A method and system for centralized statistical evaluation of urban safety performance data

Abstract

The present invention discloses a method and system for centralized statistical evaluation of urban safety performance data. The method includes: obtaining urban safety performance data uploaded by each terminal and classifying it, and selecting a performance evaluation method according to the classification result; constructing an evaluation index system based on the performance evaluation method corresponding to each type; constructing an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system; and using the evaluation model of each type to evaluate the classified data within that type. By selecting different performance evaluation methods according to different types of performance data, and then constructing different evaluation index systems and evaluation models to comprehensively evaluate the performance data of that type, it is possible to effectively use the most applicable evaluation index system for different types of performance data for evaluation, ensuring the accuracy and objectivity of the evaluation results.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a method and system for centralized statistical evaluation of urban safety performance data. Background Art

[0002] In recent years, people have paid increasing attention to the coordinated relationship between urban construction and the ecological environment in the process of social development, and it is particularly important to maintain the sustainable development of social construction. Therefore, a series of urban safety implementation systems have emerged and are carried out in an orderly manner in various departments. It has been proved that these systems have indeed played a great role in urban construction and development under the influence of urban safety systems. However, the formulated systems need to be tested in practice and need to meet the development needs of enterprises. Not every system fits perfectly with the enterprise development. Therefore, it is necessary to periodically conduct a centralized evaluation of the performance data of urban safety. The existing evaluation method is to use a pre-established evaluation system to centrally evaluate all the uploaded performance data. However, the above method has the following disadvantages: Since the types of each performance data are different, using the same evaluation system for evaluation will result in the final evaluation result not conforming to the actual situation, which will cause the modification work of the system to encounter obstacles and affect the development of the enterprise. Summary of the Invention

[0003] In view of the problems shown above, the present invention provides a method and system for centralized statistical evaluation of urban safety performance data to solve the problem that since the types of each performance data are different, using the same evaluation system for evaluation will result in the final evaluation result not conforming to the actual situation, which will cause the modification work of the system to encounter obstacles and affect the development of the enterprise as mentioned in the background art.

[0004] A method for centralized statistical evaluation of urban safety performance data includes the following steps:

[0005] Obtain the urban safety performance data uploaded by each terminal and classify it, and select a performance evaluation method according to the classification result;

[0006] Construct an evaluation index system based on the performance evaluation method corresponding to each type;

[0007] Construct an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system;

[0008] Use the evaluation model of each type to evaluate the classified data within the type.

[0009] Preferably, before obtaining the urban safety performance data uploaded by each terminal and classifying it, and selecting a performance evaluation method according to the classification result, the method further includes:

[0010] Extract the evaluation indicators from each piece of urban safety performance data;

[0011] Conduct membership degree analysis and correlation analysis on the evaluation indicators to obtain the first analysis result and the second analysis result;

[0012] Evaluate whether the urban safety performance data is qualified according to the first analysis result and the second analysis result of each piece of urban safety performance data;

[0013] Upload the unqualified first urban safety performance data to the original terminal and send a reminder that the data is unqualified, and store the qualified second urban safety performance data in a preset server.

[0014] Preferably, the method of obtaining the urban safety performance data uploaded by each terminal and classifying it, and selecting a performance evaluation method according to the classification result includes:

[0015] Receive the urban safety performance data uploaded by each terminal from a preset channel;

[0016] Determine the data type corresponding to each piece of urban safety performance data, classify the urban safety performance data uploaded by each terminal according to the data type, and obtain the classification result;

[0017] Based on the classification result, conduct factor, data, index and empirical research analysis on the urban safety performance data within each type to obtain the third analysis result;

[0018] Select a target performance evaluation method from multiple preset performance evaluation methods according to the third analysis result of each piece of urban safety performance data.

[0019] Preferably, the preset performance evaluation methods include: expert investigation method, questionnaire survey method, interview survey method, statistical analysis method, comparative analysis method, index method, experimental method, linear programming method, analytic hierarchy process and fuzzy mathematics evaluation method.

[0020] Preferably, the construction of an evaluation index system based on the performance evaluation method corresponding to each type includes:

[0021] Use the performance evaluation method corresponding to each type to analyze the influencing factors of the target urban safety performance data corresponding to this type in combination with the urban work safety standardization performance evaluation goal;

[0022] Analyze the influencing factors of each target urban safety performance data to obtain its corresponding influencing factors;

[0023] Select target influencing factors from the influencing factors according to the preset systematic principle, operability principle, effectiveness principle, comparability principle, dynamic principle, guiding principle and independence principle;

[0024] Construct an evaluation index system corresponding to each type based on the target impact factor and the evaluation index of the safety performance data of each target city.

[0025] Preferably, constructing an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system, including:

[0026] Construct a factor set and a judgment set corresponding to each type according to the content of the urban safety performance data of each type;

[0027] Judge the second set of factors in the factor set of each type according to the judgment formula and expression mode of the first set of factors in the judgment set of each type, and construct a judgment matrix according to the judgment results;

[0028] Determine the importance index of each factor in the factor set of each type;

[0029] Construct a fuzzy comprehensive evaluation model based on urban safety performance data corresponding to each type according to the factor set of each type, the importance index of each of its factors, the judgment set and the judgment matrix corresponding to each type.

[0030] Preferably, using the evaluation model of each type to evaluate the classification data within each type, including:

[0031] Determine the distribution weight of the evaluation index of the classification data within each type according to the evaluation model of each type;

[0032] Based on the distribution weight of the evaluation index, use the evaluation model of each type to calculate the evaluation result of the implementation effect of the classification data within each type;

[0033] Determine the comprehensive score of the classification data of each type according to the evaluation result of the implementation effect of the classification data within each type;

[0034] Compare the comprehensive score of each type with a preset score to evaluate the rationality of the urban safety standardization system.

[0035] Preferably, determining the distribution weight of the evaluation index of the classification data within each type according to the evaluation model of each type, including:

[0036] Construct an evaluation index matrix based on the model output result of the evaluation model of each type;

[0037] Set the same weight for each matrix factor in the evaluation index matrix;

[0038] Construct an algorithm mathematical model based on the weight factor matrix;

[0039] Use the algorithm mathematical model to determine the eigenvectors corresponding to each characteristic factor in the evaluation index matrix;

[0040] Take the eigenvectors corresponding to the characteristic factors of the evaluation index matrix of each type of evaluation model as inputs, and use the evaluation model of this type to solve the model to obtain the solution results;

[0041] Analyze the solution results to obtain the index vectors output by each type of evaluation model;

[0042] Cluster the index vectors output by each type of evaluation model to obtain the clustering results;

[0043] Determine the associated index vector sets corresponding to each evaluation index according to the clustering results;

[0044] Calculate the allocation weights of the evaluation indexes for classifying the data within this type determined by each type of evaluation model according to the ratio of the associated index vector sets corresponding to each evaluation index to all index vectors.

[0045] An urban safety performance data centralized statistical evaluation system, which includes:

[0046] A selection module, configured to obtain the urban safety performance data uploaded by each terminal, classify it, and select a performance evaluation method according to the classification results;

[0047] A first construction module, configured to construct an evaluation index system based on the performance evaluation method corresponding to each type;

[0048] A second construction module, configured to construct an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system;

[0049] An evaluation module, configured to use the evaluation model of each type to evaluate the classified data within this type.

[0050] Other features and advantages of the present invention will be described in the subsequent description, and part of them will become obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written description and the drawings.

[0051] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. Description of the Drawings

[0052] The drawings are used to provide a further understanding of the present invention, and constitute a part of the description. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention.

[0053] Figure 1 This is the flowchart of a method for centralized statistical evaluation of urban safety performance data provided by the present invention;

[0054] Figure 2 This is another flowchart of a method for centralized statistical evaluation of urban safety performance data provided by the present invention;

[0055] Figure 3 This is yet another flowchart of a method for centralized statistical evaluation of urban safety performance data provided by the present invention;

[0056] Figure 4 This is the structural schematic diagram of a system for centralized statistical evaluation of urban safety performance data provided by the present invention. Detailed implementation manners

[0057] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0058] In recent years, people have paid more and more attention to the coordination relationship between urban construction and the ecological environment in the process of social development, and it is particularly important to maintain the sustainable development of social construction. As a result, a series of urban safety implementation systems have emerged and are carried out in an orderly manner in various departments. It has been proved that the urban safety system has indeed played a great role in urban construction and development. However, the formulated systems need to be tested in practice and need to meet the development needs of enterprises. Not every system fits perfectly with the enterprise development. Therefore, it is necessary to periodically conduct a centralized evaluation of the performance data of urban safety. The existing evaluation method is to use a pre-established evaluation system to centrally evaluate all the uploaded performance data. However, the above method has the following disadvantages: Since the types of each performance data are different, using the same evaluation system for evaluation will result in the final evaluation result not conforming to the actual situation, thus causing congestion in the system modification work and affecting the development of the enterprise. To solve the above problems, this embodiment discloses a method for centralized statistical evaluation of urban safety performance data.

[0059] A method for centralized statistical evaluation of urban safety performance data, as Figure 1 shown, includes the following steps:

[0060] Step S101, obtain the urban safety performance data uploaded by each terminal and classify it, and select a performance evaluation method according to the classification result;

[0061] Step S102: Construct an evaluation index system based on the performance evaluation method corresponding to each type;

[0062] Step S103: Construct an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system;

[0063] Step S104: Use the evaluation model of each type to evaluate the classified data within that type.

[0064] The working principle of the above technical solution is as follows: Obtain the urban safety performance data uploaded by each terminal and classify it, select a performance evaluation method according to the classification result, construct an evaluation index system based on the performance evaluation method corresponding to each type, construct an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system, and use the evaluation model of each type to evaluate the classified data within that type.

[0065] The beneficial effects of the above technical solution are as follows: By selecting different performance evaluation methods according to different types of performance data and then constructing different evaluation index systems and evaluation models to comprehensively evaluate the performance data of that type, it can effectively use the most applicable evaluation index system for different types of performance data for evaluation, ensuring the accuracy and objectivity of the evaluation results, enabling the evaluation results to provide a perfect reference for the subsequent modification of the urban safety system, being beneficial to the comprehensive and sustainable development of the enterprise, and solving the problem in the prior art that since the types of each performance data are different, using the same evaluation system for evaluation will result in the final evaluation result not conforming to the actual situation, thus blocking the modification work of the system and affecting the development of the enterprise.

[0066] In one embodiment, before the step of obtaining the urban safety performance data uploaded by each terminal and classifying it, and selecting a performance evaluation method according to the classification result, the method further includes:

[0067] Extract the evaluation indexes in each piece of urban safety performance data;

[0068] Conduct membership degree analysis and correlation analysis on the evaluation indexes to obtain the first analysis result and the second analysis result;

[0069] Evaluate whether the urban safety performance data is qualified according to the first analysis result and the second analysis result of each piece of urban safety performance data;

[0070] Upload the unqualified first urban safety performance data to the original terminal and send a reminder that the data is unqualified, and store the qualified second urban safety performance data in a preset server.

[0071] The beneficial effects of the above technical solution are as follows: By evaluating whether the urban safety performance data of each city is qualified, the data validity judgment work can be carried out before the index evaluation, so as to ensure that a complete and effective performance data can be obtained, providing a guarantee for the subsequent comprehensive evaluation.

[0072] In one embodiment, as Figure 2 shown, the method of obtaining the urban safety performance data uploaded by each terminal and classifying it, and selecting a performance evaluation method according to the classification result includes:

[0073] Step S201: Receive the urban safety performance data uploaded by each terminal from a preset channel;

[0074] Step S202: Determine the data type corresponding to each piece of urban safety performance data, classify the urban safety performance data uploaded by each terminal according to the data type, and obtain the classification result;

[0075] Step S203: Based on the classification result, conduct factor, data, index, and empirical research analysis on the urban safety performance data within each type, and obtain the third analysis result;

[0076] Step S204: Select a target performance evaluation method from multiple preset performance evaluation methods according to the third analysis result of each piece of urban safety performance data.

[0077] The beneficial effects of the above technical solution are as follows: By conducting multi-factor analysis on the urban safety performance data of each type to select the target performance evaluation method corresponding to the urban safety performance data of that type, the most suitable performance evaluation method can be comprehensively selected according to the characteristic factors of each element in the urban safety performance data of each type, improving the error tolerance rate and further ensuring the accuracy of the evaluation result.

[0078] In one embodiment, the preset performance evaluation methods include: expert investigation method, questionnaire survey method, interview survey method, statistical analysis method, comparative analysis method, index method, experimental method, linear programming method, analytic hierarchy process, and fuzzy mathematics evaluation method.

[0079] The beneficial effects of the above technical solution are as follows: By providing multiple methods to evaluate the urban safety performance data, the corresponding performance evaluation method can be reasonably selected according to the actual situation of each piece of urban safety performance data, further improving the evaluation efficiency.

[0080] In one embodiment, as Figure 3 shown, the method of constructing an evaluation index system based on the performance evaluation method corresponding to each type includes:

[0081] Step S301: Analyze the influencing factors of the target urban safety performance data corresponding to each type by combining the performance evaluation methods corresponding to each type with the urban safety production standardization performance evaluation objectives;

[0082] Step S302: Analyze the influencing factors of each target urban safety performance data to obtain their corresponding influence factors;

[0083] Step S303: Screen out the target influence factors from the said influence factors according to the preset systematic principle, operability principle, effectiveness principle, comparability principle, dynamic principle, guiding principle and independence principle;

[0084] Step S304: Based on the target influence factors and the evaluation indicators of each target urban safety performance data, construct the evaluation index system corresponding to each type.

[0085] The beneficial effects of the above technical solution are as follows: By selecting the target influence factors and combining them with the evaluation indicators of each target urban safety performance data to construct the evaluation index system corresponding to each type, the evaluation indicators can be combined with their corresponding influence factors to comprehensively construct the evaluation index system, ensuring the objectivity and accuracy of the results evaluated by the evaluation index system, and improving the overall stability and practicality.

[0086] In one embodiment, constructing the evaluation model of the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system includes:

[0087] Construct the factor set and evaluation set corresponding to each type according to the content of the urban safety performance data of each type;

[0088] Judge the second set of factors in the factor set of each type according to the judgment formula and expression method of the first set of factors in the evaluation set of each type, and construct the judgment matrix according to the judgment results;

[0089] Determine the importance indicators of each factor in the factor set of each type;

[0090] Construct the fuzzy comprehensive evaluation model based on the urban safety performance data corresponding to each type according to the factor set of each type, the importance indicators of its factors, the evaluation set and the judgment matrix corresponding to each type.

[0091] The beneficial effects of the above technical solution are as follows: By using the judgment matrix, factor set and evaluation set to construct the fuzzy comprehensive evaluation model based on the urban safety performance data corresponding to each type, the classification data of each type can be effectively judged through the judgment matrix, making the final evaluation results more reasonable and improving the accuracy of the evaluation data.

[0092] In one embodiment, the data evaluation of the classification data within each type using the evaluation model of each type includes:

[0093] Determine the allocation weights of the evaluation indicators for the classification data within each type according to the evaluation model of each type;

[0094] Based on the allocation weights of the evaluation indicators, calculate the evaluation results of the implementation effects of the classification data within each type using the evaluation model of each type;

[0095] Determine the comprehensive score of the classification data of each type according to the evaluation results of the implementation effects of the classification data within each type;

[0096] Compare the comprehensive score of each type with the preset score to evaluate the rationality of the urban safety standardization system.

[0097] The beneficial effects of the above technical solution are as follows: By determining the comprehensive score of the classification data of each type according to the allocation weights of each evaluation indicator, the implementation score can be reasonably calculated effectively according to the evaluation indicators in the classification data of each type. The calculation result is simpler and more efficient, improving the evaluation efficiency and accuracy at the same time.

[0098] In one embodiment, the determining the allocation weights of the evaluation indicators for the classification data within each type according to the evaluation model of each type includes:

[0099] Construct an evaluation indicator matrix based on the model output results of the evaluation model of each type;

[0100] Set the same weight for each matrix factor in the evaluation indicator matrix;

[0101] Construct an algorithm mathematical model based on the weight factor matrix;

[0102] Use the algorithm mathematical model to determine the eigenvectors corresponding to each characteristic factor in the evaluation indicator matrix;

[0103] Take the eigenvectors corresponding to the characteristic factors of the evaluation indicator matrix of each type of evaluation model as input, and use the evaluation model of this type for model solution to obtain the solution result;

[0104] Analyze the solution result to obtain the index vectors output by each type of evaluation model;

[0105] Cluster the index vectors output by each type of evaluation model to obtain the clustering result;

[0106] Determine the associated index vector set corresponding to each evaluation indicator according to the clustering result;

[0107] Calculate the allocation weights of the evaluation indicators for the classification data within each type of evaluation model according to the ratio of the associated indicator vector set corresponding to each evaluation indicator to all indicator vectors.

[0108] The beneficial effects of the above technical solution are as follows: By using the clustering method to determine the allocation weights of the evaluation indicators for the classification data of each type of evaluation model, the indicator vectors corresponding to each evaluation indicator can be accurately determined according to the actual output results of the model. Furthermore, the allocation weights of each evaluation indicator can be quickly and accurately determined, which is simple, efficient, and accurate, improving work efficiency.

[0109] In this embodiment, the steps of clustering the indicator vectors output by each type of evaluation model to obtain the clustering results include:

[0110] Determine the number of clustering categories of the indicator vectors output by each type of evaluation model according to the number of evaluation indicators of each type of evaluation model;

[0111] Determine the central data of each evaluation indicator according to the aggregation situation of each indicator vector towards each evaluation indicator;

[0112] Calculate the membership degree of each indicator vector belonging to each evaluation indicator according to the distance between each indicator vector and the central data of each evaluation indicator, combined with the weighted coefficient and correlation coefficient of the indicator vector;

[0113] Cluster the indicator vectors output by each type of evaluation model according to the membership degree of each indicator vector belonging to each evaluation indicator to obtain the clustering results.

[0114] The beneficial effects of the above technical solution are as follows: By calculating the membership degree of each indicator vector belonging to each evaluation indicator, the indicator vectors can be more intuitively and accurately clustered quickly, further improving work efficiency.

[0115] This embodiment also discloses a centralized statistical evaluation system for urban safety performance data, as Figure 4 shown. The system includes:

[0116] A selection module 401, configured to obtain the urban safety performance data uploaded by each terminal, classify it, and select a performance evaluation method according to the classification result;

[0117] A first construction module 402, configured to construct an evaluation index system based on the performance evaluation method corresponding to each type;

[0118] A second construction module 403, configured to construct an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system;

[0119] An evaluation module 404 is configured to perform data evaluation on the classified data within each type by using an evaluation model of each type.

[0120] The working principle and beneficial effects of the above technical solution have been described in the method claims and will not be elaborated here.

[0121] Those skilled in the art should understand that the first and second in the present invention refer to different application stages.

[0122] After considering the specification and practicing the disclosure herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0123] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A method for centralized statistical evaluation of urban safety performance data, characterized in that, It includes the following steps: Obtain the urban safety performance data uploaded by each terminal, classify it, and select a performance evaluation method according to the classification result; Construct an evaluation index system based on the performance evaluation method corresponding to each type; Construct an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system; Use the evaluation model of each type to evaluate the classified data within that type; The using the evaluation model of each type to evaluate the classified data within that type includes: Determine the distribution weights of the evaluation indexes of the classified data within each type according to the evaluation model of each type; Based on the distribution weights of the evaluation indexes, use the evaluation model of each type to calculate the evaluation result of the implementation effect of the classified data within that type; Determine the comprehensive score of the classified data of each type according to the evaluation result of the implementation effect of the classified data within each type; Compare the comprehensive score of each type with a preset score to evaluate the rationality of the urban safety standardization system; The determining the distribution weights of the evaluation indexes of the classified data within each type according to the evaluation model of each type includes: Construct an evaluation index matrix based on the model output result of the evaluation model of each type; Set the same weight for each matrix factor in the evaluation index matrix; Construct an algorithm mathematical model based on the weight factor matrix; Use the algorithm mathematical model to determine the eigenvector corresponding to each characteristic factor in the evaluation index matrix; Take the eigenvector corresponding to the characteristic factor of the evaluation index matrix of each type of evaluation model as the input, use the evaluation model of that type for model solving, and obtain the solution result; Analyze the solution result to obtain the index vector output by the evaluation model of each type; Cluster the index vectors output by the evaluation model of each type to obtain a clustering result; Determine the associated index vector set corresponding to each evaluation index according to the clustering result; Calculate the distribution weights of the evaluation indexes of the classified data within each type determined by the evaluation model of each type according to the ratio of the associated index vector set corresponding to each evaluation index to all index vectors; The step of clustering the index vectors output by the evaluation model of each type to obtain a clustering result includes: Determine the number of clustering categories of the index vectors output by the evaluation model of each type according to the number of evaluation indexes of the evaluation model of each type; Determine the central data of each evaluation index according to the aggregation situation of each index vector to each evaluation index; Calculate the membership degree of each index vector belonging to each evaluation index according to the distance between each index vector and the central data of each evaluation index, combined with the weighted coefficient and correlation coefficient of the index vector; Cluster the index vectors output by the evaluation model of each type according to the membership degree of each index vector belonging to each evaluation index to obtain a clustering result.

2. The urban safety performance data centralized statistical evaluation method according to claim 1, wherein Before the obtaining the urban safety performance data uploaded by each terminal, classifying it, and selecting a performance evaluation method according to the classification result, the method further includes: Extract the evaluation indexes from each piece of urban safety performance data; Perform membership analysis and correlation analysis on the evaluation indicators to obtain the first analysis result and the second analysis result; Evaluate whether each piece of urban safety performance data is qualified according to the first analysis result and the second analysis result of the urban safety performance data; Upload the unqualified first urban safety performance data to the original terminal and send a reminder that the data is unqualified, and store the qualified second urban safety performance data in a preset server.

3. The urban safety performance data centralized statistical evaluation method according to claim 1, characterized in that The obtaining of the urban safety performance data uploaded by each terminal and classifying it, and selecting a performance evaluation method according to the classification result, includes: Receive the urban safety performance data uploaded by each terminal from a preset channel; Determine the data type corresponding to each piece of urban safety performance data, classify the urban safety performance data uploaded by each terminal according to the data type, and obtain the classification result; Based on the classification result, conduct factor, data, index, and empirical research analysis on the urban safety performance data within each type to obtain the third analysis result; Select a target performance evaluation method from multiple preset performance evaluation methods according to the third analysis result of each piece of urban safety performance data.

4. The urban safety performance data centralized statistical evaluation method according to claim 3, wherein The preset performance evaluation methods include: expert investigation method, questionnaire survey method, interview survey method, statistical analysis method, comparative analysis method, index method, experimental method, linear programming method, analytic hierarchy process, and fuzzy mathematics evaluation method.

5. The method for centralized statistical evaluation of urban safety performance data according to claim 1, characterized in that The constructing of an evaluation index system based on the performance evaluation method corresponding to each type includes: Use the performance evaluation method corresponding to each type to analyze the influencing factors of the target urban safety performance data corresponding to this type in combination with the urban work safety standardization performance evaluation goal; Analyze the influencing factors of each target urban safety performance data to obtain its corresponding influencing factors; Select target influencing factors from the influencing factors according to the preset systematic principle, operability principle, effectiveness principle, comparability principle, dynamic principle, guiding principle, and independence principle; Based on the target influencing factors and the evaluation indicators of each target urban safety performance data, construct an evaluation index system corresponding to each type.

6. The method for centralized statistical evaluation of urban safety performance data according to claim 1, wherein The constructing of an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system includes: Construct a factor set and a judgment set corresponding to each type according to the content of the urban safety performance data of each type; Judge the second set of factors in the factor set of this type according to the judgment formula and expression method of the first set of factors in the judgment set of each type, and construct a judgment matrix according to the judgment result; Determine the importance index of each factor in the factor set of each type; Construct a fuzzy comprehensive evaluation model based on the urban safety performance data corresponding to each type according to the factor set of each type, its importance index of each factor, the judgment set, and the judgment matrix corresponding to this type.

7. A centralized statistical evaluation system for urban safety performance data, characterized in that, The system includes: A selection module for obtaining the urban safety performance data uploaded by each terminal and classifying it, and selecting a performance evaluation method according to the classification result; A first construction module for constructing an evaluation index system based on the performance evaluation method corresponding to each type; A second construction module for constructing an evaluation model for the urban safety performance data corresponding to each type according to the content of the urban safety performance data of each type and its corresponding target evaluation index system; An evaluation module for evaluating the classification data within each type by using the evaluation model of each type; The evaluation of the classification data within each type by using the evaluation model of each type includes: Determining the assigned weights of the evaluation indicators for the classification data within each type according to the evaluation model of each type; Based on the assigned weights of the evaluation indicators, calculating the evaluation results of the implementation effects of the classification data within each type by using the evaluation model of each type; Determining the comprehensive score of the classification data of each type according to the evaluation results of the implementation effects of the classification data within each type; Comparing the comprehensive score of each type with a preset score to evaluate the rationality of the urban safety standardization system; The determining the assigned weights of the evaluation indicators for the classification data within each type according to the evaluation model of each type includes: Constructing an evaluation index matrix based on the model output results of the evaluation model of each type; Setting the same weight for each matrix factor in the evaluation index matrix; Constructing an algorithm mathematical model based on the weight factor matrix; Using the algorithm mathematical model to determine the eigenvector corresponding to each characteristic factor in the evaluation index matrix; Taking the eigenvector corresponding to the characteristic factor of the evaluation index matrix of each type of evaluation model as the input, and using the evaluation model of this type for model solving to obtain the solution result; Analyzing the solution result to obtain the index vector output by each type of evaluation model; Clustering the index vectors output by each type of evaluation model to obtain a clustering result; Determining the associated index vector set corresponding to each evaluation indicator according to the clustering result; Calculating the assigned weights of the evaluation indicators for the classification data within each type determined by the evaluation model of each type according to the ratio of the associated index vector set corresponding to each evaluation indicator to all index vectors; The steps of clustering the index vectors output by each type of evaluation model to obtain a clustering result include: Determining the number of clustering categories of the index vectors output by the evaluation model of each type according to the number of evaluation indicators of the evaluation model of each type; Determining the central data of each evaluation indicator according to the aggregation situation of each index vector to each evaluation indicator; Calculating the membership degree of each index vector belonging to each evaluation indicator according to the distance between each index vector and the central data of each evaluation indicator, combined with the weighted coefficient and correlation coefficient of this index vector; Clustering the index vectors output by the evaluation model of each type according to the membership degree of each index vector belonging to each evaluation indicator to obtain a clustering result.

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