Public safety video image application system health degree assessment method

Abstract

The invention discloses a health degree assessment method for a public safety video image application system. The health degree assessment method comprises the steps of collecting objective data of operation of the image application system; converting the objective data subitems into index data in a percentage form; constructing the index data into an index forward matrix, and standardizing the index forward matrix to obtain a standardized matrix; determining an entropy value weight of the index data by using a TOPSIS-entropy weight method; and calculating the health degree of the public safety video image application system and sorting. The method relates to the field of information technology and security management, and solves the problems of strong data dependence, index weight bias, insufficient overall performance evaluation and the like caused by single use of the entropy weight method through effective fusion of the entropy weight method and the TOPSIS method. According to the method, the health state of the system can be intuitively reflected, the accuracy of health degree analysis of the public safety video image application system is improved, and a scientific basis is provided for operation and maintenance management of the system.

Description

Technical Field

[0001] The invention belongs to the field of information technology and security management, relates to an image application system evaluation technology, and specifically is a public security video image application system health evaluation method. Background Art

[0002] With the continuous construction of "safe cities" and "smart cities" and the rapid development of video surveillance networks, video surveillance systems have covered all areas of society and effectively improved the city's security level. The current video surveillance system is no longer limited to basic video surveillance functions, but has developed into a comprehensive digital system that integrates high-definition images, remote access and management, and intelligent processing. However, with the continuous deepening of construction, the scale of equipment and resources of video surveillance systems is becoming increasingly large. How to guide the operation and maintenance service guarantee work by analyzing various objective data generated by the daily operation of the system has gradually become one of the pain points of public safety video image application systems.

[0003] As an objective weighting technology, the entropy weight method plays a key role in the evaluation of image application systems. It determines the weights of various indicators by analyzing the objective data generated by the operation of the system, making the evaluation results more accurate and reliable than subjective evaluation methods. However, the entropy weight method is highly dependent on data. For example, when the data dispersion of a certain indicator is large, the entropy weight method will lead to bias in the weighting of the indicator. This bias not only affects the accuracy of the evaluation, but also may cause decision makers to be overly sensitive to specific indicators, thereby ignoring other equally important indicators. In addition, the effect of the entropy weight method will be greatly reduced when faced with low data quality, because the inaccuracy of the data will directly affect the calculation of the indicator weight and the final evaluation results. At the same time, the existing video image application system evaluation methods usually only focus on single factors such as hardware failures or software errors, lack of comprehensive consideration of the overall performance and health of the system, and limit the implementation of system management and optimization measures. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a public security video image application system health assessment method to solve the technical problems of strong data dependence, indicator weight bias and insufficient system overall performance evaluation in the existing method.

[0005] To achieve the above object, the present invention provides a method for evaluating the health of a public security video image application system, comprising:

[0006] S100: Collect objective data of the operation of the image application system;

[0007] S200: converting the objective data into index data in percentage form;

[0008] S300: constructing the indicator data into an indicator forward matrix and then standardizing it to obtain a standardized matrix;

[0009] S400: Determine the entropy value weight of the indicator data by using the TOPSIS-entropy weight method;

[0010] S500: Calculate and sort the health of the public security video image application system.

[0011] Based on the above scheme, by combining TOPSIS and entropy weight method, the indicator weights can be determined more objectively, the influence of subjective judgment can be reduced, and the accuracy of evaluation can be improved. Among them, the TOPSIS part can identify and reduce the influence of outliers, making the evaluation results more stable and reliable; the entropy weight law balances the importance of indicators and avoids a single indicator from excessively affecting the results. This comprehensive method not only improves the comprehensiveness of the evaluation, but also enhances the ability to evaluate the health of the image application system.

[0012] Furthermore, the objective data includes: equipment file information, equipment basic information, GIS information, online information, clock information, subtitle information, video picture quality information, card slot image capture data, image storage information, image quality information, and image callability information.

[0013] Furthermore, the indicator data include: equipment filing rate, equipment basic information accuracy rate, GIS information accuracy rate, online rate, clock accuracy rate, subtitle information accuracy rate, video picture quality compliance rate, checkpoint picture capture rate, picture storage delay rate, picture quality compliance rate, and picture callability rate.

[0014] The present invention comprehensively evaluates the health of the system by covering multiple indicators such as equipment archiving rate, information accuracy, online rate, clock accuracy, etc., ensuring that every important performance parameter is considered. In addition, converting objective data into indicator data in percentage form not only makes the evaluation results more intuitive and easy to understand, but also facilitates comparison and analysis.

[0015] Furthermore, the percentage data is constructed into an indicator positive matrix and then standardized, including the following steps:

[0016] S301: Convert the image storage delay rate indicator in the indicator data into a very large indicator x + =max-x; where x + is the converted extremely large index, max is the maximum value of the corresponding index item data, and x is the index item data;

[0017] S302: Constructing an indicator positive matrix according to the indicator data Among them, m is the number of evaluation indicators, and n is the number of evaluation objects;

[0018] S302: Standardize the index forward matrix X to obtain a standardized matrix in, Both n and m are positive integers.

[0019] Furthermore, the method of determining the entropy weight of the indicator data by using the TOPSIS-entropy weight method comprises the following steps:

[0020] S401: By formula and Calculate the information entropy value E respectively i and weight ω i ;

[0021] S402: Constructing a weighted judgment matrix Among them, u ij =ω i *z ij ;

[0022] S403: Define the maximum value U of the weighted judgment matrix U + and the minimum value U - ;in,

[0023]

[0024] S404: By formula Calculate the mathematical distance between the positive and negative ideal solutions respectively.

[0025] Furthermore, the calculation formula of the system health is:

[0026] It should be noted that the larger the value of the system health, the healthier the system.

[0027] The TOPSIS-entropy weight method is used to determine the entropy weight of each indicator data. The calculation of healthiness is further refined by the relative distance between the evaluation object and the ideal optimal and worst points, making the evaluation results more accurate and reliable.

[0028] Beneficial effects of the present invention:

[0029] The present invention comprehensively evaluates the health of the public security video image application system based on the TOPSIS-entropy weight method based on the objective data generated by the daily operation of the system, which can intuitively reflect the health of the system in each evaluation cycle. Through the effective integration of the entropy weight method and the TOPSIS method, the problems of strong data dependence, index weight bias and insufficient overall system performance evaluation caused by the single use of the entropy weight method are avoided, and the comprehensiveness and accuracy of the health analysis of the video image application system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flowchart of the steps of the public security video image application system health assessment method provided by the present invention;

[0032] Figure 2 The indicator data provided by the present invention;

[0033] Figure 3 The objective indicator data of a public security video image application system in an embodiment of the present invention from January to July of a certain year;

[0034] Figure 4 is the weight data calculated according to the entropy weight method in the embodiment of the present invention;

[0035] Figure 5 The health degree and ranking value of a public security video image application system from January to July of a certain year calculated based on the TOPSIS-entropy weight method in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1-Figure 5 The embodiment of the present invention provides a method for evaluating the health of a public security video image application system, comprising:

[0038] S100: Collect objective data of the operation of the image application system;

[0039] S200: Convert objective data items into indicator data in percentage form;

[0040] S300: constructing the indicator data into an indicator forward matrix and then standardizing it to obtain a standardized matrix;

[0041] S400: Determine the entropy weight of the indicator data using the TOPSIS-entropy weight method;

[0042] S500: Calculate and sort the health of the public security video image application system.

[0043] Among them, objective data include: equipment file information, equipment basic information, GIS information, online information, clock information, subtitle information, video picture quality information, checkpoint image capture data, image storage information, image quality information, and image callability information.

[0044] like Figure 2 As shown, the objective data is converted into equipment filing rate, equipment basic information accuracy rate, GIS information accuracy rate, online rate, clock accuracy rate, subtitle information accuracy rate, video picture quality compliance rate, checkpoint picture capture rate, picture storage delay rate, picture quality compliance rate, and picture callability rate.

[0045] Among them, constructing the indicator data into an indicator positive matrix and then standardizing it includes the following steps:

[0046] S301: Convert the image storage delay rate indicator in the indicator data into the extremely large indicator x + =max-x; where x + is the converted extremely large index, max is the maximum value of the corresponding index item data, and x is the index item data;

[0047] S302: Construct an indicator positive matrix based on indicator data Among them, m is the number of evaluation indicators, and n is the number of evaluation objects;

[0048] S302: Standardize the index forward matrix X to obtain a standardized matrix in, Both n and m are positive integers.

[0049] The TOPSIS-entropy weight method is used to determine the entropy weight of the indicator data, including the following steps:

[0050] S401: By formula and Calculate the information entropy value E respectively i and weight ω i ;

[0051] S402: Constructing a weighted judgment matrix Among them, u ij =ω i *z ij ;

[0052] S403: Define the maximum value U of the weighted judgment matrix U + and the minimum value U - ;in,

[0053] S404: By formula Calculate the mathematical distance between the positive and negative ideal solutions respectively.

[0054] Furthermore, the calculation formula for system health is:

[0055] It should be noted that the larger the value of the system health, the healthier the system.

[0056] For example, collect objective indicator data items of a public security video image application system operation, based on Figure 2 The indicators shown are used to obtain the objective indicator data of the application system running from January to July.

[0057] like Figure 3 As shown, the above objective indicator data are converted into percentage data.

[0058] This implementation example sets the evaluation cycle as: month, and the data collection cycle is the data from January to July of a certain year. The evaluation cycle and data collection cycle can be adjusted according to the actual operation of the system.

[0059] like Figure 4 As shown in the figure, the weight of each indicator is analyzed by the entropy weight method.

[0060] The weight calculation results of the entropy weight method show that the weight of the equipment archiving rate is 6.884%, the weight of the equipment basic information accuracy is 4.081%, the weight of the GIS information accuracy is 9.286%, the weight of the online rate is 9.583%, the weight of the clock accuracy is 10.229%, the weight of the subtitle information accuracy is 10.312%, the weight of the video picture quality compliance rate is 10.701%, the weight of the card port picture capture rate is 8.846%, the weight of the picture storage delay rate is 7.031%, the weight of the picture quality compliance rate is 11.939%, and the weight of the picture callable rate is 11.108%. The maximum value of the indicator weight is the picture quality compliance rate (11.939%), and the minimum value is the equipment basic information accuracy rate (4.081%).

[0061] Figure 5 is the health degree and ranking value of the public security video image application system from January to July of a certain year calculated based on the TOPSIS-entropy weight method. Figure 5 As shown, the health of the system is 0.46397469 in January, 0.55792616 in February, 0.4874523 in March, 0.5552636 in April, 0.56068084 in May, 0.5583166 in June, and 0.69963137 in July.

[0062] Through the public security video image application system health assessment method, it was analyzed that the health ranking of a public security video image application system from January to July in a certain year was as follows: July > May > June > February > April > March > January.

[0063] The present invention combines the objective data of daily operation of the public security video image application system with the TOPSIS-entropy weight method to comprehensively evaluate the health of the system, reduce the influence of subjective factors, avoid the shortcomings of using either alone, and provide an effective method for analyzing the health of the public security video image application system, while verifying the feasibility. The present invention can intuitively reflect the health of the public security video image application system, improve the accuracy of the analysis of the health of the public security video image application system, and then guide the operation and maintenance staff to improve the stability and reliability of the system.

[0064] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0065] Working principle of the present invention: The present invention uses the TOPSIS-entropy weight method to evaluate the health of the image application system, and uses the collected objective operation data of the system to convert it into indicator data in percentage form. The indicator data is then standardized to eliminate dimensional differences, and the entropy weight method is used to determine the weight of each indicator to ensure the objectivity of the evaluation. Finally, the TOPSIS method is used to calculate the degree of closeness of the system to the ideal health state, and the health score is obtained and ranked. By analyzing these scores and rankings, the health status of the system can be intuitively understood, the operation and maintenance work can be guided, and the stability and reliability of the system can be improved.

[0066] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for evaluating the health of a public safety video image application system, characterized in that: The following steps are involved: S100: Collect objective data of the operation of the image application system; S200: converting the objective data into index data in percentage form; S300: constructing the indicator data into an indicator forward matrix and then standardizing it to obtain a standardized matrix; S400: Determine the entropy weight of the indicator data using the TOPSIS-entropy weight method; S500: Calculate and sort the health of the public security video image application system.

2. A method for evaluating the health of a public security video image application system according to claim 1, characterized in that: The objective data includes: equipment file information, equipment basic information, GIS information, online information, clock information, subtitle information, video picture quality information, card slot image capture data, image storage information, image quality information, and image callability information.

3. A method for evaluating the health of a public security video image application system according to claim 1, characterized in that: The indicator data include: equipment filing rate, equipment basic information accuracy rate, GIS information accuracy rate, online rate, clock accuracy rate, subtitle information accuracy rate, video picture quality compliance rate, checkpoint picture capture rate, picture storage delay rate, picture quality compliance rate, and picture callability rate.

4. A method for evaluating the health of a public security video image application system according to claim 1, characterized in that: The percentage data is constructed into an indicator positive matrix and then standardized, including the following steps: S301: Convert the image storage delay rate indicator in the indicator data into a very large indicator x + =max-x; where x + is the converted extremely large index, max is the maximum value of the corresponding index item data, and x is the index item data; S302: Constructing an indicator positive matrix according to the indicator data Among them, m is the number of evaluation indicators, and n is the number of evaluation objects; S302: Standardize the index forward matrix X to obtain a standardized matrix in, Both n and m are positive integers.

5. A method for evaluating the health of a public security video image application system according to claim 1, characterized in that: The method of determining the entropy weight of the indicator data by using the TOPSIS-entropy weight method comprises the following steps: S401: By formula and Calculate the information entropy value E respectively i and weight ω i ; S402: Constructing a weighted judgment matrix Among them, u ij =ω i *z ij ; S403: Define the maximum value U of the weighted judgment matrix U + and the minimum value U - ;in, S404: By formula Calculate the mathematical distance between the positive and negative ideal solutions respectively.

6. A method for evaluating the health of a public security video image application system according to claim 1, characterized in that: The calculation formula of the system health is:

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