from the massive data collected from multiple terminals and the attributes are analyzed at the same time to prepare for data modeling analysis and visualization of elderly care data. The essence of “cloud” is a service recommendation system. On the one hand, the “cloud” link selects naive Bayes, decision tree, support vector machine, recurrent neural network, reinforcement learning, and other deep learning algorithms and technologies to open up multiple terminals for the “network” link the data features are clustered, and the visualization results of part of the data are output. On the other hand, the “cloud” also contains collaborative filtering algorithms and association rule algorithms, which perform a multi-dimensional comprehensive evaluation of elderly care subjects to achieve “people grouping” and output the matching relationship between multiple sets of services and users. The “cloud” link continuously self-corrects model parameters through unsupervised learning. Multi-end pension data is divided into several clusters without category attributes. After a lot of calculations and feedback, the machine has achieved multiple cuts, increasing its data dissimilarity. Under the new big data-driven old-age care model, the “end-network-cloud” has achieved deep integration, and the integrated cloud service platform is the command core. Through the intelligent monitoring and calculation functions of the platform, the relevant data of each participant is received, identified, and processed. In this way, service instructions are formed to direct the reasonable and efficient allocation of resources such as data, funds, and services among the participating entities. Finally, the coordination and unification of data flow-driven service flow and capital flow will be realized. It is not difficult to find that all data are sent out with the elderly as the starting point, and the integrated cloud service platform is the summary end. All services are service-oriented to the elderly, and all funds come from the elderly, the government, and social security agencies [23]. The elderly, their children, and their families, as the main body of old-age care, are responsible for producing big data for old-age care. Institutions and organizations act as transmitters of big data. The integrated cloud service platform plays the role of a big data processor, serving the elderly and various organizational departments by outputting service demand content and other methods to achieve a sustainable operation state of multi-party win-win.


### 4.1.1. Construction of the Pension Insurance Correlation Analysis System

According to the design requirements of the correlation analysis system, we establish an indicator system, deal with the policies and macroeconomic factors that affect the social insurance fund from a multi-dimensional and structured perspective, and lay a solid foundation for further excavating the deep relationship between events and social security funds as follows.(1) The Index System of Pension Fund Participation in Insurance Collection and Payment. Fund collection is the main source of pension funds. Therefore, strengthening fund collection and preventing risks will help ensure the overall stability of pension funds. The main factors that affect pension fund collection are Number of people/number of people covered by the pension system × 100%); pension fund collection rate (actual number of people paying/number of people who should pay × 100%); payment base; pension insurance payment rate; pension supplement/interruption; pension account; interest rate Wait.(2) Pension Fund Payment Indicator System. Pension fund payment is mainly affected by the following factors: dependency ratio (number of pensioners/number of on-the-job workers × 100%); hidden debt ratio (hidden debt/total pension fund × 100%); pension insurance replacement rate (in the overall planning area, the number of pensions received per capita in the same period/the per capita wages of on-the-job employees × 100%).(3) The Index System of Violation of Laws and Regulations of Pension Funds. Fund fraudulent claim ratio (total fund fraudulent claim/total pension fund receivable); pension fund fraudulent payment rate (amount of fraudulent payment/total pension fund receivable).


### 4.1.2. Platform Technical Architecture

The platform technology architecture adopts the industry’s most mature SOA (service-oriented architecture) framework, follows a unified technical route, focuses on loose coupling between layers and high cohesion within layers, and realizes the componentization of business objects through business abstraction and mapping and unified service invocation; it fully considers the scalability, reusability, and configurability of the system, reduces development and maintenance costs, enables the system to change as needed, and quickly and flexibly meets the needs of business changes.(1) Data Source. We make full use of the relational data resources in the existing financial security engineering business system, combine the concept of big data technology, collect data resources on the Internet captured by file processing and web crawler tools, and synchronize them to the data storage layer through cleaning and integration.(2) Data Storage Layer. We adopt advanced mashup technology architecture and use Hadoop software to meet the storage, processing, and parallel computing of massive data of human society; Hive of Hadoop aggregates and integrates data from various sources, is used for the fast query of social security detailed data, and provides information of high concurrent query; we use the column-stored MPP database to build a human society data mart to meet the performance requirements of applications such as associated computing, analytical computing, and mining computing of various data resources and support data analysis and data services.(3) Data Service Layer. By building a unified data service platform, the heterogeneous data storage and computing capabilities are encapsulated as a whole to realize the combination of data and business, meet data analysis, mining, and special services, and improve the value mining capabilities of big data.(4) Data Application Layer. It provides various service interfaces and public components, connects to the existing ESB bus system for unified scheduling management, connects with various front-end application systems or public services (website, mobile terminal) in real time, and provides a higher level for social security business agencies and service objects of customized services.(5) Access Platform Layer. The platform adopts a dual portal (PC portal + mobile portal) design. The PC portal is mainly oriented to business agencies and solves the daily business management statistical analysis needs of business departments. On the one hand, the mobile portal is oriented to social security department and decision-making department, and it solves the real-time query needs of mobile terminals for summary statistics and decision analysis; on the other hand, it is oriented to service objects to provide personalized customized services. Both portals can implement functions such as jumping, slicing/dicing, drilling, and rotating reports on demand.The application on the PC side should be functionally convenient and quick to view professional reports and histograms, pie charts, trend charts, point charts, area charts, etc. It is equipped with dashboards, etc., which can freely combine various reports and charts to display on the same interface. Various dimension changes, data drilling, slicing, etc. can be performed through simple clicks on various graphical interfaces. Connect with other application systems through the development interface to realize data sharing. The mobile portal can realize real-time and dynamic grasp of various statistical query data through mobile applications, and support dimensional changes of data and drilling up and down. The mobile application mainly provides the functions of visual display and early warning. The current business situation needs to be evaluated from multiple dimensions (multi-index items), and different types of alerts should be carried out according to thresholds.


## 4.1.1. Construction of the Pension Insurance Correlation Analysis System

According to the design requirements of the correlation analysis system, we establish an indicator system, deal with the policies and macroeconomic factors that affect the social insurance fund from a multi-dimensional and structured perspective, and lay a solid foundation for further excavating the deep relationship between events and social security funds as follows.(1) The Index System of Pension Fund Participation in Insurance Collection and Payment. Fund collection is the main source of pension funds. Therefore, strengthening fund collection and preventing risks will help ensure the overall stability of pension funds. The main factors that affect pension fund collection are Number of people/number of people covered by the pension system × 100%); pension fund collection rate (actual number of people paying/number of people who should pay × 100%); payment base; pension insurance payment rate; pension supplement/interruption; pension account; interest rate Wait.(2) Pension Fund Payment Indicator System. Pension fund payment is mainly affected by the following factors: dependency ratio (number of pensioners/number of on-the-job workers × 100%); hidden debt ratio (hidden debt/total pension fund × 100%); pension insurance replacement rate (in the overall planning area, the number of pensions received per capita in the same period/the per capita wages of on-the-job employees × 100%).(3) The Index System of Violation of Laws and Regulations of Pension Funds. Fund fraudulent claim ratio (total fund fraudulent claim/total pension fund receivable); pension fund fraudulent payment rate (amount of fraudulent payment/total pension fund receivable).


## 4.1.2. Platform Technical Architecture

The platform technology architecture adopts the industry’s most mature SOA (service-oriented architecture) framework, follows a unified technical route, focuses on loose coupling between layers and high cohesion within layers, and realizes the componentization of business objects through business abstraction and mapping and unified service invocation; it fully considers the scalability, reusability, and configurability of the system, reduces development and maintenance costs, enables the system to change as needed, and quickly and flexibly meets the needs of business changes.(1) Data Source. We make full use of the relational data resources in the existing financial security engineering business system, combine the concept of big data technology, collect data resources on the Internet captured by file processing and web crawler tools, and synchronize them to the data storage layer through cleaning and integration.(2) Data Storage Layer. We adopt advanced mashup technology architecture and use Hadoop software to meet the storage, processing, and parallel computing of massive data of human society; Hive of Hadoop aggregates and integrates data from various sources, is used for the fast query of social security detailed data, and provides information of high concurrent query; we use the column-stored MPP database to build a human society data mart to meet the performance requirements of applications such as associated computing, analytical computing, and mining computing of various data resources and support data analysis and data services.(3) Data Service Layer. By building a unified data service platform, the heterogeneous data storage and computing capabilities are encapsulated as a whole to realize the combination of data and business, meet data analysis, mining, and special services, and improve the value mining capabilities of big data.(4) Data Application Layer. It provides various service interfaces and public components, connects to the existing ESB bus system for unified scheduling management, connects with various front-end application systems or public services (website, mobile terminal) in real time, and provides a higher level for social security business agencies and service objects of customized services.(5) Access Platform Layer. The platform adopts a dual portal (PC portal + mobile portal) design. The PC portal is mainly oriented to business agencies and solves the daily business management statistical analysis needs of business departments. On the one hand, the mobile portal is oriented to social security department and decision-making department, and it solves the real-time query needs of mobile terminals for summary statistics and decision analysis; on the other hand, it is oriented to service objects to provide personalized customized services. Both portals can implement functions such as jumping, slicing/dicing, drilling, and rotating reports on demand.The application on the PC side should be functionally convenient and quick to view professional reports and histograms, pie charts, trend charts, point charts, area charts, etc. It is equipped with dashboards, etc., which can freely combine various reports and charts to display on the same interface. Various dimension changes, data drilling, slicing, etc. can be performed through simple clicks on various graphical interfaces. Connect with other application systems through the development interface to realize data sharing. The mobile portal can realize real-time and dynamic grasp of various statistical query data through mobile applications, and support dimensional changes of data and drilling up and down. The mobile application mainly provides the functions of visual display and early warning. The current business situation needs to be evaluated from multiple dimensions (multi-index items), and different types of alerts should be carried out according to thresholds.


## 4.2. Comparison and Analysis of the Results of Various Algorithms

First, the matrix segmentation method and performance changes on 12 computing nodes (192 logic cores in total) were verified. The input matrix size given in the experiment is 30000 × 30000 × 30000. The experimental results are shown in Figures7–9. Compared with RMM and CPMM, CRMM can make better use of cluster computing resources and weigh the overall performance and the concurrency when calculating sub-block matrix multiplication. Matrix operations with different operations and scales have different computing performances on different platforms. Therefore, when there are multiple big data computing platforms at the bottom of the system, the most suitable computing platform can be selected for the matrix operation of each node in RMM during execution, so that the execution time of the entire RMM is minimized.Figure 7
RMM strategy.Figure 8
CPMM strategy.Figure 9
CRMM strategy.Then, the performance of the three strategies of RMM, CPMM, and CRMM on a cluster of 20 nodes was evaluated. In particular, when the dimensionk is much larger than the dimensions m and n, the CRMM strategy is equivalent to the CPMM strategy; when the dimensions m and n are much larger than the dimension k, the CRMM strategy is equivalent to the RMM strategy. The performance of the torch matrix multiplication execution strategy under different matrix sizes is shown in Figure 10.Figure 10
The performance of torch matrix multiplication execution strategy under different matrix sizes.Finally, the proposed optimization measures for efficiently calling the native library (Batch Calling Native Library, BCNL), efficient distributed row-block matrix transformation optimization measures (Slicing Matrix Transformation, SMT), and optimization measures to reduce shuffle data read and write were evaluated (Shuffle-Light sub-Matrix co-Grouping, SLMG). The experimental results are shown in Tables4 and 5. The performance of matrix multiplication can be improved after the optimization measures are adopted, which proves that the proposed optimization measures are effective.Table 4
Performance of batch calling local native libraries.
Matrix sizeMapMMMapMM with BCNLSpeedup ratio500k × 1k × 1k1191.17500k × 10k × 1k85213.711m × 1k × 1k15101.561m × 10k × 1k151384.225m × 1k × 1k49232.845m × 10k × 1k7261943.33Table 5
Performance of SMT and SLMG optimization measures.
Matrix sizeCRMMCRMM with SMTCRMM with SMT and SLMTSpark MLlib20k × 20k × 20k131827539725k × 25k × 25k31413812990430k × 30k × 30k580202217175135k × 35k × 35k931352331NA40k × 40k × 40k1795671468NAExperimental data unit: second; NA means failure to complete the calculation within 2500 seconds.Here, we use an uncertain sample to partition the data, and create an expression to represent the data collection, the purpose is to use HDFS as the uncertain sample to analyze and estimate the data collection, and we use a more reasonable way to ensure that each data is a random number with uncertainty:(1)
Range partition: based on a given continuous interval range, the data are allocated to different partitions; the interval should be continuous and different overlapping, and the VALUES LESS THAN operation is used to define.(2)
List partition: similar to range partition, the difference is that list partition is based on the enumerated value list partition, and RANGE partition is based on a given continuous interval range; the PARTITION BY LIST clause is used to achieve.(3)
Column partition: it can only be used after MySQL5.5. In fact, this partition is mainly generated to solve the problem that RANGE and LIST partitions cannot be partitioned using non-integer data columns, but this function is not only that because it also supports more column partitions.(4)
Hash partition: it is mainly used to disperse hot reads to ensure that data are distributed as evenly as possible in a predetermined number of partitions. When we partition a data table, MySQL will apply a hash function to the partition key. This determines in which partition of the N partitions the data should be placed. MySQL supports two hash partitions, regular partitions and linear partitions. Regular partitions use the modulo algorithm, and linear partitions use a linear power-of-2 algorithm.(5)
Key partition: similar to the HASH partition, the difference is that the HASH partition can use its expression, while the key partition can only use the hash function provided by the mysql server, and the hash partition only supports integer types, but key partition supports all field types except blob and text types.(6)
Sub-partition: it is a partition on the basis of partition, also known as compound partition.First, the mathematical definition of partition is a non-empty subset of the complete set, and each partition has no intersection with other partitions, and the union of all partitions is the complete set. The following two definitions are given according to the nature of the random sample partition data expression model.Definition 1.
Data set division: suppose a data setD contains N data samples, namely, D=x1,x2,…,xN, and T is regarded as a division of the overall data of data set D, namely, T=D1,D2,…,Dk, and meets the following conditions:(10)U1KEk=E,Ei∩Ey≠φ,i,y∈1,2,…,h,i≠y.
Therefore,T is called a partition operation of dataset D, and each Dkk=1,2,..,K is called a block of dataset D. HDFS files are partitions of dataset D, where data block D1,D2,…,DK is generated by sequentially slicing big data. Typically, these data blocks do not have similar big data distribution characteristics in HDFS files. (The partition operation of the dataset is proposed after formula (10), so please explain how to perform the partition operation.) From the abovementioned explanation, it is impossible to use uncertain samples when analyzing larger data. However, under certain conditions, uncertain sample data can also be used to define data blocks to synthesize huge uncertainty data samples.Definition 2.
Random sample division: dataset 1 is taken as a random sample of the big data as a whole. We assumeF(X) as a function to represent the distribution samples in the collection, and we assume t as a division function to divide the sum of all data, namely, T=E1,E2,…,EK. This T is called a random sample partition of D and satisfies the conditions:(11)DR˜ky=Ryk=1,2,…,K.
Among them,R˜ky represents the sample distribution function of DK, which represents the corresponding expectation. Therefore, each DK is called a random sample division block of data set D, and T is called a random sample division operation on data set D. Through the above processing, the random sample division of big data is completed, and the relevant random sample division data expression model is constructed.


## 5. Conclusion

Using big data technology to reduce the distance between the elderly and their children and external resources by connecting to smart home devices, whether video chat or family monitoring, children can be accompanied and cared for 24 hours a day, 7 days a week, even if they do not live with the elderly. At the same time, the distance between the elderly and hospitals can be reduced, whether it is remote medical consultation or 24-hour recording of portable equipment; even when the elderly are not there, children can have an accurate understanding of their health, medical institutions, and caretakers. Even if you are not next to the elderly, you can manage the health of the elderly anytime, anywhere through a mobile terminal. By recording, storing, and transmitting data, children and guardians can gain a more scientific, precise, and precise idea of the situation of older people and relate them to resources and outside services and give them access to professional healthcare, 24 hours a day, 7 days a week, as is the case in institutions for the elderly. It can be said that the new type of elderly care model driven by big data relies on the “terminal network cloud” architecture and recommendation algorithm, which guarantees the safety and health of the elderly. The proposal of the big data-driven pension concept is the reality of the vigorous development of big data technology, which requires a series of strategic deployments of top-level design. It can be said that precision elderly care is not only a strategic choice for us to face the advent of an aging society but also an important part of promoting the modernization of the national governance system. It satisfies the needs of the elderly for the elderly at a higher level and enhances the happiness and sense of achievement of the elderly group and the whole society. It is a new type of care model that is in line with the current and future social security situation of the elderly so that the life of the elderly no longer depends on the family, the community, or the medical institution, and the goal of smart elderly care is realized in a true sense. What needs to be pointed out here is that with the full deployment of 5G technology, the broadband and traffic it brings will provide more powerful technical support for precise elderly care. Promoting the widespread application of in-depth VR will make future elderly care more humanized and personalized, and more capable of humanistic care and affectionate interaction. Research conducted in countries with aging populations suggests that aging itself is less likely to lead to “out-of-control healthcare costs” for two reasons:First, according to the OECD, the main reason for rising health care costs has nothing to do with the ageing of a given population. Inefficiencies in health care delivery, the establishment of too many hospitals, payment systems that encourage prolonged hospital stays, too many medical interventions, and inappropriate use of high-cost technologies are key factors contributing to the rising cost of health care. For example, in the United States and other OECD countries, where alternative and cheaper surgical procedures exist, new technologies with relatively low marginal efficiency are sometimes rapidly introduced and used. Policymakers appear to have considerable leeway to address these issues and improve the effectiveness of healthcare.Second, the costs of long-term care are manageable if policies and programs play a role in preventive and informal care. Policies and health promotion programs to prevent chronic diseases and reduce disability in older adults enable them to live independently for longer. Another important factor is the ability and willingness of families to provide care and support to older family members, which will depend largely on female labor force participation and out-of-home work rates and public policies that recognize and support the role of caregivers.



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*Source: 9508018-2023-02-07.xml*