Power distribution network equipment fire risk assessment method and system

Abstract

The invention discloses a power distribution network equipment fire-fighting risk assessment method and system, and the method comprises the steps: obtaining power distribution network equipment data, carrying out the preprocessing of the data, and calculating a power distribution network equipment risk index based on a preprocessing result, so as to recognize an equipment risk; when the identified power distribution network equipment is high-risk equipment, carrying out risk simulation in combination with equipment environment characteristics, and implementing corresponding maintenance measures based on dynamic data of the risk simulation; and visualizing the risk simulation data and storing evaluation process data. According to the method, expert scoring and comprehensive weight calculation of the spherical fuzzy judgment matrix are combined, the evaluation precision of the fire triggering probability is improved, accurate simulation of the fire triggering probability and the extension path is achieved through the fault tree and the Bayesian network, response and maintenance measures with higher priorities are provided for high-risk equipment, and the method is suitable for popularization and application. The fire risk identification and prevention efficiency is significantly improved, the resource waste is reduced, and the safe operation and maintenance management of the power distribution network equipment is comprehensively supported.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network risk assessment, and in particular to a distribution network equipment fire risk assessment method and system. Background Art

[0002] Fire risk assessment and fire management of distribution network equipment have always been the key links in the safe operation and maintenance of power systems. With the acceleration of urbanization and the increase in the complexity of distribution network systems, the operating environment of distribution equipment has become more and more variable, resulting in more diversified fire risk factors, such as the temperature of the equipment itself, the current load, and the temperature and humidity of the surrounding environment, which may directly or indirectly cause fires. For fire prevention and control, perfluorohexanone fire extinguishing agents are often used. Therefore, traditional fire monitoring and risk assessment technologies are gradually unable to meet the needs of real-time and high-precision risk identification. In recent years, the development of technologies such as intelligent sensing technology, data analysis and machine learning has given the opportunity to study new methods for fire risk assessment of distribution networks. Through the collection, preprocessing and risk-weighted analysis of sensor data, the mechanism of electrical fires in different distribution network scenarios is studied, and the development process of electrical fires is simulated. Effective identification of fire risks has become an important direction in the operation and maintenance of distribution networks. Existing methods usually rely on single monitoring or lack multi-dimensional comprehensive data, and cannot dynamically assess fire risks in complex environments. Especially when multiple risk factors interact, traditional methods are difficult to adjust risk assessment results in real time, resulting in high-risk equipment not being identified and handled in a timely manner. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a distribution network equipment fire risk assessment method and system to solve the problem that existing methods usually rely on single monitoring or lack multi-dimensional comprehensive data and cannot dynamically assess fire risks in complex environments. In particular, when multiple risk factors interact, traditional means are difficult to adjust risk assessment results in real time, resulting in high-risk equipment not being identified and processed in a timely manner.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for fire risk assessment of distribution network equipment, comprising:

[0008] Acquire distribution network equipment data and preprocess the data, and calculate the distribution network equipment risk index based on the preprocessing result to identify equipment risks;

[0009] When the distribution network equipment is identified as high-risk equipment, risk simulation is performed in combination with the equipment environment characteristics, and corresponding maintenance measures are implemented based on the dynamic data of risk simulation;

[0010] Visualize risk simulation data and store assessment process data.

[0011] As a preferred solution of the fire risk assessment method for distribution network equipment of the present invention, before calculating the risk index of distribution network equipment based on the preprocessing result, it also includes: the preprocessing result is a risk factor, and weights are assigned to different risk factors;

[0012] Establishing spherical fuzzy judgment matrix r=(o ij ), where element o ij It is expressed as a triple: (a ij1 , a ij2 , a ij3 ), where a ij1 is the membership degree, a ij2 Non-membership degree, a ij3 It is the degree of hesitation;

[0013] Calculate the triplet data of each row in the matrix r to generate the preliminary weight value of each risk factor:

[0014]

[0015] Among them, g i is the initial weight of risk factor i, T i is the temperature data corresponding to risk factor i, I j is the current load corresponding to risk factor j, H j is the humidity corresponding to risk factor j, U is the external ambient temperature of the entire device, I is the internal current load of the device, θ ij is the temperature interaction coefficient, δ ij is the current load interaction coefficient, a ij1 , a ij2 and a ij3 is the membership degree, non-membership degree and hesitation degree between risk factor i and risk factor j, and n represents the total number of risk factors.

[0016] As a preferred solution of the fire risk assessment method for distribution network equipment described in the present invention, it further includes: taking the risk factors as nodes of the Bayesian network, and using the weight value corresponding to each risk factor as the initial probability value of each node;

[0017] Dynamically update the conditional probability P(B|A) in the Bayesian network based on real-time collected data:

[0018] P(B|A)=(a′ ij1 , a′ ij2 , a′ ij3 )

[0019] Among them, a′ ij1 It represents the support probability of risk factor B for the overall risk under given condition A, a′ ij2 It represents the negation probability of risk factor B to the overall risk under condition A, a′ ij3 It represents the uncertainty probability of risk factor B to the overall risk under condition A;

[0020] Based on the updated conditional probability value, the preliminary weight vector of each node is dynamically adjusted. The dynamic weight value calculation formula for each risk factor is:

[0021] g′ i =g i ·a′ ij1 +(1-g i )·(1-a′ ij2 )+a′ ij3 ·g i ·(1-g i )

[0022] Among them, g′ i is the adjusted weight value, g i is the initial weight, a′ ij1 is the support probability, a′ ij2 is the probability of negation, a′ ij3 is the probability of uncertainty;

[0023] The dynamically updated weights of the risk factors are traversed and calculated, the dynamically updated weights are normalized, and an updated weight vector is generated based on the adjusted weight values.

[0024] As a preferred solution of the fire risk assessment method for distribution network equipment described in the present invention, wherein: calculating the risk index of distribution network equipment to identify equipment risk includes: performing dimensionless processing on the real-time data of the risk factors after preprocessing, and performing weighted calculation on the real-time data of the risk factors after dimensionless processing according to the adjusted weight value to obtain the weighted value of each risk factor;

[0025] The weighted values ​​of each risk factor are accumulated to generate the comprehensive risk index R of each device, and the risk threshold W is set. The comprehensive fire risk index R of each device is compared with the risk threshold W:

[0026] If R ≥ W, the device is marked as a high-risk device, otherwise it is marked as a low-risk device.

[0027] As a preferred solution of the fire risk assessment method for distribution network equipment of the present invention, when the identified distribution network equipment is a high-risk equipment, the risk simulation is performed in combination with the equipment environment characteristics, including:

[0028] Collect real-time data of high-risk equipment, extract environmental feature data through feature engineering, and set critical thresholds for risk formation based on equipment safety indicators. The critical thresholds include the highest safe temperature, maximum current load, and minimum humidity.

[0029] The critical thresholds of various environmental characteristics are combined into a risk benchmark condition matrix, and based on the benchmark conditions, the risk triggering event of temperature and current load exceeding the critical threshold is selected as the initial event of risk formation, and the initial event is used as the initial event node of the fault tree;

[0030] According to the actual situation of the risk formation of distribution network equipment, select the corresponding logical relationship to link each trigger event, and according to the logical relationship, connect the trigger events step by step to form a risk fault tree;

[0031] The independent occurrence probability of each event is calculated based on historical data and real-time data, and the risk triggering probability of high-risk equipment is calculated layer by layer through the logical structure of the fault tree.

[0032] As a preferred solution of the fire risk assessment method for distribution network equipment described in the present invention, it also includes:

[0033] According to the equipment layout and installation location of the distribution network, the physical distance and spatial orientation between high-risk equipment and adjacent equipment are collected, and environmental data that affects the risk expansion are collected; the probability of high-risk equipment fire expanding to adjacent equipment is calculated based on the adjacent relationship between equipment and environmental factors:

[0034]

[0035] Among them, S XJ is the probability of fire spreading from device X to device J, S′ X is the fire triggering probability of device X, d XJ is the physical distance between equipment X and J, v is the ventilation speed, σ is the angle between the fire expansion direction and the wind direction, and α is the attenuation coefficient;

[0036] Set the probability threshold K of fire expansion, if the probability S XJIf it is greater than or equal to K, it means that the fire will spread to adjacent devices, otherwise it will not spread. According to the calculation results of the expansion probability, a path diagram is used to mark the direction and path of the fire expansion to generate a fire expansion path diagram, and the fire expansion probability, fire trigger probability and affected equipment are summarized as a dynamic data set.

[0037] As a preferred solution of the fire risk assessment method for distribution network equipment of the present invention, wherein: implementing corresponding maintenance measures based on dynamic data of risk simulation includes: classifying distribution network equipment according to the dynamic data set, marking equipment with high trigger probability and on the fire expansion path as priority processing equipment, and marking equipment with high trigger probability but not on the expansion path as second priority processing equipment;

[0038] If the equipment is a priority processing equipment, the sampling frequency of monitoring data is increased, and the technical personnel are notified to immediately go to the site to check the status of the relevant distribution network equipment;

[0039] If the equipment is a second-priority processing device, maintain the original data sampling frequency and arrange for technicians to regularly check the status of the distribution network equipment.

[0040] In a second aspect, the present invention provides a distribution network equipment fire risk assessment system, comprising:

[0041] A data acquisition and processing module, used to acquire distribution network equipment data and pre-process the data, and calculate the distribution network equipment risk index based on the pre-processing result to identify equipment risks;

[0042] The risk simulation maintenance module is used to perform risk simulation based on the equipment environment characteristics when the distribution network equipment is identified as high-risk equipment, and implement corresponding maintenance measures based on the dynamic data of risk simulation;

[0043] Presentation module for visualizing risk simulation data and storing assessment process data.

[0044] In a third aspect, the present invention provides an electronic device, comprising:

[0045] Memory and processor;

[0046] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distribution network equipment fire risk assessment method are implemented.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the distribution network equipment fire risk assessment method.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention combines expert scoring with the comprehensive weight calculation of the spherical fuzzy judgment matrix to improve the assessment accuracy of the fire trigger probability, and realizes accurate simulation of the fire trigger probability and the expansion path through the fault tree and Bayesian network, providing higher priority response and maintenance measures for high-risk equipment, significantly improving the efficiency of fire risk identification and prevention, reducing resource waste, and fully supporting the safe operation and maintenance management of distribution network equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0050] Figure 1 A method flow chart of a fire risk assessment method and system for distribution network equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0055] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0056] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] Example 1

[0058] Reference Figure 1 , is an embodiment of the present invention, which provides a method for fire risk assessment of distribution network equipment, including:

[0059] S100: Acquire distribution network equipment data and preprocess the data, and calculate the distribution network equipment risk index based on the preprocessing result to identify equipment risks;

[0060] It should be noted that the present application deploys temperature and humidity sensors and current sensors on the distribution network equipment, adjusts the data collection cycle according to the working characteristics of the equipment and the frequency of environmental changes, collects the temperature and current load inside the distribution network equipment and the ambient temperature and humidity around the distribution network equipment, cleans, standardizes, smoothes and denoises the collected data, and uses the pre-processed data as a risk factor.

[0061] In an optional embodiment, multiple sensors work together to help achieve comprehensive coverage of fire risks and ensure the diversity and accuracy of fire risk assessment data. By dynamically adjusting the collection cycle, the present application can flexibly set the frequency of data collection based on the operating characteristics of the equipment and environmental fluctuations. Data cleaning, standardization, and data smoothing and denoising can ensure the high quality and consistency of sensor data. In fire risk assessment, using preprocessed data as a risk factor can more accurately characterize the characteristics of equipment fire risks.

[0062] Furthermore, assigning weights to different fire risk factors refers to generating expert scoring data after inviting fire protection and power system experts to score the importance of internal and external risk factors.

[0063] In the embodiment of the present application, before calculating the risk index of the distribution network equipment based on the preprocessing result, the method further includes: the preprocessing result is a risk factor, and weights are assigned to different risk factors;

[0064] Establishing spherical fuzzy judgment matrix r=(o ij ), where element o ij It is expressed as a triple: (a ij1 , a ij2 , a ij3 ), where a ij1 is the membership degree, a ij2 Non-membership degree, a ij3 It is the degree of hesitation;

[0065] Specifically, membership is used to reflect the probability of factor i and factor j supporting the overall risk, non-membership is used to reflect the probability of factor i and factor j denying the overall risk, and hesitation is used to represent the probability of subjective uncertainty;

[0066] In the embodiment of the present application, the triplet data of each row in the matrix r is calculated to generate the preliminary weight value of each risk factor:

[0067]

[0068] Among them, g i is the initial weight of risk factor i, T i is the temperature data corresponding to risk factor i, I j is the current load corresponding to risk factor j, H j is the humidity corresponding to risk factor j, U is the external ambient temperature of the entire device, I is the internal current load of the device, θ ij is the temperature interaction coefficient, δ ij is the current load interaction coefficient, a ij1 , a ij2 and a ij3 is the membership degree, non-membership degree and hesitation degree between risk factor i and risk factor j, and n represents the total number of risk factors.

[0069] Specifically, the temperature interaction coefficient is used to adjust the risk impact of ambient temperature on the equipment, and is set based on historical data and actual operating conditions. The current load interaction coefficient is used to adjust the impact of the current load inside the equipment, and is set based on the actual load situation. The membership, non-membership and hesitation between risk factors i and j are generated by expert scoring. n represents the total number of risk factors. Non-Linear

[0070] The impact of humidity fluctuations can be reduced to avoid excessive interference with the weights of other factors when humidity is abnormal.

[0071] In an optional embodiment, the geometric mean method may be used to calculate the triple data of each row to generate a preliminary weight value of each risk factor:

[0072]

[0073] The above formula only considers the membership, non-membership and hesitation of each risk factor, and simply calculates the geometric mean of the triples without introducing other influencing factors. Therefore, it is not expressive enough for the fire risk assessment of distribution network equipment in complex environments. On the basis of the traditional method, actual measurement data such as equipment temperature, humidity and current load are introduced to dynamically adapt to the actual operating status of the equipment, which can more comprehensively reflect the fire risk faced by the equipment. According to the different data characteristics of temperature, current load and humidity, a more adaptable mapping function is designed, so that each factor can more truly reflect its contribution to the fire risk of the equipment in the weight calculation;

[0074] It should be noted that compared with the simple multiplication structure of the traditional geometric mean method, a nonlinear mapping function is added to more carefully balance the impact of various factors on equipment risk and improve the flexibility and accuracy of the formula. In order to consider the interaction between different environmental factors, sine and cosine interaction terms are added to the denominator so that the ambient temperature and current load of the equipment have a direct interactive effect on the overall weight. The structure of the denominator is: 1+sin(θ ij U)+cos(δ ij L), the traditional method does not introduce interaction terms, so it cannot reflect the dynamic relationship between risk factors. By introducing sine and cosine modulation of ambient temperature and internal current load, the interaction of various factors is realized and the dynamic adaptability is enhanced;

[0075] In the embodiment of the present application, it also includes: taking the risk factors as nodes of the Bayesian network, and using the weight value corresponding to each risk factor as the initial probability value of each node;

[0076] Dynamically update the conditional probability P(B|A) in the Bayesian network based on real-time collected data:

[0077] P(B|A)=(a′ ij1 , a′ ij2 , a′ ij3 )

[0078] Among them, a′ ij1 It represents the support probability of risk factor B for the overall risk under given condition A, a′ ij2 It represents the negation probability of risk factor B to the overall risk under condition A, a′ij3 It represents the uncertainty probability of risk factor B to the overall risk under condition A.

[0079] Furthermore, a′ ij1 is the support probability, which indicates the degree of support of risk factor B for the overall risk under the condition A. The higher the value, the more favorable factor B is for the occurrence of risk under the current conditions. ij2 It is the probability of negation, which indicates the degree of negation of risk factor B on the overall risk when condition A exists. The higher the value, the greater the negative impact of factor B on the risk under the current conditions. a′ ij3 It is the uncertainty probability, which reflects the ambiguity or uncertainty of risk factor B on the overall risk when condition A exists. The higher the value, the lack of clear judgment on the risk impact of factor B under the current conditions.

[0080] Specifically, a′ ij1 It indicates the support probability of risk factor B for the overall risk under given condition A. The higher the value, the greater the positive impact of factor B on the risk under the current conditions. The calculation method is as follows:

[0081]

[0082] Among them, P(A) is the prior probability of event A, which means the probability of event A occurring without any other information or conditions; P(B) is the prior probability of event B, which means the probability of event B occurring without any other restrictions; P(A|B) is the conditional probability of event A occurring under the condition that event B has occurred;

[0083] Specifically, a′ ij2 It indicates the probability of negation of risk factor B on the overall risk under condition A. The higher the value, the greater the negative effect of factor B on the risk under the current conditions. In order to ensure that the sum of the membership degree and non-membership degree is 1, this probability is defined as the complement of the support probability:

[0084] a′ ij2 =1-a′ ij1

[0085] Specifically, a′ ij3 It indicates the uncertainty probability of risk factor B on the overall risk under condition A. The hesitation reflects the ambiguity between support and denial, that is, there is a lack of definite judgment on the risk impact of factor B under the current conditions. The calculation formula is as follows:

[0086]

[0087] This formula ensures that the triplet satisfies the constraint a′ of the spherical fuzzy number 2 ij1+a′ 2 ij2 +a′ 2 ij3 =1, so that the sum of the probabilities of support, negation, and uncertainty is equal to 1;

[0088] Based on the updated conditional probability value, the preliminary weight vector of each node is dynamically adjusted. The dynamic weight value calculation formula for each risk factor is:

[0089] g′ i =g i ·a′ ij1 +(1-g i )·(1-a′ ij2 )+a′ ij3 ·g i ·(1-g i )

[0090] Among them, g′ i is the adjusted weight value, g i is the initial weight, a′ ij1 is the support probability, a′ ij2 is the probability of negation, a′ ij3 is the probability of uncertainty;

[0091] The dynamically updated weights of the risk factors are traversed and calculated, the dynamically updated weights are normalized, and an updated weight vector is generated based on the adjusted weight values.

[0092] It should be noted that by inviting fire protection and power system experts to score fire risk factors, the present invention can fully integrate professional knowledge and practical experience, generate a spherical fuzzy judgment matrix, and accurately express the subjective uncertainty and multidimensional characteristics of each risk factor. Based on the triplet scoring data, the geometric mean method is used to calculate the preliminary weight vector so that the weight distribution of each risk factor is balanced and not interfered by extreme values. Subsequently, normalization processing is performed to obtain the normalization coefficient S, unify the weight distribution and ensure that the sum of the weights of all factors is 1, thereby avoiding the problems of bias and weight imbalance. This processing method improves the fairness and consistency of weight distribution, provides a stable and unbiased benchmark for the weights of risk factors, sets the risk factors as nodes of the Bayesian network, updates the conditional probability through real-time data, and continuously adjusts the preliminary weight vector of each node, so that the network can dynamically adjust the weights of each risk factor as external data changes.

[0093] It should also be noted that this design can reflect the changes in equipment fire risk in real time, greatly improving the flexibility and response speed of risk assessment. The dynamic update of conditional probability solves the problem that traditional models cannot provide real-time feedback, so that risk assessment can cope with fire risks in dynamic environments. By decomposing the support probability, negative probability and uncertainty probability, the model can distinguish the risk support, opposition and uncertainty conditions under different conditions in the Bayesian network and calculate the dynamic weight value. This makes up for the binary logic defect of only considering "risk occurrence" or "non-occurrence" in the traditional risk assessment model, so that the present invention can capture subtle changes in risk trends under complex conditions. For example, under slight fluctuations in environmental factors, the scheme can maintain a high response accuracy and does not frequently adjust weights due to slight changes. By dynamically adjusting the weight vector based on real-time data, the present invention can automatically adjust the weights of each risk factor when the fire risk changes significantly. For example, when the temperature or current load of a certain device increases significantly, the weight of the factor will automatically increase, thereby highlighting the impact of the risk. Compared with the traditional static weight allocation method, the dynamic weight vector can reflect the risk change trend in real time, making the fire identification and response of the scheme more flexible and timely.

[0094] In an embodiment of the present application, calculating the risk index of distribution network equipment to identify equipment risk includes: performing dimensionless processing on the real-time data of risk factors after preprocessing, and performing weighted calculation on the real-time data of risk factors after dimensionless processing according to the adjusted weight value to obtain the weighted value of each risk factor;

[0095] The weighted values ​​of each risk factor are accumulated to generate the comprehensive risk index R of each device, and the risk threshold W is set. The comprehensive fire risk index R of each device is compared with the risk threshold W:

[0096] If R ≥ W, the device is marked as a high-risk device, otherwise it is marked as a low-risk device.

[0097] It should be noted that the dimensionless processing significantly enhances the evaluation capability of the present invention, which not only ensures a fair comparison between risk factors, but also improves the accuracy of the overall risk index. By assigning high weights to risk factors such as temperature and current, key risk points can be captured more accurately, thereby improving the accuracy of high-risk equipment identification. By comparing the fire risk index R with the risk threshold W, high-risk equipment can be quickly identified, and higher-priority fire safety management measures can be implemented for these equipment.

[0098] S200: When the identified distribution network equipment is a high-risk equipment, risk simulation is performed in combination with the equipment environment characteristics, and corresponding maintenance measures are implemented based on the dynamic data of the risk simulation;

[0099] In an embodiment of the present application, when the identified distribution network equipment is a high-risk equipment, the risk simulation is performed in combination with the equipment environment characteristics, including:

[0100] Collect real-time data of high-risk equipment, extract environmental feature data through feature engineering, and set critical thresholds for risk formation based on equipment safety indicators. Critical thresholds include the highest safe temperature, maximum current load, and minimum humidity.

[0101] The critical thresholds of various environmental characteristics are combined into a risk benchmark condition matrix, and based on the benchmark conditions, the risk triggering event of temperature and current load exceeding the critical threshold is selected as the initial event of risk formation, and the initial event is used as the initial event node of the fault tree;

[0102] According to the actual situation of the risk formation of distribution network equipment, select the corresponding logical relationship to link each trigger event, and according to the logical relationship, connect the trigger events step by step to form a risk fault tree;

[0103] For example, assuming that "fire trigger" needs to meet temperature exceeding the limit and current overload, then event A and event B are connected using "AND" logic, indicating that a fire will be triggered only when temperature exceeding the limit and current overload occur at the same time. If insufficient humidity (event C) is an independent fire trigger event, it is connected under the root node "fire trigger" through "OR" logic.

[0104] The independent occurrence probability of each event is calculated based on historical data and real-time data, and the risk triggering probability of high-risk equipment is calculated layer by layer through the logical structure of the fault tree.

[0105] Specifically, the logical relationships include:

[0106] If the formation of a risk requires multiple events to occur simultaneously, use "AND" logic; if any one event triggers the risk, use "OR" logic;

[0107] Specifically, according to the combination of "AND" gates and "OR" gates, the fire triggering probability of high-risk equipment is calculated layer by layer:

[0108] For the “AND” logic gate, the probability of fire triggering is the product of the probability of each event occurring;

[0109] For the “OR” logic gate, the probability of fire triggering is the complement of the probability of each event not occurring;

[0110] In the embodiment of the present application, it also includes:

[0111] According to the equipment layout and installation location of the distribution network, collect the physical distance and spatial orientation between high-risk equipment and adjacent equipment, and collect environmental data that affects the risk expansion; the environmental data includes wind speed and wind direction;

[0112] In the embodiment of the present application, the probability of a fire in a high-risk device spreading to adjacent devices is calculated based on the adjacent relationship between devices and environmental factors:

[0113]

[0114] Among them, S XJ is the probability of fire spreading from device X to device J, S′ X is the fire triggering probability of device X, d XJ is the physical distance between equipment X and J, v is the ventilation speed, σ is the angle between the fire expansion direction and the wind direction, and α is the attenuation coefficient.

[0115] Specifically, the attenuation coefficient, which is used to adjust the expansion probability as environmental factors change, is preliminarily set according to the typical attenuation relationship between devices and is obtained based on the fitting of historical fire data.

[0116] In the embodiment of the present application, the probability threshold K of fire spread is set based on the NFPA standard. XJ If it is greater than or equal to K, it means that the fire will spread to adjacent devices, otherwise it will not spread. According to the calculation results of the expansion probability, a path diagram is used to mark the direction and path of the fire expansion to generate a fire expansion path diagram, and the fire expansion probability, fire trigger probability and affected equipment are summarized as a dynamic data set.

[0117] For example, when simulating the formation of fire for high-risk equipment, the present invention combines the real-time data of the equipment and the environmental characteristic data, sets the key conditions for the formation of fire, namely the critical thresholds, such as the highest safe temperature, the maximum current load and the lowest humidity, etc., and realizes multi-dimensional monitoring of fire formation conditions, and the accuracy is greatly improved. By connecting the initial event nodes of the fire formation into a hierarchical logical structure, the present invention can clearly display the multiple conditions required for the formation of fire. This logical relationship enables each risk factor to have a clear trigger path, which not only shows the possibility of fire triggering, but also clarifies the specific conditions for causing the fire, which is convenient for tracing the key events. The "and" logic in the fault tree model is used to simulate the fire triggering conditions under the joint action of multiple factors, such as temperature, current load, humidity and other conditions exceeding the safety threshold at the same time; while the "or" logic is applicable to the independent fire triggering path of a single event, such as any device with too high temperature may cause a fire. This combination method has significant advantages in the case where traditional single monitoring cannot identify complex triggering paths, and provides a scientific logical structure and evaluation accuracy for fire risk assessment, thereby effectively reducing the missed detection rate of fire triggering. When calculating the probability of fire triggering step by step, the hierarchical design of the fault tree cooperates with the combined calculation method of "AND" gates and "OR" gates to provide a detailed hierarchical evaluation mechanism for the probability estimation of fire triggering. In the calculation of the probability of fire spread, the present invention integrates environmental data such as the physical distance between devices, spatial orientation, wind speed and wind direction to fully reflect the spread process of the fire. The fire triggering probability, expansion probability and affected equipment information obtained through the above calculations are finally summarized into a dynamic data set. The generation of dynamic data sets not only has the function of data storage, but also can be dynamically adjusted as the real-time status of the equipment is updated to ensure that the fire risk data of the equipment is always kept up to date.

[0118] In an embodiment of the present application, implementing corresponding maintenance measures based on dynamic data of risk simulation includes: classifying distribution network equipment according to the dynamic data set, marking equipment with high trigger probability and on the fire expansion path as priority processing equipment, and marking equipment with high trigger probability but not on the expansion path as second priority processing equipment;

[0119] If the equipment is a priority processing equipment, the sampling frequency of monitoring data is increased, and the technical personnel are notified to immediately go to the site to check the status of the relevant distribution network equipment;

[0120] If the equipment is a second-priority processing device, maintain the original data sampling frequency and arrange for technicians to regularly check the status of the distribution network equipment.

[0121] It should be noted that the dynamic data set records the fire trigger probability and expansion path information of each distribution network equipment. Through the classification strategy, the equipment with high trigger probability is divided into priority or sub-priority, which optimizes the resource allocation. The priority and sub-priority hierarchical maintenance method ensures the reasonable allocation of resources and the timeliness of response. High-frequency data sampling and timely notification mechanism provide reliable guarantee for early intervention of fire risks.

[0122] S300: Visualize risk simulation data and store assessment process data;

[0123] In an optional embodiment, visually displaying the risk simulation data refers to drawing a two-dimensional risk heat map through a Python data visualization library in combination with the risk trigger probability and the expansion probability, using gradient colors to represent the fire trigger probability, marking low risk to high risk with cold colors to warm colors respectively, adding a legend next to the heat map to indicate the correspondence between color and fire risk level, and integrating the generated two-dimensional fire risk heat map and fire expansion path map into Power BI for visual display.

[0124] Through the visualization of fire trigger probability and expansion probability, the fire risk level information can be intuitively conveyed to technical personnel, providing support for real-time monitoring and risk warning. Power BI supports real-time data updates and can dynamically adjust the fire trigger probability and expansion path diagram as the data changes. Technical personnel can view the latest risk status of different equipment in real time on a unified interface to ensure the timeliness of risk identification. By combining the two-dimensional heat map and expansion path map of the fire trigger probability and expansion probability, a highly targeted visualization method is provided for fire risk assessment. The display results are integrated into Power BI. Through real-time data updates and multi-dimensional data fusion displays, the efficiency of fire risk identification and expansion prediction is significantly improved.

[0125] In an embodiment of the present application, storing the data generated during the evaluation process means storing the data collected and analyzed during the evaluation process and the maintenance measures for distribution network equipment at different processing levels in a database, and the database synchronizes the stored data to the cloud for regular backup.

[0126] It should be noted that by centrally storing and synchronizing the data generated during the fire risk assessment process to the cloud for backup, the problems of decentralized data storage, incomplete information, and data loss risks in traditional fire risk management are effectively solved. At the same time, by recording equipment maintenance measures and trend analysis data at different processing levels, accurate monitoring and intelligent management of fire risks are achieved, significantly improving the safety protection capabilities of distribution network equipment, and enhancing data traceability and the scientific nature of risk assessment.

[0127] Example 2

[0128] Reference Figure 1 , is an embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a distribution network equipment fire risk assessment system.

[0129] It should be noted that the technical solution of this distribution network equipment fire risk assessment system and the technical solution of the above-mentioned distribution network equipment fire risk assessment method belong to the same concept. For details not described in detail in the technical solution of the distribution network equipment fire risk assessment system in this embodiment, please refer to the description of the technical solution of the above-mentioned distribution network equipment fire risk assessment method.

[0130] In this embodiment, a distribution network equipment fire risk assessment system includes:

[0131] A data acquisition and processing module is used to acquire distribution network equipment data and pre-process the data, and calculate the distribution network equipment risk index based on the pre-processing result to identify equipment risks;

[0132] The risk simulation maintenance module is used to perform risk simulation based on the equipment environment characteristics when the distribution network equipment is identified as high-risk equipment, and implement corresponding maintenance measures based on the dynamic data of risk simulation;

[0133] Presentation module for visualizing risk simulation data and storing assessment process data.

[0134] This embodiment also provides an electronic device, which is applicable to the fire risk assessment method for distribution network equipment, including:

[0135] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the fire risk assessment method for distribution network equipment proposed in the above embodiment.

[0136] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for fire risk assessment of distribution network equipment proposed in the above embodiment is implemented.

[0137] The storage medium proposed in this embodiment and the method for implementing fire risk assessment of distribution network equipment proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0138] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0139] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0140] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0141] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for fire risk assessment of distribution network equipment, characterized in that: include: Acquire distribution network equipment data and preprocess the data, and calculate the distribution network equipment risk index based on the preprocessing result to identify equipment risks; When the distribution network equipment is identified as high-risk equipment, risk simulation is performed in combination with the equipment environment characteristics, and corresponding maintenance measures are implemented based on the dynamic data of risk simulation; Visualize risk simulation data and store assessment process data.

2. The method for fire risk assessment of distribution network equipment according to claim 1, characterized in that: Before calculating the distribution network equipment risk index based on the preprocessing result, the method further includes: the preprocessing result is a risk factor, and weights are assigned to different risk factors; Establishing spherical fuzzy judgment matrix r=(o ij ), where element o ij It is expressed as a triple: (a ij1 , a ij2 , a ij3 ), where a ij1 is the membership degree, a ij2 Non-membership degree, a ij3 It is the degree of hesitation; Calculate the triplet data of each row in the matrix r to generate the preliminary weight value of each risk factor: Among them, g i is the initial weight of risk factor i, T i is the temperature data corresponding to risk factor i, I j is the current load corresponding to risk factor j, H j is the humidity corresponding to risk factor j, U is the external ambient temperature of the entire device, I is the internal current load of the device, θ ij is the temperature interaction coefficient, δ ij is the current load interaction coefficient, a ij1 , a ij2 and a ij3 is the membership degree, non-membership degree and hesitation degree between risk factor i and risk factor j, and n represents the total number of risk factors.

3. The method for fire risk assessment of distribution network equipment according to claim 2, characterized in that: Also includes: The risk factors are used as nodes of the Bayesian network, and the weight value corresponding to each risk factor is used as the initial probability value of each node; Dynamically update the conditional probability P(B|A) in the Bayesian network based on real-time collected data: P(B|A)=(a′ ij1 ,a′ ij2 ,a′ ij3 ) Among them, a′ ij1 It represents the support probability of risk factor B for the overall risk under given condition A, a′ ij2 It represents the negation probability of risk factor B to the overall risk under condition A, a′ ij3 It represents the uncertainty probability of risk factor B to the overall risk under condition A; Based on the updated conditional probability value, the preliminary weight vector of each node is dynamically adjusted. The dynamic weight value calculation formula for each risk factor is: g′ i =g i ·a′ ij1 +(1-g i )·(1-a′ ij2 )+a′ ij3 ·g i ·(1-g i ) Among them, g′ i is the adjusted weight value, g i is the initial weight, a′ ij1 is the support probability, a′ ij2 is the probability of negation, a′ ij3 is the probability of uncertainty; The dynamically updated weights of the risk factors are traversed and calculated, the dynamically updated weights are normalized, and an updated weight vector is generated based on the adjusted weight values.

4. The method for fire risk assessment of distribution network equipment according to claim 3, characterized in that: Calculating the distribution network equipment risk index to identify equipment risk includes: performing dimensionless processing on the pre-processed real-time data of the risk factors, and performing weighted calculation on the dimensionless real-time data of the risk factors according to the adjusted weight values ​​to obtain weighted values ​​of each risk factor; The weighted values ​​of each risk factor are accumulated to generate the comprehensive risk index R of each device, and the risk threshold W is set. The comprehensive fire risk index R of each device is compared with the risk threshold W: If R ≥ W, the device is marked as a high-risk device, otherwise it is marked as a low-risk device.

5. The method for fire risk assessment of distribution network equipment according to claim 4, characterized in that: When the distribution network equipment is identified as high-risk equipment, the risk simulation is performed in combination with the equipment environment characteristics, including: Collect real-time data of high-risk equipment, extract environmental feature data through feature engineering, and set critical thresholds for risk formation based on equipment safety indicators. The critical thresholds include the highest safe temperature, maximum current load, and minimum humidity. The critical thresholds of various environmental characteristics are combined into a risk benchmark condition matrix, and based on the benchmark conditions, the risk triggering event of temperature and current load exceeding the critical threshold is selected as the initial event of risk formation, and the initial event is used as the initial event node of the fault tree; According to the actual situation of the risk formation of distribution network equipment, select the corresponding logical relationship to link each trigger event, and according to the logical relationship, connect the trigger events step by step to form a risk fault tree; The independent occurrence probability of each event is calculated based on historical data and real-time data, and the risk triggering probability of high-risk equipment is calculated layer by layer through the logical structure of the fault tree.

6. The method for fire risk assessment of distribution network equipment according to claim 5, characterized in that: Also includes: According to the equipment layout and installation location of the distribution network, the physical distance and spatial orientation between high-risk equipment and adjacent equipment are collected, and environmental data that affects the risk expansion are collected; the probability of high-risk equipment fire expanding to adjacent equipment is calculated based on the adjacent relationship between equipment and environmental factors: Among them, S XJ is the probability of fire spreading from device X to device J, S′ X is the fire triggering probability of device X, d XJ is the physical distance between equipment X and J, v is the ventilation speed, σ is the angle between the fire expansion direction and the wind direction, and α is the attenuation coefficient; Set the probability threshold K of fire expansion, if the probability S XJ If it is greater than or equal to K, it means that the fire will spread to adjacent devices, otherwise it will not spread. According to the calculation results of the expansion probability, a path diagram is used to mark the direction and path of the fire expansion to generate a fire expansion path diagram, and the fire expansion probability, fire trigger probability and affected equipment are summarized as a dynamic data set.

7. The method for fire risk assessment of distribution network equipment according to claim 6, characterized in that: Implementing corresponding maintenance measures based on dynamic data of risk simulation includes: classifying distribution network equipment according to the dynamic data set, marking equipment with high trigger probability and on the fire expansion path as priority processing equipment, and marking equipment with high trigger probability but not on the expansion path as second priority processing equipment; If the equipment is a priority processing equipment, the sampling frequency of monitoring data is increased, and the technical personnel are notified to immediately go to the site to check the status of the relevant distribution network equipment; If the equipment is a second-priority processing device, maintain the original data sampling frequency and arrange for technicians to regularly check the status of the distribution network equipment.

8. A distribution network equipment fire risk assessment system, characterized in that: include: A data acquisition and processing module, used to acquire distribution network equipment data and pre-process the data, and calculate the distribution network equipment risk index based on the pre-processing result to identify equipment risks; The risk simulation maintenance module is used to perform risk simulation based on the equipment environment characteristics when the distribution network equipment is identified as high-risk equipment, and implement corresponding maintenance measures based on the dynamic data of risk simulation; Presentation module for visualizing risk simulation data and storing assessment process data.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distribution network equipment fire risk assessment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for fire risk assessment of distribution network equipment as described in any one of claims 1 to 7.

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