An index evaluation system based on social governance

Abstract

The present invention provides an index evaluation system based on social governance, comprising: an evaluation index library module, which captures and screens evaluation indicators through a social governance evaluation system, and establishes an evaluation index library of a target platform based on the evaluation indicators; an indicator evaluation index module is used to collect indicator information on the target platform, perform quantitative analysis on the indicator information, generate corresponding quantitative indicator data, count the quantitative indicator data, regularly score and calculate the quantitative indicator data, and generate an indicator evaluation index; a comparison module is used to compare the consistency of the indicator evaluation index and a preset standard indicator evaluation index; when the consistency is greater than or equal to a preset consistency threshold, the indicator evaluation index is transmitted to the evaluation index library for storage; when the consistency is less than the preset consistency threshold, the indicator evaluation index is uploaded to the evaluation index library, and at the same time, based on the indicator evaluation model preset by the target platform, the platform information related to the evaluation indicators in the target platform is counted and evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of index evaluation, and in particular to an index evaluation system based on social governance. Background Art

[0002] At present, safe development requires that while promoting sound and rapid economic development, more attention should be paid to social development. The impact of social civilization on the level of safe development is reflected in the degree of attention paid to science and technology and the level of education. Improving the level of science and technology and education guarantees is conducive to improving safe production conditions. Under certain conditions, this factor can be controlled to a certain extent through human influence. It is also an economic and social factor that can be regulated and shortened by enhancing people's attention to shorten the period of proneness to safe production accidents.

[0003] At present, there is a lack of a statistical system for specific indicators of social governance. It is used to conduct statistical work on social governance indicators in places with R&D capabilities such as universities, enterprises and research institutes. There is an urgent need for a social governance index evaluation method that has reasonable calculations, a wide range of applicability and good social benefits. Summary of the invention

[0004] The present invention provides an index evaluation system based on social governance to solve the above problems.

[0005] The present invention provides an index evaluation system based on social governance, comprising:

[0006] Evaluation index library module: used to capture and screen evaluation indicators about social governance through a preset social governance evaluation system, and establish an evaluation index library of the target platform based on the evaluation indicators;

[0007] Index evaluation index module: used to collect index information about each evaluation index on the target platform, perform quantitative analysis on the index information, generate corresponding quantitative index data, count the quantitative index data, regularly perform score calculation on the quantitative index data, and generate an index evaluation index;

[0008] Comparison module: used to compare the consistency of the indicator evaluation index and the preset standard indicator evaluation index, determine the comparison result, and transmit the comparison result to the evaluation indicator library for storage.

[0009] As an embodiment of the present technical solution, the evaluation index library module includes:

[0010] Screening result unit: used to capture and screen the evaluation indicators of social governance through the preset social governance evaluation system, and determine the screening results; among them,

[0011] The evaluation indicators for social governance include the proportion of research and development expenditure to GDP, the number of three types of national patents authorized per 100,000 people, the average years of education, the average life expectancy, the coverage rate of work-related injury insurance and the registered unemployment rate in urban areas;

[0012] The screening results include the proportion of research and development expenditure to GDP, the number of three types of national patents authorized per 100,000 people, and the average years of education;

[0013] Target platform database unit: used to obtain platform information of the target platform and establish a target platform database based on the platform information;

[0014] Evaluation index library unit: used to map the evaluation indexes in the screening results to the target platform database and establish an evaluation index library for the target platform.

[0015] As an embodiment of the present technical solution, the indicator evaluation index module includes:

[0016] Indicator information unit: used to collect and filter the platform information of the target platform, filter out the platform information related to the evaluation indicator, and determine the indicator information;

[0017] Separation unit: used for extracting the index features of the index information, establishing an index classifier through the index features, separating the index information corresponding to each evaluation index through the index classifier, and determining the separation result;

[0018] Quantitative indicator data unit: used to perform quantitative analysis on the indicator information in each separation result and generate corresponding quantitative indicator data;

[0019] Index evaluation index unit: used to transmit the quantitative index data to a preset Sigmoid index model, and regularly perform score calculations on the quantitative index data to generate an index evaluation index.

[0020] As an embodiment of the present technical solution, the indicator information unit includes:

[0021] Platform database subunit: used to collect and count the platform information of the target platform and establish a platform database;

[0022] Information extraction subunit: used to extract platform information that meets the characteristics of the evaluation index from the platform database based on the preset clustering model and probability correlation model, and determine the extracted information;

[0023] Index information subunit: used to calculate the correlation between the extracted information and the evaluation index, and when the correlation is greater than a preset correlation threshold, determine the index information according to the extracted information;

[0024] Deletion subunit: used to delete the extracted information in the platform database when the relevance is less than a preset relevance threshold.

[0025] As an embodiment of the present technical solution, the separation unit is used to extract the indicator features of the indicator information, establish an indicator classifier through the indicator features, separate the indicator information corresponding to each evaluation indicator through the indicator classifier, and determine the separation result, including:

[0026] Step 1: Extract indicator features of indicator information;

[0027] Step 2: Import the indicator features into a preset classifier model for extraction and establish an indicator classifier;

[0028]

[0029] Among them, x represents the preset classifier model, y represents the feature vector of the indicator feature, and x i Represents the i-th classifier model; represents the transpose of the i-th classifier model, i = 1, 2, ..., m, m represents the total number of classifier models; y j represents the feature vector of the jth batch of indicator features, j = 1, 2, ..., n, n represents the total number of feature vector batches; F(f|X, Y) represents the indicator classifier when the feature vector of the indicator feature is imported into the classifier model under the import function f, X represents the classifier model set, Y represents the feature vector set, f represents the import function of the feature vector of the indicator feature into the classifier model, f i，j The import function of the feature vector representing the jth batch of indicator features into the ith classifier model, μ represents the classification and clustering results of the feature vector of the indicator features in the classifier model, represents the normal distribution of the import function, μ1 represents the classification function in the classifier model, and p represents the conditional probability. represents the normal distribution of the classification function in the classifier model; μ2 represents the characteristic clustering function of the characteristic vector of the indicator feature, Represents the normal distribution of the eigenvectors regarding the indicator characteristics;

[0030] Step 3: Calculate the indicator feature set Y and establish the indicator feature matrix

[0031] Step 4: Import the indicator feature matrix into the indicator classifier, separate the indicator information corresponding to each evaluation indicator, and determine the separation result.

[0032] As an embodiment of the present technical solution, the quantitative indicator data unit includes:

[0033] Hierarchical analysis result subunit: used to transmit the indicator information in each separation result to the preset big data center for hierarchical analysis according to the separation order of the separation results, and obtain the hierarchical analysis results;

[0034] Impact coefficient calculation subunit: used to calculate the impact coefficients between the indicator information in each separation result based on the quantitative analysis model of economic impact factors preset by the big data center;

[0035] Difference matrix subunit: used to calculate the interference value of the influence coefficient on the stratified analysis results, statistically calculate the interference value of the stratified analysis results corresponding to each layer after the index information is classified, and generate the corresponding difference matrix;

[0036] Model weighted value subunit: used to calculate the model weighted value of the economic influencing factor quantitative analysis model based on each influencing factor and the corresponding influencing coefficient analyzed by the economic influencing factor quantitative analysis model;

[0037] Quantitative index data subunit: used to perform fuzzy complementation on the difference matrix through the model weighted value to generate quantitative index information of the evaluation index, and generate corresponding quantitative index data through the quantitative index information.

[0038] As an embodiment of the present technical solution, the quantitative index data subunit is used to perform fuzzy complementation on the difference matrix through the model weighted value to generate quantitative index information of the evaluation index, and generate corresponding quantitative index data through the quantitative index information, including the following steps:

[0039] The difference matrix is ​​fuzzily complemented by the model weighted value to generate quantitative index information of the evaluation index, and the consistency between the quantitative index information of the evaluation index and the preset index information is judged; wherein,

[0040] When the quantitative indicator information of the evaluation indicator is consistent with the preset indicator information, the corresponding quantitative indicator data is received and recorded through the economic impact factor quantitative analysis model preset by the big data center;

[0041] When the quantitative indicator information of the evaluation indicator is inconsistent with the preset indicator information, it is determined that the quantitative analysis model of the economic influencing factors cannot perform quantitative analysis.

[0042] As an embodiment of the present technical solution, the indicator evaluation index unit includes:

[0043] Indicator index value unit: used to establish indicator index values ​​of different indicator types through the quantitative indicator data;

[0044] Scoring unit: used to obtain the set standard range and critical value of each indicator index value, and transmit the indicator index value, standard range and critical value to the preset Sigmoid index model; wherein,

[0045] Sigmoid index model unit: used to activate and evaluate the indicator index value through the Sigmoid index model, regularly perform score calculation on the quantitative indicator data, and generate an indicator evaluation index.

[0046] As an embodiment of the present technical solution, the comparison module includes:

[0047] Comparison unit: used to compare the consistency of the indicator evaluation index and the preset standard indicator evaluation index, and determine the comparison result;

[0048] A storage unit is used to transmit the indicator evaluation index to an evaluation indicator library for storage when the comparison result shows that the consistency is greater than or equal to a preset consistency threshold;

[0049] Statistical evaluation unit: used to upload the indicator evaluation index to the evaluation indicator library when the comparison result is less than the preset consistency threshold, and at the same time, based on the indicator evaluation model preset by the target platform, perform statistics and evaluation on the platform information related to the evaluation indicator in the target platform.

[0050] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 This is a module flow chart of an index evaluation system based on social governance in an embodiment of the present invention;

[0054] Figure 2 This is a module flow chart of an index evaluation system based on social governance in an embodiment of the present invention;

[0055] Figure 3 The present invention is a flowchart of a module of an index evaluation system based on social governance in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0057] It should be noted that when a component is referred to as being "fixed to" or "disposed on" another component, it may be directly on the other component or indirectly on the other component. When a component is referred to as being "connected to" another component, it may be directly or indirectly connected to the other component.

[0058] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0059] In addition, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations, and "multiple" means two or more, unless otherwise clearly and specifically limited. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0061] Embodiment 1:

[0062] According to 1, an embodiment of the present invention provides an index evaluation system based on social governance, which is characterized by including:

[0063] Evaluation index library module: used to capture and screen evaluation indicators about social governance through a preset social governance evaluation system, and establish an evaluation index library of the target platform based on the evaluation indicators;

[0064] Index evaluation index module: used to collect index information about each evaluation index on the target platform, perform quantitative analysis on the index information, generate corresponding quantitative index data, count the quantitative index data, regularly perform score calculation on the quantitative index data, and generate an index evaluation index;

[0065] Comparison module: used to compare the consistency of the indicator evaluation index and the preset standard indicator evaluation index, determine the comparison result, and transmit the comparison result to the evaluation indicator library for storage.

[0066] The working principle and beneficial effects of the above technical solution are:

[0067] The embodiment of the present invention provides an index evaluation system based on social governance, wherein the evaluation index library module is used to capture and screen evaluation indicators about social governance through a preset social governance evaluation system, and establish an evaluation index library of a target platform based on the evaluation indicators; the indicator evaluation index module is used to collect indicator information about each evaluation indicator on the target platform, perform quantitative analysis on the indicator information, generate corresponding quantitative indicator data, count the quantitative indicator data, regularly score and calculate the quantitative indicator data, and generate an indicator evaluation index; the comparison module is used to compare the consistency of the indicator evaluation index and the preset standard indicator evaluation index; wherein, when the consistency is greater than or equal to a preset consistency threshold, the indicator evaluation index is transmitted to the evaluation index library for storage; when the consistency is less than the preset consistency threshold, the indicator evaluation index is uploaded to the evaluation index library, and at the same time, based on the indicator evaluation model preset by the target platform, the platform information related to the evaluation indicators in the target platform is counted and evaluated, thereby screening out enterprises and units that have made positive contributions to social governance, so as to help evaluate the social stability coefficient.

[0068] Embodiment 2:

[0069] The technical solution provides an embodiment, wherein the evaluation index library module comprises:

[0070] Screening result unit: used to capture and screen the evaluation indicators of social governance through the preset social governance evaluation system, and determine the screening results; among them,

[0071] The evaluation indicators for social governance include the proportion of research and development expenditure to GDP, the number of three types of national patents authorized per 100,000 people, the average years of education, the average life expectancy, the coverage rate of work-related injury insurance and the registered unemployment rate in urban areas;

[0072] The screening results include the proportion of research and development expenditure to GDP, the number of three types of national patents authorized per 100,000 people, and the average years of education;

[0073] Target platform database unit: used to obtain platform information of the target platform and establish a target platform database based on the platform information;

[0074] Evaluation index library unit: used to map the evaluation indexes in the screening results to the target platform database and establish an evaluation index library for the target platform.

[0075] The working principle and beneficial effects of the above technical solution are:

[0076] The evaluation index library module of the present technical solution captures and screens the evaluation indexes of social governance through the preset social governance evaluation system to determine the screening results; the evaluation indexes of social governance include the proportion of research and experimental development expenditure to GDP, the number of three national patent authorizations per 100,000 people, the average years of education, the average life expectancy, the coverage rate of work-related injury insurance and the registered urban unemployment rate; the screening results include the proportion of research and experimental development expenditure to GDP, the number of three national patent authorizations per 100,000 people and the average years of education; the platform information of the target platform is obtained, and the target platform database is established through the platform information; the evaluation indexes in the screening results are mapped to the target platform database to establish an evaluation index library for the target platform.

[0077] Embodiment 3:

[0078] The technical solution provides an embodiment, wherein the indicator evaluation index module comprises:

[0079] Indicator information unit: used to collect and filter the platform information of the target platform, filter out the platform information related to the evaluation indicator, and determine the indicator information;

[0080] Separation unit: used for extracting the index features of the index information, establishing an index classifier through the index features, separating the index information corresponding to each evaluation index through the index classifier, and determining the separation result;

[0081] Quantitative indicator data unit: used to perform quantitative analysis on the indicator information in each separation result and generate corresponding quantitative indicator data;

[0082] Index evaluation index unit: used to transmit the quantitative index data to a preset Sigmoid index model, and regularly perform score calculations on the quantitative index data to generate an index evaluation index.

[0083] The working principle and beneficial effects of the above technical solution are:

[0084] The indicator evaluation index module of the present technical solution collects and filters the platform information of the target platform, filters out the platform information related to the evaluation indicator, and determines the indicator information; extracts the indicator characteristics of the indicator information, establishes an indicator classifier through the indicator characteristics, separates the indicator information corresponding to each evaluation indicator through the indicator classifier, and determines the separation result; performs quantitative analysis on the indicator information in each separation result to generate corresponding quantitative indicator data; transmits the quantitative indicator data to a preset Sigmoid index model, and regularly performs score calculation on the quantitative indicator data to generate an indicator evaluation index. Compared with the traditional model, the Sigmoid index model can accurately describe multiple factors.

[0085] Embodiment 4:

[0086] The technical solution provides an embodiment, wherein the indicator information unit includes:

[0087] Platform database subunit: used to collect and count the platform information of the target platform and establish a platform database;

[0088] Information extraction subunit: used to extract platform information that meets the characteristics of the evaluation index from the platform database based on the preset clustering model and probability correlation model, and determine the extracted information;

[0089] Index information subunit: used to calculate the correlation between the extracted information and the evaluation index, and when the correlation is greater than a preset correlation threshold, determine the index information according to the extracted information;

[0090] Deletion subunit: used to delete the extracted information in the platform database when the relevance is less than a preset relevance threshold.

[0091] The working principle and beneficial effects of the above technical solution are:

[0092] The indicator information unit of the present technical solution, the platform database subunit is used to collect and count the platform information of the target platform and establish a platform database; the extraction information subunit is used to extract the platform information that meets the evaluation indicator characteristics from the platform database based on the preset clustering model and probability correlation model, and determine the extracted information; the indicator information subunit is used to calculate the correlation between the extracted information and the evaluation indicator, and when the correlation is greater than the preset correlation threshold, the indicator information is determined based on the extracted information; the deletion subunit is used to delete the extracted information in the platform database when the correlation is less than the preset correlation threshold, thereby providing a precise indicator information database to facilitate the accuracy of data in the extraction room.

[0093] Embodiment 5:

[0094] The technical solution provides an embodiment, wherein the separation unit is used to extract indicator features of indicator information, establish an indicator classifier through the indicator features, separate the indicator information corresponding to each evaluation indicator through the indicator classifier, and determine the separation result, including:

[0095] Step 1: Extract indicator features of indicator information;

[0096] Step 2: Import the indicator features into a preset classifier model for extraction and establish an indicator classifier;

[0097]

[0098] Among them, x represents the preset classifier model, y represents the feature vector of the indicator feature, and x i Represents the i-th classifier model; represents the transpose of the i-th classifier model, i = 1, 2, ..., m, m represents the total number of classifier models; y j represents the feature vector of the jth batch of indicator features, j = 1, 2, ..., n, n represents the total number of feature vector batches; F(f|X, Y) represents the indicator classifier when the feature vector of the indicator feature is imported into the classifier model under the import function f, X represents the classifier model set, Y represents the feature vector set, f represents the import function of the feature vector of the indicator feature into the classifier model, f i，j The import function of the feature vector representing the jth batch of indicator features into the ith classifier model, μ represents the classification and clustering results of the feature vector of the indicator features in the classifier model, represents the normal distribution of the import function, μ1 represents the classification function in the classifier model, and p represents the conditional probability. represents the normal distribution of the classification function in the classifier model; μ2 represents the characteristic clustering function of the characteristic vector of the indicator feature, Represents the normal distribution of the eigenvectors regarding the indicator characteristics;

[0099] Step 3: Calculate the indicator feature set Y and establish the indicator feature matrix

[0100] Step 4: Import the indicator feature matrix into the indicator classifier, separate the indicator information corresponding to each evaluation indicator, and determine the separation result.

[0101] The working principle and beneficial effects of the above technical solution are:

[0102] The technical solution provides an embodiment, wherein the separation unit is used to extract the indicator features of the indicator information, establish an indicator classifier through the indicator features, separate the indicator information corresponding to each evaluation indicator through the indicator classifier, determine the separation result, and extract the indicator features of the indicator information; import the indicator features into a preset classifier model for refinement, establish an indicator classifier F(f|X,Y); statistically analyze the indicator feature set Y, and establish an indicator feature matrix The indicator feature matrix is ​​imported into the indicator classifier to separate the indicator information corresponding to each evaluation indicator, and the separation result is determined, so as to accurately analyze each indicator, thereby making the evaluation result more accurate and scientific.

[0103] Embodiment 6:

[0104] The technical solution provides an embodiment, wherein the quantitative indicator data unit includes:

[0105] Hierarchical analysis result subunit: used to transmit the indicator information in each separation result to the preset big data center for hierarchical analysis according to the separation order of the separation results, and obtain the hierarchical analysis results;

[0106] Impact coefficient calculation subunit: used to calculate the impact coefficients between the indicator information in each separation result based on the quantitative analysis model of economic impact factors preset by the big data center;

[0107] Difference matrix subunit: used to calculate the interference value of the influence coefficient on the stratified analysis results, statistically calculate the interference value of the stratified analysis results corresponding to each layer after the index information is classified, and generate the corresponding difference matrix;

[0108] Model weighted value subunit: used to calculate the model weighted value of the economic influencing factor quantitative analysis model based on each influencing factor and the corresponding influencing coefficient analyzed by the economic influencing factor quantitative analysis model;

[0109] Quantitative index data subunit: used to perform fuzzy complementation on the difference matrix through the model weighted value to generate quantitative index information of the evaluation index, and generate corresponding quantitative index data through the quantitative index information.

[0110] The working principle and beneficial effects of the above technical solution are:

[0111] The quantitative indicator data unit of the present technical solution includes: a hierarchical analysis result subunit: used to transmit the indicator information in each separation result to the preset big data center for hierarchical analysis according to the separation order of the separation results, and obtain the hierarchical analysis results; an influence coefficient calculation subunit: used to calculate the influence coefficient between the indicator information in each separation result based on the quantitative analysis model of economic influencing factors preset by the big data center; a difference matrix subunit: used to calculate the interference value of the influence coefficient on the hierarchical analysis result, statistically calculate the interference value of the hierarchical analysis result corresponding to each layer after the indicator information is classified, and generate a corresponding difference matrix; a model weighting value subunit: used to calculate the model weighting value of the quantitative analysis model of economic influencing factors for each influencing factor and the corresponding influence coefficient analyzed by the quantitative analysis model of economic influencing factors; a quantitative indicator data subunit: used to perform fuzzy complementation on the difference matrix through the model weighting value, generate quantitative indicator information of the evaluation indicator, generate corresponding quantitative indicator data through the quantitative indicator information, digitize the abstract indicator factors through quantification, and improve the accuracy of the indicator factors.

[0112] Embodiment 7:

[0113] The technical solution provides an embodiment, wherein the quantitative index data subunit is used to perform fuzzy complementation on the difference matrix through the model weighted value to generate quantitative index information of the evaluation index, and generate corresponding quantitative index data through the quantitative index information, including the following steps:

[0114] The difference matrix is ​​fuzzily complemented by the model weighted value to generate quantitative index information of the evaluation index, and the consistency between the quantitative index information of the evaluation index and the preset index information is judged; wherein,

[0115] When the quantitative indicator information of the evaluation indicator is consistent with the preset indicator information, the corresponding quantitative indicator data is received and recorded through the economic impact factor quantitative analysis model preset by the big data center;

[0116] When the quantitative indicator information of the evaluation indicator is inconsistent with the preset indicator information, it is determined that the quantitative analysis model of the economic influencing factors cannot perform quantitative analysis.

[0117] The working principle and beneficial effects of the above technical solution are:

[0118] The quantitative indicator data subunit of the present technical solution is used to perform fuzzy complementation on the difference matrix through the model weighted values ​​to generate quantitative indicator information of the evaluation indicator, generate corresponding quantitative indicator data through the quantitative indicator information, perform fuzzy complementation on the difference matrix through the model weighted values ​​to generate quantitative indicator information of the evaluation indicator, and judge the consistency between the quantitative indicator information of the evaluation indicator and the preset indicator information; when the quantitative indicator information of the evaluation indicator is consistent with the preset indicator information, receive and record the corresponding quantitative indicator data through the quantitative analysis model of economic influencing factors preset by the big data center; when the quantitative indicator information of the evaluation indicator is inconsistent with the preset indicator information, it is determined that the quantitative analysis model of economic influencing factors cannot perform quantitative analysis.

[0119] Embodiment 8:

[0120] The technical solution provides an embodiment, wherein the indicator evaluation index unit comprises:

[0121] Indicator index value unit: used to establish indicator index values ​​of different indicator types through the quantitative indicator data;

[0122] Scoring unit: used to obtain the set standard range and critical value of each indicator index value, and transmit the indicator index value, standard range and critical value to the preset Sigmoid index model; wherein,

[0123] Sigmoid index model unit: used to activate and evaluate the indicator index value through the Sigmoid index model, regularly perform score calculation on the quantitative indicator data, and generate an indicator evaluation index.

[0124] The working principle and beneficial effects of the above technical solution are:

[0125] The indicator evaluation index unit of the present technical solution establishes indicator index values ​​of different indicator types through the quantitative indicator data; obtains the set standard range and critical value of each indicator index value, and transmits the indicator index value, standard range and critical value to a preset Sigmoid index model; activates and evaluates the indicator index value through the Sigmoid index model, regularly scores and calculates the quantitative indicator data, and generates an indicator evaluation index.

[0126] Embodiment 9:

[0127] The technical solution provides an embodiment, wherein the comparison module comprises:

[0128] Comparison unit: used to compare the consistency of the indicator evaluation index and the preset standard indicator evaluation index, and determine the comparison result;

[0129] A storage unit is used to transmit the indicator evaluation index to an evaluation indicator library for storage when the comparison result shows that the consistency is greater than or equal to a preset consistency threshold;

[0130] Statistical evaluation unit: used to upload the indicator evaluation index to the evaluation indicator library when the comparison result is less than the preset consistency threshold, and at the same time, based on the indicator evaluation model preset by the target platform, perform statistics and evaluation on the platform information related to the evaluation indicator in the target platform.

[0131] The working principle and beneficial effects of the above technical solution are:

[0132] The comparison module of the present technical solution compares the consistency of the comparison indicator evaluation index and the preset standard indicator evaluation index to determine the comparison result; when the comparison result is that the consistency is greater than or equal to the preset consistency threshold, the indicator evaluation index is transmitted to the evaluation indicator library for storage; when the comparison result is that the consistency is less than the preset consistency threshold, the indicator evaluation index is uploaded to the evaluation indicator library, and at the same time, based on the indicator evaluation model preset by the target platform, the platform information related to the evaluation indicator in the target platform is statistically analyzed and evaluated.

[0133] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0134] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0135] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0137] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An index evaluation system based on social governance, characterized in that: include: Evaluation index library module: used to capture and screen evaluation indicators about social governance through a preset social governance evaluation system, and establish an evaluation index library of the target platform based on the evaluation indicators; Index evaluation index module: used to collect index information about each evaluation index on the target platform, perform quantitative analysis on the index information, generate corresponding quantitative index data, count the quantitative index data, regularly perform score calculation on the quantitative index data, and generate an index evaluation index; Comparison module: used to compare the consistency of the indicator evaluation index and the preset standard indicator evaluation index, determine the comparison result, and transmit the comparison result to the evaluation index library for storage; The indicator evaluation index module includes: Indicator information unit: used to collect and filter the platform information of the target platform, filter out the platform information related to the evaluation indicator, and determine the indicator information; Separation unit: used for extracting the index features of the index information, establishing an index classifier through the index features, separating the index information corresponding to each evaluation index through the index classifier, and determining the separation result; Quantitative indicator data unit: used to perform quantitative analysis on the indicator information in each separation result and generate corresponding quantitative indicator data; Index evaluation unit: used to transmit the quantitative index data to the preset Index model, and regularly score and calculate the quantitative indicator data to generate an indicator evaluation index; The separation unit is used to extract the indicator features of the indicator information, establish an indicator classifier through the indicator features, separate the indicator information corresponding to each evaluation indicator through the indicator classifier, and determine the separation result, including: Step 1: Extract indicator features of indicator information; Step 2: Import the indicator features into a preset classifier model for extraction and establish an indicator classifier; ； in, Represents the preset classifier model. Represents the feature vector about the indicator characteristics, Representative In the classifier model; Representative The transpose of the classifier model, , Represents the total number of classifier models; Representative The feature vector of the batch index features, , Represents the total number of batches of feature vectors; Represents the import function The indicator classifier when the feature vector of the indicator feature is imported into the classifier model, represents a collection of classifier models, represents a set of feature vectors, The feature vector representing the indicator characteristics is imported into the import function of the classifier model. Representative The feature vector of the batch index feature is imported into The import function of a classifier model, The feature vector representing the indicator characteristics is classified and clustered in the classifier model. represents the normal distribution with respect to the import function, represents the classification function in the classifier model, represents the conditional probability, Represents the normal distribution of the classification function in the classifier model; The characteristic clustering function of the characteristic vector representing the indicator characteristics, Represents the normal distribution of the eigenvectors regarding the indicator characteristics; Step 3: Statistical indicator feature set , establish the indicator feature matrix ; Step 4: Import the indicator feature matrix into the indicator classifier, separate the indicator information corresponding to each evaluation indicator, and determine the separation result.

2. The index evaluation system based on social governance as claimed in claim 1, characterized in that: The evaluation index library module includes: Screening result unit: used to capture and screen the evaluation indicators of social governance through the preset social governance evaluation system, and determine the screening results; among them, The evaluation indicators for social governance include the proportion of research and development expenditure to GDP, the number of three types of national patents authorized per 100,000 people, the average years of education, the average life expectancy, the coverage rate of work-related injury insurance and the registered unemployment rate in urban areas; The screening results include the proportion of research and development expenditure to GDP, the number of three types of national patents authorized per 100,000 people, and the average years of education; Target platform database unit: used to obtain platform information of the target platform and establish a target platform database based on the platform information; Evaluation index library unit: used to map the evaluation indexes in the screening results to the target platform database and establish an evaluation index library for the target platform.

3. The index evaluation system based on social governance as claimed in claim 1, characterized in that: The indicator information unit includes: Platform database subunit: used to collect and count the platform information of the target platform and establish a platform database; Information extraction subunit: used to extract platform information that meets the characteristics of the evaluation index from the platform database based on the preset clustering model and probability correlation model, and determine the extracted information; Index information subunit: used to calculate the correlation between the extracted information and the evaluation index, and when the correlation is greater than a preset correlation threshold, determine the index information according to the extracted information; Deletion subunit: used to delete the extracted information in the platform database when the relevance is less than a preset relevance threshold.

4. The index evaluation system based on social governance as claimed in claim 1, characterized in that: The quantitative indicator data unit includes: Hierarchical analysis result subunit: used to transmit the indicator information in each separation result to the preset big data center for hierarchical analysis according to the separation order of the separation results, and obtain the hierarchical analysis results; Impact coefficient calculation subunit: used to calculate the impact coefficients between the indicator information in each separation result based on the quantitative analysis model of economic impact factors preset by the big data center; Difference matrix subunit: used to calculate the interference value of the influence coefficient on the stratified analysis results, statistically calculate the interference value of the stratified analysis results corresponding to each layer after the index information is classified, and generate the corresponding difference matrix; Model weighted value subunit: used to calculate the model weighted value of the economic influencing factor quantitative analysis model based on each influencing factor and the corresponding influencing coefficient analyzed by the economic influencing factor quantitative analysis model; Quantitative index data subunit: used to perform fuzzy complementation on the difference matrix through the model weighted value to generate quantitative index information of the evaluation index, and generate corresponding quantitative index data through the quantitative index information.

5. The index evaluation system based on social governance as claimed in claim 4, characterized in that: The quantitative index data subunit is used to perform fuzzy complementation on the difference matrix through the model weighted value to generate quantitative index information of the evaluation index, and generate corresponding quantitative index data through the quantitative index information, including the following steps: The difference matrix is ​​fuzzily complemented by the model weighted value to generate quantitative index information of the evaluation index, and the consistency between the quantitative index information of the evaluation index and the preset index information is judged; wherein, When the quantitative indicator information of the evaluation indicator is consistent with the preset indicator information, the corresponding quantitative indicator data is received and recorded through the economic impact factor quantitative analysis model preset by the big data center; When the quantitative indicator information of the evaluation indicator is inconsistent with the preset indicator information, it is determined that the quantitative analysis model of the economic influencing factors cannot perform quantitative analysis.

6. The index evaluation system based on social governance as claimed in claim 1, characterized in that: The indicator evaluation index unit includes: Indicator index value unit: used to establish indicator index values ​​of different indicator types through the quantitative indicator data; Scoring unit: used to obtain the set standard range and critical value of each indicator index value, and transmit the indicator index value, standard range and critical value to the preset Exponential model; where Index Model Unit: used to The index model activates and evaluates the indicator index value, regularly scores and calculates the quantitative indicator data, and generates an indicator evaluation index.

7. The index evaluation system based on social governance as claimed in claim 1, characterized in that: The comparison module comprises: Comparison unit: used to compare the consistency of the indicator evaluation index and the preset standard indicator evaluation index, and determine the comparison result; A storage unit is used to transmit the indicator evaluation index to an evaluation indicator library for storage when the comparison result shows that the consistency is greater than or equal to a preset consistency threshold; Statistical evaluation unit: used to upload the indicator evaluation index to the evaluation indicator library when the comparison result is less than the preset consistency threshold, and at the same time, based on the indicator evaluation model preset by the target platform, perform statistics and evaluation on the platform information related to the evaluation indicator in the target platform.

Images