# Research on the Application of Adaptive Matching Tracking Algorithm Fused with Neural Network in the Development of E-Government

Authors: Peian Lv
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

With the development of information technology and the deepening of government system reform, e-government has become one of the key points of government construction. However, both in developing countries and developed countries, there is a common phenomenon that the success rate of e-government projects is not high. Although various e-government evaluation index systems have been proposed in both theoretical research and application aspects, which provide strong support for the development of e-government, these index systems still have limitations. This paper comprehensively applies BP neural network and adaptive matching tracking, and constructs an e-government performance evaluation model based on adaptive matching neural network. We conduct simulation experiments on the constructed model to verify the accuracy and rationality of the model evaluation. It is feasible to introduce the Balanced Scorecard to construct the E-government performance evaluation index system. The constructed index system has strong objectivity, operability, comprehensiveness, guidance and sustainability, and can be applied to the practice of e-government performance evaluation. It is feasible to use the adaptive matching tracking neural network model to evaluate the performance of e-government. The model has the ability to self-learn the evaluation samples, and can grasp the mapping relationship between the evaluation indicators and the evaluation results, so as to imitate the evaluation of experts, and overcome the evaluation randomness and subjective uncertainty that are difficult to get rid of in traditional manual evaluation. In order to better evaluate the operation status of the e-government system and its adaptability to future needs, and find out its existing problems, this paper fully considers the interaction process between the government and enterprises, and establishes a corresponding responsive government. The application of adaptive matching tracking neural network model for e-government performance evaluation has better optimization results than the original BP neural network model. The specific performance is that the convergence speed is fast, the training will not fall into a local minimum, and the error of the evaluation results is small.


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## Body

## 1. Introduction

The high-efficiency, transparency, convenience, and automation features of e-government have allowed governments of all countries to see a bright future [1]. Immediately, countries around the world began to carry out e-government construction in full swing to accelerate government transformation. But after all, it is a new business. In the process of building e-government affairs in various countries, many problems have been exposed: high investment, low return, face-saving projects and so on [2]. These problems have also caused government departments and research scholars to pay attention to the scientific problems of the development, management and evaluation of e-government. The performance evaluation of e-government has begun to be scientific, and it has gradually been paid attention to by various countries and related departments [3]. In the performance evaluation of e-government, the construction goal and value orientation are very important. It is the source basis of e-government evaluation standards and directly related to the evaluation standards and results of e-government. Since the 21st century, public services have been increasingly favored by governments around the world, and government departments are constantly reforming in the direction of providing quality services to citizens [4]. The government’s public service functions have been continuously strengthened, and the concept of a service-oriented government has gradually taken root in the hearts of the people. From the analysis of the operating methods and focus of the United Nations e-government assessment report in the past ten years, it can be easily analyzed that the weight of public services is constantly increasing [5]. In the practice of various countries, due to its convenient and transparent characteristics, e-government has been welcomed by all walks of life and has become an important measure to promote the improvement of public services [6].Joining the research and exploration of the concept of “Internet +” is the inevitable direction of e-government construction to adapt to the development of the times. In the context of the development of the new era of “Internet +,” if the construction of e-government is to seek a further leap, “Internet + government” will surely become the new direction of my country’s e-government development [7]. The development of e-government is constantly adapting to new changes in Internet information technology, and making full use of emerging technologies to create a favorable environment for government services can provide a favorable external guarantee for the sustainable development of service-oriented government construction [8]. E-government actively absorbs the advanced concepts of “Internet +” and innovatively uses “user thinking” to improve the efficiency of providing services to the public. “Internet + government affairs” will bring about the in-depth development of the transparency of government affairs information, and government affairs information will achieve higher and higher practical application value and truly serve all aspects of citizens’ lives [9]. Based on the conclusions and based on theories and knowledge related to public management and e-government service management, corresponding suggestions can be made for relevant departments [10]. It is of guidance and practical significance for the future to continue to build more high-quality e-government services to accumulate experience and knowledge.In this paper, adaptive block method is used to balance the sparsity of different signal blocks. Simulation experiments have verified that the sparse images of different signal blocks are obviously different based on the fault signal of the empirical value block, indicating the sparsity of signal blocks at this time. The large difference will easily lead to different reconstruction powers among different blocks, which will affect the overall reconstruction effect of the fault signal. In the signal compression process, the adaptive block block method can be used to ensure that different signal blocks have the same sparsity, so that the reconstruction power of each signal block during the reconstruction process is similar, thereby ensuring the overall signal Reconstruction effect. In the process of reconstructing the signal, the StOMP algorithm does not need to know the sparsity of the signal, and has certain advantages for processing signals with unknown sparsity in actual engineering. However, the reconstruction power of the algorithm will decrease rapidly as the sparsity increases. For this reason, the FBStOMP algorithm in this paper is based on the StOMP algorithm, and its reconstruction support set selection method is improved, which will support centralized atomic analysis. In order to effectively support set atoms and invalid support set atoms, and through the establishment of forward selection and backward elimination mechanisms, the number of effective support set atoms in the reconstruction support set is increased until all the effective support sets are selected into the reconstruction support set. We establish a virtual organization model of e-government, so as to expand and enrich the research methods of e-government. This paper draws on the modeling methods of virtual enterprises, and proposes a set of e-government modeling methods suitable for government-enterprise interaction, and on this basis, promotes the construction of government affairs informatization. Based on the government-enterprise interactive relationship model, a suitable e-government performance evaluation system is constructed. The traditional e-government performance evaluation system does not consider the factors of government-enterprise interaction, so it is not suitable for the e-government system proposed in this article. In order to explain whether the information between the government and the enterprise is smooth, the efficiency of information transmission is used as an indicator to evaluate the operation of the e-government system.


## 2. Related Work

In compressed sensing theory, the process of signal sparse reconstruction can be equivalently regarded as the optimal process of solving the sparse representation of the signal on a given redundant dictionary. Among the various sparse representation solutions, the matching pursuit algorithm has become one of the most widely used sparse reconstruction algorithms due to its generally fast operation speed and excellent characteristics of accurate reconstruction.As a main method to solve the problem of minimum L-0 norm, the earliest proposed matching pursuit sparse reconstruction algorithms include matching pursuit (MP) algorithm and orthogonal matching pursuit (OMP) algorithm [11]. MP algorithm is a greedy algorithm of repeated iteration approximation. The basic idea is to first construct an overcomplete library of sufficient scale, that is, the measurement matrix in compressed sensing theory, and select the best matching atom for sparse approximation, and pass a certain number of times. It overcomes the shortcomings of window welfare leaf transform and wavelet transform, guarantees accuracy and also has good adaptive flexibility [12]. However, the MP algorithm also has limitations. Because the orthogonality of the signal during projection cannot be well guaranteed, the iteration results may not be ideal, so in order to achieve the purpose of convergence, it will inevitably need to consume more time [13]. Therefore, the efficiency of MP algorithm is not high.Most of the current greedy pursuit algorithms need to predict the sparsityK for accurate reconstruction. But in actual compressed sensing, this information is usually unknown. For example, most natural image signals are only compressible under noncorrelated transforms (such as wavelet transforms), not strictly sparse. For these signals, the sparsity K cannot be well defined, let alone known exactly. There have been some improvements in some existing algorithms to solve this problem. For example, we can change the iteration stop condition in OMP, that is, stop the iteration until the residual value is smaller than a certain threshold. However, we cannot know whether this improved OMP has theoretical guarantees for deterministic refactoring and its stability. In some other methods, we can guess and estimate the value of K.In foreign countries, the research on the integration of e-government information resources is regarded as an interdisciplinary project [14]. Scholars who discuss this subject have backgrounds in information systems, computer science and engineering, public management, organization science, and information science [15]. Starting from the field of discipline, scholars pay attention to this common theme, thus forming a multiangle understanding of e-government integration, involving vertical-horizontal integration, business process-information integration, government service integration, and the theoretical basis of information system integration [16]. For example, scholars studied e-government earlier in North America, built a technical execution framework and pointed out that network-based integration has exposed the “cracks” in the bureaucratic government machine, including the degree of division and government plans [17]. After the technical implementation framework, some scholars have perfected the four-stage model of e-government development, pointing out that e-government is an important force to promote organizational and government reforms [18]. However, so far, there is no comprehensive and systematic description of these viewpoints in the literature.Although the interpretation of the integration of e-government information resources has its own focus, scholars unanimously agree that the integration and sharing of e-government information resources is not only an internal requirement for e-government construction, but also the inevitable choice for economic and social development [19]. In addition, market economy interest drive, government power enforcement and social public power promotion are also regarded as important driving forces for the integration of e-government information resources. Nevertheless, due to the lack of unified planning in the initial stage, e-government construction has not yet achieved standardization and standardization. It is difficult to be compatible between various government departments, between the government and the public, and between various systems. The ideal of integration and sharing is even more difficult to achieve. At present, the factors hindering the integration of e-government information resources mainly include: the complexity of the government information resources themselves (such as the lack of unified standards and norms); the imperfect management system; the imperfect legal protection; the contradiction between information disclosure and information security [20]. This is similar to the conclusion that the influencing factors summarized in foreign research literature come from the perspective of technology and information, the perspective of organization and management, and the perspective of politics and law. It can be seen from this that the integration and sharing of information resources, whether at home or abroad, will inevitably touch the existing power structure and established interest structure. However, because the network structure involves more and more decentralized departments and powers than in traditional business processes, it is not enough to rely on formal power alone [21]. Therefore, this power sharing environment makes the integration of information resources more difficult.In the research related to e-government services, the research based on management perspective is relatively abundant. As a public service provider, in order to make the information and government services provided by the government more convenient and easy to use, research on service management has also become an important part of the research [22]. Some scholars have studied the purpose of delivering services among service participants, and used the idea of process control to design business processes [23]. Relevant scholars believe that the management methods of the private sector are increasingly regarded as suitable solutions for the public sector to solve problems, such as the use of customer relationship management. Research shows the application prospects of personalized services in e-government, such as predicting and guiding citizen behavior [24].Regarding the understanding of the connotation of information integration, despite their very different theoretical backgrounds, scholars agree that in order to maximize the benefits of information and communication technology under the use of the government, agencies need to step up cross-border information integration [8]. The process of information integration can be understood as “a process organized by different entities in order to merge government processes and share information, short-term or long-term, combined into a large unity process.” Regarding the focus of e-government information resource integration, it emphasizes technical elements, such as interoperability, data standards, or specialized technical applications. And more scholars emphasize its social and organizational significance, focusing on collaboration, trust construction, knowledge sharing, and citizen privacy [25]. The integration of information resources between departments is a complex phenomenon with both social and technical connotations. In order to have a more comprehensive and in-depth understanding of the integration of information resources, it is necessary to extensively carry out technical and social-level interdisciplinary analysis.


## 3. Method

### 3.1. E-Government Service Platform Based on Cloud Computing

The cloud computing-based e-government service platform is a cloud computing public service platform with resource sharing, complete functions, and complete systems. It is mainly composed of an infrastructure layer, a data resource layer, an operation and maintenance management layer, and a platform service layer. The model architecture of the e-government service platform based on cloud computing is shown in Figure1.Figure 1
Cloud computing-based e-government service platform model architecture.As a key link of cloud computing, the data resource layer contains great data service delivery capabilities. The construction of the data resource layer requires cluster applications, grid technology or distributed file systems to integrate different types of storage devices in the network for data storage and business access. The data resource layer, as an effective basic support for the e-government system, will effectively integrate large-scale basic application database resources such as geographic information systems, population-based databases, medical and health care, road administration and transportation, and plan to establish high-performance database clusters corresponding to each department’s business.In a massive information processing environment, e-government systems generally use distributed unstructured parallel storage systems as an ideal solution. On the one hand, they ensure reliable storage system performance, save the construction cost of large-scale storage systems, and also achieve storage reliability at the system design level. On the other hand, storage virtualization technology realizes the substantial separation of physical equipment and storage logic, making the system architecture and resource allocation more flexible and changeable. It is these characteristics of distributed storage technology that make its application as the mainstream storage architecture of the cloud computing system platform inevitable.In addition to configuring the necessary hardware facilities in the construction of the network infrastructure layer, the resources of each node of the government department are connected to the entire network through virtualization technology. Server virtualization is a technology that abstracts physical hardware facilities from network applications. Based on virtualization technology, virtual machines that undertake different system tasks can be allowed to operate independently of each other on a physical machine composed of the same hardware device to complete work tasks. The application of virtualization technology effectively improves the utilization of infrastructure resources and the flexibility of the overall operation of physical machines, reduces capital consumption, reduces operating costs, and truly realizes on-demand supply under the resource allocation strategy of rapid dynamic deployment and migration.The security management center monitors and analyzes all internal network resources such as network equipment, security equipment, servers, hosts, and business systems in the network environment, and displays the monitoring information of all resources in the network through a unified security management platform.To ensure the normal operation of the cloud computing e-government platform, it is necessary not only to provide operation and maintenance functions such as resource management, scheduling management, and monitoring management, but also to have functions such as business management and process management. Cloud management solutions must fully consider various management elements such as infrastructure (resources), business operations, and operation and maintenance services. It is necessary to quickly and flexibly process the requests of end users, and provide the main functions of on-demand application and dynamic expansion of computing resources to meet the differentiated needs of service access. The diversity of system technology and application requirements also requires that the management of cloud computing e-government platforms should be comprehensive and diverse to adapt to cloud computing platforms based on different technologies.The platform service layer is mainly to provide a platform for application development, testing, and operation. The characteristics of cloud computing technology determine that the development, testing, and deployment of system application functions will be unified based on the platform service layer. At the same time, the various application service components in the system serving different system functions will realize effective reuse and orchestration combination on the platform service layer, and flexibly meet the needs of different service forms of various information systems.


### 3.2. Sparse Matrix

If the signal itself is sparse or sparse after a certain domain transformation, the signal is considered to be a compressible sparse signal. The characteristic of sparse signal is that most of the elements in the signal are zero or close to zero.In practical applications, in order to simplify the computational complexity, the sparse representation of nonsparse signals is often obtained through a sparse dictionary. Using a suitable dictionary is an important step for the successful implementation of compressed sensing. A sparse dictionary with a high degree of matching can greatly reduce the computational complexity of the sparse process and save running time.The discrete Fourier transform dictionary is a dictionary composed of a series of sine functions that can visually display signal frequency domain characteristics. However, this dictionary cannot reflect the time domain information of the signal, that is, it cannot distinguish the time point of the abnormal frequency. Because of this defect, the dictionary cannot handle nonstationary signals. The expression of discrete Fourier transform (DFT) is(1)Xk=∏n=1Nxn+1e−2πknj/N.The structure of the Fourier dictionary can be expressed by the following formula:(2)F=−10⋯−1WN1WN2⋯WNN−1⋯⋯⋱⋯−1WN2N−1⋯WNN−1N−1,where(3)WNkN−1=e−2πknj/N−1.The Fourier transform result is in complex form, and the function image is symmetrical according to theY coordinate axis. This shows that the data located on the imaginary axis has no practical significance, which increases the storage space and computational complexity of the data to a certain extent. In order to solve this problem, a discrete cosine transform that only retains the real number axis part of the Fourier transform has appeared. Its expression is(4)Xk=∏n=1Nxn+1sin2n−12N.Standard orthogonal dictionaries and overcomplete dictionaries have better sparse representation when dealing with specific signals, but the basis functions of these two types of dictionaries are fixed, and their structures cannot adapt to different kinds of signals, so there is a problem of low adaptability. In order to increase the adaptability of such dictionaries, it is often necessary to build a dictionary atomic library, and match the target signal with the dictionary in the atomic library one by one until the optimal effect is obtained, but this process is time-consuming and labor-intensive. The emergence of learning dictionaries solves this problem.The learning dictionary generates a dictionary through self-learning. The atoms in the dictionary have a high degree of matching with the target signal, which can adapt to different types of signals and expand the application range of the dictionary. The process of dictionary learning can be described as:(5)argminBDB+2Xs.t.αi0>k.In the formula,X is the training sample, D is the learning dictionary, and B is the sparse matrix coefficient under the learning dictionary.


### 3.3. Measurement Matrix

The measurement matrix is an important step to reduce the signal storage and transmission space, and its existence can reduce the signal storage space. In the compressed sensing algorithm, the measurement matrix compresses theN-dimensional original signal X into the M-dimensional observation signal y. When the signal X is needed, it can be reconstructed by the signal y. In order to achieve accurate signal reconstruction, the observation matrix needs to ensure that the product of the observation basis matrix and the sparse basis matrix meets the condition of RIP during the design process. This restriction can ensure that two different signals with the same sparsity degree will not be mapped to the same set during the mapping process, and satisfy the one-to-one mapping relationship between the original signal space and the sparse space signal. The matrix composed of every M column vectors extracted is a nonsingular condition.The independent and identically distributed Gaussian random measurement matrix can become a universal compressed sensing measurement matrix. This shows that if the sparse basis and the measurement basis are not correlated, the RIP properties can be met to a large extent. Therefore, in compressed sensing, the Gaussian random matrix is often used as the measurement matrix.


### 3.4. Adaptive Block

In the process of signal block compression, the length of the block is usually selected based on experience. According to the empirical value, the division of the signal can be completed in a short time, so it has been widely used in practical engineering. However, the forms of fault signals of e-government affairs are changeable, and the fault signal waveforms of different bearings are significantly different. If different signals are divided into blocks with the same block length, it is difficult to ensure that each signal block has similar sparsity after sparse transformation. In order to achieve the purpose of equalizing the sparsity between signal blocks, this section studies the components of the e-government fault signal.The e-government fault signal can be considered as a composite of three parts: impact component, inherent component, and noise component, and can be further understood as the composite signal composed of impact component, inherent component and noise signal to form an e-government fault signal, as shown in the following formula:(6)yt=wt+zt.In the formula,y(t) is the e-government fault signal, wt is the composite signal of the impact component and the inherent component, and z(t) is the noise signal.Suppose the fluctuation energy of thei-th shock relative to the theoretical period T is τi, and s(t) is the shock attenuation oscillation. The model of the composite signal wt in the e-government fault signal is(7)wt=∏iAi•t•sti+T•i.This complex signal can be simplified to consist of two simple signals:(8)Ai=A0sin2πfrt−ΦA−CA,st=sin2πfnt−Φwe−0.5Bt.In the formula,fn is the natural frequency of the system, fr is the working rotation frequency, B is the attenuation coefficient, A0 is the resonance intensity from excitation, CA is an arbitrary constant, and ΦA and ϕw are the initial phases.


### 3.5. Forward and Backward Segmented Orthogonal Matching Pursuit Algorithm

In order to improve the reconstruction ability of the StOMP algorithm for signals with larger sparseness, the structure of its reconstruction support set is studied. Letα be a sparse signal with a sparsity of K, in which there are a large number of 0 elements. Set αk,1 = 0, then the k-th column element in the matrix θ is multiplied by 0, and the result is all 0. Therefore, changing the value in the k-th column does not affect the compression result. On this basis, the set of column vectors in the matrix θ corresponding to the rows of all 0 elements in α is called the invalid support set I; and the column vectors in the matrix θ corresponding to the rows of other nonzero elements in α are compressed, and the set of these columns is called the effective support set E.(9)y=∏i⟶I1−αθi.From the compression process, we can see(10)y=∏i⟶Iαθi−∏j⟶E1−αθj.Suppose that during the reconstruction process, the reconstruction support set consists of two sets,E1 and I1. E1 is a subset of E, and its atomic number is a; I1 is a subset of I, and its atomic number is b. To achieve the successful reconstruction of the signal, the reconstruction support set should contain all the E set atoms. The selection process of the support set atoms can be regarded as the process of finding all the effective support set atoms of the E set.In order to improve the reconstruction accuracy of the StOMP algorithm, the probability that all the atoms of the effective support set can be selected into the support set should be improved. Based on this, an improved algorithm of StOMP-FBStOMP algorithm is proposed. This algorithm improves the selection method of the StOMP algorithm support set, adds atom elimination and secondary selection mechanisms on the basis of the StOMP algorithm, eliminates the invalid support set atoms selected in the reconstruction support set, and removes all the remaining support set atoms. It continues to search for effective support set atoms among atoms until all atoms are filtered out. The flow of the FBStOMP algorithm is shown in Figure2.Figure 2
FBStOMP algorithm flow.


### 3.6. Process of Random Weight Network Learning and Training

Each neuron in the forward neural network accepts the input of the previous stage and outputs to the next stage, and there is no feedback in the network. This kind of network realizes the transformation of the signal from the input space to the output space, and its information processing ability comes from the multiple recombination of simple nonlinear functions. Here we use the single hidden layer forward neural network in the forward neural network as the approximation tool, and the single hidden layer forward neural network can be expressed as(11)NLx=∏i=0L−1βi+1Gx,wi,bi.Among them,G is an activation function, x is the network input vector, the internal weight wi and the bias value bi of the network constitute the learning parameters of the hidden layer, βi is the external weight connecting the i-th node and the output node, and G is the output of the i-th hidden layer node.Usually we need to use training samples for learning. The traditional BP learning algorithm is to use the gradient descent method to determine the undetermined network parameters. However, the BP network learning algorithm has some obvious shortcomings:(1) Because the BP algorithm is based on the principle of gradient descent to continuously update the weights and bias values of the network through iterations, this often requires a lot of training time; (2) the BP algorithm needs to choose a reasonable learning step size, once the step size is selected, it is possible to miss the optimal solution. If the step size is too small, more iterations will be required, which will slow down the convergence speed; (3) the BP learning algorithm may converge to the point due to improper selection of the initial point in the iteration process.In the process of optimizing network parameters, the inner weight and paranoid value of the network are randomly selected, and the outer weight of the network is obtained by solving the Moore-Penrose generalized inverse of the hidden layer output matrix. This kind of network algorithm is called a random weight network learning algorithm. Compared with the traditional BP learning algorithm, it has the following characteristics:(1) The algorithm structure is relatively simple, no need to consider the difficulty of selecting the initial point and learning step in the learning process, and there will be no convergence to a local minimum; (2) the learning speed is extremely fast, with randomly selected internal weights and biases. The setting value avoids iterative update; (3) the requirement on the transfer function is lower, unlike the BP algorithm, which requires the transfer function to be differentiable.


## 3.1. E-Government Service Platform Based on Cloud Computing

The cloud computing-based e-government service platform is a cloud computing public service platform with resource sharing, complete functions, and complete systems. It is mainly composed of an infrastructure layer, a data resource layer, an operation and maintenance management layer, and a platform service layer. The model architecture of the e-government service platform based on cloud computing is shown in Figure1.Figure 1
Cloud computing-based e-government service platform model architecture.As a key link of cloud computing, the data resource layer contains great data service delivery capabilities. The construction of the data resource layer requires cluster applications, grid technology or distributed file systems to integrate different types of storage devices in the network for data storage and business access. The data resource layer, as an effective basic support for the e-government system, will effectively integrate large-scale basic application database resources such as geographic information systems, population-based databases, medical and health care, road administration and transportation, and plan to establish high-performance database clusters corresponding to each department’s business.In a massive information processing environment, e-government systems generally use distributed unstructured parallel storage systems as an ideal solution. On the one hand, they ensure reliable storage system performance, save the construction cost of large-scale storage systems, and also achieve storage reliability at the system design level. On the other hand, storage virtualization technology realizes the substantial separation of physical equipment and storage logic, making the system architecture and resource allocation more flexible and changeable. It is these characteristics of distributed storage technology that make its application as the mainstream storage architecture of the cloud computing system platform inevitable.In addition to configuring the necessary hardware facilities in the construction of the network infrastructure layer, the resources of each node of the government department are connected to the entire network through virtualization technology. Server virtualization is a technology that abstracts physical hardware facilities from network applications. Based on virtualization technology, virtual machines that undertake different system tasks can be allowed to operate independently of each other on a physical machine composed of the same hardware device to complete work tasks. The application of virtualization technology effectively improves the utilization of infrastructure resources and the flexibility of the overall operation of physical machines, reduces capital consumption, reduces operating costs, and truly realizes on-demand supply under the resource allocation strategy of rapid dynamic deployment and migration.The security management center monitors and analyzes all internal network resources such as network equipment, security equipment, servers, hosts, and business systems in the network environment, and displays the monitoring information of all resources in the network through a unified security management platform.To ensure the normal operation of the cloud computing e-government platform, it is necessary not only to provide operation and maintenance functions such as resource management, scheduling management, and monitoring management, but also to have functions such as business management and process management. Cloud management solutions must fully consider various management elements such as infrastructure (resources), business operations, and operation and maintenance services. It is necessary to quickly and flexibly process the requests of end users, and provide the main functions of on-demand application and dynamic expansion of computing resources to meet the differentiated needs of service access. The diversity of system technology and application requirements also requires that the management of cloud computing e-government platforms should be comprehensive and diverse to adapt to cloud computing platforms based on different technologies.The platform service layer is mainly to provide a platform for application development, testing, and operation. The characteristics of cloud computing technology determine that the development, testing, and deployment of system application functions will be unified based on the platform service layer. At the same time, the various application service components in the system serving different system functions will realize effective reuse and orchestration combination on the platform service layer, and flexibly meet the needs of different service forms of various information systems.


## 3.2. Sparse Matrix

If the signal itself is sparse or sparse after a certain domain transformation, the signal is considered to be a compressible sparse signal. The characteristic of sparse signal is that most of the elements in the signal are zero or close to zero.In practical applications, in order to simplify the computational complexity, the sparse representation of nonsparse signals is often obtained through a sparse dictionary. Using a suitable dictionary is an important step for the successful implementation of compressed sensing. A sparse dictionary with a high degree of matching can greatly reduce the computational complexity of the sparse process and save running time.The discrete Fourier transform dictionary is a dictionary composed of a series of sine functions that can visually display signal frequency domain characteristics. However, this dictionary cannot reflect the time domain information of the signal, that is, it cannot distinguish the time point of the abnormal frequency. Because of this defect, the dictionary cannot handle nonstationary signals. The expression of discrete Fourier transform (DFT) is(1)Xk=∏n=1Nxn+1e−2πknj/N.The structure of the Fourier dictionary can be expressed by the following formula:(2)F=−10⋯−1WN1WN2⋯WNN−1⋯⋯⋱⋯−1WN2N−1⋯WNN−1N−1,where(3)WNkN−1=e−2πknj/N−1.The Fourier transform result is in complex form, and the function image is symmetrical according to theY coordinate axis. This shows that the data located on the imaginary axis has no practical significance, which increases the storage space and computational complexity of the data to a certain extent. In order to solve this problem, a discrete cosine transform that only retains the real number axis part of the Fourier transform has appeared. Its expression is(4)Xk=∏n=1Nxn+1sin2n−12N.Standard orthogonal dictionaries and overcomplete dictionaries have better sparse representation when dealing with specific signals, but the basis functions of these two types of dictionaries are fixed, and their structures cannot adapt to different kinds of signals, so there is a problem of low adaptability. In order to increase the adaptability of such dictionaries, it is often necessary to build a dictionary atomic library, and match the target signal with the dictionary in the atomic library one by one until the optimal effect is obtained, but this process is time-consuming and labor-intensive. The emergence of learning dictionaries solves this problem.The learning dictionary generates a dictionary through self-learning. The atoms in the dictionary have a high degree of matching with the target signal, which can adapt to different types of signals and expand the application range of the dictionary. The process of dictionary learning can be described as:(5)argminBDB+2Xs.t.αi0>k.In the formula,X is the training sample, D is the learning dictionary, and B is the sparse matrix coefficient under the learning dictionary.


## 3.3. Measurement Matrix

The measurement matrix is an important step to reduce the signal storage and transmission space, and its existence can reduce the signal storage space. In the compressed sensing algorithm, the measurement matrix compresses theN-dimensional original signal X into the M-dimensional observation signal y. When the signal X is needed, it can be reconstructed by the signal y. In order to achieve accurate signal reconstruction, the observation matrix needs to ensure that the product of the observation basis matrix and the sparse basis matrix meets the condition of RIP during the design process. This restriction can ensure that two different signals with the same sparsity degree will not be mapped to the same set during the mapping process, and satisfy the one-to-one mapping relationship between the original signal space and the sparse space signal. The matrix composed of every M column vectors extracted is a nonsingular condition.The independent and identically distributed Gaussian random measurement matrix can become a universal compressed sensing measurement matrix. This shows that if the sparse basis and the measurement basis are not correlated, the RIP properties can be met to a large extent. Therefore, in compressed sensing, the Gaussian random matrix is often used as the measurement matrix.


## 3.4. Adaptive Block

In the process of signal block compression, the length of the block is usually selected based on experience. According to the empirical value, the division of the signal can be completed in a short time, so it has been widely used in practical engineering. However, the forms of fault signals of e-government affairs are changeable, and the fault signal waveforms of different bearings are significantly different. If different signals are divided into blocks with the same block length, it is difficult to ensure that each signal block has similar sparsity after sparse transformation. In order to achieve the purpose of equalizing the sparsity between signal blocks, this section studies the components of the e-government fault signal.The e-government fault signal can be considered as a composite of three parts: impact component, inherent component, and noise component, and can be further understood as the composite signal composed of impact component, inherent component and noise signal to form an e-government fault signal, as shown in the following formula:(6)yt=wt+zt.In the formula,y(t) is the e-government fault signal, wt is the composite signal of the impact component and the inherent component, and z(t) is the noise signal.Suppose the fluctuation energy of thei-th shock relative to the theoretical period T is τi, and s(t) is the shock attenuation oscillation. The model of the composite signal wt in the e-government fault signal is(7)wt=∏iAi•t•sti+T•i.This complex signal can be simplified to consist of two simple signals:(8)Ai=A0sin2πfrt−ΦA−CA,st=sin2πfnt−Φwe−0.5Bt.In the formula,fn is the natural frequency of the system, fr is the working rotation frequency, B is the attenuation coefficient, A0 is the resonance intensity from excitation, CA is an arbitrary constant, and ΦA and ϕw are the initial phases.


## 3.5. Forward and Backward Segmented Orthogonal Matching Pursuit Algorithm

In order to improve the reconstruction ability of the StOMP algorithm for signals with larger sparseness, the structure of its reconstruction support set is studied. Letα be a sparse signal with a sparsity of K, in which there are a large number of 0 elements. Set αk,1 = 0, then the k-th column element in the matrix θ is multiplied by 0, and the result is all 0. Therefore, changing the value in the k-th column does not affect the compression result. On this basis, the set of column vectors in the matrix θ corresponding to the rows of all 0 elements in α is called the invalid support set I; and the column vectors in the matrix θ corresponding to the rows of other nonzero elements in α are compressed, and the set of these columns is called the effective support set E.(9)y=∏i⟶I1−αθi.From the compression process, we can see(10)y=∏i⟶Iαθi−∏j⟶E1−αθj.Suppose that during the reconstruction process, the reconstruction support set consists of two sets,E1 and I1. E1 is a subset of E, and its atomic number is a; I1 is a subset of I, and its atomic number is b. To achieve the successful reconstruction of the signal, the reconstruction support set should contain all the E set atoms. The selection process of the support set atoms can be regarded as the process of finding all the effective support set atoms of the E set.In order to improve the reconstruction accuracy of the StOMP algorithm, the probability that all the atoms of the effective support set can be selected into the support set should be improved. Based on this, an improved algorithm of StOMP-FBStOMP algorithm is proposed. This algorithm improves the selection method of the StOMP algorithm support set, adds atom elimination and secondary selection mechanisms on the basis of the StOMP algorithm, eliminates the invalid support set atoms selected in the reconstruction support set, and removes all the remaining support set atoms. It continues to search for effective support set atoms among atoms until all atoms are filtered out. The flow of the FBStOMP algorithm is shown in Figure2.Figure 2
FBStOMP algorithm flow.


## 3.6. Process of Random Weight Network Learning and Training

Each neuron in the forward neural network accepts the input of the previous stage and outputs to the next stage, and there is no feedback in the network. This kind of network realizes the transformation of the signal from the input space to the output space, and its information processing ability comes from the multiple recombination of simple nonlinear functions. Here we use the single hidden layer forward neural network in the forward neural network as the approximation tool, and the single hidden layer forward neural network can be expressed as(11)NLx=∏i=0L−1βi+1Gx,wi,bi.Among them,G is an activation function, x is the network input vector, the internal weight wi and the bias value bi of the network constitute the learning parameters of the hidden layer, βi is the external weight connecting the i-th node and the output node, and G is the output of the i-th hidden layer node.Usually we need to use training samples for learning. The traditional BP learning algorithm is to use the gradient descent method to determine the undetermined network parameters. However, the BP network learning algorithm has some obvious shortcomings:(1) Because the BP algorithm is based on the principle of gradient descent to continuously update the weights and bias values of the network through iterations, this often requires a lot of training time; (2) the BP algorithm needs to choose a reasonable learning step size, once the step size is selected, it is possible to miss the optimal solution. If the step size is too small, more iterations will be required, which will slow down the convergence speed; (3) the BP learning algorithm may converge to the point due to improper selection of the initial point in the iteration process.In the process of optimizing network parameters, the inner weight and paranoid value of the network are randomly selected, and the outer weight of the network is obtained by solving the Moore-Penrose generalized inverse of the hidden layer output matrix. This kind of network algorithm is called a random weight network learning algorithm. Compared with the traditional BP learning algorithm, it has the following characteristics:(1) The algorithm structure is relatively simple, no need to consider the difficulty of selecting the initial point and learning step in the learning process, and there will be no convergence to a local minimum; (2) the learning speed is extremely fast, with randomly selected internal weights and biases. The setting value avoids iterative update; (3) the requirement on the transfer function is lower, unlike the BP algorithm, which requires the transfer function to be differentiable.


## 4. Results and Analysis

### 4.1. Evaluation Subject

The subject of e-government performance evaluation mainly refers to the stakeholders of e-government. E-government is an open social public service system, covering all aspects ranging from the country to small enterprises and the public, as well as civil servants. The evaluation subjects introduced in this article are mainly individuals, groups or organizations that receive the main services of e-government or can influence the development of e-government, such as national departments, government agencies at all levels, enterprises and institutions, the public, and civil servants. Among them, the state, government agencies, and enterprises are the main driving forces. From the perspective of government agencies, the development of e-government is the primary consideration for a country. Not only that, government departments at all levels also regard e-government as the foundation and core theme of development. The ultimate service target group is not only the enterprise and the public, but also the beneficiaries of e-government.In summary, the main body of e-government evaluation can be divided into three main parts: government departments, the public, enterprises, and civil servants. The main body of e-government performance evaluation includes the public and enterprises that use e-government, civil servants who operate the e-government system, and government departments at all levels.


### 4.2. E-Government Performance Evaluation Indicators

E-government utilizes IT technology to gradually shift workflows to virtualized networks. In essence, this process is the virtualization process of the government department’s workflow on the network, so the e-government generated by the government virtualization can still be regarded as an organization of the government department. Therefore, the performance evaluation of e-government can use organizational performance evaluation models and theories. The common models and theories for organizational performance evaluation include: Balanced Scorecard model, key performance indicator model, performance prism model and stakeholder theory.When determining the index system, in order to form a streamlined index system, some less influential e-government performance variables should be eliminated, and only the more important indicators should be selected. In the process of implementing the construction of the index system, in addition to the principles of the construction of the index system as the standard, the scope of actual needs should also be considered, and the two should be combined.Starting from three aspects, the satisfaction dimension of stakeholders provides specific three-level evaluation indicators. The three aspects are government departments, the public and enterprises, and civil servants. Specific indicators are shown in Table1.Table 1
Satisfaction indicators of e-government stakeholders.
Satisfaction of stakeholdersCivil servantsPersonal work time optimization rateOperational complexityGovernment departmentEfficiency satisfactionStrategic complianceCost reduction rateThe public and businessSatisfaction rate of service effectPublic participation channelsService realization coverageIn terms of the second-level indicators, the contribution dimension of stakeholders is the same as the satisfaction latitude. The specific three-level evaluation indicators of this dimension are shown in Table2.Table 2
Contribution evaluation indicators of interest-related parties.
Contributions of stakeholdersCivil servantsSuggested initiativeOperation proficiencyGovernment departmentPolicy supportDegree of unification of technical standardsInformation accuracyThe public and businessInteractive participation enthusiasmExpected demand accuracyDevelopment forecastThe second-level indicators of the evaluation of the government process need to be divided into three aspects: process time, process quality and internal coordination. The specific three-level indicators are shown in Table3.Table 3
Evaluation indicators of government affairs process.
Government processProcess qualityBusiness error rateBusiness coverageInternal collaborationPersonnel cooperationFairnessTransparency of internal rulesProcess timeBusiness coordination timeCore business time reduction rateThe key issue involved in the capability dimension is what kind of capability they have to run e-government. The goal is to be able to create value on behalf of the organization while satisfying the subjective and objective conditions of stakeholders. The optimization process requires the use of the organization’s execution capabilities. It includes three dimensions, as shown in Table4.Table 4
Ability evaluation indicators.
AbilityTechinque levelTechnology maturitySafety evaluation and certificationStandardManpower levelProportion of people who have mastered IT application technologyNetworking and networkingInformatization training coverage of non-IT personnelInfrastructureHardware matchingNetwork construction situationInformation sharing resource catalog construction


### 4.3. Weight Determination

By inviting relevant practitioners to compare the importance of each indicator, and after a systematic and comprehensive comparison and analysis, the weight value of each indicator of the evaluation indicator system of the e-government system is obtained. The weight setting mainly includes four parts, namely the weight of each evaluation level, the weight of each factor in each evaluation level, and the weight of each secondary index and tertiary index in each evaluation factor (primary index). The following mainly takes the first-level indicator in the indicator system—the determination of the relative weights of all levels of indicators under the latitude of the stakeholders’ satisfaction as an example to illustrate the calculation process of the weights of the indicators at each level under this latitude. In the weight setting, only the specific weight of the last calculated index of each latitude is given. Figure3 shows the relative weights of indicators at all levels of the latitude of stakeholder satisfaction.Figure 3
Index weights of related interests satisfaction latitude.In the same way, the relative weights of the indicators in other latitudes can be obtained. After consistency inspection and adjustment, the weights of the indicator system obtained by each consultant are calculated and averaged to obtain the overall weight of the indicator system as shown in Figure4.Figure 4
Weights of e-government performance evaluation index system.


### 4.4. Index Processing

The processing process is based on the existing research results and the business characteristics of the project, and establishes an appropriate quantitative index division, and makes it consistent with the actual situation of the department. Then, unscrupulous tempering is performed on the quantitative indicators obtained by the research, and then a breakdown of each indicator is obtained. The relationship between the number of indicators and the accuracy of e-government development forecast is shown in Figure5.Figure 5
The relationship between the number of indicators and the accuracy of e-government development forecasts.According to the actual construction situation of the project and the statistical results of various indicators, the evaluation scores of each qualitative evaluation index and the measured values of each quantitative index are obtained. After these results are processed and preprocessed, the results shown in Figure6 are obtained.Figure 6
Score of each evaluation index.


### 4.5. Evaluation of Smart Government Development

The interior of the intelligent government system is very complex and involves a wide range of fields. From a related point of view, there are only hundreds or thousands of evaluation indicators, but if such a huge indicator system is to be considered, the operation will be very complicated, and the entire system will be unstable. Therefore, it is necessary to screen out the more important and influential indicators from a large number of indicators, as a reference for the evaluation model. Through the frequency analysis method, this paper selects the indicators suitable for the evaluation of intelligent government affairs, and analyzes their connotation and frequency, and selects a series of indicators with the highest frequency as the reference indicators of the expert questionnaire for further screening.By issuing questionnaires and interviews to experts, through the opinions of experts in different fields, more important indicators are collected, while those relatively ambiguous and inappropriate indicators are eliminated in the survey. It needs to be explained that under conditions, this article conducted on-site interviews with some experts and timely feedback. The purpose is to communicate and explain the questionnaire content face-to-face, as far as possible to ensure that experts fully understand the intent of the questionnaire, and give feedback based on the experts’ personal knowledge and experience. At the same time, in order to ensure the objectivity of the entire questionnaire results, the entire process adopts an anonymous system, and the opinions of experts do not affect each other.According to on-site interviews and questionnaires conducted by a certain government in a certain region, experts are assigned 0–100 points for each item, among which 90–100 points are good, 60–80 points are better, 30–50 points are poor, and 0–20 points are poor. Figure7 shows the annual smart government performance score of a government.Figure 7
The annual smart government performance score of a government.It can be seen from the weighted value that the performance of each quarter is continuously improving, which is also roughly the same as the feedback obtained from the field survey, because the intelligent government system is still in the development stage and there are many areas to be improved, so there is a lot of room for improvement. What needs to be added is that because the smart government is in the development stage, the general trend is progress, and there is no performance decline. However, the ANP evaluation model can indeed analyze in detail which indicators of performance affect the overall performance.


## 4.1. Evaluation Subject

The subject of e-government performance evaluation mainly refers to the stakeholders of e-government. E-government is an open social public service system, covering all aspects ranging from the country to small enterprises and the public, as well as civil servants. The evaluation subjects introduced in this article are mainly individuals, groups or organizations that receive the main services of e-government or can influence the development of e-government, such as national departments, government agencies at all levels, enterprises and institutions, the public, and civil servants. Among them, the state, government agencies, and enterprises are the main driving forces. From the perspective of government agencies, the development of e-government is the primary consideration for a country. Not only that, government departments at all levels also regard e-government as the foundation and core theme of development. The ultimate service target group is not only the enterprise and the public, but also the beneficiaries of e-government.In summary, the main body of e-government evaluation can be divided into three main parts: government departments, the public, enterprises, and civil servants. The main body of e-government performance evaluation includes the public and enterprises that use e-government, civil servants who operate the e-government system, and government departments at all levels.


## 4.2. E-Government Performance Evaluation Indicators

E-government utilizes IT technology to gradually shift workflows to virtualized networks. In essence, this process is the virtualization process of the government department’s workflow on the network, so the e-government generated by the government virtualization can still be regarded as an organization of the government department. Therefore, the performance evaluation of e-government can use organizational performance evaluation models and theories. The common models and theories for organizational performance evaluation include: Balanced Scorecard model, key performance indicator model, performance prism model and stakeholder theory.When determining the index system, in order to form a streamlined index system, some less influential e-government performance variables should be eliminated, and only the more important indicators should be selected. In the process of implementing the construction of the index system, in addition to the principles of the construction of the index system as the standard, the scope of actual needs should also be considered, and the two should be combined.Starting from three aspects, the satisfaction dimension of stakeholders provides specific three-level evaluation indicators. The three aspects are government departments, the public and enterprises, and civil servants. Specific indicators are shown in Table1.Table 1
Satisfaction indicators of e-government stakeholders.
Satisfaction of stakeholdersCivil servantsPersonal work time optimization rateOperational complexityGovernment departmentEfficiency satisfactionStrategic complianceCost reduction rateThe public and businessSatisfaction rate of service effectPublic participation channelsService realization coverageIn terms of the second-level indicators, the contribution dimension of stakeholders is the same as the satisfaction latitude. The specific three-level evaluation indicators of this dimension are shown in Table2.Table 2
Contribution evaluation indicators of interest-related parties.
Contributions of stakeholdersCivil servantsSuggested initiativeOperation proficiencyGovernment departmentPolicy supportDegree of unification of technical standardsInformation accuracyThe public and businessInteractive participation enthusiasmExpected demand accuracyDevelopment forecastThe second-level indicators of the evaluation of the government process need to be divided into three aspects: process time, process quality and internal coordination. The specific three-level indicators are shown in Table3.Table 3
Evaluation indicators of government affairs process.
Government processProcess qualityBusiness error rateBusiness coverageInternal collaborationPersonnel cooperationFairnessTransparency of internal rulesProcess timeBusiness coordination timeCore business time reduction rateThe key issue involved in the capability dimension is what kind of capability they have to run e-government. The goal is to be able to create value on behalf of the organization while satisfying the subjective and objective conditions of stakeholders. The optimization process requires the use of the organization’s execution capabilities. It includes three dimensions, as shown in Table4.Table 4
Ability evaluation indicators.
AbilityTechinque levelTechnology maturitySafety evaluation and certificationStandardManpower levelProportion of people who have mastered IT application technologyNetworking and networkingInformatization training coverage of non-IT personnelInfrastructureHardware matchingNetwork construction situationInformation sharing resource catalog construction


## 4.3. Weight Determination

By inviting relevant practitioners to compare the importance of each indicator, and after a systematic and comprehensive comparison and analysis, the weight value of each indicator of the evaluation indicator system of the e-government system is obtained. The weight setting mainly includes four parts, namely the weight of each evaluation level, the weight of each factor in each evaluation level, and the weight of each secondary index and tertiary index in each evaluation factor (primary index). The following mainly takes the first-level indicator in the indicator system—the determination of the relative weights of all levels of indicators under the latitude of the stakeholders’ satisfaction as an example to illustrate the calculation process of the weights of the indicators at each level under this latitude. In the weight setting, only the specific weight of the last calculated index of each latitude is given. Figure3 shows the relative weights of indicators at all levels of the latitude of stakeholder satisfaction.Figure 3
Index weights of related interests satisfaction latitude.In the same way, the relative weights of the indicators in other latitudes can be obtained. After consistency inspection and adjustment, the weights of the indicator system obtained by each consultant are calculated and averaged to obtain the overall weight of the indicator system as shown in Figure4.Figure 4
Weights of e-government performance evaluation index system.


## 4.4. Index Processing

The processing process is based on the existing research results and the business characteristics of the project, and establishes an appropriate quantitative index division, and makes it consistent with the actual situation of the department. Then, unscrupulous tempering is performed on the quantitative indicators obtained by the research, and then a breakdown of each indicator is obtained. The relationship between the number of indicators and the accuracy of e-government development forecast is shown in Figure5.Figure 5
The relationship between the number of indicators and the accuracy of e-government development forecasts.According to the actual construction situation of the project and the statistical results of various indicators, the evaluation scores of each qualitative evaluation index and the measured values of each quantitative index are obtained. After these results are processed and preprocessed, the results shown in Figure6 are obtained.Figure 6
Score of each evaluation index.


## 4.5. Evaluation of Smart Government Development

The interior of the intelligent government system is very complex and involves a wide range of fields. From a related point of view, there are only hundreds or thousands of evaluation indicators, but if such a huge indicator system is to be considered, the operation will be very complicated, and the entire system will be unstable. Therefore, it is necessary to screen out the more important and influential indicators from a large number of indicators, as a reference for the evaluation model. Through the frequency analysis method, this paper selects the indicators suitable for the evaluation of intelligent government affairs, and analyzes their connotation and frequency, and selects a series of indicators with the highest frequency as the reference indicators of the expert questionnaire for further screening.By issuing questionnaires and interviews to experts, through the opinions of experts in different fields, more important indicators are collected, while those relatively ambiguous and inappropriate indicators are eliminated in the survey. It needs to be explained that under conditions, this article conducted on-site interviews with some experts and timely feedback. The purpose is to communicate and explain the questionnaire content face-to-face, as far as possible to ensure that experts fully understand the intent of the questionnaire, and give feedback based on the experts’ personal knowledge and experience. At the same time, in order to ensure the objectivity of the entire questionnaire results, the entire process adopts an anonymous system, and the opinions of experts do not affect each other.According to on-site interviews and questionnaires conducted by a certain government in a certain region, experts are assigned 0–100 points for each item, among which 90–100 points are good, 60–80 points are better, 30–50 points are poor, and 0–20 points are poor. Figure7 shows the annual smart government performance score of a government.Figure 7
The annual smart government performance score of a government.It can be seen from the weighted value that the performance of each quarter is continuously improving, which is also roughly the same as the feedback obtained from the field survey, because the intelligent government system is still in the development stage and there are many areas to be improved, so there is a lot of room for improvement. What needs to be added is that because the smart government is in the development stage, the general trend is progress, and there is no performance decline. However, the ANP evaluation model can indeed analyze in detail which indicators of performance affect the overall performance.


## 5. Conclusion

In order to reduce the pressure on transmission and storage equipment caused by excessive signal acquisition, while sparse and under-sampling compression is performed on the target signal, the acquired signal is adaptively block-compressed according to the composite period, which reduces the storage space of the measurement matrix and the sparse matrix. At the same time, the sparsity between the signal blocks is equalized to ensure the overall reconstruction effect of the signal. For the compressed signal received by the monitoring terminal, the FBStOMP algorithm with fast reconstruction speed and high accuracy is used to realize the reconstruction of the compressed signal. The FBStOMP algorithm performs a secondary screening on the atoms selected into the support set, and improves the probability of selecting all the effective support set atoms into the reconstruction support set by removing invalid support set atoms and selecting effective support set atoms. In terms of economic benefits, this research creatively introduces the concept of value chain and supply chain into e-government, and analyzes the government, enterprises and the public as different objects in the value chain and supply chain. This solution is completely different from the current mode of operation. It has completely changed the embarrassing situation of “high investment and low income” in e-government, and is conducive to the smooth promotion of e-government. In terms of organizational models, this research focuses on the game relationship between the government and enterprises in the process of interaction, as the theoretical basis for virtual organization research. On the other hand, the DLA resource management framework is used to construct a virtual organization model of e-government that is interactive between government and enterprises, and realizes an e-government model that takes citizens as the center and provides high-quality and high-level online services for the public as an important goal. A set of e-government performance evaluation system for the interaction between government and enterprise is proposed to evaluate the effect of e-government system, which is conducive to promoting the improvement of e-government system.



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*Source: 1071648-2022-03-11.xml*