Method and device for identifying fire risk of energy storage power station

Abstract

The invention provides an energy storage power station fire risk identification method and device. A server establishes an index set for evaluating the fire risk of an energy storage power station; establishing an evaluation set for comprehensive evaluation of the fire risk of the energy storage power station; calculating a combined weight of each evaluation index in the index set, wherein the combined weight is associated with a subjective weight and an objective weight of each evaluation index; determining a fuzzy comprehensive evaluation matrix; establishing an energy storage power station fire risk comprehensive evaluation model according to the combined weight and the fuzzy comprehensive evaluation matrix, wherein the energy storage power station fire risk comprehensive evaluation model is used for outputting a fire risk evaluation score of the energy storage power station; and querying the evaluation set according to the fire risk evaluation score to determine the fire risk level of the energy storage power station. Therefore, according to the method, the subjective evaluation method and the objective evaluation method are fused, the multi-level evaluation model is constructed, and the fire risk of the energy storage power station is systematically identified and evaluated, so that the accuracy and comprehensiveness of fire risk identification of the energy storage power station are improved.

Description

Technical Field

[0001] The present application belongs to the field of energy storage power station safety technology, and in particular, relates to a method and device for identifying fire risks in energy storage power stations. Background Art

[0002] With the rapid development of renewable energy, energy storage power stations play an increasingly important role in power systems. However, the operating environment of energy storage power stations is complex, involving multiple subsystems such as battery management systems (BMS), energy management systems (EMS), and power electronic equipment. There are many safety risks such as fire, explosion, overcharge, overdischarge, thermal runaway, etc., especially the fire risk of high energy density energy storage equipment such as lithium-ion batteries. Existing fire risk assessment methods mostly rely on empirical judgment or single indicator assessment, lack of systematicity and scientificity, and it is difficult to fully identify and quantify fire risks. Summary of the invention

[0003] The present application provides a method and device for identifying fire risks in energy storage power stations. By integrating subjective evaluation methods and objective evaluation methods and constructing a multi-level evaluation model, the fire risks of energy storage power stations are systematically identified and evaluated, thereby improving the accuracy and comprehensiveness of fire risk identification in energy storage power stations.

[0004] In a first aspect, the present application provides a method for identifying fire risks of energy storage power stations, the method comprising: establishing an indicator set for evaluating the fire risks of the energy storage power station; establishing an evaluation set for comprehensive evaluation of the fire risks of the energy storage power station; calculating the combined weights of the evaluation indicators in the indicator set, the combined weights being associated with the subjective weights and objective weights of the evaluation indicators; determining a fuzzy comprehensive evaluation matrix; establishing a comprehensive evaluation model for fire risks of energy storage power stations based on the combined weights and the fuzzy comprehensive evaluation matrix, the comprehensive evaluation model for fire risks of energy storage power stations being used to output a fire risk evaluation score of the energy storage power station; and querying the evaluation set based on the fire risk evaluation score to determine the fire risk level of the energy storage power station.

[0005] In some embodiments, the calculation process of the subjective weight includes: ranking the evaluation indicators by experts, and assigning values ​​to the ranking of each evaluation indicator to obtain the importance index of each evaluation indicator; calculating the subjective weight of each evaluation indicator according to the importance index of each evaluation indicator according to the following formula (1); obtaining the subjective weight set of the indicator set according to the subjective weight of each evaluation indicator; Formula (1) Among them, ρ is the important index of the evaluation index, ω is the subjective weight of the evaluation index, is the subjective weight set of the indicator set, and n is the total number of evaluation indicators.

[0006] In some embodiments, the objective weight calculation process includes: obtaining sample data of each evaluation indicator of the energy storage power station; calculating the standard deviation of each evaluation indicator based on the sample data; calculating the correlation coefficient of each evaluation indicator based on the sample data; calculating the amount of information contained in each evaluation indicator based on the standard deviation and the correlation coefficient; obtaining the objective weight of each evaluation indicator based on the information amount; and obtaining the objective weight set of the indicator set based on the objective weight of each evaluation indicator.

[0007] In some embodiments, the standard deviation is calculated as follows: Formula (2) in, is the i-th sample data of the j-th evaluation index, m is the total number of sample data, j is the serial number of the evaluation index, is the sample average value of the jth evaluation index, is the standard deviation; The calculation formula of the correlation coefficient is as follows: Formula (3) in, is the correlation coefficient between the i-th evaluation index and the j-th evaluation index, is the sample data of the i-th evaluation index, is the sample average value of the i-th evaluation index, is the sample data of the jth evaluation index, is the sample average value of the jth evaluation index; The calculation formula of the information volume is as follows: Formula (4) in, is the information content of the jth evaluation index; The calculation formula of the objective weight is as follows: Formula (5) Among them, n is the total number of evaluation indicators, is the objective weight of the jth evaluation index, is the objective weight set of the indicator set.

[0008] In some embodiments, the calculation formula of the combined weight is as follows: Formula (6) in, is the combined weight of the jth evaluation index, is the subjective weight of the jth evaluation index, is the objective weight of the jth evaluation index, and n is the total number of evaluation indicators.

[0009] In some embodiments, the comprehensive evaluation model for fire risk of energy storage power station is as follows: Formula (7) Among them, n is the total number of evaluation indicators, m is the total number of risk levels in the evaluation set V, represents the combined weight of the nth evaluation index, W is the combined weight set of each evaluation index, F is the fuzzy comprehensive evaluation matrix, is the membership degree of the nth evaluation index to the mth risk level in the evaluation set V, B represents the comprehensive membership degree of the fire risk of the energy storage power station to each risk level in the evaluation set V, It indicates the degree to which the fire risk of the energy storage power station belongs to the mth risk level in the evaluation set V, J is the grade corresponding to each risk level in the evaluation set V, and P is the fire risk evaluation score of the energy storage power station.

[0010] In some embodiments, before calculating the standard deviation of each evaluation indicator, the weight calculation module 503 is further used to: standardize the sample data of each evaluation indicator, and the standardization formula is as follows: Formula (8): Formula (8) Among them, i is the serial number of the sample data in a single evaluation index, j is the serial number of the evaluation index, represents the i-th sample of the j-th indicator, is the standardized sample data, To standardize the sample data, is the smallest sample data among multiple sample data of the jth evaluation index, It is the largest sample data among multiple sample data of the jth evaluation index.

[0011] In a second aspect, the present application provides a device for identifying fire risks in energy storage power stations, the device comprising: a processing unit, for establishing an indicator set for evaluating the fire risk of the energy storage power station; establishing an evaluation set for comprehensive evaluation of the fire risk of the energy storage power station; calculating the combined weights of each evaluation indicator in the indicator set, the combined weights being associated with the subjective weights and objective weights of each evaluation indicator; determining a fuzzy comprehensive evaluation matrix; establishing a comprehensive evaluation model for fire risk of an energy storage power station based on the combined weights and the fuzzy comprehensive evaluation matrix, the comprehensive evaluation model for fire risk of an energy storage power station being used to output a fire risk evaluation score of the energy storage power station; and querying the evaluation set based on the fire risk evaluation score to determine the fire risk level of the energy storage power station.

[0012] In a third aspect, the present application provides a fire risk identification system for an energy storage power station, the system comprising: a data acquisition module for collecting equipment status, environmental data and management information of the energy storage power station; an indicator construction module for establishing an indicator set for evaluating the fire risk of the energy storage power station; a weight calculation module for calculating the combined weight of each evaluation indicator in the indicator set, the combined weight being associated with the subjective weight and objective weight of each evaluation indicator; a risk assessment module for establishing an evaluation set for comprehensive evaluation of the fire risk of the energy storage power station; and, determining a fuzzy comprehensive evaluation matrix; and, establishing a comprehensive evaluation model for the fire risk of the energy storage power station based on the combined weight and the fuzzy comprehensive evaluation matrix, the comprehensive evaluation model for the fire risk of the energy storage power station being used to output the fire risk evaluation score of the energy storage power station; and, querying the evaluation set based on the fire risk evaluation score to determine the fire risk level of the energy storage power station.

[0013] In a fourth aspect, the present application provides a server, comprising a processor and a memory, wherein the memory stores one or more programs, and the processor is used to execute the step instructions in the method as described in any one of the first aspects according to the one or more programs.

[0014] It can be seen that in the embodiment of the present application, the server establishes an indicator set for evaluating the fire risk of the energy storage power station; establishes an evaluation set for the comprehensive evaluation of the fire risk of the energy storage power station; calculates the combined weight of each evaluation indicator in the indicator set, and the combined weight is related to the subjective weight and objective weight of each evaluation indicator; determines the fuzzy comprehensive evaluation matrix; establishes a comprehensive evaluation model for the fire risk of the energy storage power station based on the combined weight and the fuzzy comprehensive evaluation matrix, and the comprehensive evaluation model for the fire risk of the energy storage power station is used to output the fire risk evaluation score of the energy storage power station; and determines the fire risk level of the energy storage power station by querying the evaluation set based on the fire risk evaluation score. Therefore, in the present application, by integrating the subjective evaluation method and the objective evaluation method and constructing a multi-level evaluation model, the fire risk of the energy storage power station is systematically identified and evaluated, thereby improving the accuracy and comprehensiveness of the fire risk identification of the energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them, Figure 1 A schematic diagram of the structure of a server provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of a method for identifying fire risks in an energy storage power station provided in an embodiment of the present application; Figure 3 A diagram showing a fire risk assessment indicator prompt structure for an energy storage power station provided in an embodiment of the present application; Figure 4 A fire risk identification device for an energy storage power station provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of a fire risk identification system for an energy storage power station provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0017] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but in some embodiments also includes steps or units that are not listed, or in some embodiments also includes other steps or units inherent to these processes, methods, products or devices.

[0018] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0019] In the embodiments of the present application, "and / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent the following three situations: A exists alone; A and B exist at the same time; B exists alone. Among them, A and B can be singular or plural.

[0020] In the embodiment of the present application, the symbol " / " can indicate that the objects associated with each other are in an "or" relationship. In addition, the symbol " / " can also indicate a division sign, that is, performing a division operation. For example, A / B can indicate A divided by B.

[0021] In the embodiments of the present application, "at least one item" or similar expressions refer to any combination of these items, including any combination of single items or plural items, and refer to one or more, and multiple refers to two or more. For example, at least one item of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0022] In the embodiments of the present application, "equal to" can be used in conjunction with greater than, and is applicable to the technical solution adopted when greater than, and can also be used in conjunction with less than, and is applicable to the technical solution adopted when less than. When equal to is used in conjunction with greater than, it is not used in conjunction with less than; when equal to is used in conjunction with less than, it is not used in conjunction with greater than.

[0023] In order to solve the above technical problems, the present application provides a method and device for identifying fire risks in energy storage power stations. By integrating subjective evaluation methods and objective evaluation methods and constructing a multi-level evaluation model, the fire risks of energy storage power stations are systematically identified and evaluated, thereby improving the accuracy and comprehensiveness of fire risk identification in energy storage power stations.

[0024] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0025] The application subject of the method for mining the associated clusters of power grid reserve projects provided by the present application is a server, wherein the server may specifically include a server for data processing in the background that is applied to one side of the network platform and can realize functions such as data transmission and data processing. It may be a physical server or a server cluster or distributed system composed of multiple physical servers. In this embodiment, the number of servers is not specifically limited. Alternatively, it may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0026] For specific implementation, please refer to Figure 1 , Figure 1 A schematic diagram of the structure of a server provided in an embodiment of the present application, such as Figure 1As shown, the server 10 includes a processor 11, a memory 13, a communication interface 12, and one or more programs 131. Among them, the one or more programs 131 are stored in the memory 13 and are configured to be executed by the above-mentioned processor 11, and the one or more programs 131 include instructions for executing any step in the following embodiment of the method for mining associated clusters of power grid reserve projects.

[0027] See also Figure 2 , Figure 2 A flow chart of a method for identifying fire risks in an energy storage power station provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method is Figure 1 The server execution shown specifically includes the following steps S201 to S206: Step S201: establishing an index set for evaluating the fire risk of the energy storage power station.

[0028] The establishment process of the indicator set is as follows: See also Figure 3 , Figure 3 The fire risk assessment indicator prompt structure diagram of the energy storage power station provided in the embodiment of the present application is as follows: Figure 3 As shown, target layer A is established as the fire risk of energy storage power stations. Criteria layer B is established as the major factors of fire risk, including but not limited to equipment factor B1, environmental factor B2, management factor B3 and emergency response factor B4. Indicator layer C is established to subdivide each criterion layer into specific quantifiable indicators for actual evaluation, specifically: The indicators under the equipment factor B1 of the criterion layer include but are not limited to: C11 battery type (denoted as )、C12 battery aging degree (denoted as )、C13 battery temperature abnormality (recorded as )、C14 battery overcharge / over discharge (recorded as ); Indicators under the criteria layer environmental factor B2 include but are not limited to: C21 ambient temperature (denoted as )、C22 environmental humidity (denoted as )、C23 ventilation conditions (denoted as )、C24 dust concentration (denoted as ); Indicators under the management factor B3 of the criterion level include but are not limited to: C31 Equipment inspection frequency (denoted as )、C32 Safety management system integrity (denoted as )、C33 Emergency plan completeness (denoted as )、C34 historical accident records (recorded as ); Indicators under the emergency response factor B4 of the criterion level include but are not limited to: C41 Fire alarm system sensitivity (denoted as )、C42 Fire extinguishing equipment configuration (denoted as )、C43 Emergency response time (denoted as )、C44 Emergency drill frequency (denoted as ).

[0029] In summary, the index set of fire risk of energy storage power station is: .

[0030] It should be noted that, depending on the actual situation of the energy storage power station, the specific evaluation factors in the criterion layer may be more or less than the specific factors listed in the above embodiments. Similarly, the indicators contained in the indicator layer under each criterion layer may be more or less than the specific indicators listed in the above embodiments. This application does not impose specific restrictions on this.

[0031] The above indicators can be determined based on expert experience, or the server can obtain relevant data of the energy storage power station, determine the main evaluation indicators based on historical fire risks and changes in related data, and then determine the final indicator set for evaluating the fire risk of the energy storage power station based on expert opinions.

[0032] The relevant data of the energy storage power station includes but is not limited to the equipment status, environmental data and management information of the energy storage power station.

[0033] Step S202: establishing an evaluation set for comprehensive evaluation of fire risk of the energy storage power station.

[0034] The evaluation set is used to query the corresponding risk level according to the corresponding score after determining the fire risk evaluation score of the energy storage power station.

[0035] Exemplarily, the evaluation set V={high, relatively high, medium, relatively low, low}, and the risk levels high, relatively high, medium, relatively low, and low are assigned values ​​J={100, 90, 75, 55, 30} respectively.

[0036] Step S203, calculating the combined weight of each evaluation index in the index set.

[0037] The combined weight is associated with the subjective weight and the objective weight of each evaluation index.

[0038] In some embodiments, the calculation process of the subjective weight includes: ranking the evaluation indicators by experts, and assigning values ​​to the ranking of each evaluation indicator to obtain the importance index of each evaluation indicator; calculating the subjective weight of each evaluation indicator according to the importance index of each evaluation indicator according to the following formula (1); obtaining the subjective weight set of the indicator set according to the subjective weight of each evaluation indicator; Formula (1) Among them, ρ is the important index of the evaluation index, ω is the subjective weight of the evaluation index, is the subjective weight set of the indicator set, and n is the total number of evaluation indicators.

[0039] The experts rank the evaluation indicators and assign values ​​to the ranking of each evaluation indicator to obtain the importance index of each evaluation indicator, which specifically refers to: Experts ranked the evaluation indicators in the index set based on fire cases and experience of energy storage power stations. Replace the n ranked indicators respectively, and then assign the ranking of each evaluation indicator according to the following formula (9) to obtain the importance index of each evaluation indicator.

[0040] Formula (9) in, is an important index for evaluating indicators, indicating that and indicators The weight ratio of .

[0041] Among them, the indicator Weight The calculation formula is: .

[0042] against , the value range of j is [1,n], which means that the important indexes of n evaluation indicators are multiplied together, that is, .

[0043] against , the value range of i is [2,n], so Indicates that The result is repeated (n-1) times, that is, .

[0044] Among them, the weight coefficients of other indicators are: .

[0045] For example, when n=16 and i=16, = ; When i=15, ; … When i=2, .

[0046] Therefore, the subjective weight set .

[0047] It can be seen that in this embodiment, by experts ranking and assigning values ​​to the indicators, and then calculating the subjective weight of each evaluation indicator according to formula (1), their understanding and judgment of the importance of each indicator can be incorporated into the weight calculation. This makes the determination of the weight more professional and authoritative, and can more accurately reflect the importance of each indicator in the actual evaluation.

[0048] In some embodiments, the objective weight calculation process includes: obtaining sample data of each evaluation indicator of the energy storage power station; calculating the standard deviation of each evaluation indicator based on the sample data; calculating the correlation coefficient of each evaluation indicator based on the sample data; calculating the amount of information contained in each evaluation indicator based on the standard deviation and the correlation coefficient; obtaining the objective weight of each evaluation indicator based on the information amount; and obtaining the objective weight set of the indicator set based on the objective weight of each evaluation indicator.

[0049] Assuming that the energy storage power station collects m groups of data samples, the original sample data can be expressed by formula (10): Formula (10) Among them, m is the total number of sample data, n is the total number of evaluation indicators, Represents the mth sample of the nth indicator.

[0050] In some embodiments, before calculating the standard deviation of each evaluation indicator, the method further includes: standardizing the sample data of each evaluation indicator, and the standardization formula is as follows: Formula (8) Among them, i is the serial number of the sample data in a single evaluation index, j is the serial number of the evaluation index, represents the i-th sample of the j-th indicator, is the standardized sample data, To standardize the sample data before, is the smallest sample data among multiple sample data of the jth evaluation index, It is the largest sample data among multiple sample data of the jth evaluation index.

[0051] In some embodiments, the standard deviation is calculated as follows: Formula (2) in, is the i-th sample data of the j-th evaluation index, m is the total number of sample data, j is the serial number of the evaluation index, is the sample average value of the jth evaluation index, is the standard deviation; The calculation formula of the correlation coefficient is as follows: Formula (3) in, is the correlation coefficient between the i-th evaluation index and the j-th evaluation index, is the sample data of the i-th evaluation index, is the sample average value of the i-th evaluation index, is the sample data of the jth evaluation index, is the sample average value of the jth evaluation index; The calculation formula of the information volume is as follows: Formula (4) in, is the information content of the jth evaluation index; The calculation formula of the objective weight is as follows: Formula (5) Among them, n is the total number of evaluation indicators, is the objective weight of the jth evaluation index, is the objective weight set of the indicator set.

[0052] It can be seen that in this embodiment, by obtaining sample data of each evaluation index, calculating the correlation coefficient and standard deviation of each evaluation index through each sample data, and calculating the objective weight of each evaluation index through the standard deviation and correlation coefficient, it is possible to reflect the degree of variation and mutual relationship of each evaluation index based on actual data, reduce the interference of human subjective factors, and make the weight allocation more objective and scientific. For example, for indicators such as efficiency, life, and cost of energy storage power stations, by analyzing the standard deviation and correlation coefficient of a large amount of actual operation data, the objective importance of each indicator can be accurately grasped, avoiding the deviation that may be caused by relying solely on subjective judgment.

[0053] In some embodiments, the calculation formula of the combined weight is as follows: Formula (6) in, is the combined weight of the jth evaluation index, is the subjective weight of the jth evaluation index, is the objective weight of the jth evaluation index, and n is the total number of evaluation indicators.

[0054] It can be seen that in this embodiment, by combining the subjective weight and the objective weight to obtain the combined weight, the expert's knowledge and experience are fully utilized, and the characteristics of the data itself are taken into account. The subjective weight reflects the expert's subjective judgment on the importance of each indicator, reflecting the professional knowledge and practical experience in a specific field; the objective weight is based on the statistical analysis of the data, and is objective and data-driven. The combination of the two can more comprehensively and comprehensively consider the various factors that affect the evaluation of energy storage power stations, making the weight more reasonable and accurate. Systematic risk assessment and early warning functions help to take preventive measures in a timely manner to reduce the probability and loss of fire.

[0055] Step S204, determining the fuzzy comprehensive evaluation matrix.

[0056] Among them, fuzzy evaluation is a method to deal with uncertainty problems, which is suitable for situations where evaluation indicators are difficult to quantify accurately. To implement fuzzy evaluation for each evaluation indicator is to determine the affiliation between each indicator and different evaluation levels, and then combine these relationships to form an overall fuzzy relationship, which is represented by the fuzzy comprehensive evaluation matrix F.

[0057] In the specific implementation, relevant experts are invited to score and grade the fuzzy relationship matrix. The experts evaluate all indicators and finally determine the fuzzy comprehensive evaluation matrix as follows (11): Formula (11) Among them, n is the total number of evaluation indicators, m is the total number of risk levels in the evaluation set V, is the membership degree of the nth evaluation index to the mth risk level in the evaluation set V. Membership degree is a concept in fuzzy mathematics, which indicates the degree to which an element belongs to a set, and its value range is between 0 and 1. For example, Indicates the degree of membership of the second evaluation index to the first risk level in the evaluation set V. If =0.7, which means that 70% of the second evaluation indicator belongs to the first risk level.

[0058] By constructing a fuzzy comprehensive evaluation matrix, the complex relationship between multiple evaluation indicators and different evaluation levels can be clearly expressed in the form of a mathematical matrix, providing a basis for subsequent comprehensive evaluation analysis based on fuzzy mathematics theory, so as to more reasonably conduct a comprehensive evaluation of energy storage power stations.

[0059] Step S205: establishing a comprehensive evaluation model for fire risk of an energy storage power station according to the combined weights and the fuzzy comprehensive evaluation matrix.

[0060] The comprehensive fire risk assessment model for the energy storage power station is used to output the fire risk assessment score of the energy storage power station.

[0061] In some embodiments, the comprehensive evaluation model for fire risk of energy storage power station is as follows: Formula (7) Among them, n is the total number of evaluation indicators, m is the total number of risk levels in the evaluation set V, represents the combined weight of the nth evaluation index, W is the combined weight set of each evaluation index, F is the fuzzy comprehensive evaluation matrix, is the membership degree of the nth evaluation index to the mth risk level in the evaluation set V, B represents the comprehensive membership degree of the fire risk of the energy storage power station to each risk level in the evaluation set V, It indicates the degree to which the fire risk of the energy storage power station belongs to the mth risk level in the evaluation set V, J is the grade corresponding to each risk level in the evaluation set V, and P is the fire risk evaluation score of the energy storage power station.

[0062] For example, the evaluation set V = {high, relatively high, medium, relatively low, low}, and the risk levels high, relatively high, medium, relatively low, and low are assigned values ​​J = {100, 90, 75, 55, 30} respectively, then m = [1, 5], B = [ ], where b1 represents the degree to which the fire risk of the energy storage power station belongs to the risk level of "high", b2 represents the degree to which the fire risk of the energy storage power station belongs to the risk level of "relatively high", and b3, b4, and b5 are the same. The fire risk assessment score of the energy storage power station is P: P=[ ]·[100 90 75 55 30]=b1×100+b2×90+b3×75+b4×55+b5×30.

[0063] Step S206: query the evaluation set according to the fire risk evaluation score to determine the fire risk level of the energy storage power station.

[0064] For example, the fire risk level of the energy storage power station is obtained by querying the following formula (12). If the fire risk assessment score belongs to , the fire risk level is determined to be low risk; if the fire risk assessment is classified as , the fire risk level is determined to be low risk; if the fire risk assessment is classified as , the fire risk level is determined to be medium risk; if the fire risk assessment is classified as , the fire risk level is determined to be high risk; if the fire risk assessment is classified as , the fire risk level is determined to be high risk.

[0065] Formula (12) It can be seen that in the embodiment of the present application, the server establishes an indicator set for evaluating the fire risk of the energy storage power station; establishes an evaluation set for the comprehensive evaluation of the fire risk of the energy storage power station; calculates the combined weight of each evaluation indicator in the indicator set, and the combined weight is related to the subjective weight and objective weight of each evaluation indicator; determines the fuzzy comprehensive evaluation matrix; establishes a comprehensive evaluation model for the fire risk of the energy storage power station based on the combined weight and the fuzzy comprehensive evaluation matrix, and the comprehensive evaluation model for the fire risk of the energy storage power station is used to output the fire risk evaluation score of the energy storage power station; and determines the fire risk level of the energy storage power station by querying the evaluation set based on the fire risk evaluation score. Therefore, in the present application, by integrating the subjective evaluation method and the objective evaluation method and constructing a multi-level evaluation model, the fire risk of the energy storage power station is systematically identified and evaluated, thereby improving the accuracy and comprehensiveness of the fire risk identification of the energy storage power station.

[0066] After the comprehensive evaluation model is established, the equipment status, environmental data and management information of the energy storage power station that actually needs to be tested are collected, and the fire risk level of the energy storage power station is determined according to the steps described in the above method embodiment.

[0067] It can be seen that this application has constructed a systematic fire risk assessment model through the hierarchical analysis method, which can comprehensively identify and quantify the fire risk of energy storage power stations. And by combining expert scoring with actual data, the scientificity and accuracy of the assessment results are improved. Through systematic risk assessment and early warning functions, it is helpful to take preventive measures in time to reduce the probability and loss of fire.

[0068] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the server includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0069] The embodiment of the present application can divide the server into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated unit can be implemented in the form of hardware or in the form of a software program module. It should be noted that the division of units in the embodiment of the present application is schematic, which is only a logical function division, and there may be other division methods in actual implementation.

[0070] In the case of integrated units, see Figure 4 , Figure 4 An energy storage power station fire risk identification device provided in an embodiment of the present application, the energy storage power station fire risk identification device 4 includes: Processing unit 401 is used to establish an indicator set for evaluating the fire risk of the energy storage power station; establish an evaluation set for comprehensive evaluation of the fire risk of the energy storage power station; calculate the combined weight of each evaluation indicator in the indicator set, and the combined weight is associated with the subjective weight and objective weight of each evaluation indicator; determine a fuzzy comprehensive evaluation matrix; establish a comprehensive evaluation model for the fire risk of the energy storage power station according to the combined weight and the fuzzy comprehensive evaluation matrix, and the comprehensive evaluation model for the fire risk of the energy storage power station is used to output the fire risk evaluation score of the energy storage power station; query the evaluation set according to the fire risk evaluation score to determine the fire risk level of the energy storage power station.

[0071] It can be seen that in the embodiment of the present application, the server establishes an indicator set for evaluating the fire risk of the energy storage power station; establishes an evaluation set for the comprehensive evaluation of the fire risk of the energy storage power station; calculates the combined weight of each evaluation indicator in the indicator set, and the combined weight is related to the subjective weight and objective weight of each evaluation indicator; determines the fuzzy comprehensive evaluation matrix; establishes a comprehensive evaluation model for the fire risk of the energy storage power station based on the combined weight and the fuzzy comprehensive evaluation matrix, and the comprehensive evaluation model for the fire risk of the energy storage power station is used to output the fire risk evaluation score of the energy storage power station; and determines the fire risk level of the energy storage power station by querying the evaluation set based on the fire risk evaluation score. Therefore, in the present application, by integrating the subjective evaluation method and the objective evaluation method and constructing a multi-level evaluation model, the fire risk of the energy storage power station is systematically identified and evaluated, thereby improving the accuracy and comprehensiveness of the fire risk identification of the energy storage power station.

[0072] In some embodiments, the calculation process of the subjective weight includes: ranking the evaluation indicators by experts, and assigning values ​​to the ranking of each evaluation indicator to obtain the importance index of each evaluation indicator; calculating the subjective weight of each evaluation indicator according to the importance index of each evaluation indicator according to the following formula (1); obtaining the subjective weight set of the indicator set according to the subjective weight of each evaluation indicator; Formula (1) Among them, ρ is the important index of the evaluation index, ω is the subjective weight of the evaluation index, is the subjective weight set of the indicator set, and n is the total number of evaluation indicators.

[0073] In some embodiments, the objective weight calculation process includes: obtaining sample data of each evaluation indicator of the energy storage power station; calculating the standard deviation of each evaluation indicator based on the sample data; calculating the correlation coefficient of each evaluation indicator based on the sample data; calculating the amount of information contained in each evaluation indicator based on the standard deviation and the correlation coefficient; obtaining the objective weight of each evaluation indicator based on the information amount; and obtaining the objective weight set of the indicator set based on the objective weight of each evaluation indicator.

[0074] In some embodiments, the standard deviation is calculated as follows: Formula (2) in, is the i-th sample data of the j-th evaluation index, m is the total number of sample data, j is the serial number of the evaluation index, is the sample average value of the jth evaluation index, is the standard deviation; The calculation formula of the correlation coefficient is as follows: Formula (3) in, is the correlation coefficient between the i-th evaluation index and the j-th evaluation index, is the sample data of the i-th evaluation index, is the sample average value of the i-th evaluation index, is the sample data of the jth evaluation index, is the sample average value of the jth evaluation index; The calculation formula of the information volume is as follows: Formula (4) in, is the information amount of the jth evaluation index; The calculation formula of the objective weight is as follows: Formula (5) Among them, n is the total number of evaluation indicators, is the objective weight of the jth evaluation index, is the objective weight set of the indicator set.

[0075] In some embodiments, the calculation formula of the combined weight is as follows: Formula (6) in, is the combined weight of the jth evaluation index, is the subjective weight of the jth evaluation index, is the objective weight of the jth evaluation index, and n is the total number of evaluation indicators.

[0076] In some embodiments, the comprehensive evaluation model for fire risk of energy storage power station is as follows: Formula (7) Among them, n is the total number of evaluation indicators, m is the total number of risk levels in the evaluation set V, represents the combined weight of the nth evaluation index, W is the combined weight set of each evaluation index, F is the fuzzy comprehensive evaluation matrix, is the membership degree of the nth evaluation index to the mth risk level in the evaluation set V, B represents the comprehensive membership degree of the fire risk of the energy storage power station to each risk level in the evaluation set V, It indicates the degree to which the fire risk of the energy storage power station belongs to the mth risk level in the evaluation set V, J is the grade corresponding to each risk level in the evaluation set V, and P is the fire risk evaluation score of the energy storage power station.

[0077] In some embodiments, before calculating the standard deviation of each evaluation indicator, the processing unit 401 is further used to: standardize the sample data of each evaluation indicator, and the standardization formula is as follows: Formula (8): Formula (8) Among them, i is the serial number of the sample data in a single evaluation index, j is the serial number of the evaluation index, represents the i-th sample of the j-th indicator, is the standardized sample data, To standardize the sample data before, is the smallest sample data among multiple sample data of the jth evaluation index, It is the largest sample data among multiple sample data of the jth evaluation index.

[0078] See also Figure 5 , Figure 5 A schematic diagram of the structure of a fire risk identification system for an energy storage power station provided in an embodiment of the present application, wherein the fire risk identification system 5 for an energy storage power station includes: Data acquisition module 501, used to collect equipment status, environmental data and management information of the energy storage power station; An indicator construction module 502 is used to establish an indicator set for evaluating the fire risk of an energy storage power station; A weight calculation module 503, used to calculate the combined weight of each evaluation indicator in the indicator set, wherein the combined weight is associated with the subjective weight and the objective weight of each evaluation indicator; The risk assessment module 504 is used to establish an evaluation set for the comprehensive evaluation of the fire risk of the energy storage power station; and, determine a fuzzy comprehensive evaluation matrix; and, establish a comprehensive evaluation model for the fire risk of the energy storage power station based on the combined weights and the fuzzy comprehensive evaluation matrix, wherein the comprehensive evaluation model for the fire risk of the energy storage power station is used to output a fire risk evaluation score of the energy storage power station; and, query the evaluation set based on the fire risk evaluation score to determine the fire risk level of the energy storage power station.

[0079] It can be seen that in the embodiment of the present application, the server establishes an indicator set for evaluating the fire risk of the energy storage power station; establishes an evaluation set for the comprehensive evaluation of the fire risk of the energy storage power station; calculates the combined weight of each evaluation indicator in the indicator set, and the combined weight is related to the subjective weight and objective weight of each evaluation indicator; determines the fuzzy comprehensive evaluation matrix; establishes a comprehensive evaluation model for the fire risk of the energy storage power station based on the combined weight and the fuzzy comprehensive evaluation matrix, and the comprehensive evaluation model for the fire risk of the energy storage power station is used to output the fire risk evaluation score of the energy storage power station; and determines the fire risk level of the energy storage power station by querying the evaluation set based on the fire risk evaluation score. Therefore, in the present application, by integrating the subjective evaluation method and the objective evaluation method and constructing a multi-level evaluation model, the fire risk of the energy storage power station is systematically identified and evaluated, thereby improving the accuracy and comprehensiveness of the fire risk identification of the energy storage power station.

[0080] In some embodiments, the calculation process of the subjective weight includes: ranking the evaluation indicators by experts, and assigning values ​​to the ranking of each evaluation indicator to obtain the importance index of each evaluation indicator; calculating the subjective weight of each evaluation indicator according to the importance index of each evaluation indicator according to the following formula (1); obtaining the subjective weight set of the indicator set according to the subjective weight of each evaluation indicator; Formula (1) Among them, ρ is the important index of the evaluation index, ω is the subjective weight of the evaluation index, is the subjective weight set of the indicator set, and n is the total number of evaluation indicators.

[0081] In some embodiments, the objective weight calculation process includes: obtaining sample data of each evaluation indicator of the energy storage power station; calculating the standard deviation of each evaluation indicator based on the sample data; calculating the correlation coefficient of each evaluation indicator based on the sample data; calculating the amount of information contained in each evaluation indicator based on the standard deviation and the correlation coefficient; obtaining the objective weight of each evaluation indicator based on the information amount; and obtaining the objective weight set of the indicator set based on the objective weight of each evaluation indicator.

[0082] In some embodiments, the standard deviation is calculated as follows: Formula (2) in, is the i-th sample data of the j-th evaluation index, m is the total number of sample data, j is the serial number of the evaluation index, is the sample average value of the jth evaluation index, is the standard deviation; The calculation formula of the correlation coefficient is as follows: Formula (3) in, is the correlation coefficient between the i-th evaluation index and the j-th evaluation index, is the sample data of the i-th evaluation index, is the sample average value of the i-th evaluation index, is the sample data of the jth evaluation index, is the sample average value of the jth evaluation index; The calculation formula of the information volume is as follows: Formula (4) in, is the information content of the jth evaluation index; The calculation formula of the objective weight is as follows: Formula (5) Among them, n is the total number of evaluation indicators, is the objective weight of the jth evaluation index, is the objective weight set of the indicator set.

[0083] In some embodiments, the calculation formula of the combined weight is as follows: Formula (6) in, is the combined weight of the jth evaluation index, is the subjective weight of the jth evaluation index, is the objective weight of the jth evaluation index, and n is the total number of evaluation indicators.

[0084] In some embodiments, the comprehensive evaluation model for fire risk of energy storage power station is as follows: Formula (7) Among them, n is the total number of evaluation indicators, m is the total number of risk levels in the evaluation set V, represents the combined weight of the nth evaluation index, W is the combined weight set of each evaluation index, F is the fuzzy comprehensive evaluation matrix, is the membership degree of the nth evaluation index to the mth risk level in the evaluation set V, B represents the comprehensive membership degree of the fire risk of the energy storage power station to each risk level in the evaluation set V, It indicates the degree to which the fire risk of the energy storage power station belongs to the mth risk level in the evaluation set V, J is the grade corresponding to each risk level in the evaluation set V, and P is the fire risk evaluation score of the energy storage power station.

[0085] In some embodiments, before calculating the standard deviation of each evaluation indicator, the weight calculation module 503 is further used to: standardize the sample data of each evaluation indicator, and the standardization formula is as follows: Formula (8): Formula (8) Among them, i is the serial number of the sample data in a single evaluation index, j is the serial number of the evaluation index, represents the i-th sample of the j-th indicator, is the standardized sample data, To standardize the sample data, is the smallest sample data among multiple sample data of the jth evaluation index, It is the largest sample data among multiple sample data of the jth evaluation index.

[0086] An embodiment of the present application provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the method described in any possible embodiment are implemented.

[0087] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0088] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0090] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0092] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0093] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0094] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for identifying fire risks in energy storage power plants, characterized in that: The method comprises: Establishing a set of indicators for evaluating the fire risk of the energy storage power station; Establish an evaluation set for comprehensive evaluation of fire risk in energy storage power plants; Calculating a combined weight of each evaluation indicator in the indicator set, wherein the combined weight is associated with a subjective weight and an objective weight of each evaluation indicator; Determine the fuzzy comprehensive evaluation matrix; Establishing a comprehensive fire risk evaluation model for an energy storage power station according to the combined weight and the fuzzy comprehensive evaluation matrix, wherein the comprehensive fire risk evaluation model for an energy storage power station is used to output a fire risk evaluation score for the energy storage power station; The fire risk level of the energy storage power station is determined by querying the evaluation set according to the fire risk evaluation score.

2. The method according to claim 1, characterized in that The calculation process of the subjective weight includes: Experts rank the evaluation indicators and assign values ​​to the ranking of each evaluation indicator to obtain an important index of each evaluation indicator; Calculate the subjective weight of each evaluation indicator according to the following formula (1) based on the importance index of each evaluation indicator; Obtaining a subjective weight set of the indicator set according to the subjective weight of each evaluation indicator; Formula (1) Among them, ρ is the important index of the evaluation index, ω is the subjective weight of the evaluation index, is the subjective weight set of the indicator set, and n is the total number of evaluation indicators.

3. The method according to claim 1, characterized in that The calculation process of the objective weight includes: Obtain sample data for each evaluation indicator of the energy storage power station; Calculate the standard deviation of each evaluation index according to the sample data; Calculate the correlation coefficient of each evaluation index according to the sample data; Calculate the amount of information contained in each evaluation index according to the standard deviation and the correlation coefficient; Obtaining an objective weight of each evaluation indicator according to the amount of information; The objective weight set of the indicator set is obtained according to the objective weight of each evaluation indicator.

4. The method according to claim 3, characterized in that: The calculation formula of the standard deviation is as follows (2): Formula (2) in, is the i-th sample data of the j-th evaluation index, m is the total number of sample data, j is the serial number of the evaluation index, is the sample average value of the jth evaluation index, is the standard deviation; The calculation formula of the correlation coefficient is as follows: Formula (3) in, is the correlation coefficient between the i-th evaluation index and the j-th evaluation index, is the sample data of the i-th evaluation index, is the sample average value of the i-th evaluation index, is the sample data of the jth evaluation index, is the sample average value of the jth evaluation index; The calculation formula of the information volume is as follows: Formula (4) in, is the information amount of the jth evaluation index; The calculation formula of the objective weight is as follows: Formula (5) Among them, n is the total number of evaluation indicators, is the objective weight of the jth evaluation index, is the objective weight set of the indicator set.

5. The method according to claim 1, characterized in that The calculation formula of the combined weight is as follows: Formula(6) in, is the combined weight of the jth evaluation index, is the subjective weight of the jth evaluation index, is the objective weight of the jth evaluation index, and n is the total number of evaluation indicators.

6. The method according to claim 1, characterized in that The comprehensive evaluation model of fire risk of energy storage power station is as follows: Formulas (7) Among them, n is the total number of evaluation indicators, m is the total number of risk levels in the evaluation set V, represents the combined weight of the nth evaluation index, W is the combined weight set of each evaluation index, F is the fuzzy comprehensive evaluation matrix, is the membership degree of the nth evaluation index to the mth risk level in the evaluation set V, B represents the comprehensive membership degree of the fire risk of the energy storage power station to each risk level in the evaluation set V, It indicates the degree to which the fire risk of the energy storage power station belongs to the mth risk level in the evaluation set V, J is the grade corresponding to each risk level in the evaluation set V, and P is the fire risk evaluation score of the energy storage power station.

7. The method according to claim 3, characterized in that Before calculating the standard deviation of each evaluation index, the method further includes: The sample data of each evaluation index is standardized, and the standardized formula is as follows (8): Formulas (8) Among them, i is the serial number of the sample data in a single evaluation index, j is the serial number of the evaluation index, represents the i-th sample of the j-th indicator, is the standardized sample data, To standardize the sample data before, is the smallest sample data among multiple sample data of the jth evaluation index, It is the largest sample data among multiple sample data of the jth evaluation index.

8. A fire risk identification device for an energy storage power station, characterized in that: The device comprises: A processing unit is used to establish an indicator set for evaluating the fire risk of the energy storage power station; establish an evaluation set for comprehensive evaluation of the fire risk of the energy storage power station; calculate the combined weight of each evaluation indicator in the indicator set, wherein the combined weight is associated with the subjective weight and the objective weight of each evaluation indicator; determine a fuzzy comprehensive evaluation matrix; establish a comprehensive evaluation model for the fire risk of the energy storage power station according to the combined weight and the fuzzy comprehensive evaluation matrix, wherein the comprehensive evaluation model for the fire risk of the energy storage power station is used to output a fire risk evaluation score of the energy storage power station; and query the evaluation set according to the fire risk evaluation score to determine the fire risk level of the energy storage power station.

9. A fire risk identification system for an energy storage power station, characterized in that: The system comprises: Data acquisition module, used to collect equipment status, environmental data and management information of energy storage power stations; An indicator construction module, used to establish an indicator set for evaluating the fire risk of energy storage power plants; A weight calculation module, used to calculate the combined weight of each evaluation indicator in the indicator set, wherein the combined weight is associated with the subjective weight and the objective weight of each evaluation indicator; A risk assessment module is used to establish an evaluation set for the comprehensive evaluation of the fire risk of the energy storage power station; and, determine a fuzzy comprehensive evaluation matrix; and, establish a comprehensive evaluation model for the fire risk of the energy storage power station based on the combined weights and the fuzzy comprehensive evaluation matrix, wherein the comprehensive evaluation model for the fire risk of the energy storage power station is used to output a fire risk evaluation score of the energy storage power station; and, query the evaluation set based on the fire risk evaluation score to determine the fire risk level of the energy storage power station.

10. A server, characterized in that: The method comprises a processor and a memory, wherein one or more programs are stored in the memory, and the one or more programs are called by the processor to execute the step instructions in the method according to any one of claims 1 to 7.

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