# A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Authors: Jie Ren; Lijuan Liu
Journal: Journal of Electrical and Computer Engineering
 (2022)

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## Abstract

Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on “API key” and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data.


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## Body

## 1. Introduction

Cloud computing mainly focuses on the analysis and processing of massive data, using the Internet as a framework to share data resources in a virtualized manner. The core idea of cloud computing is to unify the management of data resources through a “cloud” platform to achieve optimal allocation of resources so that users can obtain the services they need in real-time and pay for them according to their needs, reducing economic costs and other needs. Cloud computing has been recognized as a great innovation in the tertiary industry, which has brought fundamental changes to the network model and business model [1]. The emergence of cloud computing technology has precisely solved the biggest problem of massive data processing. For the massive data mining to be smoothly realized, not only the cloud computing platform has to be built, but also the traditional data mining algorithms need to be improved to meet the requirements of massive data processing, and the improved algorithms are implanted on the cloud platform. The storage, processing, and analysis of massive multisource heterogeneous IoT data are the core key technology that can bring into play the potential value of IoT. In the IoT architecture, the data center provides services such as large-scale and fast access to the perception layer, and the information base to the application layer. The rapid development of IoT and cloud computing is bound to greatly change people's perception and life, providing smarter services for humans and enabling them to better manage and control IoT devices in the physical world and promote the progress of the Internet era; both are based on the Internet, and cloud computing technology can provide basic support for IoT technology, while IoT technology provides a rich application scenario for cloud computing technology [2]. The storage, processing, and analysis of massive multisource heterogeneous IoT data are the core key technology that can bring into play the potential value of IoT. In the IoT architecture, the data center provides services such as large-scale and fast access to the perception layer and the information base to the application layer. The rapid development of IoT and cloud computing is bound to greatly change people's perception and life and provide smarter services for humans Cloud computing technology can provide basic support for IoT technology, while IoT technology provides rich application scenarios for cloud computing technology. As an important part of the new generation of information technology, IoT and cloud computing will also generate various kinds of problems in the process of mutual integration, mutual promotion, and mutual development; therefore, carrying out research related to IoT technology based on cloud computing platform has an important role in promoting the development of information technology.The idea of a group intelligence algorithm is introduced into the field of clustering to form a new intelligent clustering model, which promotes the development and improvement of clustering analysis technology. The decentralized control mechanism of group intelligence has strong robustness, while group intelligence has the advantage of adapting to the environment that changes at any time and has more flexibility. In addition, the group intelligence system is based on group operation, which is easy to implement in parallel. Therefore, population intelligence reveals group behavior and self-organization phenomena from a new perspective and effectively solves the problems in algorithm optimization and engineering optimization of massively parallel computing through the stronger merit-seeking ability of the group intelligence algorithm to provide the clustering algorithm with certain necessary prior knowledge of clustering, to relatively weaken the influence of the input parameters on the clustering results, achieve the purpose of improving the adaptability of the algorithm, reduce the burden of the user, improve the accuracy and robustness of the clustering algorithm, and achieve the purpose of improving the clustering results [3]. In this paper, we take cloud computing data center information classification storage as the research background, aim to improve the utilization of cloud computing data center resources and data service capability, combine the characteristics of cloud data computing center user requirements and data mining levels, construct the framework of big data federation data mining service model, and carry out in-depth research on it.


## 2. Related Work

Research related to clustering is analyzed from a statistical perspective, clustering analysis uses data modeling techniques to model and analyze the internal structure and distribution of data. Analyzed from a data mining perspective, clustering is an unsupervised learning model in which data is divided by clustering algorithms to form labeled clusters in the absence of a priori knowledge. In recent years, along with cluster analysis techniques being widely used in bioinformatics, image processing, computer vision, medical diagnosis, business decision making, information retrieval, resource management, and other fields, the theoretical research related to the field of cluster analysis has become richer. Kaur et al. [4] researched an artificial intelligence-based automatic adjustment data preprocessing method that can preprocess data for different data, but the drawback is that they could not select useful information. Jiang et al. [5] proposed a new efficient perceptual data control and a scheduling method to determine different entities based on the number; however, this method lacks the extraction of entity information. Literature [6], on the other hand, researched this with regard to data classification models between similar datasets to obtain the distribution of datasets based on the judgment of distance, which can improve the data classification accuracy. Literature [7] proposed their model about classifying data by using k-means to implement clustering operation on different data; this model has poor performance in most of the data sets. The common feature of the above methods is that they have poor performance and cannot satisfy some specific scenarios. Researchers have researched the area of big data. Literature [8] proposes a data preprocessing model based on big data. This model is characterized by fast convergence and good classification results. Kumar and Vivekanandan [9] propose a feature similar fuzzy method, which can be applied to the field of detecting roads with high accuracy of data classification. Literature [10] studied a deep learning data preprocessing model, where the records of the data set are considered as the input to the model, and the information of the entities is continuously extracted to finally get the prediction results. In the above model, there is a loss of too much valuable information when the information of entities is extracted. Data classification is also very much sought after by researchers in the field of IoT. Literature [11] proposed a data monitoring and data preprocessing method to perform data preprocessing operation of water source information by this model; this model was combined with artificial intelligence techniques for classification of water source data with high accuracy. Javadpour et al. [12] proposed a machine learning-based data classification model, which is widely used in health services and has high accuracy in classifying health information. Literature [13] proposed a data classification model in matrix space; this model modified the weight calculation method, and the data classification accuracy was improved. In the above studies, all of them do not have a comprehensive extraction of data attribute information, which will reduce the classification accuracy of the model. The methods and models for conducting wireless sensor networks, big data, and IoT domains are not suitable for image feature extraction and classification and do not retain most of the features of the images.However, today, for the image classification problem, deep learning of Capsule Network (CN) is more widely used; literature [14] proposed to combine the capsule network in deep learning for data classification operation on special images, which largely improves the accuracy of data classification and also reflects that the capsule network retains attribute information very strong ability. Comparing the capsule network with data preprocessing models applied in major fields, the capsule network does not require much amount of data during training to fit the data operation and can get a very accurate model. Training and mining of deep learning models for user uploaded image data in a population collaborative intelligence platform can effectively solve the problem of perceptual data classification. Naranjo et al. [15] investigate the control and scheduling mechanism of perceptual tasks. The energy consumption management method in the mechanism enables the transfer of information between different participants, which can be achieved to reduce the energy consumption for the group collaborative intelligence platform. This mechanism can be broadly summarized in two aspects: the initial stage of dimensionality reduction of the perceptual data and later, the control and scheduling of different participants for perceptual data collection. Erhan et al. [16] researched an unprecedented network of group collaborative intelligence systems; this network uses the latest preprocessing algorithms for perceptual information, combines the daily action trajectories and contribution values of users, and provides an incentive mechanism for users, and a large number of users voluntarily perform the collection of perceptual data. This can maximize the benefits of the group collaborative intelligence network and reduce the cost of consumption, which can achieve the expected results. Bi et al. [17] researched a system that aggregates information from mobile devices carried by mobile users and uploads it to the group collaborative intelligence system, which can reduce the energy consumption of the group collaborative intelligence system by analyzing the information of mobile devices and, thus, the collection of perceptual information. The system is good at capturing some unimportant information for analysis and processing, so that it can achieve both the perception task and the minimum consumption of resources.


## 3. An Algorithmic Model for Collaborative Intelligent Clustering of Groups

### 3.1. A Framework for Collaborative Intelligent Clustering Algorithms for Groups

The theory related to population intelligence first appeared in the late 1990s, and any model and problem solution constructed by the behavior of a socially motivated population of organisms were defined as population intelligence research, and the algorithms evolving from it were collectively referred to as population intelligence algorithms. In the last two decades of research, numerous scholars have proposed a large number of population intelligence algorithm models, such as particle swarm algorithm, artificial bee colony algorithm, cuckoo search algorithm, and fruit fly optimization algorithm, by providing the clustering algorithm with certain necessary prior knowledge of clustering through the stronger merit-seeking ability of the population intelligence algorithm, thus relatively weakening the influence of input parameters on the clustering results, achieving the purpose of improving the adaptability of the algorithm, reducing the burden of the user, improving the accuracy and robustness of the clustering algorithm, and achieving the purpose of improving the clustering results. Compared with traditional optimization algorithms such as genetic algorithms and artificial immune algorithms, swarm intelligence algorithms have the features of the simple framework and fast convergence and give intelligent search capability to such models to solve realistic optimization problems by simulating the population intelligence behavior of organisms. The particle swarm algorithm design is inspired by the observation and analysis of the foraging behavior of a flock of birds. The bird flock shows obvious social and discipline in the process of foraging, and individuals in the population guide the overall behavior of the population by analyzing local information, which can be applied to solve complex optimization problems; similar to the particle swarm algorithm, the artificial bee swarm algorithm simulates the intelligent behavior of bees; the cuckoo search algorithm is a group intelligence model based on the unique parasitic behavior of cuckoos in nature. Scholars have proposed a huge amount of optimization methods in the process of solving optimization problems, such as Newton's method, conjugate gradient method (unconstrained optimization algorithm), Monte Carlo method (constrained optimization algorithm), and metaheuristic algorithm (intelligent optimization algorithm), depending on the way of the problem being solved and the constraints. Population intelligence algorithms, as a branch of metaheuristic algorithms, belong to the same category of intelligent optimization algorithms as metaheuristic algorithms [18]. According to the distinction of algorithms simulating evolutionary features, metaheuristic algorithms can be divided into three categories: ecosystem simulation algorithms, population intelligence optimization algorithms, and evolutionary algorithms. Figure 1 shows the specific classification of population intelligence algorithms.Figure 1
Group intelligence algorithm classification diagram.The group collaborative intelligence contains many different types of perceptual data classification, and a large number of participants provide different perceptual data such as videos and images to the group collaborative intelligence platform. The group collaborative intelligence system includes data requestors, mobile users, data classification models, and a mobile group collaborative intelligence platform. One of the data classification models is used to fit the operation to these perceptual data, which can effectively solve the data classification accuracy problem. In this paper, the perceptual data is collected for the vehicle situation of a regional traffic section, and the capsule network data classification model is introduced into the group collaborative intelligence to classify the image data uploaded by the users to improve the perceptual data classification accuracy. When completing a single task, the group collaborative intelligence platform does not need to perform data classification processing. In a multitask concurrent environment, the group collaborative intelligence platform needs to issue multiple tasks, and participants need to complete multiple tasks and upload data collected for different tasks, and the task server is based on classifying the images of different tasks to provide the corresponding data to the requesters more easily. The multitask concurrent environment involves different data for example images of different environments, which requires the application of neural networks to the group collaborative intelligence platform, which can solve the problem of accuracy in classifying images.


### 3.2. Capsule Network-Based Data Classification Model

With exploration and research on capsule networks, it is generally believed that capsule networks are ahead of other neural networks in terms of computational power, convergence speed, stability, and feature extraction because they have dynamic routing algorithms. Feature selection can make the capsule network model converge faster by continuously extracting more useful feature information from the feature matrix in the previous network layer to form a completely new set of feature information, i.e., feature selection. This solves the problem of vanishing gradients in the capsule network and also improves the accuracy, fitting ability, and training speed of the capsule network to filter the features useful for the current learning task from the given feature set. Since the core of capsule networks is that each vector retains different properties of the entity, capsule networks have a good fitting ability. The capsule network consists of a large number of capsule layers, each of which is divided into multiple capsules and vectors that are the output of the capsule network, where the probability of the existence of an entity is represented by the length of the vector, and the direction of the vector represents the entity attributes (shape, size, hue, etc.). The capsule network is composed of a convolutional layer, a primary capsule layer, and a digital capsule layer and is embedded with an Adam optimizer and a loss function. During the training process, operations related to the calculation of gradients, updating of parameters, and saving of the model are performed, and the final result of the capsule network output is a digital capsule layer. The next layer of the capsule network is inferred from the previous layer of the capsule network using a dynamic routing algorithm, and if the result obtained is the same, the next layer of the capsule network is woken up. The capsule network can retain most of the feature information while fitting compared to the convolutional neural network, where the network layer retains more feature information. So, capsule network-based population collaborative intelligence data classification model for image classification improves the accuracy of population collaborative intelligence data classification [19].The process of the data classification algorithm based on capsule network is as follows: R data objects are randomly read from the database, t objects are randomly selected in this dataset of packets of n data objects, and these t objects are also the initial clustering centers we select.(1)v¯i=δxδtn!r!n−r!xγ+μx.Calculate the remaining data objects and get their Euclidean distances to the initial clustering centersRSUi:(2)RSUi=R⋅MN⋅∑i=1nVi−V¯2.Lβ is the probability density function of the Levy distribution function:(3)Lβ=∏βVi+θ+η.Calculate the odor concentration judgment value for each locationsmelli:(4)smelli=∑m≠a,n≠biαm2tiβn2tj.After manipulation by the language model, the text data is transformed into the form of a vector, and a common similarity measure is cosine similarityGJ :(5)GJ=j∂γ∂j+1n∑i=1nXiYi.Cluster analysis is considered to be the most studied and widely used method for unsupervised learning tasks. The class approach is based on the similarity between all samples in a data set and divides the data set into several independent subsets, each defined as a “class cluster,” and each class cluster has one and only one clustering center. The clustering process automatically forms the cluster structure only. In the field of pattern recognition, clustering analysis is seen as a separate process for finding the intrinsic distribution structure of data; in data mining tasks, it is mostly used as a data preprocessing technique to organize and analyze data for other classification and prediction models. The biggest advantage of capsule networks over convolutional networks is that the length of the vector is used to represent the probability of the existence of an entity; it is a set of vectors consisting of different neurons that become capsules; capsule networks focus not only on these features, but also on the attribute information of the features; capsules of capsule networks are linked by dynamic routing. Convolutional neural networks can obtain feature information through pooling operations, but some local information is also lost. Convolutional neural network extracts feature information incomplete for some datasets with the interference of noise. Convolutional neural networks are difficult to interpret the positional relationship between the part and the whole and have weak generalization ability. The power of the capsule network is that it has dynamic routing algorithms, and the capsule network does not need much data to have the strong fitting ability. Therefore, for a class of tasks with very high requirements on time, i.e., emergency events, the group collaborative intelligence platform enters the capsule network to classify and store the data, and the data requestor can realize the classification query when obtaining the data, which improves the data classification accuracy of the group collaborative intelligence platform and provides the data to the data requestor more accurately. The power of capsule networks is that they have dynamic routing algorithms, and capsule networks do not need much data to have the strong fitting ability. For convolutional neural networks, capsule networks can then be trained to fit on very few image datasets with much better results than convolutional neural networks; however, convolutional neural networks can only be trained to fit on a large number of datasets. The capsule network can handle ambiguous scenes well, while the convolutional neural network does not work very well in handling ambiguous scenes. Convolutional neural networks do not retain a lot of feature information, and a small change in the input does not produce a large change in the output, whereas capsule networks retain a lot of feature information, and a small change in the input has a large impact on the output. This is the reason why capsule networks are more accurate than convolutional neural networks. The disadvantages of capsule networks compared to convolutional neural networks are more computationally intensive and more energy-consuming, and naturally, the hardware requirements are very high. The power of the capsule complex is that it has a dynamic routing algorithm, and the capsule network does not need much data to have a strong fitting ability [20]. Therefore, for a class of tasks with very high time requirements, i.e., emergency events, the group collaborative intelligence platform into the capsule network for data classification and storage, data requesters can achieve classification queries when obtaining data, which improves the accuracy of the group collaborative intelligence platform for data classification and provides data to data requesters more accurately. The capsule network data classification model is shown in Figure 2.Figure 2
Capsule network data classification model.Decision-level data mining is the highest level of data mining technique, which starts from the requirements of the decision problem and makes full use of the feature information extracted at the feature level for the mining implementation of specific decision objectives. Unlike some supervised machine learning methods, clustering analysis cannot measure the reasonableness of the delineation due to the lack of labeled data, so the reasonable construction of a criterion function to evaluate the quality of clustering (clustering evaluation index) becomes another research direction in the study of clustering algorithms. According to the difference of evaluation principles and methods, the commonly used clustering evaluation indexes at this stage are roughly divided into two categories, which are internal evaluation indexes and external evaluation indexes. The internal evaluation mainly evaluates the degree of separation and compactness between class clusters; external evaluation metrics apply to the case where the data category labels are known and measure the algorithm clustering accuracy by some statistical methods. Based on the above analysis and the type of data used in this paper, Silhouette metrics (internal evaluation metrics) and F-measure metrics (external evaluation metrics) are selected as full-text clustering evaluation metrics.


## 3.1. A Framework for Collaborative Intelligent Clustering Algorithms for Groups

The theory related to population intelligence first appeared in the late 1990s, and any model and problem solution constructed by the behavior of a socially motivated population of organisms were defined as population intelligence research, and the algorithms evolving from it were collectively referred to as population intelligence algorithms. In the last two decades of research, numerous scholars have proposed a large number of population intelligence algorithm models, such as particle swarm algorithm, artificial bee colony algorithm, cuckoo search algorithm, and fruit fly optimization algorithm, by providing the clustering algorithm with certain necessary prior knowledge of clustering through the stronger merit-seeking ability of the population intelligence algorithm, thus relatively weakening the influence of input parameters on the clustering results, achieving the purpose of improving the adaptability of the algorithm, reducing the burden of the user, improving the accuracy and robustness of the clustering algorithm, and achieving the purpose of improving the clustering results. Compared with traditional optimization algorithms such as genetic algorithms and artificial immune algorithms, swarm intelligence algorithms have the features of the simple framework and fast convergence and give intelligent search capability to such models to solve realistic optimization problems by simulating the population intelligence behavior of organisms. The particle swarm algorithm design is inspired by the observation and analysis of the foraging behavior of a flock of birds. The bird flock shows obvious social and discipline in the process of foraging, and individuals in the population guide the overall behavior of the population by analyzing local information, which can be applied to solve complex optimization problems; similar to the particle swarm algorithm, the artificial bee swarm algorithm simulates the intelligent behavior of bees; the cuckoo search algorithm is a group intelligence model based on the unique parasitic behavior of cuckoos in nature. Scholars have proposed a huge amount of optimization methods in the process of solving optimization problems, such as Newton's method, conjugate gradient method (unconstrained optimization algorithm), Monte Carlo method (constrained optimization algorithm), and metaheuristic algorithm (intelligent optimization algorithm), depending on the way of the problem being solved and the constraints. Population intelligence algorithms, as a branch of metaheuristic algorithms, belong to the same category of intelligent optimization algorithms as metaheuristic algorithms [18]. According to the distinction of algorithms simulating evolutionary features, metaheuristic algorithms can be divided into three categories: ecosystem simulation algorithms, population intelligence optimization algorithms, and evolutionary algorithms. Figure 1 shows the specific classification of population intelligence algorithms.Figure 1
Group intelligence algorithm classification diagram.The group collaborative intelligence contains many different types of perceptual data classification, and a large number of participants provide different perceptual data such as videos and images to the group collaborative intelligence platform. The group collaborative intelligence system includes data requestors, mobile users, data classification models, and a mobile group collaborative intelligence platform. One of the data classification models is used to fit the operation to these perceptual data, which can effectively solve the data classification accuracy problem. In this paper, the perceptual data is collected for the vehicle situation of a regional traffic section, and the capsule network data classification model is introduced into the group collaborative intelligence to classify the image data uploaded by the users to improve the perceptual data classification accuracy. When completing a single task, the group collaborative intelligence platform does not need to perform data classification processing. In a multitask concurrent environment, the group collaborative intelligence platform needs to issue multiple tasks, and participants need to complete multiple tasks and upload data collected for different tasks, and the task server is based on classifying the images of different tasks to provide the corresponding data to the requesters more easily. The multitask concurrent environment involves different data for example images of different environments, which requires the application of neural networks to the group collaborative intelligence platform, which can solve the problem of accuracy in classifying images.


## 3.2. Capsule Network-Based Data Classification Model

With exploration and research on capsule networks, it is generally believed that capsule networks are ahead of other neural networks in terms of computational power, convergence speed, stability, and feature extraction because they have dynamic routing algorithms. Feature selection can make the capsule network model converge faster by continuously extracting more useful feature information from the feature matrix in the previous network layer to form a completely new set of feature information, i.e., feature selection. This solves the problem of vanishing gradients in the capsule network and also improves the accuracy, fitting ability, and training speed of the capsule network to filter the features useful for the current learning task from the given feature set. Since the core of capsule networks is that each vector retains different properties of the entity, capsule networks have a good fitting ability. The capsule network consists of a large number of capsule layers, each of which is divided into multiple capsules and vectors that are the output of the capsule network, where the probability of the existence of an entity is represented by the length of the vector, and the direction of the vector represents the entity attributes (shape, size, hue, etc.). The capsule network is composed of a convolutional layer, a primary capsule layer, and a digital capsule layer and is embedded with an Adam optimizer and a loss function. During the training process, operations related to the calculation of gradients, updating of parameters, and saving of the model are performed, and the final result of the capsule network output is a digital capsule layer. The next layer of the capsule network is inferred from the previous layer of the capsule network using a dynamic routing algorithm, and if the result obtained is the same, the next layer of the capsule network is woken up. The capsule network can retain most of the feature information while fitting compared to the convolutional neural network, where the network layer retains more feature information. So, capsule network-based population collaborative intelligence data classification model for image classification improves the accuracy of population collaborative intelligence data classification [19].The process of the data classification algorithm based on capsule network is as follows: R data objects are randomly read from the database, t objects are randomly selected in this dataset of packets of n data objects, and these t objects are also the initial clustering centers we select.(1)v¯i=δxδtn!r!n−r!xγ+μx.Calculate the remaining data objects and get their Euclidean distances to the initial clustering centersRSUi:(2)RSUi=R⋅MN⋅∑i=1nVi−V¯2.Lβ is the probability density function of the Levy distribution function:(3)Lβ=∏βVi+θ+η.Calculate the odor concentration judgment value for each locationsmelli:(4)smelli=∑m≠a,n≠biαm2tiβn2tj.After manipulation by the language model, the text data is transformed into the form of a vector, and a common similarity measure is cosine similarityGJ :(5)GJ=j∂γ∂j+1n∑i=1nXiYi.Cluster analysis is considered to be the most studied and widely used method for unsupervised learning tasks. The class approach is based on the similarity between all samples in a data set and divides the data set into several independent subsets, each defined as a “class cluster,” and each class cluster has one and only one clustering center. The clustering process automatically forms the cluster structure only. In the field of pattern recognition, clustering analysis is seen as a separate process for finding the intrinsic distribution structure of data; in data mining tasks, it is mostly used as a data preprocessing technique to organize and analyze data for other classification and prediction models. The biggest advantage of capsule networks over convolutional networks is that the length of the vector is used to represent the probability of the existence of an entity; it is a set of vectors consisting of different neurons that become capsules; capsule networks focus not only on these features, but also on the attribute information of the features; capsules of capsule networks are linked by dynamic routing. Convolutional neural networks can obtain feature information through pooling operations, but some local information is also lost. Convolutional neural network extracts feature information incomplete for some datasets with the interference of noise. Convolutional neural networks are difficult to interpret the positional relationship between the part and the whole and have weak generalization ability. The power of the capsule network is that it has dynamic routing algorithms, and the capsule network does not need much data to have the strong fitting ability. Therefore, for a class of tasks with very high requirements on time, i.e., emergency events, the group collaborative intelligence platform enters the capsule network to classify and store the data, and the data requestor can realize the classification query when obtaining the data, which improves the data classification accuracy of the group collaborative intelligence platform and provides the data to the data requestor more accurately. The power of capsule networks is that they have dynamic routing algorithms, and capsule networks do not need much data to have the strong fitting ability. For convolutional neural networks, capsule networks can then be trained to fit on very few image datasets with much better results than convolutional neural networks; however, convolutional neural networks can only be trained to fit on a large number of datasets. The capsule network can handle ambiguous scenes well, while the convolutional neural network does not work very well in handling ambiguous scenes. Convolutional neural networks do not retain a lot of feature information, and a small change in the input does not produce a large change in the output, whereas capsule networks retain a lot of feature information, and a small change in the input has a large impact on the output. This is the reason why capsule networks are more accurate than convolutional neural networks. The disadvantages of capsule networks compared to convolutional neural networks are more computationally intensive and more energy-consuming, and naturally, the hardware requirements are very high. The power of the capsule complex is that it has a dynamic routing algorithm, and the capsule network does not need much data to have a strong fitting ability [20]. Therefore, for a class of tasks with very high time requirements, i.e., emergency events, the group collaborative intelligence platform into the capsule network for data classification and storage, data requesters can achieve classification queries when obtaining data, which improves the accuracy of the group collaborative intelligence platform for data classification and provides data to data requesters more accurately. The capsule network data classification model is shown in Figure 2.Figure 2
Capsule network data classification model.Decision-level data mining is the highest level of data mining technique, which starts from the requirements of the decision problem and makes full use of the feature information extracted at the feature level for the mining implementation of specific decision objectives. Unlike some supervised machine learning methods, clustering analysis cannot measure the reasonableness of the delineation due to the lack of labeled data, so the reasonable construction of a criterion function to evaluate the quality of clustering (clustering evaluation index) becomes another research direction in the study of clustering algorithms. According to the difference of evaluation principles and methods, the commonly used clustering evaluation indexes at this stage are roughly divided into two categories, which are internal evaluation indexes and external evaluation indexes. The internal evaluation mainly evaluates the degree of separation and compactness between class clusters; external evaluation metrics apply to the case where the data category labels are known and measure the algorithm clustering accuracy by some statistical methods. Based on the above analysis and the type of data used in this paper, Silhouette metrics (internal evaluation metrics) and F-measure metrics (external evaluation metrics) are selected as full-text clustering evaluation metrics.


## 4. A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

The basic principle of cloud computing is to structure cloud computing in its distributed computer clusters based on network protocols, instead of distributing it in remote servers or local computers and synchronizing data between local computers and remote servers through the Internet. Enterprises can customize their services on the client side according to their needs and access the server system according to their needs, which also reduces the computing burden of client servers Cloud computing allocates resources or provides services through virtualization technology with efficient real-time scalability, while web-based services provide analysis and processing of massive amounts of data. As a great change in the computer era, it not only provides users with a reliable database to store data, but also does not have to worry about their local server system down resulting in data loss and other troubles and, at the same time, does not require high equipment on the client-side, reducing the user's dependence on IT expertise [21]. The basic principle of cloud computing is to structure cloud computing in its distributed computer clusters based on network protocols instead of distributing it in remote servers or local computers and synchronizing data between local computers and remote servers through the Internet. The goal of cloud technology is to provide personalized services with high reliability, versatility, and scalability in an extremely inexpensive way, enabling the full and efficient use of IT resources. With this goal in mind, there is a need for rational management of database centers, optimized resource allocation, enhanced virtualization techniques as well as improved data mining techniques and processing technologies, and enhanced data privacy and security properties.The system can not only realize the distributed storage of data and parallel computing of data, but also support the expansion of relevant standards under the cloud computing platform to ensure good advancement to adapt to the future development of technology. The graphical user interface is the window of the system function, and the main function of the interaction layer is to ensure the interaction experience between the system and the user. In the interaction layer, users can customize detailed mining tasks according to their needs, according to the results they want, and view or download the data results they need through the results returned by the system. The interaction layer is the bridge between the user and the system. The user enters the request command through the interaction layer, and the command is sent to the system through the next layer (algorithm layer) by calling the corresponding algorithm to complete the task, and the system returns the processing result to the user. The algorithm layer is the core processing layer of the whole system, and the effective implementation of the algorithm can cope with a variety of data processing requests. The traditional data mining algorithm or the improved mining algorithm is deployed on the cloud platform to achieve parallel computing of the algorithm, which can meet various big data mining requirements. To realize the parallel design of the algorithm on the cloud platform, the Map function, Combine function, and Reduce function are designed to implement the algorithm execution process in Figure3, respectively.Figure 3
Design flow of population collaborative intelligent clustering algorithm for cloud computing data center.After the Map task is completed, the new cluster center results are compressed and sent to the Combine function. The Combine task merges the cluster IDs of the same results without incurring cluster communication overhead due to client-server interaction, as the intermediate result data is stored on the local hard disk. In the Combine function, new cluster centers are obtained by summing the local cluster IDs with the same clusters, and the number of records in each cluster is counted. The Combine operation gets the data for each node by using the HTTP network protocol for the input of the Reduce function and copies the result to the server host, which consists of two parts, the cumulative sum of the recorded vectors and the total number of vectors. Immediately after this, Reduce function manipulates the record vectors of uniform clusters on all nodes and cumulates them to determine the total number of vectors and obtain new clusters, which can be used as new cluster centers in the next iteration of the operation. The convergence of the cluster centers is used to determine whether the next computation of the iterative operation is still required.


## 5. Experimental Verification and Conclusions

Figure4 shows the experimental results for the eight Benchmark functions with 1000 iterations and a function dimension of 30. The experimental results show that, for the unimodal Benchmark functions, the KM-FOA algorithm outperforms the other three algorithms on F1, F2, and F4 functions, especially on the F3 function, where the search accuracy is significantly better than that of the more and FOA algorithms. For the multimodal Benchmark functions with many local extremes, the KM-FOA algorithm significantly improves the search accuracy on functions F5, F6, F7, and F8 and makes progress in the stability of the search, and the average and mean squared deviation of the search are better than those of the other three algorithms. Similar conclusions to the traditional experiments are obtained for the increased dimensionality of the solution problem. For the unimodal Benchmark function, the average finding accuracy of the KM-FOA algorithm is 18, and the average finding accuracy of the mFOA, FOA, and PSO algorithms is about 25, 23, and 27, respectively; for the multimodal Benchmark function, the average finding accuracy of the KM-FOA algorithm is 19, and the average finding accuracy of the mFOA, FOA, and PSO algorithms is 19. For the multimodal Benchmark functions, the average finding accuracy of the KM-FOA algorithm is 19, and the average finding accuracy of the mFOA, FOA, and PSO algorithms is 29, 31, and 26, respectively; again, the overall performance of the KM-FOA algorithm in terms of finding accuracy is better than that of the other three algorithms. The experimental data all show that the knowledge memory model enables the KM-FOA algorithm to improve the search efficiency of the algorithm for the local solution space on most functions, and to improve the algorithm's search accuracy within a limited number of iterations to achieve the purpose of improving the performance of the algorithm.Figure 4
Benchmark function optimization test results.Figure5 shows the average number of iterations and success rate of the eight Benchmark functions after 50 independent experiments with a target accuracy of 1 × 10−6. The average number of iterations is obtained by calculating the average number of iterations to achieve the target accuracy for each experiment, and the success rate is the percentage of the number of experiments to achieve the target accuracy over the total number of experiments. The experimental results show that, for the unimodal Benchmark function, the average number of iterations of the KM-FOA algorithm is significantly less than that of the FOA algorithm, the FOA algorithm, and the PSO algorithm, and the success rate of the search is also better than that of the other three algorithms. Typically, the KM-FOA algorithm achieves a 100% success rate for all four unimodal Benchmark functions, while the mFOA algorithm also achieves a 100% success rate for F1, F2, and F4 but fails to achieve the target accuracy within the maximum number of iterations by failing to optimize on function F3. The average number of iterations of the KM-FOA algorithm on functions F1, F2, and F4 is significantly less than that of the mFOA algorithm, which proves that the KM-FOA algorithm has a faster convergence speed compared with the most algorithm. In Figures 3–5, the success rates of the FOA algorithm for functions F1, F2, and F4 are 80%, 5%, and 45%, respectively, which are lower than those of the KM-FOA algorithm and the mFOA algorithm, while the success rate of the FOA algorithm for function F3 is 0, which is the same as that of the mFOA algorithm and significantly lower than that of the KM-FOA algorithm. The PSO algorithm has a success rate of 0 for all four functions, which is the worst performance among the four algorithms, and the number of iterations is significantly higher than that of the other three algorithms. For the multimodal Benchmark function, only the KM-FOA algorithm and the mFOA algorithm are successful in finding the target accuracy for functions F5 and F7, but the success rate of the most algorithm is only 10% and 20%, and the number of iterations is 9 times more than that of the KM-FOA algorithm. The success rate of mFOA and FOA algorithms reaches 100% and 65%, respectively. The difference with the KM-FOA algorithm is not large, but the average number of iterations of the latter is about 1/4 of the PFOA algorithm and 1/60 of the FOA algorithm, so the difference is more obvious; moreover, on function F8, only the KM-FOA algorithm achieves the target accuracy and completes 50 experiments with a 100% success rate of finding the best. The above conclusions can objectively show that KM-FOA has a greater improvement in convergence speed and convergence stability when solving the optimization problem, and the overall performance of the algorithm is better than the algorithms involved in the comparison. The curve in Figure 5 shows a step shape, and the curve steps show a trend of gradually increasing with the increase of the number of iterations. This phenomenon indicates that the improved algorithm has a strong ability in jumping out of the local extremes, and this escape ability will be enhanced gradually with the increase of the number of optimization searches, so that the algorithm has a stronger global survey ability in the later stage of the optimization search. To further illustrate the faster convergence speed of the KM-FOA algorithm, the convergence curves of the KM-FOA algorithm, the mFOA algorithm, and the FOA algorithm on the eight Benchmark functions are graphically presented. The convergence curves of the KM-FOA algorithm on functions F3, F5, F7, and F8 are relatively undulating, and the curves are step-like, and with the increasing number of iterations, the curve steps show a trend of gradually increasing; this phenomenon indicates that the improved algorithm has a strong ability to jump out of the local extremes, and this escape ability will be gradually enhanced with the increase of the number of times of seeking, so that the algorithm has a stronger ability and have stronger global surveying ability in the late stage of the optimization search.Figure 5
Convergence curve of the Benchmark function.Figure6 shows the clustering results of the IDPCA algorithm, DPCG algorithm, and GDPC algorithm for datasets R15, Path-based Flame, and Spiral. For the dataset Flame, only the aggregation effect of the IDPCA algorithm agrees with the information in the real dataset, while the clustering result of DPCG reaches the correct number of clusters, but the obtained clustering result is obviously in error compared with the real clustering effect; the GDPC algorithm yields the wrong number of clusters. From the Path-based and Spiral datasets, the results of the GDPC algorithm do not match the true number of clusters although they are similar to the true clustering effect; the aggregation results of the IDPCA algorithm match the number and information of the true dataset. This further proves that the clustering effect of the IDPCA algorithm is better than that of DPCG and GDPC algorithms. The Silhouette indicator and the clustering accuracy F-measure indicator of the IDPCA algorithm are better than those of DPCG and GDPC algorithms in Seeds, Wine, D31, and Flame datasets. In particular, both metrics are close to 1 in the Seeds dataset. The Metric of the IDPCA algorithm is 24% higher in the Seeds dataset compared with the DPCG algorithm; the FM metric is 35% higher. Solmetric of IDPCA algorithm is 30% higher in the Seeds dataset compared with GPDC algorithm; F-measure metric is 36% higher. The F-measure metric of the IDPCA algorithm in the Flame dataset is 100% more accurate than that of the DPCG algorithm and GDPC algorithm. The accuracy FM metrics of the IDPCA algorithm in datasets Ecoli and Path-based are 28% and 31% higher compared to the DPCG algorithm, and 32% and 17% higher compared to the GDPC algorithm, respectively.Figure 6
Clustering results of different clustering algorithms.The experimental results in Figure7 show that the user-based collaborative filtering algorithm (UCF) does not take into account the change of user interest over time, so the recommendation result is relatively less satisfactory, with an MAE optimal value of 2.7650, and of course, the algorithm runs relatively fast; the user-attribute time-based collaborative filtering recommendation (UATCF) takes into account the change of user interest and reduces the effect of data sparsity, so the algorithm's effect is improved by nearly half compared to UCF, which is about 1.5170. The running time of UATCF is negligible compared to the whole algorithm due to the addition of user attribute calculation and time factor, so the running time of UATCF does not change much, the collaborative filtering algorithm of user attribute time with IPK-means. The recommendation algorithm (IUATCF) is better than the first two algorithms in terms of the number of neighbors, reaching 0.9027. This is because the algorithm classifies the users into clusters based on UATCF, which makes similar users in the same class and has more reference value. The algorithm is applied to the recommendation system, the clustering operation of users can be done offline, and the system needs to calculate the similarity between target users and predict the rating online according to the online users and feed the recommendation results to the target users. When the algorithm is applied to the recommendation system, the clustering operation of users can be done offline, and the system calculates the similarity between target users and predicts the rating online according to the needs of online users and feeds the recommendation results to the target users.Figure 7
MAE values of the algorithm on different training sets.When the cloud IoT platform storage system satisfies the concurrency of IoT sensing data requests, adding the Memcached caching subsystem will result in slower response time when processing sensing data than without the distributed caching system; that is, adding the distributed caching subsystem will improve the sensing data processing performance by about three times. Since the distributed caching subsystem described in this paper uses two Memcached nodes, all the data that need to be cached are stored with memcached1 in the background program segment of the cloud IoT platform, all the query requests are also executed in the memcached1 node, and when memcached1 is down, all the caching operations are executed in memcache2 node. Therefore, theoretically, when the concurrency of sensor data storage requests does not reach the maximum allowed value of the distributed cache subsystem, one Memcached node works with the same performance as two Memcached nodes, and this conclusion is verified below. The tests were performed as described in the above method and requirements in this subsection, and the data was obtained as the average processing time of 36.8 ms for single node working sensing data and 36.6 ms for two-node working sensing data, removing the chance factor and concluding that the analysis is correct, and the test results are shown in Figure8. When the cloud IoT platform storage system meets the concurrency of IoT sensing data requests, adding a Memcached caching subsystem is lower than the response time when processing sensing data without distributed caching system, and adding distributed caching subsystem will improve the sensing data processing performance by about 3 times.Figure 8
Memcached single-node versus dual-node processing time.


## 6. Conclusions

When IoT grows to a certain scale, integration with cloud computing is an inevitable trend, and cloud computing technology can provide virtualization, computing, storage, and other support services for IoT technology. Data mining has become one of the most active branches of database research, development, and application, and every enterprise is facing a huge challenge in large-scale data processing and analysis technology. This paper is rooted in the context of data mining becoming a natural need to achieve analytical processing of massive data by combining data mining techniques with a clustering framework in the era of cloud computing. In this paper, we provide an overview of the theories related to collaborative group intelligence and deep learning, synthesize and analyze existing data classification models of collaborative group intelligence, and further introduce capsule networks into the data classification of collaborative group intelligence platforms for the accuracy problem of collaborative group intelligence data classification. By establishing the overall framework of the system of group collaborative intelligence and classifying the perceptual tasks, the foundation for building the capsule network structure is laid. The capsule network loss function uses interval loss and reconstruction loss to improve the speed of model parameter optimization during training. This algorithm needs to rescan the entire dataset traversal cycle for each iteration of the operation, which is very time-consuming and can be used for other module layers of the system to build a more powerful data mining platform in future work. The data classification model accuracy is improved by introducing a dynamic routing algorithm for sample data to optimize the parameters of the hidden layer during the capsule network training process. The reason why the convolutional neural network is not as accurate as the capsule network in classifying perceptual information is that the capsule network has a dynamic routing algorithm to iteratively update the parameters and still has the structure of convolution, using the length of the matrix to represent the odds of things. The simulation experimental results show that the perceptual information classification model of the capsule network is more accurate than the perceptual information classification model class of the residual network and is more precise in providing the required valuable information to the perceptual information demander.



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*Source: 1476661-2022-03-25.xml*