Social public security safety index assessment method and system, electronic equipment and storage medium
By obtaining and analyzing the element information of public opinion on social networks, street security conditions and alarm conditions, and using weight analysis models and neural network models, the problem of inaccurate and objective evaluation results in the existing technology is solved, and the social security conditions are achieved Dynamic real-time monitoring and accurate evaluation.
Patent Information
- Application Number
- CN202411249566.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-06
- Publication Date
- 2024-12-13
AI Technical Summary
The existing social security and security index evaluation system is difficult to achieve dynamic real-time monitoring and accurate evaluation, resulting in insufficient accuracy and objective evaluation.
By obtaining factor information on social network public opinion, street security status and alarm conditions, using the weight analysis model algorithm to calculate the weighted value, and weighted operations are performed in combination with the comprehensive population and economic parameters to obtain the social security and safety index. This method includes hierarchical structure construction, judgment matrix calculation and neural network model application to ensure the objectivity and accuracy of the evaluation results.
It improves the accuracy and objectivity of the social security index assessment, can dynamically monitor and predict social security situations in real time, and provide more accurate assessment results.
Smart Images
Figure CN119130192A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of public safety technology, and in particular to a method, system, medium and device for evaluating a public security index. Background Art
[0002] The social security situation is restricted and influenced by many factors such as social politics, economy, culture, and education, and is a comprehensive reflection of various social contradictions. According to relevant research, criminal crimes in my country are currently on the rise, various violent crimes and cyber crimes are increasing, and the means of crime are becoming more intelligent and technological. The social security index is an objective indicator value that measures the social security status and the subjective indicator value of residents' sense of security. It includes two parts: the objectively evaluated social security index and the subjectively evaluated residents' inner security index.
[0003] At present, the social security index evaluation system constructed in various places mostly adopts a static indicator evaluation system, which performs weighted combination calculations on statistical indicators such as police incidents and cases, and combines the permanent population base to approximately judge the social security situation in a certain area. This evaluation system cannot dynamically monitor the social security situation in real time, and it is difficult to provide timely feedback and predictions on the social security situation; in addition, since the safety index is obtained by simply manually classifying cases and the weight of the harm of the cases after classification, the evaluation method is relatively rough, resulting in inaccurate evaluation results; in addition, the subjective classification of cases by manual means is not objective enough, which further leads to inaccurate evaluation results.
[0004] Therefore, how to obtain accurate evaluation results is a technical problem that relevant personnel urgently need to solve. Summary of the invention
[0005] In order to at least partially solve the above technical problems, the embodiments of the present invention provide a method, system, electronic device and storage medium for evaluating a social security index.
[0006] In the first aspect, the social security and safety index evaluation method disclosed in the embodiment of the present invention includes: according to the area where the social security and safety index to be evaluated is located, obtaining factor information related to the social security and safety index to be evaluated, wherein the factor information includes social network public opinion, street security conditions and alarm conditions; matching the corresponding factor information according to preset indicator extraction rules, and calculating the evaluation index value corresponding to each evaluation index respectively; calculating the corresponding weighted value of each evaluation index according to the weight analysis model algorithm; performing weighted operation based on the corresponding weighted value, the evaluation index value and the regional population and economic comprehensive parameters to obtain the social security and safety index.
[0007] In some optional embodiments, according to the weight analysis model algorithm, calculating the corresponding weighted value of each evaluation indicator includes: establishing a hierarchical structure, including a target layer, a criterion layer and an indicator layer; constructing a judgment matrix according to the hierarchical structure, and the elements in the judgment matrix represent the relative importance level between two factors; calculating the weight according to the judgment matrix to obtain the corresponding weighted value.
[0008] In some optional implementations, the social security index evaluation method further includes calculating a consistency ratio so that the consistency ratio of the judgment matrix falls within a range of <0.1.
[0009] In some optional embodiments, weights are calculated according to the judgment matrix to obtain the corresponding weighted values, specifically including: extracting the eigenvector corresponding to the maximum eigenroot from the judgment matrix; normalizing the eigenvector to obtain a weight vector; according to the weight vector, for each criterion layer, calculating the weight of the next level indicator; synthesizing the weights of different levels to obtain the final indicator weight.
[0010] In some optional implementations, the weights of different levels are synthesized to obtain the final indicator weight, specifically including: the judgment matrix performs hierarchical single sorting on all elements of the same layer with respect to a certain element of the previous layer, and obtains the single sorting weight of each factor of the same layer with respect to a certain factor of the previous layer; and the single sorting weights between all layers and their previous layers are merged to obtain the final indicator weight.
[0011] In some optional implementations, any of the methods for assessing a public security index further includes: obtaining a historical element information set based on the element information; cleaning the historical element information set; and standardizing or normalizing the cleaned historical element information set.
[0012] On the second aspect, a social security and safety index evaluation system is also proposed, including: an information collection module, configured to obtain factor information associated with the social security and safety index to be evaluated according to the area where the social security and safety index to be evaluated is located, wherein the factor information includes social network public opinion, street security conditions and alarm conditions; an evaluation module, configured to match the corresponding factor information according to preset indicator extraction rules, respectively calculate the evaluation index values corresponding to each evaluation index, and calculate the corresponding weighted value of each evaluation index according to the weight analysis model algorithm; perform weighted operation based on the corresponding weighted value, the evaluation index value and the comprehensive parameters of regional population and economy to obtain the social security and safety index.
[0013] In some optional implementations, the evaluation module includes a neural network model, and the neural network model includes a social perception layer, an expert reasoning layer, and an event layer to form an evaluation index system.
[0014] In a third aspect, an electronic device disclosed in an embodiment of the present invention includes a processor and a memory storing computer program instructions; when the electronic device executes the computer program instructions, it implements the social security index evaluation method disclosed in the first aspect.
[0015] In a fourth aspect, a computer-readable storage medium disclosed in an embodiment of the present invention stores computer program instructions, and when the computer program instructions are executed by a processor, the method for evaluating the social security index disclosed in the first aspect is implemented.
[0016] Compared with the prior art, the social security index evaluation method, system, medium and device provided by the embodiments of the present invention have the following beneficial effects:
[0017] By using a trained computational model to evaluate the social security index, the accuracy of the evaluation results is improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the embodiments of the present disclosure 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 disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0019] Figure 1 It is a flow chart of a method for evaluating a social security index disclosed in an embodiment of the present invention.
[0020] Figure 2 It is a structural schematic diagram of a social security index evaluation system disclosed in an embodiment of the present invention.
[0021] Figure 3 It is a schematic diagram of the index hierarchy structure of the social security index evaluation system disclosed in the embodiment of the present invention.
[0022] Figure 4 It is a principle block diagram of an electronic device as a classical computing device according to an embodiment of the present invention. DETAILED DESCRIPTION
[0023] The following is a further detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the present disclosure, but cannot be used to limit the scope of the present disclosure. The present disclosure can be implemented in many different forms and is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.
[0024] like Figure 1 As shown, the social security index evaluation method provided by the embodiment of the present invention includes the following steps:
[0025] Step S101: According to the area where the social security index to be evaluated is located, element information associated with the social security index to be evaluated is obtained, wherein the element information includes social network public opinion, street security conditions and alarm conditions.
[0026] Step S103: Match the corresponding element information according to the preset indicator extraction rule, and calculate the evaluation indicator value corresponding to each evaluation indicator respectively.
[0027] Step S103: Calculate the corresponding weighted value of each evaluation index according to the weight analysis model algorithm.
[0028] Step S103: Perform weighted calculation according to the corresponding weighted value, evaluation index value and regional population and economic comprehensive parameters to obtain a social security index.
[0029] In this embodiment, element information related to the social security to be evaluated occurring in a set area is collected according to a set frequency.
[0030] The information collection module can be used to collect social network public opinion, alarm conditions at the police receiving station, and street security conditions, and store the collected events in the server's element database to form element information associated with the social security index to be evaluated. According to the predetermined index extraction rules, the evaluation module retrieves the element information in the server's database and the evaluation indicators pre-stored in the server's index library, and calculates the specific evaluation index values of each evaluation indicator. The evaluation module performs correlation operations on the indicators of the index system according to the weight analysis model algorithm, and calculates the corresponding weighted value of each indicator. Then, the evaluation index values of the total event indicators in the index system are weighted with the weighted values and the regional population and economic comprehensive parameters to obtain the final score of the social security index, completing the comprehensive evaluation of the security situation in a certain area.
[0031] The purpose of the safety index is to reflect the overall social security situation in a certain area by using the size and changes of numbers, so as to facilitate the analysis of differences in the security situation in different areas and important response factors. Therefore, when planning the components of the safety index, it is necessary to focus on the important impact of the security situation on social stability and residents' lives, and to reversely evaluate the overall security situation through these collectible social events or situations. Only by establishing an effective link between the safety index and social activities can the residents' true feelings about the social security situation be truly reflected.
[0032] Among them, several important areas related to social security are as follows:
[0033] Social stability: Good public security is one of the foundations of social stability. When public security is effectively maintained, people's lives and property safety are guaranteed, social order is in good order, and various economic, cultural and social activities can proceed normally. On the contrary, if public security deteriorates, criminal activities are frequent, and society is in turmoil, it will pose a serious threat to social stability.
[0034] Residents' sense of security: The public security situation directly affects residents' sense of security and quality of life. When the public security of a community or city is good, residents can live and work in a safe environment, go out without worry, and enjoy social activities and entertainment. On the contrary, if the public security situation deteriorates, residents will feel fear and anxiety, their quality of life will be affected, and they may even choose to move or leave.
[0035] Economic development: A good public security environment is conducive to economic development and investment attraction. Enterprises and investors tend to choose to do business in areas with good public security, because this can reduce operating risks and losses. On the contrary, poor public security will hinder business activities and investment, affecting the development and prosperity of the local economy.
[0036] Social harmony: Public security also has an important impact on social harmony and neighborhood relations. When the community has good public security, the trust and cooperation between residents will be strengthened, and mutual help and support between neighbors will be more frequent. On the contrary, poor public security will lead to suspicion and alienation among residents, exacerbating social division and disharmony.
[0037] Mental health: The quality of the public security environment directly affects the mental health of residents. In a good public security environment, people are more likely to maintain a good mentality and mental health, and can better cope with various pressures and challenges in life. In a poor public security environment, people may feel anxious, fearful and depressed, and even have mental health problems.
[0038] In summary, a good public security situation is of great significance to social stability, residents' sense of security, economic development, social harmony and mental health, and is one of the important guarantees for social development and progress. Therefore, when designing the social security index, it is necessary to collect information on factors closely related to the above social conditions and people's lives, and conduct data analysis to obtain a comprehensive evaluation index.
[0039] Among them, according to the weight analysis model algorithm, calculating the corresponding weighted value of each evaluation indicator includes the following steps:
[0040] Step S201: Establish a hierarchical structure, including a target layer, a criterion layer and an indicator layer.
[0041] Step S203: constructing a judgment matrix according to the hierarchical structure, where the elements in the judgment matrix represent the relative importance levels between two factors.
[0042] Step S205: Calculate weights according to the judgment matrix to obtain corresponding weighted values.
[0043] In this embodiment, a hierarchical structure can be established through a neural network model, in which the target layer is the safety index, the criterion layer includes the social perception layer, the expert reasoning layer and the event layer, and the indicator layer includes various specific indicators, such as the number of cases, the number of conflicts and disputes, etc.
[0044] The process of constructing the judgment matrix is to compare each criterion layer with the target layer, and the indicator layer with the criterion layer, so that the judgment matrix can be constructed. Using the calculated weights, the weighted sum of each indicator is performed to obtain the final safety index.
[0045] Furthermore, the social security safety index evaluation method also includes calculating the consistency ratio so that the consistency ratio of the judgment matrix is within a range of <0.1. After the weights are calculated, the consistency ratio (CR) is calculated for consistency testing to ensure that the consistency of the judgment matrix is within an acceptable range. For example, CR is usually <0.1. The fifth is to synthesize weights: synthesize weights at different levels to obtain the final indicator weight. The sixth is to calculate the safety index:
[0046] Furthermore, the weights are calculated according to the judgment matrix to obtain corresponding weighted values, which specifically includes the following steps:
[0047] Step S301: extracting the eigenvector corresponding to the maximum eigenvalue from the judgment matrix.
[0048] Step S303: normalize the feature vector to obtain a weight vector.
[0049] Step S305: According to the weight vector, for each criterion layer, the weight of the next level indicator is calculated.
[0050] Step S307: synthesize the weights of different levels to obtain the final indicator weight.
[0051] The elements in the judgment matrix represent the relative importance level between two factors, usually using a scale of 1-9. When calculating the weight, the eigenvector method or geometric mean method is used to extract the eigenvector corresponding to the largest eigenroot from the judgment matrix, and the weight vector is obtained after normalization. For each criterion layer, the weight of the next level indicator is calculated.
[0052] Among them, the weights of different levels are synthesized to obtain the final indicator weight, which specifically includes the following steps:
[0053] Step S401: The judgment matrix performs hierarchical single sorting on all elements of the same layer with respect to a certain element of the previous layer, and obtains the single sorting weight of each factor of the same layer with respect to a certain element of the previous layer.
[0054] Step S403: Merge the single ranking weights between all layers and their upper layers to obtain the final indicator weight.
[0055] After completing the construction of the judgment matrix, you can also generate a judgment matrix list, as shown below. The judgment matrix performs a hierarchical single sorting of all elements in the same layer for a certain element in the previous layer, and obtains the single sorting weight of each factor in the same layer for a certain factor in the previous layer.
[0056] As an example, let's focus on the output layer evaluation index: campus safety. Indicators such as conflicts, public security cases, mass requests for help, and street activities are used as relevant input evaluation indicators. All historical input evaluation indicators and output indicators are constructed into a judgment matrix, which expresses the relative importance of each element in each layer relative to each element in the upper layer in the form of a matrix, where the greater the value of the relative importance, the deeper the impact of the former on the latter. Specifically, the relative importance of a certain element in the judgment matrix to another element can be obtained by analyzing historical data. For example, as shown in the following judgment matrix list, the relative importance between the victim's age and the campus safety perception index is 5, the relative importance between the crime location and the campus safety perception index is 4, the relative importance between the case type and the campus safety perception index is 2, and the relative importance between the alarm time and the campus safety perception index is 1.
[0057] Judgment matrix list
[0058] Importance Age of victim Location Case Type Alarm time Campus safety indicators 5 4 2 1 Burglary 0.2 5 4 5 Telecommunications fraud 4 1 4 1
[0059] Among them, hierarchical single ranking is to rank all the elements of this layer in order of comparison with a certain element of the previous layer, and this order is expressed by relative numerical values. Specifically, the weight analysis module in the evaluation module normalizes the relative importance of all the elements of this layer and a certain element of the previous layer in the judgment matrix, that is, the sum of the relative importance of all the elements of this layer and a certain element of the previous layer is 1, and the single ranking weight of each factor of the same layer for a certain factor of the previous layer is obtained.
[0060] The total ranking weight analysis module merges the single ranking weights between all layers and their upper layers to obtain the total ranking weight between the evaluation indicators as the final indicator weight, and uses the total ranking weight as the importance weight.
[0061] The evaluation module determines the safety index score of the evaluation scope within the evaluation period according to the importance weights between the evaluation indicators, the evaluation period and the evaluation indicator values within the evaluation scope, and the personnel economic parameters of the area.
[0062] The evaluation module can summarize the product of all event-level evaluation indicators and weight indexes and divide them by the regional reference parameters to obtain the safety index score. The regional reference parameters are mainly the base numbers of actual population, actual units, employees and other factor information.
[0063] Furthermore, the social security index evaluation method also includes the following steps:
[0064] Step S501: Obtain a historical element information set according to element information;
[0065] Step S503: cleaning the historical element information set;
[0066] Step S505: Standardize or normalize the cleaned historical element information set.
[0067] In this embodiment, the data preprocessing work to be carried out at the collection layer includes: cleaning and transforming the original data, including missing value filling, outlier detection, feature selection, etc. Standardization or normalization is to standardize or normalize the input data to reduce the impact of data magnitude differences on model training.
[0068] like Figure 2 As shown,
[0069] The present invention also proposes a social security and safety index evaluation system, including: an information collection module, configured to obtain factor information associated with the social security and safety index to be evaluated according to the area where the social security and safety index to be evaluated is located, wherein the factor information includes social network public opinion, street security conditions and alarm conditions; an evaluation module, configured to match the corresponding factor information according to preset indicator extraction rules, respectively calculate the evaluation index values corresponding to each evaluation index, and calculate the corresponding weighted value of each evaluation index according to the weight analysis model algorithm; perform weighted operations based on the corresponding weighted values, evaluation index values and regional population and economic comprehensive parameters to obtain the social security and safety index.
[0070] In this embodiment, the information collection module can connect to the social network public opinion, street sensing equipment and 110 police call-handling system already built by the public security organs, collect relevant element information from three aspects: network public opinion, street public security conditions and people's calls for help, and send the collected various types of information to the evaluation module. The evaluation module extracts various types of element information according to the data preprocessing rules, and completes the comprehensive evaluation of the public security situation in a certain area according to the three steps of indicator analysis, weighted analysis and safety index analysis.
[0071] The social security index evaluation system includes an information collection module and an evaluation module, wherein the evaluation module runs on a computer.
[0072] The collection module collects element data according to a predetermined collection method and stores the collected data in a database.
[0073] In a predetermined evaluation area, such as a city district or a township, the collection module collects element information in a predetermined collection manner, such as collecting once at a collection interval or collecting in real time.
[0074] Specifically, the information collection module can be the camera in Skynet, the monitoring equipment of merchants, the 110 police call handling system, the public security public opinion system, etc. It can be street activity information collected by the camera, or it can be connected to the police system to collect case event information and public opinion information in the police system.
[0075] The information collection module can collect the events, places or people related to public security that are rising dynamically in public opinion; it can collect the changing trends of street activities in the selected area of the city; it can collect the information on people's requests for help, conflicts and disputes, and public security and criminal cases in the 110 police call system. The above collected dynamic information is stored according to the preset hierarchical element system, and according to the needs of subsequent data mining and analysis, feature subsets useful for indicator analysis are selected from the original data, and pre-processing such as clustering, classification, and association is performed on the data to reduce the dimension and complexity of the data.
[0076] Since the data collected by the camera is image data, the feature information of the image data is analyzed to determine whether the image data contains human activities and events; since the data collected by the police system is text data or image data, the feature information of the text data and the feature information of the image data are analyzed to determine whether the text data and the image data contain the defined element information.
[0077] The evaluation module retrieves element information from the database according to predetermined rules, generates three levels of evaluation indicators through the indicator analysis model, determines the importance weights between the evaluation indicators through the weight analysis model, and obtains the safety index through weighted calculation.
[0078] Specifically, the evaluation module in the present application retrieves the element information stored in the database according to predetermined evaluation rules such as the evaluation interval and whether the evaluation conditions have been met, and also retrieves the pre-stored evaluation indicators in the indicator library, and then analyzes the importance weights between the evaluation indicators based on the correlation between the evaluation indicators. Finally, the evaluation indicators, weight values and regional parameter values are passed to the safety index analysis module, and the final safety index is weightedly calculated.
[0079] On the basis of the above, the evaluation module includes: indicator analysis submodule, weight analysis submodule and safety index analysis submodule.
[0080] The index analysis is mainly based on the information of the masses calling the police for help, objectively evaluating various incidents that actually occur every day, and using the multi-level evaluation indicators stored in the index library to calculate the current score of each indicator. The weighted analysis is mainly based on the dynamic basic elements of the urban area, such as the current actual population, the number of entertainment venues, the number of online rental houses, etc., social network public opinion and street dynamic collection data, and dynamically adjusts the evaluation weights of relevant indicators to determine the safety index score of the evaluation range within the evaluation period; the final safety index analysis multiplies the indicator score within the evaluation period by the weighting coefficient, and conducts comprehensive statistics according to the latitude of administrative jurisdiction and hot spots, so as to obtain the final safety index score.
[0081] The indicator analysis submodule predetermines the evaluation indicators and stores the evaluation indicators in the indicator library.
[0082] The indicator analysis submodule can receive the evaluation indicators pre-entered by the user through computer hardware, such as a computer keyboard, and then store the evaluation indicators in the indicator library, so that the indicator analysis submodule can pre-determine the evaluation indicators. The indicator analysis submodule can also input the historical element data into the computer memory through computer hardware, such as a computer keyboard, a computer camera, etc., and then the indicator analysis submodule retrieves the historical element data stored in the computer memory, and pre-determines the evaluation indicators by analyzing the historical data, for example: the case quantity evaluation indicator B1, such as Figure 2 As shown, the indicator analysis submodule then stores the evaluation indicators in the indicator library.
[0083] The indicator analysis submodule performs data analysis based on predetermined evaluation indicators and the latest collected factor data, obtains the specific value of each indicator, and stores the evaluation indicator value in the indicator library.
[0084] The indicator analysis module can calculate the corresponding indicator value from the collected factor data according to the calculation rules of the evaluation indicator, and store the evaluation indicator in the indicator library. For example: according to the police information received and handled, the number of criminal case police reports, administrative case police reports, conflict and dispute police reports, and public assistance police reports within a specified period can be statistically analyzed; the overall attention of hot events within a specified period can be analyzed based on public opinion information; the activity intensity and change trend rate of people in a specified area can be analyzed based on street monitoring data, etc.
[0085] Among them, Figure 3 As shown, the evaluation module includes a neural network model, and the neural network model includes a social perception layer, an expert reasoning layer and an event layer to form an evaluation index system.
[0086] Specifically, the hierarchical indicator model includes a social perception layer, an expert reasoning layer, and an event layer. Various indicators that are closely related to the masses and intuitively perceived are included in the perception layer, and the analysis results based on the field-related expert model are included in the expert reasoning layer. The reasoning layer can have a multi-layer structure, and a specific type of event or help information is included in the event layer. The correlation between indicators at all levels is dynamically calculated to form a complete evaluation indicator system.
[0087] The weight analysis submodule uses a neural network model to construct the evaluation indicators into multiple layers of neurons, where the event layer indicators are constructed as the input layer, the expert reasoning layer indicators are constructed as the hidden layer, and the social perception layer indicators are constructed as the output layer. The average weight coefficient between each layer is calculated through the historical cycle evaluation indicator values, and the influence weight relationship between different indicators is analyzed.
[0088] The event layer in the indicator system is used as the input layer of the neural network, including but not limited to social public opinion, police information, street activity data, etc., representing the collected objective event data. These data can be organized into numerical features, such as the number of cases, attention, etc., for model processing. The middle expert reasoning layer is used as the hidden layer of the neural network, representing the analysis indicators under different problem domains. There can be multiple hidden layers, which are used to process the data of the input layer and extract high-level features. The hidden layer can use the ReLU activation function to introduce nonlinear characteristics. The social perception layer is used as the output layer, representing the subjective perception of the people, matching the specific value of the safety index, which can be a regression task. The output layer uses a linear activation function. Through the back propagation algorithm, the linkage relationship between the three layers is dynamically calculated, and the hyperparameters such as the learning rate, batch size, number and size of hidden layers are adjusted through grid search or random search, etc., to find the weighted value between the indicator values, and pass this weighted value into the final safety index calculation model.
[0089] The safety index evaluation method and system provided by the present invention can calculate the safety index in a timely and accurate manner within the evaluation period, thereby objectively and reasonably evaluating the safety situation in a predetermined area, and fully utilizes the domain expert model to obtain the degree of direct mutual influence of various factors, obtains the weight of each factor through hierarchical analysis, and calculates the score of indicators at all levels in each district and county based on the obtained weight, and also dynamically adjusts the coefficient as a balancing algorithm through coefficient adjustment and objective factors such as population and economy in each region, so that the obtained results are more valuable for reference.
[0090] The present invention also provides an electronic device, Figure 4 The hardware structure principle block diagram of an embodiment of the electronic device provided by the present invention is shown. The electronic device includes a processor 601 and a memory 602 storing computer program instructions. When the processor executes the computer program instructions, the method for improving the micro-motion processing of ions in an ion trap according to any of the above embodiments is implemented.
[0091] Specifically, the processor 601 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.
[0092] The memory 602 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 602 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 602 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 602 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 602 is a non-volatile solid-state memory.
[0093] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.
[0094] In one example, the electronic device may further include a communication interface 603 and a bus 610. Figure 4 As shown, the processor 601, the memory 602, and the communication interface 603 are connected and communicate with each other via a bus 610. The electronic device in the embodiment of the present invention may be a server or other computing device, or may be a cloud server.
[0095] The communication interface 603 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiment of the present invention.
[0096] Bus 610 includes hardware, software or both, and the components of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 610 may include one or more buses. Although the embodiment of the present invention describes and shows a specific bus, the present invention considers any suitable bus or interconnection.
[0097] The present invention also provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, any one of the methods for evaluating the social security index is implemented. Computer program products include software installation packages, plug-ins compatible with related software systems, and the like.
[0098] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.
[0099] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the system, module and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.
[0100] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the embodiments of this application.
[0101] It should be understood that in various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0102] It can be understood that the device embodiments described above are schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
[0103] The basic principles disclosed in the present invention are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment disclosed in the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limiting. The above details do not limit the present invention to being implemented by adopting the above specific details.
[0104] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0105] The block diagrams of the devices, apparatuses, equipment, and systems involved in the disclosure of the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.
[0106] The method and apparatus disclosed in the present invention may be implemented in many ways. For example, the method and apparatus disclosed in the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above sequence of steps for the method is for illustration only, and the steps of the method disclosed in the present invention are not limited to the sequence specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method disclosed in the present invention. Therefore, the present invention also covers a recording medium storing a program for executing the method disclosed in the present invention.
[0107] It should also be noted that in the apparatus, equipment and method disclosed in the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes disclosed in the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but to the widest range consistent with the principles and novel features disclosed herein.
[0108] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments disclosed in the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.
[0109] In addition, the words “first”, “second” and the like used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different parts.
[0110] It should be noted that the above embodiments do not limit the present invention in any form, and any technical solutions obtained by equivalent replacement or equivalent transformation shall fall within the protection scope of the present invention.
Claims
1. A method for evaluating a social security index, characterized in that: The following steps are involved: Obtaining, based on the area where the social security index to be assessed is located, factor information associated with the social security index to be assessed, wherein the factor information includes social network public opinion, street security conditions, and alarm status; Match the corresponding element information according to the preset indicator extraction rules, and calculate the evaluation indicator value corresponding to each evaluation indicator respectively; According to the weight analysis model algorithm, the corresponding weighted value of each evaluation indicator is calculated; A weighted calculation is performed based on the corresponding weighted values, the evaluation index values and the regional population and economic comprehensive parameters to obtain a social security index.
2. The social security index evaluation method according to claim 1 is characterized in that: According to the weight analysis model algorithm, the corresponding weighted values of each evaluation indicator are calculated, including: Establish a hierarchical structure, including the goal layer, the criteria layer, and the indicator layer; constructing a judgment matrix according to the hierarchical structure, wherein the elements in the judgment matrix represent the relative importance levels between two factors; The weights are calculated according to the judgment matrix to obtain the corresponding weighted values.
3. The social security index evaluation method according to claim 2 is characterized in that: The method further includes calculating a consistency ratio so that the consistency ratio of the judgment matrix ranges from <0.
1.
4. The social security index evaluation method according to claim 2, characterized in that: Calculating weights according to the judgment matrix to obtain the corresponding weighted values specifically includes: Extracting the eigenvector corresponding to the maximum eigenvalue from the judgment matrix; Normalizing the feature vector to obtain a weight vector; Calculating the weight of the next level indicator for each of the criterion layers according to the weight vector; The weights at different levels are synthesized to obtain the final indicator weight.
5. The social security index evaluation method according to claim 4 is characterized in that: The weights at different levels are synthesized to obtain the final indicator weights, which specifically include: The judgment matrix performs hierarchical single sorting on all elements of the same layer with respect to a certain element of the previous layer, and obtains a single sorting weight of each factor of the same layer with respect to a certain factor of the previous layer; The single ranking weights between all layers and the layer above them are merged to obtain the final indicator weight.
6. The method for evaluating the social security index according to any one of claims 1 to 5, characterized in that: Also includes: Obtaining a historical element information set based on the element information; Cleaning the historical element information set; The cleaned historical element information set is standardized or normalized.
7. A social security index evaluation system, characterized in that: include: An information collection module is configured to obtain factor information associated with the social security index to be evaluated based on the area where the social security index to be evaluated is located, wherein the factor information includes social network public opinion, street security conditions, and alarm status; The evaluation module is configured to match the corresponding element information according to the preset indicator extraction rules, calculate the evaluation index value corresponding to each evaluation index respectively, and calculate the corresponding weighted value of each evaluation index according to the weight analysis model algorithm; perform weighted operation based on the corresponding weighted value, the evaluation index value and the regional population and economic comprehensive parameters to obtain the social security index.
8. The social security index evaluation system according to claim 7 is characterized in that: The evaluation module includes a neural network model, which includes a social perception layer, an expert reasoning layer and an event layer to form an evaluation index system.
9. An electronic device, characterized in that: The electronic device includes a processor and a memory storing computer program instructions; when the electronic device executes the computer program instructions, the social security index evaluation method according to any one of claims 1 to 6 is implemented.
10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the social security index evaluation method according to any one of claims 1 to 6.