Distribution network equipment fire risk quantitative evaluation method and system

Abstract

The invention discloses a distribution network equipment fire risk quantitative evaluation method and system, and the method comprises the steps: obtaining distribution network equipment data, and constructing a risk quantitative first evaluation model based on the data; performing preliminary risk judgment based on the risk quantification first evaluation model, analyzing a fault transmission risk, and calculating an overall risk state value; constructing a risk quantification second evaluation model, wherein the risk quantification second evaluation model is used for predicting the change condition of the risk parameters; and dynamically updating the overall risk state value, and carrying out fire risk early warning in combination with the change condition of the risk parameters. According to the method, the starting condition value and the propagation threshold value are calculated according to the fuzzy membership degrees of different risk factor nodes, the transmission risk between the equipment risk factors can be dynamically monitored, the risk contribution degree is calculated by using a nonlinear function, the nodes with the high risk membership degrees are amplified, the influence of the nodes on the overall risk is more remarkable, and the reliability of the system is improved. And the influence of the key node can be highlighted when the overall risk of the system is evaluated.

Description

Technical Field

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

[0002] With the continuous expansion of the scale and complexity of the distribution network system, the safe operation of distribution network equipment faces severe challenges. Fire extinguishing equipment made of perfluorohexanone material has begun to be used for fire fighting. According to its chemical properties, conductive properties, and mechanism of inhibiting electrical fire discharge, it can be highly compatible with the fire fighting operation of distribution equipment.

[0003] However, in the process of using fire extinguishing equipment made of perfluorohexanone materials, the existing technology for fire risk assessment of distribution network equipment mainly relies on manual regular inspections and traditional fault detection methods, such as temperature monitoring and overload protection. However, these methods often have problems with insufficient monitoring timeliness and accuracy, and are mostly focused on the analysis of single data features or states. There is a lack of multi-level and multi-dimensional comprehensive risk assessment methods, especially in terms of fault transmission and dynamic monitoring of fire risks, which reduces the ability to adaptively adjust to dynamic changes in risks and reduces the efficiency of fire risk assessment. Summary of the invention

[0004] 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.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for quantitatively assessing fire risk of distribution network equipment to solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for quantitatively assessing fire risk of distribution network equipment, including: acquiring distribution network equipment data, and constructing a first quantitative risk assessment model based on the data;

[0009] Performing preliminary risk determination based on the first risk quantitative assessment model, analyzing the fault transmission risk, and calculating the overall risk status value;

[0010] Constructing a second risk quantitative assessment model, wherein the second risk quantitative assessment model is used to predict changes in risk parameters;

[0011] The overall risk status value is dynamically updated, and fire risk warnings are issued in combination with changes in the risk parameters.

[0012] As a preferred solution of the method for quantitatively assessing fire risk of distribution network equipment described in the present invention, the acquisition of distribution network equipment data includes: operation data and status data of the distribution network equipment, and setting operation risk factors and status risk factors based on the operation data and status data.

[0013] As a preferred solution of the method for quantitatively assessing fire risk of distribution network equipment of the present invention, wherein: constructing a first quantitative risk assessment model comprises: the first quantitative risk assessment model adopts a fuzzy Petri network structure;

[0014] Each item of data of the operation risk factor and the state risk factor is defined as a location node; and the triggering condition of each risk factor is defined as a transfer node according to the fault propagation path;

[0015] The directed arcs connecting the location nodes and the transfer nodes represent the relationship between risk factors, and the first quantitative risk assessment model is constructed.

[0016] As a preferred solution of the method for quantitatively assessing fire risk of distribution network equipment described in the present invention, the preliminary risk determination based on the first quantitative risk assessment model includes: calculating the fuzzy membership of the Petri network position node, calculating the corresponding fuzzy membership according to the current collected data value of each position node, and setting the fuzzy membership range [0,1], which is expressed as:

[0017]

[0018] in, represents the fuzzy membership of the ith risk factor, Ip,i represents the actual measured value of the ith risk factor, and Im,i represents the maximum reference value of the ith risk factor;

[0019] Define fuzzy rules, based on the historical data membership of risk factors, calculate the sum of the mean and standard deviation as the risk threshold. If the calculated membership is greater than the risk threshold, it is judged as high risk. If the calculated membership is less than or equal to the risk threshold, it is judged as low risk.

[0020] As a preferred solution of the method for quantitatively assessing fire risk of distribution network equipment of the present invention, analyzing the risk of fault transmission and calculating the overall risk status value includes:

[0021] According to the fuzzy membership of each risk factor node, the activation condition value is calculated, which is expressed as:

[0022]

[0023] Among them, Θij represents the transmission intensity of risk factor node Pi to node Pj, and They represent the fuzzy membership of risk factor node Pi and node Pj respectively;

[0024] Based on historical data, the fault propagation data between different risk factors are counted, and the mean and double standard deviation of the corresponding membership degree are used as the propagation threshold of the corresponding risk factor node. If the calculated condition value is greater than or equal to the propagation threshold, it means that the risk of fault transmission between the two corresponding risk factors is high. If the calculated condition value is less than the propagation threshold, it means that the risk of fault transmission between the two corresponding risk factors is low.

[0025] Based on the judgment that the risk of fault transmission is high, the fuzzy membership of the risk factor is updated by risk superposition, which is expressed as:

[0026]

[0027] in, represents the fuzzy membership of the updated risk factor node Pj;

[0028] Based on the updated fuzzy membership, the risk is re-determined, and the risk contribution of each location node to the overall risk of the system is calculated, which is expressed as:

[0029]

[0030] in, represents the risk contribution of the i-th position node, represents the nonlinear function of the fuzzy membership of the i position nodes after the update, and k represents the natural constant for adjusting the nonlinear influence;

[0031] The contribution is combined into an overall risk status value using the fuzzy overlay method, expressed as:

[0032]

[0033] Among them, Rsy represents the overall risk status value, and n represents the total number of risk factor nodes;

[0034] The mean and three times the standard deviation of the overall risk status value based on historical data are used as the status threshold. If the overall risk status value is greater than or equal to the status threshold, the overall risk is judged to be high.

[0035] As a preferred solution of the method for quantitatively assessing fire risk of distribution network equipment described in the present invention, wherein: constructing a second quantitative risk assessment model, the second quantitative risk assessment model is used to predict the change of risk parameters, including: using the Fourier heat conduction model to determine the heat diffusion rate, combining the material combustion heat to calculate the dynamic heat release rate expressed as:

[0036]

[0037] Where HRR(t) represents the dynamic heat release rate at time t, and ΔH represents the heat release rate constant of the material;

[0038] Calculate the change rate of the dynamic heat release rate ΔHRR(t) based on the time change;

[0039] A risk quantitative second assessment model is constructed as an LSTM model, and the risk quantitative second assessment model includes an input layer, an LSTM layer, and an output layer;

[0040] The input layer inputs the data of the change rate of heat release rate at different times, the LSTM layer extracts the features of the data of the change rate of heat release rate at different times, retains the time series information, and the output layer outputs the predicted value of the change rate of heat release rate at the next moment;

[0041] Use the training set to train the LSTM model, select the cross entropy loss function to calculate the difference between the predicted result and the actual result, use the Adam optimizer to perform gradient descent optimization, update the model weights, and stop iterating if the model loss no longer decreases significantly during continuous iterations to output the model parameters and complete the model training;

[0042] The real-time heat release rate change rate data is input into the LSTM model to predict the heat release rate change rate value at the next moment;

[0043] According to the change rate of heat release rate, the overall risk state is adaptively and dynamically adjusted, which can be expressed as:

[0044] R'sy=Rsy·(1+ΔHRR(t))

[0045] Among them, R'sy represents the updated overall risk status value.

[0046] As a preferred solution of the method for quantitatively assessing fire risk of distribution network equipment described in the present invention, wherein: dynamically updating the overall risk status value and performing fire risk warning in combination with the change of the risk parameter includes: taking the sum of the average value and the standard deviation of the historical heat release rate change rate as the heat release rate threshold;

[0047] If the predicted heat release rate change value at the next moment is greater than or equal to the heat release rate threshold, the overall risk state value at the current moment is increased to the maximum value, and the overall risk state value is updated;

[0048] After the overall risk status value at the current moment is increased to the maximum value, if the heat release rate change rate value at the next moment is less than the heat release rate threshold, the risk status value will be restored within the time window of the heat release rate change rate;

[0049] If the overall risk is judged to be high according to the updated overall risk status value, a warning signal will be triggered, and obvious sound and visual prompts will be issued through the sound and light alarm system to remind the operator that there is a fire risk in the equipment.

[0050] In a second aspect, the present invention provides a distribution network equipment fire risk quantitative assessment system, comprising:

[0051] A first assessment model building module, used to obtain distribution network equipment data and build a first risk quantitative assessment model based on the data;

[0052] A risk status assessment module, used to make a preliminary risk determination based on the first risk quantitative assessment model, analyze the fault transmission risk, and calculate the overall risk status value;

[0053] A second assessment model building module, used to build a second risk quantitative assessment model, wherein the second risk quantitative assessment model is used to predict changes in risk parameters;

[0054] The risk warning module is used to dynamically update the overall risk status value and issue a fire risk warning based on the changes in the risk parameters.

[0055] In a third aspect, the present invention provides an electronic device, comprising:

[0056] Memory and processor;

[0057] 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 method for quantitatively assessing fire risks of distribution network equipment are implemented.

[0058] 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 method for quantitatively assessing fire risks of distribution network equipment.

[0059] Compared with the prior art, the present invention has the following beneficial effects: by defining the operation risk factor and the state risk factor as the position nodes of the fuzzy Petri network structure, the present invention can systematically model the relationship between the risk factors; by calculating the enabling condition value and the propagation threshold according to the fuzzy membership of different risk factor nodes, the transmission risk between the equipment risk factors can be dynamically monitored; by calculating the risk contribution by using a nonlinear function, the nodes with high risk membership are amplified to make their impact on the overall risk more significant, which helps to highlight the impact of key nodes when evaluating the overall risk of the system; the dynamic heat release rate is calculated by the heat diffusion rate, the combustion rate and the combustion heat of the material, so that the heat release rate can reflect the combustion characteristics of the equipment in real time; the time series prediction of the heat release rate change rate is performed based on the LSTM model, the time series characteristics of the heat release rate can be retained, and the changing trend of the fire risk can be analyzed prospectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] 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:

[0061] Figure 1 A method flow diagram of a method and system for quantitatively assessing fire risk of distribution network equipment according to an embodiment of the present invention;

[0062] Figure 2 A detailed flow chart of a method and system for quantitatively assessing fire risk of distribution network equipment according to an embodiment of the present invention;

[0063] Figure 3 A schematic diagram of a flow chart of calculating the overall risk status of a method and system for quantitatively assessing fire risk of distribution network equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] Example 1

[0071] Reference Figure 1 to Figure 3 , is an embodiment of the present invention, which provides a method for quantitatively assessing fire risk of distribution network equipment, including:

[0072] S100: Obtain distribution network equipment data and build a first quantitative risk assessment model based on the data;

[0073] In an embodiment of the present application, obtaining the network distribution equipment data includes: operation data and status data of the network distribution equipment, and setting the operation risk factor and status risk factor based on the operation data and status data.

[0074] Specifically, the operation data is collected based on the distribution network equipment, including the operation current of the equipment, the design rated current value corresponding to the equipment, the current voltage of the equipment, the design rated voltage value corresponding to the equipment, the operating temperature of the equipment and the design temperature upper limit corresponding to the equipment;

[0075] Specifically, status data collection is performed, including the number of years since the equipment was put into use, the designed service life of the equipment, the resistance value of the equipment insulation layer and the minimum insulation resistance value of the safety standard, the contact resistance value of the equipment connection point measured by a contact resistance tester, and the contact resistance value determined based on the standard of the connection device;

[0076] Specifically, the operation risk factors are set, including the current overload rate, the voltage deviation rate and the temperature overlimit ratio value. The current overload rate is determined based on the ratio of the current current to the rated current. The voltage deviation rate is determined based on the deviation ratio of the current voltage to the rated voltage. The temperature overlimit ratio value is determined based on the ratio of the current operating temperature exceeding the upper limit of the safe temperature.

[0077] Specifically, the state risk factors are set, including the aging coefficient, the insulation loss rate and the continuous oxidation rate. The aging coefficient is determined based on the ratio of the current equipment service life to the expected life span. The insulation loss rate is determined based on the ratio of the insulation resistance to the minimum insulation resistance value. The continuous oxidation rate is determined based on the ratio of the contact resistance to the standard contact resistance value.

[0078] By collecting the real-time operation data of the equipment, such as current, voltage and temperature, combined with the design parameters of the equipment, such as rated current, rated voltage and upper temperature limit, the current overload rate, voltage deviation rate and temperature overlimit ratio of the equipment can be calculated in real time, which directly reflects the current operation status of the equipment and helps to identify key operation risks such as overload, overvoltage and overheating in real time, making the assessment more accurate and avoiding the accumulation of hidden dangers that may be caused by traditional regular maintenance.

[0079] In the embodiment of the present application, constructing the first risk quantitative assessment model includes: the first risk quantitative assessment model adopts a fuzzy Petri network structure;

[0080] Each data item of the operation risk factor and the state risk factor is defined as a location node; the triggering condition of each risk factor is defined as a transfer node according to the fault propagation path;

[0081] The directed arcs connecting the location nodes and the transfer nodes represent the relationship between risk factors, and the first quantitative risk assessment model is constructed.

[0082] Specifically, a fuzzy Petri network structure is defined, and each data of the operation risk factor and the state risk factor is defined as a position node, where the position node is marked as Pi, indicating the current risk state of the factor, including current overload risk node P1, voltage deviation risk node P2, temperature overlimit risk node P3, aging coefficient risk node P4, insulation loss risk node P5 and connection oxidation risk node P6;

[0083] According to the fault propagation path, the triggering condition of each risk factor is defined as a transfer node, and the transfer node is marked as Tij, where i and j are the numbers of the relevant risk factors, including the transfer node T13 from current overload risk to temperature overlimit risk, the transfer node T25 from voltage deviation risk to insulation loss risk, the transfer node T34 from temperature overlimit risk to aging coefficient risk, the transfer node T45 from aging coefficient risk to insulation loss risk, the transfer node T56 from insulation loss risk to connection oxidation risk, and the transfer node T36 from temperature overlimit risk to connection oxidation risk;

[0084] The directed arcs connecting the location nodes and the transfer nodes represent the relationship between the risk factors and constitute the fuzzy Petri network FPN model.

[0085] By defining the operation risk factor and the status risk factor as the position nodes in the FPN model, and defining the fault propagation path between the risk factors as the transfer node, FPN can systematically model the relationship between the risk factors. By connecting the position nodes and the transfer nodes with directed arcs, the fault propagation path can be visualized, so that risk managers can intuitively observe which risks will cause subsequent failures, which helps to identify key risk paths and understand their transmission rules, making it easier for equipment managers to take key protective measures for high-risk paths. The fuzzy reasoning method can still predict the overall risk status of the system even when the risk factor status is not clearly "high risk" or "low risk". Make accurate predictions, improve the flexibility and accuracy of risk assessment, connect location nodes into a network structure by transferring nodes, and the FPN model can dynamically display multiple stages of fault propagation. For example, when the current overload risk triggers the temperature overlimit risk, and then further transmits to the aging coefficient risk, FPN can dynamically display this series of fault evolution paths, and equipment managers can clearly observe the gradual development process of the risk, which is convenient for formulating phased intervention measures or early warning mechanisms to prevent small risks from gradually evolving into serious accidents; it can achieve the effect of quantitatively outputting the overall risk status of the system and the possibility of risk propagation, which is convenient for managers to conduct detailed risk management and analysis of the system.

[0086] S200: Perform preliminary risk determination based on the first risk quantitative assessment model, analyze the fault transmission risk, and calculate the overall risk status value;

[0087] The preliminary risk determination based on the first risk quantitative assessment model includes: calculating the fuzzy membership of the Petri network location node, calculating the corresponding fuzzy membership according to the current collected data value of each location node, and setting the fuzzy membership range [0,1], which is expressed as:

[0088]

[0089] in, represents the fuzzy membership of the ith risk factor, Ip,i represents the actual measured value of the ith risk factor, including the current overload rate, voltage deviation rate, temperature overlimit ratio, aging coefficient, insulation loss ratio and connection oxidation rate, Im,i represents the maximum reference value of the ith risk factor, including the maximum value of the current overload rate, the maximum deviation of the voltage deviation rate, the maximum value of the temperature overlimit, the maximum value of the service life, the minimum insulation resistance threshold and the maximum value of the connection oxidation rate;

[0090] Define fuzzy rules, based on the historical data membership of risk factors, calculate the sum of the mean and standard deviation as the risk threshold. If the calculated membership is greater than the risk threshold, it is judged as high risk. If the calculated membership is less than or equal to the risk threshold, it is judged as low risk.

[0091] By converting the collected data into fuzzy membership (ranging from [0,1]), each risk factor of the distribution network equipment can be effectively quantified, making the risk status more intuitive and easy to read. By defining the "IF-THEN" fuzzy rules, the historical data of the risk factors are combined to calculate the mean and standard deviation and set the risk threshold, making the risk threshold more scientific and dynamic, and able to reflect the actual risk level of the equipment at different times and states. The "high risk" and "low risk" judgment criteria defined by the fuzzy rules provide multi-level risk identification for each risk factor. By calculating the comparison between the fuzzy membership and the risk threshold, the system can quickly identify factors in a high-risk state, thereby giving priority to the most serious risks. The multi-level identification method greatly improves the sensitivity of the early warning system, ensuring that equipment managers can receive early warning information in a timely manner under high-risk conditions.

[0092] In the embodiment of the present application, analyzing the fault transmission risk and calculating the overall risk status value includes:

[0093] According to the fuzzy membership of each risk factor node, the activation condition value is calculated, which is expressed as:

[0094]

[0095] Among them, Θij represents the transmission intensity of risk factor node Pi to node Pj, and They represent the fuzzy membership of risk factor node Pi and node Pj respectively;

[0096] Based on historical data, the fault propagation data between different risk factors are counted, and the mean and double standard deviation of the corresponding membership degree are used as the propagation threshold of the corresponding risk factor node. If the calculated condition value is greater than or equal to the propagation threshold, it means that the risk of fault transmission between the two corresponding risk factors is high. If the calculated condition value is less than the propagation threshold, it means that the risk of fault transmission between the two corresponding risk factors is low.

[0097] Based on the judgment that the risk of fault transmission is high, the fuzzy membership of the risk factor is updated by risk superposition, which is expressed as:

[0098]

[0099] in, represents the fuzzy membership of the updated risk factor node Pj;

[0100] Based on the updated fuzzy membership, the risk is re-determined, and the risk contribution of each location node to the overall risk of the system is calculated, which is expressed as:

[0101]

[0102] Among them, CP i represents the risk contribution of the i-th position node, represents the nonlinear function of the fuzzy membership of the i position nodes after the update, and k represents the natural constant for adjusting the nonlinear influence;

[0103] The contribution is combined into an overall risk status value using the fuzzy overlay method, expressed as:

[0104]

[0105] Among them, Rsy represents the overall risk status value, and n represents the total number of risk factor nodes;

[0106] The mean and three times the standard deviation of the overall risk status value based on historical data are used as the status threshold. If the overall risk status value is greater than or equal to the status threshold, the overall risk is judged to be high.

[0107] The mean and three times the standard deviation of the overall risk status value based on historical data are used as the status threshold. If the overall risk status value is greater than or equal to the status threshold, the overall risk is judged to be high.

[0108] By calculating the enabling condition value and propagation threshold according to the fuzzy membership of different risk factor nodes, the transmission risk between equipment risk factors can be dynamically monitored. The mean and double standard deviation determined based on historical data are used as the propagation threshold, so that the system can identify risk transmission based on real-time data and historical trends. By superimposing and updating the risk of fuzzy membership of nodes with high fault transmission risks, the mutual influence between risk factors can be reflected in a refined manner. By using nonlinear functions to calculate the risk contribution, nodes with high risk membership are amplified to make their impact on the overall risk more significant, which helps to highlight the impact of key nodes when assessing the overall risk of the system and ensure that high-risk nodes can be timely introduced. Attention should be paid to improving the early warning sensitivity of the system to avoid the underestimation of important risks. The risk contribution of each location node is combined into an overall risk status value using the fuzzy superposition method, so that the risk status of each location node is taken into consideration, which improves the comprehensiveness of the overall risk status assessment. Through the risk contribution calculation and the change of the overall risk status value, the system can trace back to the specific high-risk location node when the overall risk status value is high, so as to quickly lock the source of the risk. In addition, the threshold setting combining historical data and standard deviation provides trend analysis of risk changes, provides data support for risk prediction and long-term management of equipment, and helps to take preventive measures in advance before risks accumulate or the status deteriorates.

[0109] S300: constructing a second risk quantitative assessment model, where the second risk quantitative assessment model is used to predict changes in risk parameters;

[0110] It should be noted that the Fourier heat conduction model is used to determine the heat diffusion rate, the dynamic heat release rate is calculated in combination with the material combustion heat, and the LSTM model is used to predict the rate of change of the dynamic heat release rate at the next moment, including:

[0111] Based on the real-time temperature data, the Fourier heat conduction model is used to calculate the temperature diffusion characteristics in the material, which is expressed as:

[0112]

[0113] Among them, α represents the thermal diffusion coefficient of the equipment material, T(t) represents the temperature data at the acquisition location, represents the heat diffusion rate;

[0114] Using the thermal diffusion rate and the heat flux of the device, the real-time combustion rate is calculated and expressed as:

[0115]

[0116] in, represents the burning rate, ρ represents the material density, and A represents the surface area of ​​the equipment;

[0117] In the embodiment of the present application, a second risk quantitative assessment model is constructed. The second risk quantitative assessment model is used to predict the change of risk parameters, including: using the Fourier heat conduction model to determine the heat diffusion rate, and combining the material combustion heat to calculate the dynamic heat release rate expressed as:

[0118]

[0119] Where HRR(t) represents the dynamic heat release rate at time t, and ΔH represents the heat release rate constant of the material;

[0120] Calculate the change rate of the dynamic heat release rate ΔHRR(t) based on the time change;

[0121] A risk quantitative second assessment model is constructed as an LSTM model, and the risk quantitative second assessment model includes an input layer, an LSTM layer, and an output layer;

[0122] The input layer inputs the data of the change rate of heat release rate at different times, the LSTM layer extracts the features of the data of the change rate of heat release rate at different times, retains the time series information, and the output layer outputs the predicted value of the change rate of heat release rate at the next moment;

[0123] Use the training set to train the LSTM model, select the cross entropy loss function to calculate the difference between the predicted result and the actual result, use the Adam optimizer to perform gradient descent optimization, update the model weights, and stop iterating if the model loss no longer decreases significantly during continuous iterations to output the model parameters and complete the model training;

[0124] The real-time heat release rate change rate data is input into the LSTM model to predict the heat release rate change rate value at the next moment;

[0125] According to the change rate of heat release rate, the overall risk state is adaptively and dynamically adjusted, which can be expressed as:

[0126] R'sy=Rsy·(1+ΔHRR(t))

[0127] Among them, R'sy represents the updated overall risk status value.

[0128] It should be noted that by calculating the temperature diffusion rate through the Fourier heat conduction model, the heat diffusion characteristics inside the equipment material can be accurately monitored based on real-time temperature data. Combined with the characteristics of material temperature changes, it is helpful to identify temperature anomalies and potential combustion risks, so that risk warnings can be issued when fire precursors appear. The dynamic heat release rate is calculated by the heat diffusion rate, combustion rate and combustion heat of the material, so that HRR can reflect the combustion characteristics of the equipment in real time. The time series prediction of the HRR change rate based on the LSTM model can retain the time series characteristics of HRR and conduct forward-looking analysis on the changing trend of fire risks. By predicting the HRR change rate at the next moment, this method can be used in An early warning is issued before the fire risk reaches its peak, which effectively improves the ability to predict fire risks and provides sufficient reaction time for system managers. According to the HRR change rate prediction results, the overall risk status value is adaptively adjusted so that the system can automatically adapt to the current status as the fire risk changes. The LSTM model uses historical HRR change rate data for learning during the training process. It is not only used for real-time prediction, but also for analyzing the historical risk data of equipment to help equipment managers understand the long-term operating status of the equipment. Through the accumulation and trend analysis of HRR historical data, managers can make accurate equipment maintenance arrangements to avoid unnecessary downtime and maintenance and improve the operation and maintenance efficiency of equipment.

[0129] S400: Dynamically update the overall risk status value and issue a fire risk warning based on the changes in risk parameters;

[0130] In the embodiment of the present application, dynamically updating the overall risk status value and performing fire risk warning in combination with the change of risk parameters includes: taking the sum of the average value and the standard deviation of the historical heat release rate change rate as the heat release rate threshold;

[0131] If the predicted heat release rate change value at the next moment is greater than or equal to the heat release rate threshold, the overall risk state value at the current moment is increased to the maximum value, and the overall risk state value is updated;

[0132] After the overall risk status value at the current moment is increased to the maximum value, if the heat release rate change rate value at the next moment is less than the heat release rate threshold, the risk status value will be restored within the time window of the heat release rate change rate;

[0133] It should be noted that by setting the heat release rate threshold through the average value and standard deviation of the historical HRR change rate, the system can identify and respond in advance before the predicted HRR change rate reaches the high-risk moment, and automatically increase the overall risk status value to the maximum value when the HRR change rate prediction value reaches the threshold, thereby realizing adaptive dynamic adjustment of the risk status and ensuring that the overall risk status can instantly reflect the peak value of the fire risk, effectively improving the accuracy and timeliness of the system's risk assessment, avoiding the lag of manual intervention, and ensuring that the fire risk not only depends on the current HRR value, but also on the dynamic change trend and historical pattern, providing more accurate fire precursor monitoring and reducing false alarms and missed alarms.

[0134] In an embodiment of the present application, if the overall risk is judged to be high according to the updated overall risk status value, a warning signal is triggered, and an obvious sound and visual prompt is emitted through the sound and light alarm system to remind the operator that there is a fire risk in the equipment.

[0135] It should also be noted that the time, location and key risk parameters that trigger the high-risk state are automatically recorded, including the current HRR value, temperature, and the membership value of each risk factor;

[0136] The warning information is sent to the control center, and a "High Risk Warning" dialog box pops up on the monitoring interface, and corresponding fire warning measures are implemented according to the fire protection standards of the distribution network equipment.

[0137] Through the sound and visual prompts of the sound and light alarm system, the system can immediately remind the operator after judging the high-risk status. The objectivity and integrity of the data are guaranteed through automatic recording, avoiding the omission or inaccuracy of data that may be caused by manual recording, and providing a reliable basis for subsequent analysis. By sending the warning information to the control center and popping up the "High Risk Warning" dialog box on the monitoring interface, it ensures that the management and relevant persons in charge can understand the high-risk status of the equipment in a timely manner.

[0138] Example 2

[0139] Reference Figures 1 to 3 , 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 quantitative assessment system.

[0140] It should be noted that the technical solution of the distribution network equipment fire risk quantitative assessment system and the technical solution of the distribution network equipment fire risk quantitative assessment method mentioned above belong to the same concept. For the details not described in detail in the technical solution of the distribution network equipment fire risk quantitative assessment system in this embodiment, please refer to the description of the technical solution of the distribution network equipment fire risk quantitative assessment method mentioned above.

[0141] In this embodiment, a distribution network equipment fire risk quantitative assessment system includes:

[0142] A first assessment model building module, used to obtain distribution network equipment data and build a first risk quantitative assessment model based on the data;

[0143] A risk status assessment module is used to make a preliminary risk determination based on the first risk quantitative assessment model, analyze the fault transmission risk, and calculate the overall risk status value;

[0144] A second assessment model building module is used to build a risk quantitative second assessment model, and the risk quantitative second assessment model is used to predict changes in risk parameters;

[0145] The risk warning module is used to dynamically update the overall risk status value and issue fire risk warnings based on changes in risk parameters.

[0146] This embodiment further provides an electronic device, which is applicable to the case of the quantitative assessment method for fire risk of distribution network equipment, including:

[0147] 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 method for quantitatively assessing fire risk of distribution network equipment as proposed in the above embodiment.

[0148] 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 quantitatively assessing fire risk of distribution network equipment proposed in the above embodiment is implemented.

[0149] The storage medium proposed in this embodiment and the method for implementing the quantitative assessment of fire risk of distribution network equipment proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0150] 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, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0151] 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 quantitative assessment method for fire risk of distribution network equipment, characterized in that: include: Obtain distribution network equipment data and build the first quantitative risk assessment model based on the data; Conduct preliminary risk assessment based on the first quantitative risk assessment model, analyze the failure transmission risk, and calculate the overall risk status value; Constructing a second quantitative risk assessment model, which is used to predict changes in risk parameters; Dynamically update the overall risk status value and issue fire risk warnings based on changes in risk parameters.

2. The method for quantitatively assessing fire risk of distribution network equipment according to claim 1, characterized in that: The acquiring of the network distribution equipment data includes: operation data and status data of the network distribution equipment, and setting an operation risk factor and a status risk factor based on the operation data and status data.

3. The method for quantitatively assessing fire risk of distribution network equipment according to claim 2, characterized in that: The construction of the first quantitative risk assessment model includes: the first quantitative risk assessment model adopts a fuzzy Petri network structure; Each item of data of the operation risk factor and the state risk factor is defined as a location node; and the triggering condition of each risk factor is defined as a transfer node according to the fault propagation path; The directed arcs connecting the location nodes and the transfer nodes represent the relationship between risk factors, and the first quantitative risk assessment model is constructed.

4. The method for quantitatively assessing fire risk of distribution network equipment according to claim 3, characterized in that: The preliminary risk determination based on the first risk quantitative assessment model includes: calculating the fuzzy membership of the Petri network location node, calculating the corresponding fuzzy membership according to the current collected data value of each location node, and setting the fuzzy membership range [0,1], which is expressed as: in, represents the fuzzy membership of the ith risk factor, Ip,i represents the actual measured value of the ith risk factor, and Im,i represents the maximum reference value of the ith risk factor; Define fuzzy rules, based on the historical data membership of risk factors, calculate the sum of the mean and standard deviation as the risk threshold. If the calculated membership is greater than the risk threshold, it is judged as high risk. If the calculated membership is less than or equal to the risk threshold, it is judged as low risk.

5. The method for quantitatively assessing fire risk of distribution network equipment according to claim 4, characterized in that: Analyze the failure transmission risk and calculate the overall risk status value including: According to the fuzzy membership of each risk factor node, the activation condition value is calculated, which is expressed as: Among them, Θij represents the transmission intensity of the risk factor node Pi to the node Pj, and They represent the fuzzy membership of risk factor node Pi and node Pj respectively; Based on historical data, the fault propagation data between different risk factors are counted, and the mean and double standard deviation of the corresponding membership degree are used as the propagation threshold of the corresponding risk factor node. If the calculated condition value is greater than or equal to the propagation threshold, it means that the risk of fault transmission between the two corresponding risk factors is high. If the calculated condition value is less than the propagation threshold, it means that the risk of fault transmission between the two corresponding risk factors is low. Based on the judgment that the risk of fault transmission is high, the fuzzy membership of the risk factor is updated by risk superposition, which is expressed as: in, represents the fuzzy membership of the updated risk factor node Pj; Based on the updated fuzzy membership, the risk is re-determined, and the risk contribution of each location node to the overall risk of the system is calculated, which is expressed as: in, represents the risk contribution of the i-th position node, represents the nonlinear function of the fuzzy membership of the i position nodes after the update, and k represents the natural constant for adjusting the nonlinear influence; The contribution is combined into an overall risk status value using the fuzzy overlay method, expressed as: Among them, Rsy represents the overall risk status value, and n represents the total number of risk factor nodes; The mean and three times the standard deviation of the overall risk status value based on historical data are used as the status threshold. If the overall risk status value is greater than or equal to the status threshold, the overall risk is judged to be high.

6. The method for quantitatively assessing fire risk of distribution network equipment according to claim 5, characterized in that: Constructing a second risk quantitative assessment model, which is used to predict the changes in risk parameters, includes: using the Fourier heat conduction model to determine the heat diffusion rate, and combining the material combustion heat to calculate the dynamic heat release rate, which is expressed as: Where HRR(t) represents the dynamic heat release rate at time t, and ΔH represents the heat release rate constant of the material; Calculate the change rate of the dynamic heat release rate ΔHRR(t) based on the time change; A risk quantitative second assessment model is constructed as an LSTM model, and the risk quantitative second assessment model includes an input layer, an LSTM layer, and an output layer; The input layer inputs the data of the change rate of heat release rate at different times, the LSTM layer extracts the features of the data of the change rate of heat release rate at different times, retains the time series information, and the output layer outputs the predicted value of the change rate of heat release rate at the next moment; Use the training set to train the LSTM model, select the cross entropy loss function to calculate the difference between the predicted result and the actual result, use the Adam optimizer to perform gradient descent optimization, update the model weights, and stop iterating if the model loss no longer decreases significantly during continuous iterations to output the model parameters and complete the model training; The real-time heat release rate change rate data is input into the LSTM model to predict the heat release rate change rate value at the next moment; According to the change rate of heat release rate, the overall risk state is adaptively and dynamically adjusted, which can be expressed as: R'sy=Rsy·(1+ΔHRR(t)) Among them, R'sy represents the updated overall risk status value.

7. The method for quantitatively assessing fire risk of distribution network equipment according to claim 6, characterized in that: Dynamically updating the overall risk status value and conducting fire risk warning in combination with the change of the risk parameters includes: taking the sum of the average value and the standard deviation of the historical heat release rate change rate as the heat release rate threshold; If the predicted heat release rate change rate value at the next moment is greater than or equal to the heat release rate threshold, the overall risk state value at the current moment is increased to the maximum value, and the overall risk state value is updated; After the overall risk status value at the current moment is increased to the maximum value, if the heat release rate change rate value at the next moment is less than the heat release rate threshold, the risk status value will be restored within the time window of the heat release rate change rate; If the overall risk is judged to be high according to the updated overall risk status value, a warning signal will be triggered, and obvious sound and visual prompts will be issued through the sound and light alarm system to remind the operator that there is a fire risk in the equipment.

8. A distribution network equipment fire risk quantitative assessment system, characterized in that: include: A first assessment model building module, used to obtain distribution network equipment data and build a first risk quantitative assessment model based on the data; A risk status assessment module, used to make a preliminary risk determination based on the first risk quantitative assessment model, analyze the fault transmission risk, and calculate the overall risk status value; A second assessment model building module, used to build a second risk quantitative assessment model, wherein the second risk quantitative assessment model is used to predict changes in risk parameters; The risk warning module is used to dynamically update the overall risk status value and issue a fire risk warning based on the changes in the risk parameters.

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 method for quantitatively assessing fire risks of distribution network equipment 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 quantitatively assessing fire risk of distribution network equipment as described in any one of claims 1 to 7.

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