Electrochemical energy storage fire safety management system

Abstract

This invention provides an electrochemical energy storage fire safety management system, relating to the field of fire safety management technology. It includes: a score acquisition module for determining the original scores of each sub-evaluation dimension; a weight determination module for performing a dual analysis of the relative importance of each sub-evaluation dimension to determine its weight; a score correction module for setting a score correction mechanism based on causal relationship judgment to correct the original scores of each sub-evaluation dimension to the final score; and a management evaluation module for performing a weighted summation of the final scores based on the weights to determine the fire safety management score. The dual analysis of this invention fully considers the relative importance between each pair of sub-evaluation dimensions, making the final determined weights more reasonable and comprehensive. It also identifies and corrects unreasonable original data based on the rationality of causal logic, thereby ensuring the scientific and reasonable nature of the fire safety management score.

Description

Technical Field

[0001] This invention relates to the field of fire safety management technology, specifically to an electrochemical energy storage fire safety management system. Background Technology

[0002] Currently, the field of electrochemical energy storage has not yet established a systematic and standardized assessment system specifically for its fire safety management. The assessment process is often based on scattered checklists or general fire requirements. It is difficult to integrate and quantify the score data between different evaluation dimensions. Moreover, most of the score data for each evaluation dimension comes from subjective scoring that heavily relies on the experience level of the scorer, and there is a lack of reasonable identification and correction constraints to ensure the rationality of the score data.

[0003] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide an electrochemical energy storage fire safety management system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An electrochemical energy storage fire safety management system, comprising:

[0007] The scoring module is used to combine the maintenance report and annual fire safety evaluation report of the electrochemical energy storage power station under test, and use expert scoring to determine the original score of the electrochemical energy storage power station under test in each sub-evaluation dimension.

[0008] The weight determination module uses expert scoring to determine triples that characterize the importance of each sub-evaluation dimension, and combines fuzzy hierarchical analysis to perform a dual analysis of the relative importance between each sub-evaluation dimension in order to determine the weight of each sub-evaluation dimension.

[0009] The scoring correction module is used to determine whether there is a causal relationship between each sub-evaluation dimension, and to set a scoring correction mechanism based on the causal relationship judgment. The original score is corrected based on the scoring correction mechanism to determine the final score of the electrochemical energy storage power station under test in each sub-evaluation dimension.

[0010] The management evaluation module, based on the weights of each sub-evaluation dimension, performs a weighted summation of the final scores of the electrochemical energy storage power station under test in each sub-evaluation dimension to determine the fire safety management score of the electrochemical energy storage power station under test.

[0011] Furthermore, the sub-evaluation dimensions are categorized to determine the parent evaluation dimension to which each sub-evaluation dimension belongs. There are four parent evaluation dimensions: fire management level, comprehensiveness of fire facility maintenance, fire fighting and rescue level, and fire supervision and inspection status.

[0012] Among them, for the parent evaluation dimension of fire management level, there are five sub-evaluation dimensions belonging to this parent evaluation dimension, namely, the legality of fire administrative approval, the completeness of fire safety system, the standardization of fire safety operation, the completeness of fire prevention patrol and rectification system, and the degree of implementation of fire safety publicity and education.

[0013] Among them, the comprehensiveness of fire protection facility maintenance is the main evaluation dimension, which includes ten sub-evaluation dimensions: fire power supply and distribution integrity rate, automatic fire alarm system integrity rate, automatic sprinkler system integrity rate, smoke exhaust system integrity rate, fire compartmentation facility integrity rate, fire elevator integrity rate, emergency lighting evacuation guidance integrity rate, emergency broadcast system integrity rate, fire water supply facility integrity rate, and maintenance and rectification rate.

[0014] Among them, for the parent evaluation dimension of fire fighting and rescue level, there is one sub-evaluation dimension belonging to this parent evaluation dimension, namely fire fighting and rescue capability;

[0015] Among them, the fire safety supervision and inspection situation has two sub-evaluation dimensions belonging to this parent evaluation dimension, namely the inspection situation of electrical and gas systems and the integrity of building fire prevention and evacuation facilities.

[0016] Furthermore, for any two sub-evaluation dimensions that have a causal relationship, the coupling type between them is determined based on whether they belong to the same parent evaluation dimension. Specifically, if they belong to the same parent evaluation dimension, the coupling type between them is determined to be consistent coupling; otherwise, the coupling type between them is determined to be heterogeneous coupling.

[0017] Furthermore, for any sub-evaluation dimension, the logic for determining the triplet used to represent its importance is as follows: invite multiple experts to score the importance of this sub-evaluation dimension according to a 1-9 rating scale, summarize the importance score results of all experts for this sub-evaluation dimension, and extract the minimum, maximum, and mode values ​​to form a triplet used to represent the importance of this sub-evaluation dimension. This triplet includes the minimum possible value of importance, the most likely value of importance, and the maximum possible value of importance. Specifically, the minimum, mode, and maximum values ​​of the importance score results of all experts for a sub-evaluation dimension are used to represent the minimum possible value of importance, the most likely value of importance, and the maximum possible value of importance of this sub-evaluation dimension.

[0018] Furthermore, the specific logic for determining the weights of each sub-evaluation dimension is as follows:

[0019] 1) For any sub-evaluation dimension, divide it element by element with the triples of each sub-evaluation dimension to obtain the relative importance triples of the sub-evaluation dimension relative to each sub-evaluation dimension, and obtain the relative importance triangular fuzzy value of the sub-evaluation dimension relative to each sub-evaluation dimension by querying the triangular fuzzy value selection table.

[0020] Specifically, for any sub-evaluation dimension, the logic for dividing it element-wise with the triples of each sub-evaluation dimension is as follows: divide the minimum possible value in its triple by the maximum possible value in the triples of each sub-evaluation dimension, divide the most likely value in its triple by the most likely value in the triples of each sub-evaluation dimension, and divide the maximum possible value in its triple by the minimum possible value in the triples of each sub-evaluation dimension.

[0021] 2) Based on the triangular fuzzy values ​​of the relative importance of each sub-evaluation dimension to the other sub-evaluation dimensions, construct a triangular fuzzy matrix and sum the rows of the triangular fuzzy matrix to obtain the cumulative fuzzy importance value of each sub-evaluation dimension. Based on the triangular fuzzy matrix, normalize the cumulative fuzzy importance value of each sub-evaluation dimension to obtain the comprehensive fuzzy importance degree of each sub-evaluation dimension.

[0022] 3) For any sub-evaluation dimension, compare its importance with that of each other to determine the importance of the sub-evaluation dimension relative to each other.

[0023] 4) Based on the importance of each sub-evaluation dimension relative to the other sub-evaluation dimensions, construct an importance comparison matrix, and take the minimum value of each row of the importance comparison matrix to obtain the initial weight of each sub-evaluation dimension. With the cumulative value equal to 1 as a constraint, scale the initial weight of each sub-evaluation dimension proportionally to determine the weight of each sub-evaluation dimension.

[0024] Furthermore, when determining the importance of one sub-evaluation dimension relative to another, let the former sub-evaluation dimension be the sub-evaluation dimension to be analyzed and the latter sub-evaluation dimension be the benchmark sub-evaluation dimension. Combine the following scenarios to determine the importance of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension.

[0025] Scenario 1: The most likely value of the fuzzy comprehensive degree of importance of the sub-evaluation dimension to be analyzed is not less than the most likely value of the fuzzy comprehensive degree of importance of the benchmark sub-evaluation dimension. When Scenario 1 is satisfied, let the importance degree of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension be 1.

[0026] Scenario 2: The minimum possible value of the importance fuzzy comprehensive degree of the benchmark sub-evaluation dimension is not less than the maximum possible value of the importance fuzzy comprehensive degree of the sub-evaluation dimension to be analyzed. When Scenario 2 is satisfied, the importance degree of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension is set to 0.

[0027] When scenarios one and two are not met, the minimum possible value of the fuzzy comprehensive importance degree of the sub-evaluation dimension to be analyzed is subtracted from the maximum possible value of the fuzzy comprehensive importance degree of the sub-evaluation dimension to be analyzed to obtain the limit difference in importance between the sub-evaluation dimension to be analyzed and the benchmark sub-evaluation dimension. The most likely value of the fuzzy comprehensive importance degree of the benchmark sub-evaluation dimension is subtracted from the most likely value of the fuzzy comprehensive importance degree of the sub-evaluation dimension to be analyzed to obtain the most likely difference in importance between the sub-evaluation dimension to be analyzed and the benchmark sub-evaluation dimension. The ratio of the most likely difference in importance to the limit difference in importance is calculated. The reciprocal of the sum of this ratio and 1 is used to characterize the importance degree of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension.

[0028] Furthermore, the triangular fuzzy value selection table consists of multiple triangular fuzzy values ​​and triples corresponding to each triangular fuzzy value. For any relative importance triple, the Euclidean distance between it and each triple in the triangular fuzzy value selection table is calculated. The triple with the smallest Euclidean distance is selected as the reference triple, and the triangular fuzzy value corresponding to this reference triple is used as the relative importance triangular fuzzy value corresponding to the relative importance triple. This determines the relative importance triangular fuzzy value of each sub-evaluation dimension relative to each other sub-evaluation dimension.

[0029] If, for a relative importance triplet, the triplet with the smallest Euclidean distance is not unique, then the reference triplet is determined by successively progressively using the following constraints: the minimum absolute error of the most likely value, the minimum mean of the absolute errors of the minimum and maximum possible values, and the range of possible values ​​of the relative importance triplet being within the range of possible values ​​of the triplet.

[0030] Furthermore, the logic for determining the final score is as follows:

[0031] 1) For any sub-evaluation dimension, determine whether it has a causal relationship with other sub-evaluation dimensions. If the sub-evaluation dimension has no causal relationship with any other sub-evaluation dimensions, the original score of the electrochemical energy storage power station under test in that sub-evaluation dimension is directly used as the final score; otherwise, proceed to step 2).

[0032] 2) Traverse all causal relationships of the sub-evaluation dimension. If the sub-evaluation dimension is a cause in all causal relationships, use the original score of the electrochemical energy storage power station under test in the sub-evaluation dimension as the final score; otherwise, proceed to step 3).

[0033] 3) Take this sub-evaluation dimension as the sub-evaluation dimension to be corrected, and summarize all the sub-evaluation dimensions that are the cause in the causal relationship with it, so as to take them as its coupled sub-evaluation dimensions. Calculate the coupling coefficient between each coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected based on the original score.

[0034] 4) For any evaluation dimension to be corrected, traverse each of its coupled evaluation dimensions. Based on the relative magnitude of the original scores of the energy storage power station under test on the evaluation dimension to be corrected and each of its coupled evaluation dimensions, as well as the coupling coefficient and coupling type of the evaluation dimension to be corrected and each of its coupled evaluation dimensions, determine the final score of the evaluation dimension to be corrected relative to each of its coupled evaluation dimensions, and take the average as the final score of the electrochemical energy storage power station under test on the evaluation dimension to be corrected.

[0035] Furthermore, for any coupled sub-evaluation dimension of the sub-evaluation dimension to be corrected, the logic for calculating the coupling coefficient between the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected is as follows: calculate the relative closeness index of the original scores of the electrochemical energy storage power station under test on both the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected, preset an enhancement factor, and the enhancement factor is not less than one, construct a value with the relative closeness index as the base and the enhancement factor as the exponent, as the coupling coefficient between the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected;

[0036] The calculation logic for the relative proximity index is as follows: Calculate the geometric mean and arithmetic mean of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected. The geometric mean is the square root of the product of the two original scores, and the arithmetic mean is the ratio of the sum of the two original scores to 2. Calculate the ratio of the geometric mean and the arithmetic mean to obtain the relative mean of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected. Calculate the square of the relative mean to obtain the relative proximity index of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected.

[0037] Furthermore, the logic for determining the final score of the sub-evaluation dimension to be corrected relative to its various coupled sub-evaluation dimensions is as follows:

[0038] For the evaluation dimension to be corrected and any of its coupled evaluation dimensions, the original scores of the electrochemical energy storage power station under test are extracted on both. If the original score of the electrochemical energy storage power station under test on the former is less than the original score on the latter, the original score of the electrochemical energy storage power station under test on the former is directly used as the final score of the former relative to the latter.

[0039] Conversely, if the original score of the electrochemical energy storage power station under test in the former is not less than its original score in the latter, then the original score of the electrochemical energy storage power station under test in the former is reduced and corrected by combining the original scores of the electrochemical energy storage power station under test in both, the coupling coefficient and the coupling type of both, in order to determine the final score of the former relative to the latter. The greater the difference in the original scores of the electrochemical energy storage power station under test in both, and the greater the coupling coefficient of both, the greater the degree of reduction and correction. Moreover, the degree of reduction and correction when the coupling type is uniform coupling is greater than the degree of reduction when the coupling type is heterogeneous coupling.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] The electrochemical energy storage fire safety management system of the present invention uses fuzzy hierarchical analysis to perform a dual analysis of the relative importance between each sub-evaluation dimension, fully considering the relative importance between each pair of sub-evaluation dimensions, so that the final determined weights are more reasonable and comprehensive, providing a scientific basis for weighted fusion of the score data of each sub-evaluation dimension, and setting a score correction mechanism for the original score of the sub-evaluation dimension by combining causal relationship judgment, identifying unreasonable original data from the perspective of whether the causal logic is reasonable and reducing and correcting it, thereby ensuring the scientificity and rationality of the fire safety management score. Attached Figure Description

[0042] Figure 1 This is a modular unit diagram of the overall system of the present invention;

[0043] Figure 2 This is a schematic diagram illustrating the coupling types of the various sub-evaluation dimensions in this invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0045] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0046] Example:

[0047] Please see Figures 1-2 This invention provides an electrochemical energy storage fire safety management system, comprising:

[0048] The scoring module is used to combine the maintenance report and annual fire safety evaluation report of the electrochemical energy storage power station under test, and use expert scoring to determine the original score of the electrochemical energy storage power station under test in each sub-evaluation dimension.

[0049] The specific steps for determining the original score are as follows: collect the maintenance reports and annual fire safety evaluation reports of the electrochemical energy storage station under test for the past year, set the full score for each sub-evaluation dimension to 100 points, invite multiple experts to read the maintenance reports and annual fire safety evaluation reports to score the performance of the electrochemical energy storage station under test in each sub-evaluation dimension. Generally, the number of experts invited is between 5 and 9 to ensure the stability of the scoring results. Summarize the scoring results of all experts for the same sub-evaluation dimension, remove the maximum and minimum values, and take the average value as the original score of the electrochemical energy storage station under test in that sub-evaluation dimension. The specific scoring basis and process are determined based on common knowledge in the art and will not be elaborated here.

[0050] Each sub-evaluation dimension is categorized to determine the parent evaluation dimension to which each sub-evaluation dimension belongs. In this technical solution, there are four parent evaluation dimensions: fire management level, comprehensiveness of fire facility maintenance, fire fighting and rescue level, and fire supervision and inspection status.

[0051] As one implementation method, for the parent evaluation dimension of fire management level, there are five sub-evaluation dimensions belonging to this parent evaluation dimension, namely, the legality of fire administrative approval, the completeness of fire safety system, the standardization of fire safety operation, the completeness of fire prevention patrol and rectification system, and the degree of implementation of fire safety publicity and education.

[0052] As one implementation method, for the parent evaluation dimension of comprehensive maintenance of fire protection facilities, there are ten sub-evaluation dimensions belonging to this parent evaluation dimension, namely, the integrity rate of fire power supply and distribution, the integrity rate of automatic fire alarm system, the integrity rate of automatic sprinkler system, the integrity rate of smoke exhaust system, the integrity rate of fire compartmentation facilities, the integrity rate of fire elevator, the integrity rate of emergency lighting and evacuation guidance, the integrity rate of emergency broadcast system, the integrity rate of fire water supply facilities, and the maintenance and rectification rate.

[0053] As one implementation method, for the parent evaluation dimension of fire fighting and rescue level, there is a sub-evaluation dimension belonging to this parent evaluation dimension, namely fire fighting and rescue capability;

[0054] As one implementation method, for fire supervision and inspection, there are two sub-evaluation dimensions belonging to this parent evaluation dimension: the inspection of electrical and gas facilities and the integrity of building fire prevention and evacuation facilities.

[0055] It should be noted that the four parent evaluation dimensions and eighteen sub-evaluation dimensions mentioned above are specifically determined based on national standards, maintenance reports, and annual fire safety evaluation reports of the unit related to electrochemical energy storage fire protection. The national standards used include GB / T 42288-2022 "Safety Regulations for Electrochemical Energy Storage Power Stations" and GB 51048-2014 "Design Code for Electrochemical Energy Storage Power Stations". This ensures that this technical solution comprehensively considers multiple dimensions such as the operating status of fire protection facilities, fire management level, fire protection facility maintenance level, fire fighting and rescue capabilities, and fire supervision and inspection.

[0056] The weight determination module uses expert scoring to determine triples that characterize the importance of each sub-evaluation dimension, and combines fuzzy hierarchical analysis to perform a dual analysis of the relative importance between each sub-evaluation dimension in order to determine the weight of each sub-evaluation dimension.

[0057] For any given sub-evaluation dimension, the logic for determining the triplet representing its importance is as follows: Multiple experts are invited to rate the importance of this sub-evaluation dimension using a 1-9 rating scale. Generally, 5-9 experts are invited to ensure the stability of the importance rating results. A higher importance rating indicates greater importance for this sub-evaluation dimension. The importance ratings from all experts for this sub-evaluation dimension are then compiled, and the minimum, maximum, and mode values ​​are extracted to form the triplet representing the importance of this sub-evaluation dimension. This can be specifically represented as follows: In the formula, They represent the first The minimum possible value of importance, the most likely value of importance, and the most likely value of importance for each sub-evaluation dimension, where all experts are used for the first... The minimum, mode, and maximum values ​​of the importance rating results for each sub-evaluation dimension, to represent the importance of the first sub-evaluation dimension. The minimum possible value of importance, the most likely value of importance, and the maximum possible value of importance for each sub-evaluation dimension. For the index of the sub-evaluation dimension, and , Let be the total number of sub-evaluation dimensions. For example, in this technical solution, the four parent evaluation dimensions include a total of eighteen sub-evaluation dimensions. Therefore, let be the total number of sub-evaluation dimensions. ;

[0058] It should be noted that the minimum and maximum values ​​represent the minimum and maximum expectations of experts regarding the importance of the same sub-evaluation dimension, respectively. Therefore, the minimum and maximum values ​​are used here to represent the minimum and maximum possible values, respectively. The mode, as the value that appears most frequently, represents the mainstream expectation of experts regarding the importance of this sub-evaluation dimension. Therefore, the mode value is used here to represent the most likely value.

[0059] Furthermore, if the mode values ​​are not unique in the importance score results of a sub-evaluation dimension, the mode value with the smallest absolute error from the mean is selected as the most likely value. Since the mean also reflects the tendency of the most likely value of the importance score, the absolute error is used here to quantify the difference between the mean and each mode value, and the mode value with the smallest difference from the mean is selected as the most likely value. If the mode value with the smallest absolute error from the mean is still not unique on the basis that the mode values ​​are not unique, then a random one is selected from the mode values ​​with the smallest absolute error from the mean as the most likely value.

[0060] The specific logic for determining the weights of each sub-evaluation dimension is as follows:

[0061] 1) For any sub-evaluation dimension, divide it element by element with the triples of each sub-evaluation dimension to obtain the relative importance triples of the sub-evaluation dimension relative to each sub-evaluation dimension, and obtain the relative importance triangular fuzzy value of the sub-evaluation dimension relative to each sub-evaluation dimension by querying the triangular fuzzy value selection table.

[0062] Specifically, for any sub-evaluation dimension, the logic for dividing it element-wise with the triples of each sub-evaluation dimension is as follows: divide the minimum possible value in its triple by the maximum possible value in the triples of each sub-evaluation dimension, divide the most likely value in its triple by the most likely value in the triples of each sub-evaluation dimension, and divide the maximum possible value in its triple by the minimum possible value in the triples of each sub-evaluation dimension.

[0063] It should be noted that the setting of dividing the minimum possible value by the maximum possible value is used to quantify the relative importance in the worst-case scenario, while the setting of dividing the maximum possible value by the minimum possible value is used to quantify the relative importance in the best-case scenario. Therefore, the relative importance triples generated in this way can fully express the relative importance of one sub-evaluation dimension relative to another sub-evaluation dimension.

[0064] Specifically, the first Individual evaluation dimensions compared to the first The relative importance of individual evaluation dimensions in a triplet is specifically represented as follows: In the formula, They represent the first The minimum possible value of importance, the most likely value of importance, and the maximum possible value of importance for each sub-evaluation dimension. It is also an index for the sub-evaluation dimension, and ;

[0065] The triangular fuzzy value selection table consists of multiple triangular fuzzy values ​​and the triples corresponding to each triangular fuzzy value. For any relative importance triple, the Euclidean distance between it and each triple in the triangular fuzzy value selection table is calculated. The triple with the smallest Euclidean distance is selected as the reference triple, and the triangular fuzzy value corresponding to this reference triple is used as the relative importance triangular fuzzy value corresponding to the relative importance triple. This determines the relative importance triangular fuzzy value of each sub-evaluation dimension relative to each other sub-evaluation dimension.

[0066] As one implementation method, the triangular fuzzy value selection table is shown in Table 1 below:

[0067] Table 1. Selection Table of Triangular Blur Values

[0068]

[0069] It should be noted that the above triangular fuzzy value selection table provides a benchmark for evaluating the relative importance of each pair of sub-evaluation dimensions. Its specific values ​​are determined based on common knowledge known to those skilled in the art. For example, regarding triangular fuzzy values... In this context, it represents a triangular fuzzy value with a minimum possible value of 7, a maximum possible value of 9, and a maximum possible value of 11. Specifically, it is represented by a triple (7,9,11). Other triangular fuzzy values ​​are similar and will not be elaborated here.

[0070] Furthermore, if for a relative importance triplet, the triplet with the smallest Euclidean distance is not unique, then the reference triplet is determined by successively progressively using the following constraints: the minimum absolute error of the most likely value, the minimum mean of the absolute errors of the minimum and maximum possible values, and the fact that the possible value range of the relative importance triplet lies within the possible value range of the triplet.

[0071] It should be noted that the constraint of minimizing the absolute error of the most probable value is set to find the triplet that is closest to the most probable value of the relative importance triplet. The most probable value is the most important parameter of the triangular fuzzy value. Therefore, the constraint of minimizing the absolute error of the most probable value is used as the first-level constraint to find the triplet that is most similar to the relative importance triplet from multiple triplets with the smallest Euclidean distance from the relative importance triplet, using the most probable value as the similarity evaluation benchmark. If only one triplet is found based on this constraint, then this triplet is directly used as the reference triplet. Otherwise, further screening is carried out based on subsequent constraints.

[0072] It should be noted that the constraint of minimizing the mean of the minimum possible absolute error and the maximum possible absolute error is used to find the triplet that is closest to the boundary of the relative importance triplet. Therefore, the minimum mean of the minimum possible absolute error and the maximum possible absolute error is used as the second-level constraint to find the triplet that is most similar to the relative importance triplet from the triplets that pass the first-level constraint, using the boundary as the similarity evaluation benchmark. If only one triplet is found based on this constraint, it is directly used as the reference triplet; otherwise, further screening is carried out based on subsequent constraints.

[0073] It should be noted that if the minimum and maximum possible values ​​of a relative importance triplet are both between the minimum and maximum possible values ​​of a triplet, then the possible value range of the relative importance triplet is considered to be within the possible value range of this triplet. This constraint is set to find triplets that completely contain the relative importance triplet, that is, to find triplets that can summarize the relative importance triplet. Therefore, the possibility range of the relative importance triplet is used as the third-level constraint to filter out triplets that completely contain the relative importance triplet from the triplets that pass the second-level constraint, using the summary description as the evaluation criterion. If only one triplet is found based on this constraint, then this triplet is directly used as the reference triplet; otherwise, a reference triplet is randomly selected from among them.

[0074] 2) Based on the triangular fuzzy values ​​of the relative importance of each sub-evaluation dimension to the other sub-evaluation dimensions, construct a triangular fuzzy matrix and sum the rows of the triangular fuzzy matrix to obtain the cumulative fuzzy importance value of each sub-evaluation dimension. Based on the triangular fuzzy matrix, normalize the cumulative fuzzy importance value of each sub-evaluation dimension to obtain the comprehensive fuzzy importance degree of each sub-evaluation dimension.

[0075] Wherein, the triangular fuzzy matrix is ​​a OK Column matrices, also using As the row index of the triangular fuzzy matrix, use As the column index of the triangular fuzzy matrix, the first... Line 1 The element value of the column is the first one. Individual evaluation dimensions compared to the first The relative importance triangle fuzzy value of individual evaluation dimensions;

[0076] Specifically, the triangular fuzzy matrix is ​​expressed as follows:

[0077]

[0078] In the formula, Represents a triangular fuzzy matrix. The triangular fuzzy matrix is ​​the first... Line 1 The element value of the column represents the first element. Individual evaluation dimensions compared to the first The relative importance triangle fuzzy value of individual evaluation dimensions;

[0079] It should be noted that for the triangular fuzzy matrix, the elements in the same row are the triangular fuzzy values ​​of the relative importance of the same sub-evaluation dimension compared to each other. The triangular fuzzy matrix is ​​set to sum row by row to obtain the sum of the triangular fuzzy values ​​of the relative importance of each sub-evaluation dimension compared to all sub-evaluation dimensions. This is used as the cumulative fuzzy value of the importance of the corresponding sub-evaluation dimension. The larger the value, the more important the sub-evaluation dimension is.

[0080] The logic for normalizing the cumulative importance value is as follows: sum all elements in the triangular fuzzy matrix to obtain the cumulative importance value. For any sub-evaluation dimension, multiply its cumulative importance value by the reciprocal of the cumulative importance value to normalize the cumulative importance value of each sub-evaluation dimension, thus obtaining the comprehensive importance degree of each sub-evaluation dimension. Normalizing the cumulative importance value here serves to set the comprehensive importance degree, unifying the dimensions and facilitating subsequent comparison of the comprehensive importance degree of each sub-evaluation dimension.

[0081] Specifically, the mathematical expression for calculating the degree of importance fuzzy comprehensiveness is as follows:

[0082]

[0083] In the formula, For the first The importance of individual evaluation dimensions is represented by the fuzzy cumulative value. For the first The importance of each sub-evaluation dimension is determined by the fuzzy comprehensiveness of both, and both are triangular fuzzy number structures. This is the multiplication operator for fuzzy numbers, representing element-wise multiplication;

[0084] 3) For any given sub-evaluation dimension, compare its importance with that of each of the other sub-evaluation dimensions to determine the relative importance of that sub-evaluation dimension compared to the other sub-evaluation dimensions. The specific logic is as follows:

[0085] When determining the importance of one sub-evaluation dimension relative to another, let the former sub-evaluation dimension be the sub-evaluation dimension to be analyzed and the latter sub-evaluation dimension be the benchmark sub-evaluation dimension. Combine the following scenarios to determine the importance of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension.

[0086] Scenario 1: The most likely value of the fuzzy comprehensive degree of importance of the sub-evaluation dimension to be analyzed is not less than the most likely value of the fuzzy comprehensive degree of importance of the benchmark sub-evaluation dimension. When Scenario 1 is satisfied, it means that the sub-evaluation dimension to be analyzed is more likely to be more important than the benchmark sub-evaluation dimension. Therefore, the importance degree of the sub-evaluation dimension to be analyzed compared with the benchmark sub-evaluation dimension is set to 1, which means that the sub-evaluation dimension to be analyzed is more important than the benchmark sub-evaluation dimension.

[0087] Scenario 2: The minimum possible value of the fuzzy comprehensive degree of importance of the benchmark sub-evaluation dimension is not less than the maximum possible value of the fuzzy comprehensive degree of importance of the sub-evaluation dimension to be analyzed. When Scenario 2 is satisfied, it means that the sub-evaluation dimension to be analyzed is highly likely to be more important than the benchmark sub-evaluation dimension. Therefore, the importance degree of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension is set to 0, which means that the sub-evaluation dimension to be analyzed is not important relative to the benchmark sub-evaluation dimension.

[0088] When scenarios one and two are not met, the minimum possible value of the fuzzy comprehensive importance degree of the sub-evaluation dimension to be analyzed is subtracted from the maximum possible value of the fuzzy comprehensive importance degree of the sub-evaluation dimension to be analyzed. This yields the limit difference in importance between the sub-evaluation dimension to be analyzed and the benchmark sub-evaluation dimension. The larger this value, the higher the probability that the sub-evaluation dimension to be analyzed is more important than the benchmark sub-evaluation dimension. The most probable difference in importance between the sub-evaluation dimension to be analyzed is then subtracted from the most probable value of the fuzzy comprehensive importance degree of the benchmark sub-evaluation dimension. This yields the most probable difference in importance between the sub-evaluation dimension to be analyzed and the benchmark sub-evaluation dimension. The larger this value, the lower the probability that the sub-evaluation dimension to be analyzed is more important than the benchmark sub-evaluation dimension. The most probable difference in importance is then calculated. The ratio of importance difference to importance limit difference is used. The smaller the ratio, the higher the probability that the sub-evaluation dimension to be analyzed is more important than the benchmark sub-evaluation dimension. The reciprocal of the sum of this ratio and 1 is used to characterize the importance of the sub-evaluation dimension to be analyzed compared to the benchmark sub-evaluation dimension. The setting of taking the reciprocal after adding 1 to the ratio is used to quantify the importance level between 0 and 1. The setting of calculating the importance level through importance limit difference and most likely importance difference comprehensively considers the minimum, most likely, and maximum possible values ​​of the sub-evaluation dimension to be analyzed and the benchmark sub-evaluation dimension in the importance representation. This ensures that the final calculated importance level can comprehensively reflect the probability that the sub-evaluation dimension to be analyzed is more important than the benchmark sub-evaluation dimension.

[0089] It should be noted that since the importance fuzzy comprehensive degree is a triangular fuzzy number, it is not an accurate value, but a possible number within a certain range. It cannot be directly used to measure the relative importance between two sub-evaluation dimensions. Therefore, the above is based on the comparison of possible values ​​and the analysis of importance differences to quantify the relative importance between two sub-evaluation dimensions into an importance degree between 0 and 1. This importance degree is the probability value of the sub-evaluation dimension to be analyzed being more important than the benchmark sub-evaluation dimension. The larger the value, the higher the probability that the sub-evaluation dimension to be analyzed is more important than the benchmark sub-evaluation dimension.

[0090] Specifically, the mathematical expression for calculating the degree of importance is as follows:

[0091]

[0092] In the formula, For the first The most probable value in the fuzzy comprehensive degree of importance of individual evaluation dimensions For the first The most probable value in the fuzzy comprehensive degree of importance of individual evaluation dimensions For the first The minimum possible value in the fuzzy comprehensive degree of importance of each sub-evaluation dimension. For the first The maximum possible value in the fuzzy comprehensive degree of importance of individual evaluation dimensions. For the first Individual evaluation dimensions compared to the first The degree of importance of individual evaluation dimensions;

[0093] 4) Based on the importance of each sub-evaluation dimension relative to the other sub-evaluation dimensions, construct an importance comparison matrix, and take the minimum value of each row of the importance comparison matrix to obtain the initial weight of each sub-evaluation dimension. With the cumulative value equal to 1 as a constraint, scale the initial weight of each sub-evaluation dimension proportionally to determine the weight of each sub-evaluation dimension.

[0094] The importance comparison matrix is ​​a single matrix. OK Column matrices, also using As row indexes in the importance comparison matrix, used As the column index of the importance comparison matrix, the [number]th [column index] in the importance comparison matrix Line 1 The element value of the column is the first one. Individual evaluation dimensions compared to the first The degree of importance of individual evaluation dimensions;

[0095] Specifically, the importance comparison matrix is ​​expressed as follows:

[0096]

[0097] In the formula, This represents an importance comparison matrix;

[0098] It should be noted that for the importance comparison matrix, the elements in the same row represent the importance of the same sub-evaluation dimension relative to each other. Here, the minimum value is set based on a conservative strategy to strictly constrain the importance evaluation results of the sub-evaluation dimensions and avoid overestimating the weight of the sub-evaluation dimensions in the calculation of fire safety management scores.

[0099] Among them, the initial weights of each sub-evaluation dimension are scaled proportionally with the cumulative value equal to 1 as a constraint, so as to determine the weight settings of each sub-evaluation dimension. This is used to ensure that the sum of the weight proportions of each sub-evaluation dimension in the fire safety management score calculation is 1, so as to ensure the rationality of the fire safety management score calculation process.

[0100] It should be noted that in the technical solution for determining the weights of each sub-evaluation dimension in this invention, firstly, based on step 1), the elements of the triples of each sub-evaluation dimension are divided to obtain triangular fuzzy values ​​representing the pairwise importance of each sub-evaluation dimension, thus realizing a primary analysis of the relative importance of each sub-evaluation dimension. Then, combined with the row-by-row addition process in step 2), a comprehensive fuzzy comprehensive degree representing the importance of each sub-evaluation dimension is obtained. Then, based on step 3), the fuzzy comprehensive degree of each sub-evaluation dimension is compared pairwise, thus realizing a secondary analysis of the relative importance of each sub-evaluation dimension. Finally, based on the conservative strategy in step 4), the minimum value is taken to determine the weight of each sub-evaluation dimension. This method fully considers the relative importance of each sub-evaluation dimension through the setting of primary and secondary analyses when determining the weights, so that the final determined weights are more reasonable and comprehensive.

[0101] The scoring correction module is used to determine whether there is a causal relationship between each sub-evaluation dimension, and to set a scoring correction mechanism based on the causal relationship judgment. The original score is corrected based on the scoring correction mechanism to determine the final score of the electrochemical energy storage power station under test in each sub-evaluation dimension.

[0102] For any two sub-evaluation dimensions, the existence of a causal relationship between them is determined based on expert consultation and common knowledge in this technical field. This is existing technology. Taking the degree of implementation of fire safety publicity and education and fire fighting and rescue capabilities as examples, if the degree of implementation of fire safety publicity and education is poor, it will lead to employees lacking fire fighting and fire prevention knowledge, which will directly affect the unit's fire fighting and rescue capabilities and reduce its fire fighting and rescue capabilities. Therefore, it can be considered that there is a causal relationship between the degree of implementation of fire safety publicity and education and fire fighting and rescue capabilities, with the degree of implementation of fire safety publicity and education as the cause and fire fighting and rescue capabilities as the effect. The determination of whether there is a causal relationship between other sub-evaluation dimensions is similar and will not be elaborated here.

[0103] Furthermore, for any two sub-evaluation dimensions that have a causal relationship, the coupling type between them is determined based on whether they belong to the same parent evaluation dimension. Specifically, if they belong to the same parent evaluation dimension, the coupling type between them is determined to be consistent coupling; otherwise, the coupling type between them is determined to be heterogeneous coupling.

[0104] It should be noted that the sub-evaluation dimensions are not completely independent, but rather interconnected, mutually influential, and causally related. For example, if an energy storage power station fails to effectively implement fire safety education and awareness campaigns, resulting in employees lacking fire prevention and extinguishing knowledge, this will directly impact the unit's fire-fighting and rescue capabilities, causing a decline in their effectiveness. However, traditional evaluation and scoring typically focus only on the content of the evaluation items themselves, ignoring these interrelationships. Therefore, the scores often fail to comprehensively reflect the overall level of fire safety management. In light of this, this technical solution introduces causal relationship analysis when analyzing the sub-evaluation dimensions, quantifying the mutual influence relationships between the 18 sub-evaluation dimensions. This facilitates subsequent adjustments and corrections to the scores of each sub-evaluation dimension, resulting in a more objective and accurate revised score.

[0105] As one implementation method, the coupling types among the above eighteen evaluation dimensions are as follows:

[0106] Regarding the degree of implementation of fire safety publicity and education, its coupling type with fire fighting and rescue capabilities is heterogeneous coupling. Moreover, in the causal relationship of the above sub-evaluation dimensions, the degree of implementation of fire safety publicity and education is the cause, and fire fighting and rescue capabilities are the effect.

[0107] Regarding the completeness of the fire prevention inspection and rectification system, its coupling type with the integrity of building fire evacuation and escape facilities, maintenance and rectification rate, and electrical and gas monitoring status is heterogeneous coupling. Moreover, in the causal relationship of the above sub-evaluation dimensions, the completeness of the fire prevention inspection and rectification system is the cause, and the integrity of building fire evacuation and escape facilities, maintenance and rectification rate, and electrical and gas monitoring status are all effects.

[0108] Regarding the integrity of building fire evacuation and escape facilities, its coupling type with fire fighting and rescue capabilities is heterogeneous coupling. In the causal relationship of the above sub-evaluation dimensions, the integrity of building fire evacuation and escape facilities is the cause, and fire fighting and rescue capabilities are the effect.

[0109] Regarding the completeness of fire safety systems, its coupling type with maintenance and rectification rate and electrical and gas monitoring status is heterogeneous coupling, while its coupling type with fire safety operation standardization is consistent coupling. In the causal relationship of the above sub-evaluation dimensions, the completeness of fire safety systems is the cause, and the maintenance and rectification rate, electrical and gas monitoring status, and fire safety operation standardization status are all effects.

[0110] Regarding the standardization of fire safety operations, its coupling type with the integrity rates of emergency broadcast systems, automatic sprinkler systems, automatic fire alarm systems, fire compartmentation facilities, smoke exhaust systems, fire power supply and distribution systems, emergency lighting and evacuation guidance systems, and fire elevators is heterogeneous coupling. Furthermore, in the causal relationships of the above sub-evaluation dimensions, the standardization of fire safety operations is the cause, while the integrity rates of emergency broadcast systems, automatic sprinkler systems, automatic fire alarm systems, fire compartmentation facilities, smoke exhaust systems, fire power supply and distribution systems, emergency lighting and evacuation guidance systems, and fire elevators are all effects.

[0111] Regarding the maintenance and rectification rate, its coupling type with the integrity rates of emergency broadcast systems, automatic sprinkler systems, automatic fire alarm systems, fire compartmentation facilities, smoke exhaust systems, fire power supply and distribution systems, emergency lighting and evacuation indicators, and fire elevators is consistent coupling. Furthermore, in the causal relationships of the above sub-evaluation dimensions, the maintenance and rectification rate is the cause, while the integrity rates of emergency broadcast systems, automatic sprinkler systems, automatic fire alarm systems, fire compartmentation facilities, smoke exhaust systems, fire power supply and distribution systems, emergency lighting and evacuation indicators, and fire elevators are all effects.

[0112] The logic for determining the final score is as follows:

[0113] 1) For any sub-evaluation dimension, determine whether it has a causal relationship with other sub-evaluation dimensions. If the sub-evaluation dimension has no causal relationship with any other sub-evaluation dimensions, it means that the sub-evaluation dimension does not need to be corrected. In this case, the original score of the electrochemical energy storage power station under test in this sub-evaluation dimension is directly used as the final score. Otherwise, proceed to step 2).

[0114] 2) Traverse all causal relationships of the sub-evaluation dimension. If the sub-evaluation dimension is a cause in all causal relationships, it means that the sub-evaluation dimension does not need to be corrected. Then, the original score of the electrochemical energy storage power station under test in the sub-evaluation dimension is directly used as the final score. Otherwise, proceed to step 3).

[0115] 3) Treat this sub-evaluation dimension as the sub-evaluation dimension to be corrected, and summarize all sub-evaluation dimensions that are causes in the causal relationship with it, as its coupled sub-evaluation dimensions. Calculate the coupling coefficient between each coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected based on the original score. The specific calculation logic is as follows:

[0116] For any coupled sub-evaluation dimension of the sub-evaluation dimension to be corrected, calculate the relative closeness index of the original scores of the electrochemical energy storage power station under test on both the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected. Preset an enhancement factor, and the enhancement factor is not less than one. Construct a value with the relative closeness index as the base and the enhancement factor as the exponent, as the coupling coefficient between the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected.

[0117] The calculation logic for the relative proximity index is as follows: Calculate the geometric mean and arithmetic mean of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected. The geometric mean is the square root of the product of the two original scores, and the arithmetic mean is the ratio of the sum of the two original scores to 2. Calculate the ratio of the geometric mean and the arithmetic mean to obtain the relative mean of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected. Calculate the square of the relative mean to obtain the relative proximity index of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected.

[0118] Specifically, the mathematical expression for calculating the coupling coefficient is as follows:

[0119]

[0120] In the formula, For the electrochemical energy storage power station under test in the first The original scores of the evaluation dimensions to be corrected. For the index of the sub-dimension to be corrected, In order to target the Regarding the evaluation dimensions to be corrected, the electrochemical energy storage power station under test is in its first... The original scores of each coupled sub-evaluation dimension For the index of the evaluation dimension of the coupling sub-evaluation, As an enhancing factor, For the first The evaluation dimensions to be corrected and their first... The coupling coefficients of each coupled sub-evaluation dimension;

[0121] It should be noted that, and The greater the similarity, the stronger the coupling between the two, meaning that the evaluation dimension of the coupled sub-factor as a cause will affect the evaluation dimension of the sub-factor to be corrected as an effect. Therefore, the square of the relative mean is used here. Calculate in the form of and The relative differences, in order to and The similarity is quantified as a value between 0 and 1. and The more similar they are, the higher their relative proximity index. The closer it gets to 1;

[0122] It should be noted that the enhancement factor setting is used to amplify... and The subtle differences between the original scores of the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected are better represented by the coupling coefficient, thus avoiding the problem of setting the coupling coefficient to too large a value, which would lead to the final score of the sub-evaluation dimension to be corrected being too low. The enhancement factor can be set between 3 and 7, and the specific value can be determined by experts. For example, in this embodiment, the value of 5 is selected as the enhancement factor, so as to more accurately represent the coupling coefficient between the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected.

[0123] 4) For any evaluation dimension to be corrected, iterate through each of its coupled evaluation dimensions. Based on the relative magnitude of the original scores of the energy storage power station under test on the evaluation dimension to be corrected and each of its coupled evaluation dimensions, as well as the coupling coefficient and coupling type of the evaluation dimension to be corrected and each of its coupled evaluation dimensions, determine the final score of the evaluation dimension to be corrected relative to each of its coupled evaluation dimensions, and take the average as the final score of the electrochemical energy storage power station under test on the evaluation dimension to be corrected.

[0124] It should be noted that the final score of the evaluation dimension to be corrected relative to its various coupled evaluation dimensions is determined only based on the causal relationship between the evaluation dimension to be corrected and its individual coupled evaluation dimensions, which makes the final score one-sided. Therefore, the average of the final scores of the evaluation dimension to be corrected relative to its various coupled evaluation dimensions is taken as the final score of the electrochemical energy storage power station under test in that evaluation dimension. This comprehensively considers the causal relationship between the evaluation dimension to be corrected and all its coupled evaluation dimensions, making the final result more objective and accurate.

[0125] The logic for determining the final score of the sub-evaluation dimension to be corrected relative to its various coupled sub-evaluation dimensions is as follows:

[0126] For the evaluation dimension to be corrected and any of its coupled evaluation dimensions, the original scores of the electrochemical energy storage power station under test are extracted on both. If the original score of the electrochemical energy storage power station under test on the former is less than the original score on the latter, it means that in the causal relationship between the two, the original score as the effect is less than the original score as the cause. This state can correctly characterize the characteristic that the deficiency of the cause has an adverse effect on the effect. That is, the original scores of the two are normal in causal logic. Therefore, it means that the original score of the former does not need to be corrected relative to the latter. So the original score of the electrochemical energy storage power station under test on the former is directly used as the final score of the former relative to the latter.

[0127] Conversely, if the original score of the tested electrochemical energy storage power station in the former is not less than its original score in the latter, it indicates that in the causal relationship between the two, the original score as the effect is not less than the original score as the cause. This state cannot correctly characterize the characteristic that the insufficiency of the cause has an adverse effect on the effect. That is, there is an anomaly in the causal logic of the original scores of the two, indicating that the original score of the former needs to be corrected relative to the latter. Therefore, combining the original scores of the tested electrochemical energy storage power station in both, the coupling coefficient and the coupling type, the original score of the tested electrochemical energy storage power station in the former is reduced and corrected to determine the final score of the former relative to the latter. The greater the difference in the original scores of the tested electrochemical energy storage power station in both and the greater the coupling coefficient, the greater the degree of reduction and correction. Moreover, the degree of reduction and correction is greater when the coupling type is uniform coupling than when the coupling type is heterogeneous coupling. The specific mathematical expression for reduction and correction is as follows:

[0128]

[0129] In the formula, For the first The evaluation dimensions to be corrected and their first... The coupling correction coefficient for each coupled sub-evaluation dimension is determined based on the coupling type. Since two sub-evaluation dimensions satisfying consistent coupling belong to the same parent evaluation dimension, while two sub-evaluation dimensions satisfying heterogeneous coupling belong to different parent evaluation dimensions, the coupling correlation between two sub-evaluation dimensions satisfying consistent coupling is better than that between two sub-evaluation dimensions satisfying heterogeneous coupling. Therefore, the coupling correction coefficient corresponding to the coupling type of consistent coupling is set to be greater than that corresponding to the coupling type of heterogeneous coupling. Specifically, the coupling correction coefficient corresponding to the coupling type of heterogeneous coupling can be defined as 1 to set the benchmark, and the coupling correction coefficient corresponding to the coupling type of consistent coupling is set to a value greater than 1 to characterize that the coupling correlation of consistent coupling is higher than that of heterogeneous coupling. Its value can be set between 1.1 and 1.3 to characterize its superior coupling correlation compared to heterogeneous coupling. The specific value is set by the staff according to the actual situation, and will not be elaborated here.

[0130] In the formula, For the first The evaluation dimension to be corrected is relative to its first... The core logic of the final score for each coupled sub-evaluation dimension is as follows: An exponential decay function with continuity and differentiability is used as the application function. The correction strength is defined based on the irrationality of the original score and the coupling correlation, in order to reduce and correct the original score. The specific logic is as follows:

[0131] For the The evaluation dimensions to be corrected and their first... In terms of the evaluation dimension of the couplers, the coupling coefficient represents the coupling correlation between the two in terms of numerical similarity. The larger the value, the higher the coupling correlation between the two. Regarding the raw score The higher the reduction correction strength, the better. Therefore, the coupling coefficient is considered as a factor in the correction strength. Similarly, the coupling correction coefficient represents the coupling correlation between the two from the perspective of whether they belong to the same parent evaluation dimension. The larger the value, the higher the coupling correlation between the two. Therefore, the coupling correction coefficient is also considered as a factor in the correction strength. Regarding the raw score The stronger the reduction and correction, the higher the intensity. Furthermore, as can be seen from the above description... Greater than There is an anomaly in the causal logic, and Greater than The higher the degree, the more it indicates relative to In terms of raw score The more unreasonable it is, the more it needs to be drastically reduced. Use here To characterize compared to In terms of raw score The irrationality Set the original score The irrationality is quantified as a value greater than 1, and the larger the value, the greater the irrationality compared to... In terms of raw score The more unreasonable the result, the more the square root is used to reduce the impact of extreme values, making the result smoother.

[0132] It should be noted that, and Used to characterize the The evaluation dimensions to be corrected and their first... The coupling correlation of each coupled sub-evaluation dimension, and Used to characterize relative to In terms of raw score The irrationality of all three is related to the original score. The degree of reduction and correction required is closely related to this, so the three factors are multiplied together to construct an interaction term. The negative value of this interaction term is used as the correction strength to adjust the original score. The larger the values ​​of the three factors, the larger the interaction term, indicating a change in the original score. The greater the degree of reduction and correction required, the smaller the correction strength, and consequently the impact on the original score. The greater the reduction, the better;

[0133] Furthermore, when calculating the coupling coefficient, the relative average is first squared to obtain the relative proximity index. This squared process amplifies the subtle differences between the original scores for the first time. Then, the relative proximity index is raised to the power of k to obtain the coupling coefficient. This power of k amplifies the subtle differences between the original scores for the second time. This leads to the coupling coefficient fluctuating drastically due to subtle differences, which in turn causes the correction strength to fluctuate excessively. Therefore, 2+k is used to normalize the coupling coefficient to smooth the correction strength, making the final reduction correction result more stable and controllable.

[0134] The management evaluation module, based on the weights of each sub-evaluation dimension, performs a weighted summation of the final scores of the electrochemical energy storage power station under test in each sub-evaluation dimension to determine the fire safety management score of the electrochemical energy storage power station under test.

[0135] The specific logic for determining the fire safety management score through weighted summation is as follows: For the final score of the electrochemical energy storage power station under test in each sub-evaluation dimension, multiply it by the weight of that sub-evaluation dimension to obtain the weighted score of the electrochemical energy storage power station under test in that sub-evaluation dimension. Then, sum the weighted scores of the electrochemical energy storage power station under test in each sub-evaluation dimension to obtain the fire safety management score of the electrochemical energy storage power station under test. The higher the fire safety management score, the higher the level of fire safety management of the electrochemical energy storage power station under test.

[0136] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0137] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0139] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An electrochemical energy storage fire safety management system, characterized in that, include: The scoring module is used to combine the maintenance report and annual fire safety evaluation report of the electrochemical energy storage power station under test, and use expert scoring to determine the original score of the electrochemical energy storage power station under test in each sub-evaluation dimension. The weight determination module uses expert scoring to determine triples that characterize the importance of each sub-evaluation dimension, and combines fuzzy hierarchical analysis to perform a dual analysis of the relative importance between each sub-evaluation dimension in order to determine the weight of each sub-evaluation dimension. The scoring correction module is used to determine whether there is a causal relationship between each sub-evaluation dimension, and to set a scoring correction mechanism based on the causal relationship judgment. The original score is corrected based on the scoring correction mechanism to determine the final score of the electrochemical energy storage power station under test in each sub-evaluation dimension. The management evaluation module, based on the weights of each sub-evaluation dimension, performs a weighted summation of the final scores of the electrochemical energy storage power station under test in each sub-evaluation dimension to determine the fire safety management score of the electrochemical energy storage power station under test.

2. The electrochemical energy storage fire safety management system of claim 1, wherein: Each sub-evaluation dimension is categorized to determine the parent evaluation dimension to which each sub-evaluation dimension belongs. There are four parent evaluation dimensions: fire management level, comprehensive maintenance of fire facilities, fire fighting and rescue level, and fire supervision and inspection status. Among them, for the parent evaluation dimension of fire management level, there are five sub-evaluation dimensions belonging to this parent evaluation dimension, namely, the legality of fire administrative approval, the completeness of fire safety system, the standardization of fire safety operation, the completeness of fire prevention patrol and rectification system, and the degree of implementation of fire safety publicity and education. Among them, the comprehensiveness of fire protection facility maintenance is the main evaluation dimension, which includes ten sub-evaluation dimensions: fire power supply and distribution integrity rate, automatic fire alarm system integrity rate, automatic sprinkler system integrity rate, smoke exhaust system integrity rate, fire compartmentation facility integrity rate, fire elevator integrity rate, emergency lighting evacuation guidance integrity rate, emergency broadcast system integrity rate, fire water supply facility integrity rate, and maintenance and rectification rate. Among them, for the parent evaluation dimension of fire fighting and rescue level, there is one sub-evaluation dimension belonging to this parent evaluation dimension, namely fire fighting and rescue capability; Among them, the fire safety supervision and inspection situation has two sub-evaluation dimensions belonging to this parent evaluation dimension, namely the inspection situation of electrical and gas systems and the integrity of building fire prevention and evacuation facilities.

3. The electrochemical energy storage fire safety management system according to claim 2, characterized in that, For any two sub-evaluation dimensions that have a causal relationship, the coupling type between them is determined based on whether they belong to the same parent evaluation dimension. Specifically, if they belong to the same parent evaluation dimension, the coupling type between them is determined to be consistent coupling; otherwise, the coupling type between them is determined to be heterogeneous coupling.

4. The electrochemical energy storage fire safety management system according to claim 1, characterized in that, For any sub-evaluation dimension, the logic for determining the triplet used to represent its importance is as follows: Invite multiple experts to rate the importance of this sub-evaluation dimension according to a 1-9 rating scale, summarize the importance rating results of all experts for this sub-evaluation dimension, and extract the minimum, maximum, and mode values ​​to form a triplet used to represent the importance of this sub-evaluation dimension. This triplet includes the minimum possible value of importance, the most likely value of importance, and the maximum possible value of importance. Specifically, the minimum, mode, and maximum values ​​of the importance rating results of all experts for a sub-evaluation dimension are used to represent the minimum possible value of importance, the most likely value of importance, and the maximum possible value of importance of this sub-evaluation dimension.

5. The electrochemical energy storage fire safety management system according to claim 4, characterized in that, The specific logic for determining the weights of each sub-evaluation dimension is as follows: 1) For any sub-evaluation dimension, divide it element by element with the triples of each sub-evaluation dimension to obtain the relative importance triples of the sub-evaluation dimension relative to each sub-evaluation dimension, and obtain the relative importance triangular fuzzy value of the sub-evaluation dimension relative to each sub-evaluation dimension by querying the triangular fuzzy value selection table. Specifically, for any sub-evaluation dimension, the logic for dividing it element-wise with the triples of each sub-evaluation dimension is as follows: divide the minimum possible value in its triple by the maximum possible value in the triples of each sub-evaluation dimension, divide the most likely value in its triple by the most likely value in the triples of each sub-evaluation dimension, and divide the maximum possible value in its triple by the minimum possible value in the triples of each sub-evaluation dimension. 2) Based on the triangular fuzzy values ​​of the relative importance of each sub-evaluation dimension to the other sub-evaluation dimensions, construct a triangular fuzzy matrix and sum the rows of the triangular fuzzy matrix to obtain the cumulative fuzzy importance value of each sub-evaluation dimension. Based on the triangular fuzzy matrix, normalize the cumulative fuzzy importance value of each sub-evaluation dimension to obtain the comprehensive fuzzy importance degree of each sub-evaluation dimension. 3) For any sub-evaluation dimension, compare its importance with that of each other to determine the importance of the sub-evaluation dimension relative to each other. 4) Based on the importance of each sub-evaluation dimension relative to the other sub-evaluation dimensions, construct an importance comparison matrix, and take the minimum value of each row of the importance comparison matrix to obtain the initial weight of each sub-evaluation dimension. With the cumulative value equal to 1 as a constraint, scale the initial weight of each sub-evaluation dimension proportionally to determine the weight of each sub-evaluation dimension.

6. The electrochemical energy storage fire safety management system according to claim 5, characterized in that, When determining the importance of one sub-evaluation dimension relative to another, let the former sub-evaluation dimension be the sub-evaluation dimension to be analyzed and the latter sub-evaluation dimension be the benchmark sub-evaluation dimension. Combine the following scenarios to determine the importance of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension. Scenario 1: The most likely value of the fuzzy comprehensive degree of importance of the sub-evaluation dimension to be analyzed is not less than the most likely value of the fuzzy comprehensive degree of importance of the benchmark sub-evaluation dimension. When Scenario 1 is satisfied, let the importance degree of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension be 1. Scenario 2: The minimum possible value of the importance fuzzy comprehensive degree of the benchmark sub-evaluation dimension is not less than the maximum possible value of the importance fuzzy comprehensive degree of the sub-evaluation dimension to be analyzed. When Scenario 2 is satisfied, the importance degree of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension is set to 0. When scenarios one and two are not met, the minimum possible value of the fuzzy comprehensive importance degree of the sub-evaluation dimension to be analyzed is subtracted from the maximum possible value of the fuzzy comprehensive importance degree of the sub-evaluation dimension to be analyzed to obtain the limit difference in importance between the sub-evaluation dimension to be analyzed and the benchmark sub-evaluation dimension. The most likely value of the fuzzy comprehensive importance degree of the benchmark sub-evaluation dimension is subtracted from the most likely value of the fuzzy comprehensive importance degree of the sub-evaluation dimension to be analyzed to obtain the most likely difference in importance between the sub-evaluation dimension to be analyzed and the benchmark sub-evaluation dimension. The ratio of the most likely difference in importance to the limit difference in importance is calculated. The reciprocal of the sum of this ratio and 1 is used to characterize the importance degree of the sub-evaluation dimension to be analyzed relative to the benchmark sub-evaluation dimension.

7. The electrochemical energy storage fire safety management system according to claim 5, characterized in that, The triangular fuzzy value selection table consists of multiple triangular fuzzy values ​​and triples corresponding to each triangular fuzzy value. For any relative importance triple, the Euclidean distance between it and each triple in the triangular fuzzy value selection table is calculated. The triple with the smallest Euclidean distance is selected as the reference triple, and the triangular fuzzy value corresponding to this reference triple is used as the relative importance triangular fuzzy value corresponding to the relative importance triple. This determines the relative importance triangular fuzzy value of each sub-evaluation dimension relative to each other sub-evaluation dimension. If, for a relative importance triplet, the triplet with the smallest Euclidean distance is not unique, then the reference triplet is determined by successively progressively using the following constraints: the minimum absolute error of the most likely value, the minimum mean of the absolute errors of the minimum and maximum possible values, and the range of possible values ​​of the relative importance triplet being within the range of possible values ​​of the triplet.

8. The electrochemical energy storage fire safety management system according to claim 3, characterized in that, The logic for determining the final score is as follows: 1) For any sub-evaluation dimension, determine whether it has a causal relationship with other sub-evaluation dimensions. If the sub-evaluation dimension has no causal relationship with any other sub-evaluation dimensions, the original score of the electrochemical energy storage power station under test in that sub-evaluation dimension is directly used as the final score; otherwise, proceed to step 2). 2) Traverse all causal relationships of the sub-evaluation dimension. If the sub-evaluation dimension is a cause in all causal relationships, use the original score of the electrochemical energy storage power station under test in the sub-evaluation dimension as the final score; otherwise, proceed to step 3). 3) Take this sub-evaluation dimension as the sub-evaluation dimension to be corrected, and summarize all the sub-evaluation dimensions that are the cause in the causal relationship with it, so as to take them as its coupled sub-evaluation dimensions. Calculate the coupling coefficient between each coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected based on the original score. 4) For any evaluation dimension to be corrected, traverse each of its coupled evaluation dimensions. Based on the relative magnitude of the original scores of the energy storage power station under test on the evaluation dimension to be corrected and each of its coupled evaluation dimensions, as well as the coupling coefficient and coupling type of the evaluation dimension to be corrected and each of its coupled evaluation dimensions, determine the final score of the evaluation dimension to be corrected relative to each of its coupled evaluation dimensions, and take the average as the final score of the electrochemical energy storage power station under test on the evaluation dimension to be corrected.

9. The electrochemical energy storage fire safety management system according to claim 8, characterized in that, For any coupled sub-evaluation dimension of the sub-evaluation dimension to be corrected, the logic for calculating the coupling coefficient between the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected is as follows: calculate the relative closeness index of the original scores of the electrochemical energy storage power station under test on both the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected, preset an enhancement factor, and the enhancement factor is not less than one, construct a value with the relative closeness index as the base and the enhancement factor as the exponent, as the coupling coefficient between the coupled sub-evaluation dimension and the sub-evaluation dimension to be corrected; The calculation logic for the relative proximity index is as follows: Calculate the geometric mean and arithmetic mean of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected. The geometric mean is the square root of the product of the two original scores, and the arithmetic mean is the ratio of the sum of the two original scores to 2. Calculate the ratio of the geometric mean and the arithmetic mean to obtain the relative mean of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected. Calculate the square of the relative mean to obtain the relative proximity index of the original scores of the electrochemical energy storage power station under test on both the evaluation dimension of the coupler and the evaluation dimension to be corrected.

10. The electrochemical energy storage fire safety management system according to claim 8, characterized in that, The logic for determining the final score of the sub-evaluation dimension to be corrected relative to its various coupled sub-evaluation dimensions is as follows: For the evaluation dimension to be corrected and any of its coupled evaluation dimensions, the original scores of the electrochemical energy storage power station under test are extracted on both. If the original score of the electrochemical energy storage power station under test on the former is less than the original score on the latter, the original score of the electrochemical energy storage power station under test on the former is directly used as the final score of the former relative to the latter. Conversely, if the original score of the electrochemical energy storage power station under test in the former is not less than its original score in the latter, then the original score of the electrochemical energy storage power station under test in the former is reduced and corrected by combining the original scores of the electrochemical energy storage power station under test in both, the coupling coefficient and the coupling type of both, in order to determine the final score of the former relative to the latter. The greater the difference in the original scores of the electrochemical energy storage power station under test in both, and the greater the coupling coefficient of both, the greater the degree of reduction and correction. Moreover, the degree of reduction and correction when the coupling type is uniform coupling is greater than the degree of reduction when the coupling type is heterogeneous coupling.

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