A Dynamic Evaluation Method and System for the Capability of Cultural Relics Security Governance within a Region

Abstract

This application relates to the technical field of cultural relics protection, and particularly to a dynamic evaluation method and system for the cultural relics security governance ability within a region, which includes the following steps: obtaining the quantity, level information, and category information of protected units within the region; generating a first evaluation coefficient for objects of different levels based on the level information, and generating a second evaluation coefficient for specific category objects based on the category information; generating several scoring dimensions and preset parameter information for scoring; obtaining a conventional scoring calculation method and a specific item scoring calculation method; generating a scoring system in combination with the scoring dimensions, and adding the conventional scoring calculation method or the specific item scoring calculation method to the scoring system to train and generate a scoring portrait model; obtaining the security governance parameters corresponding to the protected units and sending them to the scoring portrait model to obtain the unit scores; calculating the total regional score according to several unit scores and the quantity of protected units. This application has the effect of establishing a standardized evaluation model for the cultural relics security governance ability.

Description

Technical Field

[0001] The present application relates to the technical field of cultural relics protection, and in particular, to a method and system for dynamically evaluating the cultural relics safety governance ability within a region. Background Art

[0002] Cultural relics are precious and non-renewable historical resources. They are not only the witnesses of historical culture but also important carriers of national cultural and historical security. There are a large number of cultural relics protection units in various regions and areas in the country.

[0003] Currently, there is a lack of a standardized evaluation model for the safety governance ability of cultural relics. Different cultural relics protection units in a region also apply different forms of safety governance plans. Different evaluators and different evaluation criteria may result in different ability evaluation results for each cultural relics protection unit in a region. Moreover, multiple ability evaluation dimensions are not comprehensive. At the same time, the commonly used expert inspection and scoring system on the one hand requires a large amount of manpower, and on the other hand, its results only represent the safety governance ability of the cultural relics protection unit at that time and are not suitable for evaluating the ability over a wide range of time.

[0004] The absence of a standardized evaluation model makes it impossible to scientifically and accurately evaluate the overall cultural relics protection ability within a region, and thus it is impossible to effectively improve the corresponding cultural relics safety governance plan in the follow-up. Summary of the Invention

[0005] In order to establish a standardized evaluation model for the safety governance ability of cultural relics, the present application provides a method and system for dynamically evaluating the cultural relics safety governance ability within a region.

[0006] In a first aspect, the present application provides a method for dynamically evaluating the cultural relics safety governance ability within a region, adopting the following technical solution:

[0007] A method for dynamically evaluating the cultural relics safety governance ability within a region includes the following steps:

[0008] Obtain the quantity, level information, and category information of the protection units within the region;

[0009] Generate a first evaluation coefficient for objects of different levels based on the level information, and generate a second evaluation coefficient for objects of a specific category based on the category information;

[0010] Generate a number of scoring dimensions, and obtain preset parameter information for scoring, and classify each piece of the preset parameter information into the corresponding scoring dimension;

[0011] Obtain a preset scoring calculation method, adjust the preset scoring calculation method based on the first evaluation coefficient to obtain a regular scoring calculation method, and adjust the regular scoring calculation method based on the second evaluation coefficient to obtain a specific item scoring calculation method;

[0012] Combine the scoring dimensions to generate a scoring system, and add a regular scoring calculation method or a specific item scoring calculation method to the scoring system to train and generate a scoring portrait model;

[0013] Obtain the safety governance parameters corresponding to the protection unit, and send each of the safety governance parameters to the corresponding scoring dimension in the scoring portrait model for scoring calculation to obtain a unit score;

[0014] Calculate the regional total score according to a number of the unit scores and the number of protection units.

[0015] In some of these embodiments, the level objects in the level information include national level, provincial level, and city / county level, and the first evaluation coefficient corresponding to the national level is less than the first evaluation coefficient corresponding to the provincial level, the first evaluation coefficient corresponding to the provincial level is less than the first evaluation coefficient corresponding to the city / county level, and all the first evaluation coefficients are greater than or equal to 1;

[0016] The category objects of the category information at least include ancient cultural sites, ancient buildings, ancient tombs, grottoes and stone inscriptions, modern important historical sites, and representative buildings, and different category objects correspond to different evaluation emphases. The evaluation emphases include human risk bias, natural risk bias, and balance. If the evaluation emphasis is balance, then one level object corresponds to one second evaluation coefficient. If the evaluation emphasis is human risk bias, then one category object corresponds to a relatively high second evaluation coefficient on the human side and a relatively low second evaluation coefficient on the natural side. If the evaluation emphasis is natural risk bias, then one category object corresponds to a relatively low second evaluation coefficient on the human side and a relatively high second evaluation coefficient on the natural side.

[0017] In some of these embodiments, the scoring dimensions include:

[0018] Artificial protection, which characterizes the personnel cultural relics protection configuration ability of the protection unit, and is specifically used to configure the following preset parameter information: personnel configuration including the number of personnel, composition, and age structure, assessment situation, and inspection intensity;

[0019] Technical protection, which characterizes the security coverage and operation situation of the protection unit, and is specifically used to configure the following preset parameter information: equipment types associated with specific protection types, equipment online rate, and equipment coverage rate;

[0020] Problem perception, characterized by the ability to perceive the safety status and potential hazards, is specifically used to configure the following preset parameter information: evaluation elements of perception ability including but not limited to security perception, fire perception, environmental perception, weather and geological disaster perception, pest perception, illegal intrusion perception, and behavior perception;

[0021] Hidden danger rectification, characterized by the ability to handle the discovered hidden dangers, is specifically used to configure the following preset parameter information: hidden danger types including but not limited to surface weathering, plant roots, combustibles, and environmental pollution, and feedback on the treatment situation;

[0022] Case handling, characterized by the ability to handle the filed events, is specifically used to configure the following preset parameter information: whether it is completed, the case closing cycle, the number of cases occurred, the number of cases closed, the nature of the case, and the social impact;

[0023] Emergency response, characterized by the ability to respond to emergency events affecting the safety of cultural relics, is specifically used to configure the following preset parameter information: emergency response plan, emergency personnel, emergency supplies, and the degree of improvement of the emergency system.

[0024] In some of the embodiments, each of the said scoring dimensions corresponds to a corresponding scoring comparison table, which is used to represent the scoring values corresponding to the preset parameter information. Among them, the formulation of the scoring comparison table includes the following steps:

[0025] Generate a determined scoring group and an undetermined scoring group. The determined scoring group is characterized by scoring items that can be directly or indirectly determined through the corresponding parameters. The undetermined scoring group includes scoring items that cannot be directly or indirectly determined through the corresponding parameters. The determined scoring group includes manual protection, technical protection, case handling, and hidden danger rectification. The undetermined scoring group includes problem perception and emergency response;

[0026] One of the scoring dimensions in the undetermined scoring group selects one or more of the scoring dimensions in the determined scoring group as verification objects;

[0027] Set a standard scoring comparison table for each of the scoring dimensions in the determined scoring group, and add several verification parameter information to obtain the standard score;

[0028] Generate the predicted score of its corresponding verification object based on the standard score;

[0029] Set an empirical scoring comparison table for the undetermined scoring group, and add several verification parameters to obtain the simulated score;

[0030] Compare the predicted score and the simulated score to calculate the correlation trend value, and determine whether it is a positive feedback correlation or a negative feedback correlation based on the correlation trend value. Update the standard score comparison table and / or the empirical score comparison table through the positive feedback correlation or the negative feedback correlation to obtain a score comparison table.

[0031] In some of these embodiments, updating the standard score comparison table and / or the empirical score comparison table through the positive feedback correlation or the negative feedback correlation to obtain a score comparison table includes the following steps:

[0032] Under the positive feedback correlation, when the correlation trend value is less than a preset value, move the simulated score closer to the predicted score for updating, and update the empirical score comparison table according to the updated simulated score to obtain a score comparison table;

[0033] Under the negative feedback correlation, when the correlation trend value is greater than a preset value, judge the accuracy rates of the predicted score and the verification score based on the cross-validation method, select the score with a higher accuracy rate as the reference score, generate a feedback parameter based on the reference score and send it to the corresponding determined score group or non-determined score group. The determined score group or non-determined score group updates the standard score comparison table or the empirical score comparison table based on the feedback parameter, and repeat the above steps until the correlation trend value is less than the preset value.

[0034] In some of these embodiments, generating a scoring system in combination with the scoring dimension includes the following steps:

[0035] Single-end scoring system:

[0036] Correspondingly distribute the security governance parameters to each of the scoring dimensions for independent scoring;

[0037] Combined scoring system:

[0038] Divide each of the scoring dimensions into a detection group and a response group. Generate the detection time of different objects based on the security governance parameters within the detection group, generate the response time of different objects based on the security governance parameters within the response group, generate the minimum delay time based on the level information and category information of the protection unit, and perform combined scoring based on the detection time, the response time, and the minimum delay time.

[0039] In some of these embodiments, adding a conventional scoring calculation method or a specific item scoring calculation method to the scoring system to train and generate a scoring portrait model includes the following steps:

[0040] Based on the conventional scoring calculation method, a conventional scoring portrait model corresponding to each level is generated. In the conventional scoring portrait model, the preset parameter information of each scoring dimension is configured with a standard calculation weight matching the first scoring coefficient;

[0041] Based on the specific item scoring calculation method, a specific item scoring portrait model corresponding to each level is generated. In the specific item scoring portrait model, the preset parameter information that does not belong to the specific category object in each scoring dimension is configured with a standard calculation weight, and the preset parameter information that belongs to the specific category object in each scoring dimension is configured with a specific calculation weight set based on the second evaluation coefficient on the human side or the second evaluation coefficient on the natural side.

[0042] In some of the embodiments, sending each of the security governance parameters to the corresponding scoring dimension in the scoring portrait model for scoring calculation to obtain a unit score includes the following steps:

[0043] Setting corresponding weight ratios for each scoring dimension based on a preset segmentation standard;

[0044] After obtaining the sub-scores corresponding to each scoring dimension according to the security governance parameters, multiplying each of the sub-scores by the weight ratio to obtain a dimension score;

[0045] Adding up the dimension scores to obtain the unit score of the protection unit.

[0046] In some of the embodiments, after calculating the total regional score according to several of the unit scores and the number of protection units, the following steps are further included:

[0047] Generating a unit score portrait based on the unit scores, and generating unit rectification opinions based on the scoring dimensions with low scores in each of the unit score portraits;

[0048] Generating a regional score portrait based on the total regional score, and generating regional rectification opinions based on the scoring dimensions with low scores in the regional score portrait;

[0049] Establishing a rectification list corresponding to each of the units, where the rectification list includes rectification instruction information and rectification warning information;

[0050] Adding the unit rectification opinions to the rectification instruction information, adding the regional rectification opinions to the rectification warning information, and deleting the content corresponding to the scoring dimensions that are the same as the unit rectification opinions.

[0051] In a second aspect, the present application provides a dynamic evaluation system for the cultural relics security governance ability within a region, adopting the following technical solution:

[0052] A dynamic evaluation system for the ability of cultural relics safety governance within a region, comprising:

[0053] A protection unit docking module, configured to obtain the number, level information, and category information of protection units within the region;

[0054] A calculation coefficient generation module, configured to generate a first evaluation coefficient for objects of different levels based on the level information, and generate a second evaluation coefficient for specific category objects based on the category information;

[0055] A planar portrait model training module, configured to generate a number of scoring dimensions, obtain preset parameter information for scoring, and classify each piece of the preset parameter information into the corresponding scoring dimension; obtain a preset scoring calculation method, and adjust the preset scoring calculation method based on the first evaluation coefficient to obtain a conventional scoring calculation method, and adjust the conventional scoring calculation method based on the second evaluation coefficient to obtain a specific item scoring calculation method; combine the scoring dimensions to generate a scoring system, and add the conventional scoring calculation method or the specific item scoring calculation method in the scoring system to train and generate a scoring portrait model;

[0056] A scoring module, configured to obtain the safety governance parameters corresponding to the protection unit, and send each of the safety governance parameters to the corresponding scoring dimension in the scoring portrait model for scoring calculation to obtain a unit score; calculate the total region score based on a number of the unit scores and the number of protection units.

[0057] Through the technical solution provided by the embodiments of the present application, the following technical effects exist:

[0058] Establish a set of standardized evaluation models for the ability of cultural relics safety governance. This evaluation model objectively and accurately scores each protection unit in combination with the actual situation of different levels and categories within a region, and conducts an ability portrait through the unit score and the total region score to realize the determination of the cultural relics safety index within the region, clearly and accurately understand the overall level and implementation ability of the cultural relics safety governance ability within the region, and thus can discover the short boards of the cultural relics protection ability in each region. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a schematic flowchart of a dynamic evaluation method for the ability of cultural relics safety governance within a region in an embodiment of the present application.

[0060] Figure 2 is a schematic diagram of the module connection of a dynamic evaluation system for the ability of cultural relics safety governance within a region in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] In the description of the present application, the meaning of "a number of" is one or more, the meaning of "a plurality of" is more than two, and understandings such as "greater than", "less than", and "exceeding" do not include the base number, and understandings such as "above", "below", and "within" include the base number. If the first and second are described only for the purpose of distinguishing technical features, they cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0062] In the description of the present application, the descriptions with reference to terms such as "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a combined manner.

[0063] An embodiment of the present application discloses a dynamic evaluation method and system for the cultural relic safety governance ability within a region.

[0064] As Figure 1 shown, a dynamic evaluation method for the cultural relic safety governance ability within a region includes the following steps:

[0065] S100, obtain the quantity, level information, and category information of the protected units within the region.

[0066] The system accesses each protected unit within the region to obtain the quantity of the existing protected units within the region, and judges and obtains the corresponding level information according to its scale, the quantity of cultural relics, the rarity of cultural relics, etc., and judges the corresponding category of the protected unit according to the type of cultural relics within the protected unit.

[0067] Obtaining the quantity of the protected units within the region is used for subsequent calculation of the total score of the cultural relic safety governance ability within a region, and the level information and category information are used to specifically individualize each protected unit to calculate its score through a specific calculation method.

[0068] S200, generate a first evaluation coefficient for different-level objects based on the level information, and generate a second evaluation coefficient for specific-category objects based on the category information.

[0069] For protection units at different levels, different first evaluation coefficients are corresponding. Different first evaluation coefficients can enable the mutual conversion of scores between protection units at different levels. This is because the personnel scale, operating costs, and security levels of protection units at different levels are all different, and the required security governance capabilities of protection units at different levels are also different. Therefore, different first evaluation coefficients are needed to balance the scores of protection units at different levels.

[0070] For different categories of protection units, the evaluation tendencies to be considered are different. For example, ancient buildings are immovable and have few risks such as theft, so the evaluation tendency in terms of human factors is relatively low for them, while the evaluation tendency in terms of natural disasters such as storms and thunder is relatively high. Therefore, in order to improve the accuracy of scoring, some specific protection units need to perform specific scoring calculations through a second evaluation coefficient.

[0071] S300, generate several scoring dimensions, and obtain the preset parameter information for scoring, and classify each preset parameter information into the corresponding scoring dimension.

[0072] The scoring dimension is characterized by multiple aspects that need to be considered for the corresponding standard security governance capabilities. Different preset parameter information exists in different scoring dimensions. In the embodiments of the present application, the scoring dimension includes six dimensions: manual protection, technical protection, problem perception, hidden danger rectification, case handling, and emergency response. The finally obtained score corresponds to a six-dimensional ability portrait of a protection unit or an area.

[0073] The preset parameter information is the item to be scored under different scoring dimensions.

[0074] S400, obtain the preset scoring calculation method, and adjust the preset scoring calculation method based on the first evaluation coefficient to obtain the conventional scoring calculation method, and adjust the conventional scoring calculation method based on the second evaluation coefficient to obtain the specific item scoring calculation method.

[0075] The preset scoring calculation method is characterized by a basic calculation method set according to the real-time environment, policies, etc. in an area. It has universality for scoring the security governance capabilities of cultural relics, and it serves as the overall logical framework for subsequent specific scoring calculation methods.

[0076] The conventional scoring calculation method is characterized by the calculation method corresponding to some conventional comprehensive protection units. For such protection units, the evaluation of security governance capabilities for human problems or natural problems is relatively balanced, and there is no need to perform evaluation calculations for specific contents. At this time, adding the first evaluation coefficient of the protection unit level to the preset scoring calculation method can calculate the corresponding score.

[0077] The specific item scoring calculation method is characterized as a calculation method for some protection units with a strong tendency of potential hazards and specific protection requirements. Such protection units have relatively large potential hazards in the face of some specific security risks. For example, for the protection units corresponding to ancient buildings, their protection requirements for fire protection are relatively high, and for ancient tombs, the anti-theft requirements are also relatively high. When generating the scoring calculation method for such protection units, a second evaluation coefficient for specific items should be added on the basis of the conventional scoring calculation method.

[0078] S500, generate a scoring system in combination with the scoring dimensions, and add a conventional scoring calculation method or a specific item scoring calculation method to the scoring system to train and generate a scoring portrait model.

[0079] After obtaining the scoring dimensions, a scoring system can be built according to the scoring dimensions. This scoring system is applicable to each protection unit in different regions. After adding a conventional scoring calculation method or a specific item scoring calculation method to the scoring system according to the level and category of the specific protection unit, a scoring portrait model for scoring the protection unit can be obtained through neural network algorithms.

[0080] S600, obtain the safety governance parameters corresponding to the protection unit, and send each safety governance parameter to the corresponding scoring dimension in the scoring portrait model for scoring calculation to obtain the unit score.

[0081] After building the scoring portrait model, the currently detected and manually uploaded safety governance parameters of each protection unit are sent to the model, and the scores corresponding to different scoring dimensions can be obtained. The scores corresponding to the six scoring dimensions together constitute the unit score of the protection unit. The unit score represents an evaluation of a unit's safety governance ability for cultural relics.

[0082] Among them, the above-mentioned safety governance parameters are all detailed parameter values that can be specifically measured or calculated through corresponding devices, detection sensors, etc. in combination with natural laws.

[0083] S700, calculate the total regional score according to several unit scores and the number of protection units.

[0084] After calculating the unit scores corresponding to each protection unit in the region, the total regional score can be calculated in combination with the total number of protection units in the region. The total regional score represents an evaluation of the overall safety governance ability of a region for cultural relics.

[0085] Through the above steps, a standardized evaluation model for the ability of cultural relics security governance is established. This evaluation model objectively and accurately scores each protection unit in combination with the actual situation of different levels and categories within a region, and conducts an ability portrait through the unit score and the total regional score to realize the determination of the cultural relics security index in the region, clearly and accurately understand the overall level and implementation ability of the cultural relics security governance ability in the region, and thus discover the short board of the cultural relics protection ability in each region.

[0086] In some other embodiments, the level objects in the level information include national level, provincial level, and city / county level, and the corresponding first evaluation coefficient values for each level object are different, but all the first evaluation coefficients are greater than one. Specifically, the first evaluation coefficients from large to small correspond to: city / county level - provincial level - national level.

[0087] The category objects of the category information at least include ancient cultural sites, ancient buildings, ancient tombs, grottoes and stone carvings, important historical sites of modern times, and representative buildings. Different category objects correspond to different evaluation emphases, and the evaluation emphasis is characterized by the bias of its key evaluation content, specifically including human risk bias, natural risk bias, and balance.

[0088] The human risk bias is characterized in that the key evaluation content is biased towards risks caused by human factors such as fire protection, anti-theft, security prevention, intrusion, etc., while the natural risk bias is characterized in that the key evaluation content is biased towards risks caused by natural environmental reasons such as geological disasters, temperature and humidity changes, etc., and the balance is characterized by the absence of key evaluation content, and the evaluation criteria for each part are the same.

[0089] Different evaluation emphases correspond to different second evaluation coefficients. If the evaluation emphasis is balance, then each parameter corresponds to the same second evaluation coefficient, and at this time the second evaluation coefficient is defaulted to 1. If the evaluation emphasis is human risk bias, it means that the evaluation of human reasons needs to be focused on, and the parameters corresponding to natural reasons have relatively little impact on the score. Therefore, at this time, this category object corresponds to two different second evaluation coefficients. Among them, the parameters corresponding to the human risk aspect correspond to the higher human-side second evaluation coefficient, and the parameters corresponding to the natural risk aspect correspond to the lower natural-side second evaluation coefficient. Similarly, if the evaluation emphasis is natural risk bias, this category object corresponds to a lower human-side second evaluation coefficient and a higher natural-side second evaluation coefficient.

[0090] Among them, according to the actual situation, the human-side second evaluation coefficient and the natural-side second evaluation coefficient can be further numerically adjusted. For example, the key protection for ancient tombs is anti-theft, so only the parameters corresponding to anti-theft will be assigned the higher human-side second evaluation coefficient, and the second evaluation coefficients of the parameters corresponding to other aspects such as fire protection can be taken as 1.

[0091] Meanwhile, for different types of protection units, their corresponding human risk biases and natural risk biases are the bias values fixed for their corresponding types. Since the types of protection units are limited, after training a large number of samples before determining the second evaluation coefficient, only by obtaining the type of the target protection unit or the cultural relic information existing in the protection unit, the corresponding second evaluation coefficient can be obtained.

[0092] In some other embodiments, the scoring dimensions include the following six categories:

[0093] Artificial protection, which is characterized by the cultural relic protection configuration ability of the personnel of the protection unit, and is expressed as an organized preventive measure of personnel and / or personnel groups with corresponding qualities performing security prevention tasks. It configures the following preset parameter information: personnel configuration, assessment situation, and inspection intensity.

[0094] Personnel configuration includes the number of personnel, personnel composition, and personnel age structure. The number of personnel includes the number of full-time and part-time personnel. The personnel composition includes the configuration information of cultural relic protectors, firefighters, security guards, and management personnel. The assessment situation includes the completion of learning tasks, daily assessment situation, and ability improvement assessment situation of cultural relic protectors. The inspection intensity includes the frequency, coverage area, and completion situation of daily patrols by personnel such as cultural relic protectors and security guards.

[0095] The personnel configuration information is obtained by the Internet of Things module accessed by the system, which inputs and synchronously updates the information of on-the-job employees, part-time employees, patrol information, and learning information within the protection unit through Internet of Things technology.

[0096] Technical protection, which is characterized by the security coverage and operation situation of the protection unit, and may also include the richness of the categories of various security equipment. Specifically configured are: equipment type, equipment online rate, and equipment coverage rate.

[0097] The equipment type includes security, fire protection, lightning protection, access control, perimeter, etc. The equipment online rate is characterized by the ratio of the number of operating equipment to the number of operable equipment, and the equipment coverage rate is characterized by the ratio of the range that various types of equipment can detect and protect to the total area of the protection unit.

[0098] The parameter information of technical protection is obtained and recorded by the Internet of Things module. The Internet of Things module connects various types of equipment within the protection unit through networking, monitors the operation status of the equipment while synchronizing the protection information of the equipment.

[0099] Problem perception, characterized by the ability to perceive safety status and potential hazards, depends on whether security equipment can detect various explicit and / or implicit risk events. It is configured with the following preset parameter information: including but not limited to the evaluation elements of security perception, fire perception, environmental perception, weather and geological disaster perception, pest perception, illegal intrusion perception, and behavior perception.

[0100] Problem perception cannot be evaluated solely by the number of personnel and equipment. It requires combining empirical algorithms and subsequent simulations and drills to refine the evaluation accuracy and logic.

[0101] Hidden danger rectification, characterized by the ability to handle discovered hidden dangers, such as whether the discovered hidden danger problems have been rectified, the degree of rectification completion, and the rectification effect. It is specifically used to configure the following preset parameter information: including but not limited to hidden danger types, feedback on treatment situations, number of hidden danger rectifications, rectification efficiency, and rectification quality. Hidden danger types include surface weathering, plant roots, combustibles, environmental pollution, etc.

[0102] The parameter information of hidden danger rectification is obtained by the personnel in the protection unit through daily patrols and inspections on the one hand, and on the other hand, the information recording module in the system records the parameters, such as when what hidden danger occurred and how long it took to solve the hidden danger, and generates feedback for daily or other regular time periods through the daily reported information.

[0103] Case handling, characterized by the ability to handle cases that have been filed, such as the degree of case handling completion and whether the case is fully closed. It specifically includes the following preset parameter information: whether it is completed, case closing cycle, number of cases occurred, number of cases closed, nature of the case, and social impact. When various man-made or natural events occur, each event is recorded as a case, and cases can also include patrol anomalies, personnel complaints, etc.

[0104] The parameter information of case handling is recorded and updated by the module responsible for case management in the system. When various daily or accidental events occur in the protection unit, this module records the case content, case start time, personnel responsible for the case, case type, etc., and can be called directly later.

[0105] Emergency response, characterized by the ability to respond to emergency events affecting cultural relics safety, such as whether the emergency response plan, personnel, materials, and system are perfect and effective. It is specifically configured with the following preset parameter information: perfection degree of the emergency response plan, emergency personnel configuration, emergency materials, emergency system, and also includes the handling situation of the occurred emergency events, completeness of the emergency response plan, completeness of the emergency drill records, frequency of safety education and training carried out, etc.

[0106] The parameters for emergency response are obtained from two aspects. On the one hand, information such as personnel allocation, fire protection, medical treatment, etc. that can be connected within a certain emergency time, as well as information on the corresponding materials currently entered and saved for the protected unit, are obtained through the Internet of Things module. On the other hand, various personnel transfer information, material transfer information, etc. during the simulation process are obtained through regular fire emergency drills to continuously update and record the existing several emergency response plans.

[0107] Through these six scoring dimensions, a comprehensive evaluation coverage of the various safety governance capabilities of cultural relics protection units can be carried out. Combining human, physical, response ability, work ability, etc., a full - dimensional portrait can be depicted, making the final score more scientific and comprehensive when scoring subsequently.

[0108] Among them, most of the above - mentioned data can be objectively detected, recorded, and collected by the corresponding modules. Although some data on problem perception cannot be evaluated by the objectively detected data, the objective expression of subjective data can be inferred through the combination and mutual feedback correlation of some objective data according to the subsequent disclosed methods, so as to replace the information content in problem perception with some other detectable and computable values.

[0109] Each of these six scoring dimensions is scored independently, and the sum of the scores of the six scoring dimensions represents the total score of the unit.

[0110] In some other embodiments, each scoring dimension corresponds to a corresponding scoring comparison table. The scoring comparison table is used to represent the scoring values corresponding to each preset parameter information. The scoring comparison table is used to compare the safety governance parameters with each value in the scoring comparison table when safety governance parameters are issued subsequently, and obtain the corresponding score according to the comparison result. Therefore, the values of all preset parameter information and their corresponding scores should exist in the scoring comparison table. The formulation of the scoring comparison table includes the following steps:

[0111] S310, generate a determined scoring group and an undetermined scoring group.

[0112] The determined scoring group is used to place the content of some scoring dimensions that can be objectively evaluated through the corresponding parameters. For example, the number of people in the protected unit, training quality, coverage rate and types of security equipment, etc. These data can all be used as objective certainty benchmarks for scoring according to specific parameters.

[0113] The undetermined scoring group is used to place the content of scoring dimensions that cannot be objectively evaluated through the corresponding parameters. For example, the perception of various dangers can only obtain an accurate description of the perception ability when a danger occurs, but the ability description cannot be accurately obtained through the corresponding parameters when no danger occurs. This type of data often requires in - depth analysis through certain historical data analysis, daily drills, and danger handling results.

[0114] S311, select one or more scoring dimensions in the definite scoring group as the verification objects for one of the scoring dimensions in the indefinite scoring group.

[0115] Select one or more reference items for each scoring dimension in the indefinite scoring group, which are used to verify the parameters that cannot be objectively evaluated within the indefinite scoring group. The reference items are one or more scoring dimensions in the definite scoring group.

[0116] Since all the scoring dimensions in the indefinite scoring group are somewhat related to the scoring dimensions in the definite scoring group. For example, for the security perception in the problem perception dimension, although it cannot be objectively evaluated by specific parameter data, its relationship is related to the types of security equipment, the number of security equipment, the coverage rate of security equipment, personnel training, and the intensity of personnel patrol in technical protection. Then, the objective data can be used as a comparison reference group for non-objective data to verify the accuracy of non-objective data.

[0117] In this way, some of the evaluation contents that originally needed to be formulated through human experience can be verified and feedback-adjusted through some objectively detected data, and then replaced with actual parameter values.

[0118] S312, set a standard scoring comparison table for each scoring dimension in the definite scoring group, and add several verification parameter information to obtain the standard score.

[0119] First, set a standard scoring comparison table for each scoring dimension in the definite scoring group. The values of all preset parameter information and their corresponding scores exist in this standard scoring comparison table. Through this standard scoring comparison table, the corresponding security governance parameters in the definite scoring group can be scored. And in the generation stage of the standard scoring comparison table, verification parameter information for verifying the scoring table needs to be added. This step is the verification link for the standard scoring comparison table. When the corresponding parameters are input, the standard score corresponding to each verification parameter information is obtained according to the standard scoring comparison table.

[0120] When presetting the standard scoring comparison table, the corresponding comparison range and corresponding scores can be set according to expert evaluation, policy requirements, etc. Since the specific accurate scoring values will be determined according to the comparison later, it is not necessary to require completely accurate formulation of the standard scoring comparison table during presetting.

[0121] And the standard scoring comparison table only needs to be set once and can be applied to any level and any type of protection unit. The standard scoring comparison table is a standard reference for converting the various security governance parameters corresponding to the protection unit from the detection values of different types and different units into unified scoring system values.

[0122] S313. Generate the predicted score of the corresponding verification object based on the standard score.

[0123] After calculating the standard score, obtain the predicted score corresponding to the standard score through empirical algorithms or historical data operations.

[0124] For example, based on traversing experience and historical data, it is found that when the human protection score is x points and the technical protection score is y points, the security perception in problem perception is a score between z1 - z2, and the fire protection perception is a score between z3 - z4. Then, any score within the selected value or the selected score range can be used as the predicted score.

[0125] S314. Set up an empirical score comparison table for the non - determined score group and add several verification parameters to obtain the simulated score.

[0126] Set up an empirical score comparison table for each scoring dimension in the non - determined score group through empirical algorithms and historical data. Among them, each reference item in the empirical score comparison table is the same as the reference item in the standard score comparison table. That is to say, the corresponding content in the empirical score comparison table is also for dimensions such as human protection and technical protection. Subsequently, the score of the non - objective dimension can be obtained through objective numerical values. This empirical score comparison table is not the objective and accurate score of this scoring dimension, but a score comparison table speculated based on past experience.

[0127] Put the same verification parameters as the above - mentioned standard score comparison table into the empirical score comparison table to obtain a set of scores. This set of scores is the speculative scores obtained through cumulative past experience.

[0128] S315. Compare the predicted score and the simulated score to calculate the correlation trend value, and judge whether it is a positive - feedback correlation or a negative - feedback correlation based on the correlation trend value. Update the standard score comparison table and / or the empirical score comparison table through positive - feedback correlation or negative - feedback correlation to obtain the score comparison table.

[0129] Both the predicted score and the simulated score are non - objective score values. The difference between them is that the predicted score is a non - objective score deduced from the objective standard score, and there is a certain data - support benchmark corresponding to the protected unit to be scored. While the simulated score is a non - objective score simulated based on empirical algorithms, which is independent and decoupled from the standard score.

[0130] By comparing the predicted score and the simulated score, if the difference between the predicted score and the simulated score is small, the correlation trend value is small; if the difference between the predicted score and the simulated score is large, the correlation trend value is relatively large. The correlation trend value is the determination coefficient for judging the numerical size correlation between two values.

[0131] If the associated trend value is small, it can be considered that the parameters and scoring criteria in the empirical scoring comparison table are close to those in the standard scoring comparison table. When performing dimension scoring subsequently, there will be no abnormal differences in scores between different dimensions. At this time, it is considered that there is a positive feedback association between the two. The positive feedback association is characterized by a set of parameters having a positive impact on another set of parameters, which is a healthy comparison and analysis relationship. At this time, only the two scoring tables need to be dynamically refined according to the specific differences.

[0132] If the associated trend value is large, it is considered that the parameters and scoring criteria in the empirical scoring comparison table are quite different from those in the standard scoring comparison table. When performing dimension scoring subsequently, there may be problems such as extremely high scores for human protection and technical protection, but extremely low scores for fire perception, or extremely low scores for human protection and technical protection, but extremely high scores for fire perception, for example. At this time, it is considered that there is a negative feedback association between the two. The negative feedback is characterized by a set of parameters having a negative impact on another set of parameters. At this time, it is an unhealthy comparison and analysis relationship. At this time, the two scoring comparison tables need to be updated significantly through an algorithm.

[0133] In some other embodiments, the standard scoring comparison table and / or the empirical scoring comparison table are updated through positive feedback association or negative feedback association to obtain a scoring comparison table, including the following steps:

[0134] S3151, under positive feedback association, when the associated trend value is less than a preset value, the simulated score is adjusted towards the predicted score for update, and the empirical scoring comparison table is updated according to the updated simulated score to obtain a scoring comparison table.

[0135] Under positive feedback association, since the difference between the simulated score and the predicted score is not large, only the simulated score can be corrected at this time. The correction process is to modify the simulated score to the predicted score. This is because although both scores are non-objective scores, the predicted score has specific and objective to-be-scored parameters in the protection unit as data support, so its numerical accuracy is greater than that of the simulated score.

[0136] After the simulated score is updated, the empirical scoring comparison table is updated through this simulated score to obtain the final scoring comparison table.

[0137] For example, when the simulated score is specifically 70 points corresponding to the case of having 10 security cameras, and the predicted score is specifically 63 points corresponding to the case of having 10 security cameras, then first change the score from 70 points to 63 points, and then update the changed simulated score to the empirical scoring comparison table, so that the score of security camera quantity * 10 in the empirical scoring comparison table changes from 70 points to 63 points, thereby updating the empirical scoring comparison table.

[0138] S3152. Under inverse feedback association, when the association trend value is greater than the preset value, the accuracy rates of the predicted score and the verification score are judged based on the cross-validation method, and the score with a higher accuracy rate is selected as the reference score. Then, feedback parameters are generated based on the reference score and sent to the corresponding determined score group or undetermined score group. The determined score group or undetermined score group updates the standard score comparison table or the empirical score comparison table based on the feedback parameters. After the update, the above steps are repeated until the association trend value is less than the preset value.

[0139] When there is inverse feedback association, the difference between the simulated score and the predicted score is large. At this time, the cross-validation method is needed to judge the accuracy rates of the predicted score and the verification score.

[0140] Since the parameters of the predicted score and the parameters of the verification score are simulated and specified under uncertain conditions, the parameters specified according to experience may not be accurate. Cross-validation is used to judge the accuracy of these specified parameters.

[0141] First, the parameters and their corresponding scores in the predicted score and the parameters and their corresponding parameters in the verification score are respectively divided into a training set and a test set according to a proportion. The data in the training set is used for n-fold verification. The training set is divided into n parts, and each part of the parameters in the n parts takes turns as the CV set, and the remaining n - 1 parts are used as the Training set. After training n times, the average value of the CV error obtained in the n times is taken as the final result to perform parameter optimization.

[0142] Then, a model is trained with the entire training set under the selected optimal model parameters, and finally the final accuracy rate result is tested in the test set B.

[0143] The higher the accuracy rate, the more accurate the scoring result of the score for each parameter. Then, the score with a higher accuracy rate is selected as the reference score.

[0144] If the reference score is the predicted score, the reference score is sent to the undetermined score group corresponding to the empirical score with a lower accuracy rate, so that the empirical score comparison table can adjust the score setting in the score table based on the reference score with a better accuracy rate; conversely, if the reference score is the empirical score, the reference score is sent to the determined score group corresponding to the standard score with a lower accuracy rate, so that the standard score comparison table can adjust the score setting in the score table based on the reference score with a higher accuracy rate.

[0145] Generally speaking, the accuracy rate of the scoring parameters corresponding to the standard scoring is relatively high. However, in some cases, a large number of personnel resulting in bloated manpower, a large number of technical protection devices but with low setting accuracy, etc. will all cause a high standard scoring, but the actual problem perception ability is poor. Therefore, the standard scoring can only be used as the basis for standardized scoring under normal circumstances, but there may be abnormal scoring in special cases.

[0146] In some other embodiments, a scoring system is generated in combination with the scoring dimensions, including the following steps:

[0147] Single-end scoring system:

[0148] S510, Independently score according to the security governance parameters corresponding to each scoring dimension.

[0149] Combined scoring system:

[0150] S520, Divide each scoring dimension into a detection group and a response group. Generate the detection time of different objects based on the security governance parameters within the detection group, generate the response time of different objects based on the security governance parameters within the response group, generate the minimum delay time based on the level information and category information of the protection unit, and perform combined scoring based on the detection time, response time, and minimum delay time.

[0151] There are two scoring modes in the established scoring system. In the single-end scoring system, each issued security governance parameter is an independent parameter, and there is no comparison and association considered between each independent parameter. When the security governance parameter is obtained, only need to find the corresponding position and the score corresponding to the corresponding data size of each security governance parameter in the scoring comparison table.

[0152] In the combined scoring system, some scoring dimensions are integrated, and at the same time, some parameters are associated to evaluate the overall or combined corresponding security governance ability. First, divide each scoring dimension into a detection group and a response group.

[0153] What is included in the detection group are the assessment items for the detection ability of various security hazards and problems, such as manual protection, technical protection, and problem perception. And the detection time is characterized as the time required for various detection personnel and detection entities to generate alarm status information after detecting the security problem target until the corresponding device or person receives this information and issues an alarm signal.

[0154] While in the response group are the assessment items for the timely response ability when various security hazards and problems occur, such as emergency response. And the response time is characterized as the time from the corresponding device or person receiving the alarm information to the corresponding security, fire protection, and cultural protection personnel arriving at the alarm site.

[0155] Secondly, a minimum delay time is generated based on the level information and category information of the protected entity. The minimum delay time is characterized as the minimum time from when a security issue or potential hazard is detected until the security issue or potential hazard starts to commit an infringement act on the protected object. The minimum delay time shortens as the level of the protected entity increases, that is, the higher the level of the protected entity, the shorter the required response time. For different categories of protected entities, their indoor or outdoor location, floor area, terrain, etc. are all different, so they also correspond to different minimum delay times.

[0156] In the combined scoring system, it is necessary to determine whether the detection time + response time is less than or equal to the minimum delay time. If it is greater, it means that when a security issue or potential hazard occurs, the protected entity cannot timely handle the security hazard or issue through the existing security governance capabilities, which will lead to the security issue causing harm. At this time, it is necessary to deduct points from the scores of some dimensions in the detection group or response group accordingly.

[0157] In some other embodiments, a scoring portrait model is trained by adding a conventional scoring calculation method or a specific item scoring calculation method to the scoring system, including the following steps:

[0158] S530, generate a conventional scoring portrait model corresponding to each level based on the conventional scoring calculation method. In the conventional scoring portrait model, the preset parameter information of each scoring dimension is configured with a standard calculation weight matching the first scoring coefficient.

[0159] Under the conventional scoring calculation method, a conventional scoring portrait model is trained correspondingly. This model scores the corresponding security governance parameters in combination with the corresponding scoring comparison table. The full score of each security governance parameter is 100 points. The total score of a scoring dimension is composed of the scores corresponding to several security governance parameters. Therefore, the corresponding weight coefficients are set according to the importance of each parameter, so that the scores corresponding to multiple security governance parameters are added together through the weight coefficient calculation to obtain the dimension score, and the full score of the dimension score is also 100 points. The generation of this weight coefficient also needs to be based on the first scoring coefficient. Since the reference numerical values of the scoring comparison tables corresponding to protected entities at different levels are the same, for example, the number of personnel is 30 to correspond to a score of 80 points, while the number of personnel in a national-level protected entity may exceed 30, and the number of personnel in a county-level protected entity may only be 10. However, because the required number of personnel at different levels is also different, it is necessary to adjust the standard calculation weight through the first scoring coefficient so that the final score is adapted to protected entities at each level.

[0160] S531. Generate specific item scoring portrait models corresponding to each level based on a specific item scoring calculation method. In the specific item scoring portrait models, standard calculation weights are configured for the preset parameter information of objects that do not belong to specific categories in each scoring dimension, and specific calculation weights set based on the second evaluation coefficient on the human side or the second evaluation coefficient on the natural side are configured for the preset parameter information of objects that belong to specific categories in each scoring dimension.

[0161] For protection units of specific categories, scoring needs to be carried out through specific item scoring portrait models trained by specific item scoring calculation methods. For each scoring dimension, there is parameter information that has an impact association with its specific category. For example, for ancient tombs, emphasis needs to be placed on anti-theft security management. Then, parameters such as the inspection intensity, number of security guards, security guard training and assessment situation in manual protection corresponding to anti-theft, the number of security cameras, coverage rate in technical protection, and security perception and illegal intrusion perception in problem perception cannot be calculated through conventional standard calculation weights. They require higher weight coefficients for determination. Such parameters need to correspond to specific calculation weights set based on the second evaluation coefficient on the human side or the second evaluation coefficient on the natural side, and such specific calculation weights are higher or lower than the standard calculation weights.

[0162] In protection units of specific categories, parameter information that has nothing to do with the determination bias of their types still applies to standard calculation weights.

[0163] In some other embodiments, sending each security management parameter to the corresponding scoring dimension in the scoring portrait model for scoring calculation to obtain a unit score includes the following steps:

[0164] S610. Set corresponding weight ratios for each scoring dimension based on a preset segmentation standard.

[0165] The segmentation standard is characterized by the proportion in the overall score of the protection unit in six scoring dimensions. In the embodiments of the present application, the segmentation standard is: problem perception (20%), hidden danger rectification (15%), technical protection (12%), human protection (15%), case handling (18%), emergency response (20%), and the total score of the unit score is 100 points.

[0166] S620. After obtaining the sub-scores corresponding to each scoring dimension according to the security management parameters, multiply each sub-score by the weight ratio to obtain the dimension score.

[0167] Define the total score corresponding to each scoring dimension as the sub-score, and multiply each sub-score by the weight ratio to obtain the dimension score corresponding to that scoring dimension. For example, if the sub-score of a protection unit in the problem perception dimension is 86 points, then its dimension score is 86 * 0.2 = 17.2 points.

[0168] S630, add up the scores of each dimension to obtain the unit score of this protected entity.

[0169] Add up the dimension scores corresponding to each scoring dimension to obtain the unit score corresponding to this protected entity, with a full score of 100 points.

[0170] Finally, add up the unit scores corresponding to each protected entity within a region to obtain the region score corresponding to this region. The full score of the region score is the quantity in this region * 100. In some other embodiments, for the convenience of comparing and analyzing the region scores, after calculating the full score of the region score, the full scores of all regions can be converted to 100 score values.

[0171] In some other embodiments, after calculating the matching total score based on several unit scores and the number of protected entities, the following steps are further included:

[0172] S710, generate a unit score portrait based on the unit scores, and generate unit rectification opinions based on the scoring dimensions with low scores in each unit score portrait.

[0173] The unit score portrait is a portrait of the safety governance ability of the corresponding independent unit individual. Through this portrait, the safety governance short board of this protected entity can be clearly seen, and unit rectification opinions can be generated based on this short board.

[0174] S720, generate a region score portrait based on the region total score, and generate region rectification opinions based on the scoring dimensions with low scores in the region score portrait.

[0175] The region total score portrait is a portrait of the safety governance ability of a region. Through this portrait, the safety governance short board of this region can be clearly seen, and region rectification opinions can be generated based on this short board.

[0176] Among them, the content of the unit rectification opinions is more detailed and accurate, while the region rectification opinions are overall direction coordination opinions. The content of the opinions involves a wide range but is relatively general, and often the region rectification opinions include some unit rectification opinions.

[0177] S730, establish a rectification list corresponding to each unit.

[0178] The rectification list includes rectification instruction information and rectification warning information.

[0179] The rectification instruction information represents the rectification requirements that this protected entity needs to enforce based on the safety governance short board, while the rectification warning information represents the information for controlling, reminding, and suggesting aspects without safety governance short boards in each protected entity.

[0180] S740, add the unit's rectification opinions to the rectification instruction information, add the area rectification opinions to the rectification warning information, and delete the content corresponding to the scoring dimensions that are the same as the unit's rectification opinions.

[0181] First, the unit's rectification opinions indicate the problems identified in the protected unit, and it is necessary for the protected unit to implement them in accordance with the rectification requirements, so they are added to the rectification instruction information. For some dimensions with relatively low scores in the overall score of a certain area, it shows that many protected units in this area generally have shortcomings in these aspects. If the protected unit does not have such shortcomings, these rectification opinions can be added to the rectification warning information to remind the protected unit that the following problems are common in this area, and it is recommended that the protected unit maintain or strengthen the supervision and prevention of such problems.

[0182] As Figure 2 shown, this application also discloses a dynamic evaluation system for the cultural relics safety governance ability within a region, including:

[0183] A protected unit docking module, used to obtain the quantity, level information, and category information of protected units within the region.

[0184] A calculation coefficient generation module, used to generate the first evaluation coefficient for objects of different levels based on the level information, and generate the second evaluation coefficient for specific category objects based on the category information.

[0185] A planar portrait model training module, used to generate several scoring dimensions, obtain the preset parameter information for scoring, and classify each preset parameter information into the corresponding scoring dimensions; obtain the preset scoring calculation method, and adjust the preset scoring calculation method based on the first evaluation coefficient to obtain the general scoring calculation method, and adjust the general scoring calculation method based on the second evaluation coefficient to obtain the specific item scoring calculation method; combine the scoring dimensions to generate a scoring system, and add the general scoring calculation method or the specific item scoring calculation method in the scoring system to train and generate a scoring portrait model.

[0186] A scoring module, used to obtain the safety governance parameters corresponding to the protected unit, and send each safety governance parameter to the corresponding scoring dimension in the scoring portrait model for scoring calculation to obtain the unit score; calculate the overall area score based on several unit scores and the quantity of protected units.

[0187] The implementation principle is as follows:

[0188] Establish a standardized evaluation model for the ability of cultural relics security governance. This evaluation model objectively and accurately scores each protection unit in combination with the actual situation of different levels and categories within a region, and conducts an ability portrait through the unit score and the total regional score to realize the determination of the cultural relics security index within the region, clearly and accurately knowing the overall level and implementation ability of the cultural relics security governance ability within the region, so as to discover the short board of the cultural relics protection ability in each region.

[0189] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order restriction and can be executed in other orders.

[0190] The above are all preferred embodiments of the present application, and do not limit the protection scope of the present application accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A dynamic evaluation method for the security management capacity of cultural relics in a region, characterized by: The following steps are involved: Obtain the number, level information and category information of protected units in the area, wherein the level objects in the level information include national, provincial, municipal and county levels; Generate first assessment coefficients for objects of different levels based on the level information, and generate second assessment coefficients for objects of a specific category based on the category information, wherein the first assessment coefficient corresponding to the national level is smaller than the first assessment coefficient corresponding to the provincial level, and the first assessment coefficient corresponding to the provincial level is smaller than the first assessment coefficient corresponding to the city and county level, and the first assessment coefficients are both greater than or equal to 1; Generate several scoring dimensions, obtain preset parameter information for scoring, and classify each preset parameter information into a corresponding scoring dimension. Specifically, Generate a definite scoring group and an indefinite scoring group, wherein the definite scoring group is characterized by scoring items that can be directly or indirectly determined by corresponding parameters, and the indefinite scoring group includes scoring items that cannot be directly or indirectly determined by corresponding parameters. The definite scoring group includes manual protection, technical protection, case handling, and hidden danger rectification, and the indefinite scoring group includes problem perception and emergency response; One of the scoring dimensions in the non-determined scoring group selects one or more of the scoring dimensions in the determined scoring group as verification objects; Setting a standard scoring comparison table for each of the scoring dimensions in the determined scoring group, and adding some verification parameter information to obtain a standard score; Generate a prediction score for the corresponding verification object based on the standard score; Setting an experience score comparison table for the non-deterministic score group and adding several verification parameters to obtain a simulated score; The predicted score and the simulated score are compared to calculate a correlation trend value, and a positive feedback correlation or a negative feedback correlation is determined based on the correlation trend value, and the standard score comparison table and / or the experience score comparison table are updated through the positive feedback correlation or the negative feedback correlation to obtain a score comparison table; Under the positive feedback association, the association trend value is less than a preset value, the simulation score is brought closer to the predicted score for updating, and the experience score comparison table is updated according to the updated simulation score to obtain a score comparison table; Under the inverse feedback association, the association trend value is greater than a preset value, the accuracy of the predicted score and the experience score is determined based on a cross-validation method, and the score with a higher accuracy is selected as a reference score, and a feedback parameter is generated based on the reference score and sent to the corresponding determined score group or the non-determined score group, and the determined score group or the non-determined score group updates the standard score comparison table or the experience score comparison table based on the feedback parameter, and repeats the above steps after the update until the association trend value is less than the preset value; Obtaining a preset scoring calculation method, and adjusting the preset scoring calculation method based on the first evaluation coefficient to obtain a conventional scoring calculation method, and adjusting the conventional scoring calculation method based on the second evaluation coefficient to obtain a specific item scoring calculation method; Generate a scoring system in combination with the scoring dimensions, and add a conventional scoring calculation method or a specific item scoring calculation method to the scoring system to train and generate a scoring portrait model; Obtaining security governance parameters corresponding to the protection unit, and sending each of the security governance parameters to the corresponding scoring dimensions in the scoring portrait model for scoring calculation to obtain a unit score; The overall regional score is calculated based on the scores of several of the units and the number of protected units.

2. The dynamic assessment method for regional cultural relics security management capabilities according to claim 1 is characterized by: The category objects of the category information include at least ancient cultural relics, ancient buildings, ancient tombs, grottoes and stone carvings, important modern historical sites, and representative buildings, and different category objects correspond to different assessment biases, which include man-made risk bias, natural risk bias, and balance. If the assessment bias is balance, one level object corresponds to one second assessment coefficient. If the assessment bias is man-made risk bias, one category object corresponds to a second assessment coefficient on the man-made side with a higher value and a second assessment coefficient on the natural side with a lower value. If the assessment bias is natural risk bias, one category object corresponds to a second assessment coefficient on the man-made side with a lower value and a second assessment coefficient on the natural side with a higher value.

3. The dynamic evaluation method for regional cultural relics security management capabilities according to claim 1 is characterized in that: The scoring dimensions include: Artificial protection, which represents the personnel protection configuration capability of the protection unit, is specifically used to configure the following preset parameter information: personnel configuration including the number, composition, age structure, assessment status, and patrol intensity of personnel; Technical protection, which is characterized by the security coverage and operation status of the protection unit, is specifically used to configure the following preset parameter information: device type, device online rate, and device coverage rate associated with the specific protection type; Problem perception, which is characterized by the ability to perceive safety status and potential safety hazards, is specifically used to configure the following preset parameter information: including but not limited to the perception capability evaluation factors of security perception, fire perception, environmental perception, weather and geological disaster perception, pest perception, illegal intrusion perception, and behavior perception; Hidden danger rectification refers to the ability to deal with discovered hidden dangers. It is specifically used to configure the following preset parameter information: including but not limited to hidden danger types such as surface weathering, plant roots, combustibles, and environmental pollution, and feedback on governance; Case handling, which refers to the ability to handle the cases filed, is used to configure the following preset parameter information: whether the case is completed, case closing cycle, number of cases, number of closed cases, nature of the case, and social impact; Emergency response refers to the ability to respond to emergency events that affect the safety of cultural relics. It is specifically used to configure the following preset parameter information: emergency response plan, emergency personnel, emergency supplies, and the degree of perfection of the emergency system.

4. The dynamic evaluation method for regional cultural relics security management capabilities according to claim 1 is characterized in that: Generating a scoring system in combination with the scoring dimensions includes the following steps: Single-ended scoring system: According to the security governance parameters, each scoring dimension is sent down to perform independent scoring; Combined scoring system: Each scoring dimension is divided into a detection group and a response group, and the detection time of different objects is generated based on the security governance parameters in the detection group. The response time of different objects is generated based on the security governance parameters in the response group. The minimum delay time is generated based on the level information and category information of the protection unit. A combined score is performed based on the detection time, the response time and the minimum delay time.

5. The dynamic evaluation method for regional cultural relics security management capabilities according to claim 2 is characterized in that: Adding a conventional scoring calculation method or a specific scoring calculation method to the scoring system to train and generate a scoring portrait model includes the following steps: Generate a conventional scoring portrait model corresponding to each level based on the conventional scoring calculation method, in which the preset parameter information of each scoring dimension is configured with a standard calculation weight matching the first evaluation coefficient; Based on the specific item scoring calculation method, a specific item scoring portrait model corresponding to each level is generated. In the specific item scoring portrait model, the preset parameter information that does not belong to the specific category object in each scoring dimension is configured with a standard calculation weight, and the preset parameter information that belongs to the specific category object in each scoring dimension is configured with a specific calculation weight set based on the second evaluation coefficient on the artificial side or the second evaluation coefficient on the natural side.

6. The dynamic evaluation method for regional cultural relics security management capabilities according to claim 5 is characterized in that: Sending each of the security governance parameters to the corresponding scoring dimensions in the scoring profile model for scoring calculation to obtain a unit score includes the following steps: Setting corresponding weight ratios for each of the scoring dimensions based on a preset segmentation standard; After obtaining the sub-scores corresponding to each scoring dimension according to the security governance parameters, multiply each of the sub-scores by the weight ratio to obtain a dimension score; The scores of each dimension are added together to obtain the unit score of the protection unit.

7. The dynamic evaluation method for regional cultural relics security management capabilities according to claim 6 is characterized in that: After calculating the regional total score based on the scores of the units and the number of protected units, the following steps are also included: Generate a unit rating profile based on the unit rating, and generate unit rectification opinions based on the rating dimensions with low scores in each of the unit rating profiles; Generate a regional scoring profile based on the total score of the region, and generate regional rectification opinions based on the scoring dimensions with low scores in the regional scoring profile; Establishing a rectification list corresponding to each of the units, the rectification list including rectification instruction information and rectification warning information; The unit rectification opinions are added to the rectification instruction information, the regional rectification opinions are added to the rectification warning information, and the content corresponding to the scoring dimension that is the same as the unit rectification opinions is deleted.

8. A dynamic assessment system for the security management capabilities of cultural relics in a region, characterized by: include: The protection unit docking module is used to obtain the number, level information and category information of the protection units in the region, wherein the level objects in the level information include national, provincial, municipal and county levels; A calculation coefficient generation module, used to generate first evaluation coefficients for objects of different levels based on the level information, and to generate second evaluation coefficients for objects of a specific category based on the category information, wherein the first evaluation coefficient corresponding to the national level is smaller than the first evaluation coefficient corresponding to the provincial level, and the first evaluation coefficient corresponding to the provincial level is smaller than the first evaluation coefficient corresponding to the city and county level, and the first evaluation coefficients are both greater than or equal to 1; The plane portrait model training module is used to generate several scoring dimensions, obtain preset parameter information for scoring, and classify each preset parameter information into a corresponding scoring dimension; obtain a preset scoring calculation method, and adjust the preset scoring calculation method based on the first evaluation coefficient to obtain a conventional scoring calculation method, and adjust the conventional scoring calculation method based on the second evaluation coefficient to obtain a specific item scoring calculation method; generate a scoring system in combination with the scoring dimensions, and add a conventional scoring calculation method or a specific item scoring calculation method to the scoring system to train and generate a scoring portrait model. Specifically, Generate a definite scoring group and an indefinite scoring group, wherein the definite scoring group is characterized by scoring items that can be directly or indirectly determined by corresponding parameters, and the indefinite scoring group includes scoring items that cannot be directly or indirectly determined by corresponding parameters. The definite scoring group includes manual protection, technical protection, case handling, and hidden danger rectification, and the indefinite scoring group includes problem perception and emergency response; One of the scoring dimensions in the non-determined scoring group selects one or more of the scoring dimensions in the determined scoring group as verification objects; Setting a standard scoring comparison table for each of the scoring dimensions in the determined scoring group, and adding some verification parameter information to obtain a standard score; Generate a prediction score for the corresponding verification object based on the standard score; Setting an experience score comparison table for the non-deterministic score group and adding several verification parameters to obtain a simulated score; The predicted score and the simulated score are compared to calculate a correlation trend value, and a positive feedback correlation or a negative feedback correlation is determined based on the correlation trend value, and the standard score comparison table and / or the experience score comparison table are updated through the positive feedback correlation or the negative feedback correlation to obtain a score comparison table; Under the inverse feedback association, the association trend value is greater than a preset value, the accuracy of the predicted score and the experience score is determined based on a cross-validation method, and the score with a higher accuracy is selected as a reference score, and a feedback parameter is generated based on the reference score and sent to the corresponding determined score group or the non-determined score group, and the determined score group or the non-determined score group updates the standard score comparison table or the experience score comparison table based on the feedback parameter, and repeats the above steps after the update until the association trend value is less than the preset value; The scoring module is used to obtain the security governance parameters corresponding to the protection unit, and send each of the security governance parameters to the corresponding scoring dimensions in the scoring portrait model for scoring calculation to obtain the unit score; the total regional score is calculated based on several of the unit scores and the number of protection units.

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