Power battery alarm handling force dispatching method and system based on large fire-fighting model

Abstract

The invention relates to the technical field of emergency management, in particular to a power battery alarm handling strength scheduling method and system based on a fire-fighting large model, and the method comprises the following steps: carrying out text summarization through a large language model based on power battery parameter data, extracting material components and design structure information of various batteries, and carrying out data analysis; and analyzing the heat released by the multiple types of new energy batteries during combustion, the combustion rate and the combustion characteristics, and generating a power battery combustion model. According to the method, combustion characteristics of various types of new energy batteries are analyzed, fire diffusion and spreading processes in various external environments are simulated, flame propagation paths and battery fire characteristics are predicted, and the emergency degrees of various fire alarm conditions are evaluated and priority scores are calculated by analyzing historical alarm condition data. The efficiency and effectiveness of resource scheduling are optimized, the alarm response speed is improved, the number and type of required resources are predicted by analyzing the geographic position, the fire level and the environmental condition of the real-time fire, efficient deployment of fire-fighting power is carried out, and fire-fighting power waste and resource redundancy are reduced.

Description

Technical Field

[0001] The present invention relates to the field of emergency management technology, and in particular to a method and system for dispatching power battery emergency response forces based on a large fire model. Background Art

[0002] The field of emergency management technology prevents, prepares, responds to and recovers from various emergencies and emergency situations by formulating and implementing strategies, processes and technical solutions. It optimizes the response speed when natural disasters, industrial accidents or public safety incidents occur by combining the optimization of decision-making processes, resource allocation and operational efficiency. It uses real-time data analysis, large language models, risk assessment models, communication systems and command control centers to reduce risks, mitigate the impact of disasters, and ensure public safety and social stability.

[0003] Among them, the power battery alarm handling force dispatch method based on the fire model is an emergency response and resource dispatch plan for power battery system safety accidents. It uses large-scale data models to predict and assess risks, and achieves rapid and effective response to alarms, including accurate extraction and analysis of alarm information, optimization of the allocation and dispatch of fire resources, and ensures that alarms can be quickly controlled and handled to reduce casualties and property losses. It combines modern information technology and intelligent algorithms to improve the accuracy and efficiency of alarm handling.

[0004] Traditional police dispatch methods cannot make full use of real-time data for dynamic resource allocation and rapid response. When dealing with power battery system safety accidents, they cannot automatically extract useful information from the alarm information accurately and comprehensively, and cannot effectively respond to the rapid changes in the fire situation, resulting in response delays and improper resource allocation. It is difficult to solve the real-time impact of various fire types and external environmental changes on resource demand, resulting in excessive and insufficient resource deployment, increasing the accident handling time and the degree of fire damage. It is impossible to evaluate the development trend of the fire in real time, making it difficult to carry out accurate early warning and rapid disposal, which increases the response time in new energy power battery fires and leads to the failure of rescue operations and unnecessary losses. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a power battery alarm handling force dispatching method and system based on a large fire model.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a method for dispatching power battery alarm handling force based on a large fire model, comprising the following steps:

[0007] S1: Based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the heat released, combustion rate, and combustion characteristics of various types of new energy batteries during combustion, and generate a power battery combustion model;

[0008] S2: Based on the power battery combustion model, simulate the diffusion and spread process of battery fire under various external environments, analyze the flame propagation path and characteristics of battery fire, and generate diffusion trend analysis results;

[0009] S3: Based on the diffusion trend analysis results, combined with historical alarm data, multiple fire alarms are analyzed and the urgency is evaluated, priority scores are calculated, and an alarm priority ranking model is generated;

[0010] S4: Based on the alarm priority ranking model, analyze the real-time fire situation and police resource status, identify the urgency and resource requirements of the real-time alarm situation, adjust the police resource scheduling order, and generate a real-time alarm response sequence;

[0011] S5: Based on the real-time alarm response sequence, the geographical location, fire level and environmental conditions of the real-time fire are analyzed, and by comparing with historical fire information, the required number of firefighters, vehicle types and equipment are analyzed to obtain resource demand prediction information;

[0012] S6: Based on the resource demand forecast information, analyze the real-time firefighting force deployment and equipment inventory, calculate the firefighting resource mobilization parameters and firefighting response path, and generate the alarm handling dispatch parameters.

[0013] As a further solution of the present invention, the power battery combustion model specifically includes the thermal response characteristics of the battery material, the heat release rate, and the battery design structure; the diffusion trend analysis results include the flame propagation speed, the flame propagation path, and the affected battery structure characteristics; the alarm priority ranking model specifically includes the fire hazard rating, historical response time, and resource input statistics; the real-time alarm response sequence includes alarm time information, police force availability information, and fire location information; the resource demand forecast information includes the expected number of firefighters, fire truck types and quantity information, and estimated equipment type information; the alarm handling dispatch parameters specifically include the mobilization time of the fire brigade, the response path optimization plan, and the equipment deployment strategy.

[0014] As a further solution of the present invention, based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the heat released, combustion rate, and combustion characteristics of various types of new energy batteries during combustion, and generate a power battery combustion model in the following specific steps:

[0015] S101: Based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the material composition and design structure of various batteries, identify the characteristics and combustion characteristics of various batteries, and generate basic data on battery combustion characteristics;

[0016] S102: Based on the basic data of battery combustion characteristics, a Monte Carlo simulation method is used to calculate heat release rates of various types of batteries by simulating the temperature and oxygen concentration in the combustion environment, and generate battery heat release and rate information;

[0017] S103: Based on the battery heat release and rate information, analyze the combustion behaviors of multiple batteries under the same conditions, identify the combustion modes and characteristics of the batteries, and generate a power battery combustion model.

[0018] As a further solution of the present invention, the Monte Carlo simulation method is through the formula:

[0019]

[0020] Calculate the heat release rate of the battery in a simulated combustion environment, where Q′ is the total heat release rate, ∑ represents the sum, i is the index variable, N is the total number of simulations, and w i is the weight coefficient of the ith simulation, q i is the heat release rate obtained from the ith simulation, T i is the temperature during the ith simulation, O i is the oxygen concentration in the ith simulation, E i is the energy state of the battery during the i-th simulation.

[0021] As a further solution of the present invention, based on the power battery combustion model, the diffusion and spread process of battery fire under various external environments is simulated, the flame propagation path and the characteristics of battery fire are analyzed, and the steps of generating diffusion trend analysis results are specifically as follows:

[0022] S201: Based on the power battery combustion model, simulate the flame propagation process under differentiated external conditions, including oxygen content, humidity, temperature, and space size, and generate an environmental factor impact model;

[0023] S202: Based on the environmental factor impact model, analyze the fire spread rate and path of various battery types in different external environments, and generate a fire spread path simulation result in combination with the influence of battery arrangement and installation position;

[0024] S203: Based on the fire spread path simulation results, analyze the characteristics and spread trends of various power battery fires, evaluate the required fire extinguisher types and fire extinguishing steps, and generate spread trend analysis results.

[0025] As a further solution of the present invention, based on the diffusion trend analysis results, combined with historical alarm data, multiple fire alarms are analyzed and the urgency is evaluated, priority scores are calculated, and the steps of generating an alarm priority ranking model are specifically as follows:

[0026] S301: Based on the diffusion trend analysis result, analyze the historical alarm data, identify the data associated with the fire type, including the duration and fire intensity data, and generate the fire type data classification;

[0027] S302: Based on the fire type data classification, by evaluating the frequency and response time of various fire types, calculating the urgency of various power battery fires, and generating a fire urgency assessment score;

[0028] S303: Based on the fire urgency assessment scores, the response priorities of various types of fires are ranked, and a warning priority ranking model is generated in combination with resource requirements and availability.

[0029] As a further solution of the present invention, based on the alarm priority ranking model, the steps of analyzing the real-time fire situation and police resource status, identifying the urgency and resource requirements of the real-time alarm situation, adjusting the police resource scheduling order, and generating the real-time alarm response sequence are specifically as follows:

[0030] S401: Based on the alarm priority ranking model, real-time monitoring of the fire situation and firefighting resource status, including the location of the fire brigade, available vehicles and equipment, and generating firefighting resource assessment information;

[0031] S402: Based on the fire resource assessment information, analyzing the matching degree between fire resource configuration and disaster demand, identifying the priority of resource allocation, and generating a resource scheduling priority sequence;

[0032] S403: Based on the resource scheduling priority sequence, police resources are adjusted and the deployment plan is optimized to match the real-time fire situation, and a real-time alarm response sequence is generated.

[0033] As a further solution of the present invention, based on the real-time alarm response sequence, the geographical location, fire level and environmental conditions of the real-time fire are analyzed, and by comparing with historical fire information, the required number of firefighters, vehicle types and equipment are analyzed to obtain resource demand forecast information. Specifically, the steps are as follows:

[0034] S501: Based on the real-time alarm response sequence, compare the historical dispatch records with the real-time fire parameters, including the geographical location, fire level and environmental conditions of the fire, and generate real-time disaster analysis results in combination with the number of firefighters, vehicle types and equipment lists;

[0035] S502: Based on the real-time disaster analysis results, by analyzing the dispatch information of firefighters, vehicles and equipment for similar fire situations in historical records, the quantity and type of resources required for various fire situations are calculated, and a dispatch resource evaluation list is generated;

[0036] S503: Based on the dispatch resource assessment list, combined with the real-time fire development, including fire level and environmental conditions, the number of firefighters, vehicle types and equipment requirements are analyzed to generate resource demand forecast information.

[0037] As a further solution of the present invention, based on the resource demand forecast information, the steps of analyzing the real-time firefighting force deployment and equipment inventory, calculating the firefighting resource mobilization parameters and the firefighting response path, and generating the alarm handling dispatch parameters are specifically as follows:

[0038] S601: Based on the resource demand forecast information, analyze the real-time firefighting force deployment and equipment inventory, including the available firefighter positions, vehicles and equipment status, and generate real-time firefighting resource information;

[0039] S602: Based on the real-time firefighting resource information, combined with the fire level, geographical location and current environment, the firefighter deployment, vehicle driving routes and equipment configuration of multiple fire stations are calculated to generate firefighting resource configuration parameters;

[0040] S603: Based on the fire resource configuration parameters, by matching the vehicle dispatch sequence and the fire station location, the fire response path is calculated to generate the alarm handling dispatch parameters.

[0041] A power battery alarm handling force dispatching system based on a large fire model, the power battery alarm handling force dispatching system based on a large fire model is used to execute the above-mentioned power battery alarm handling force dispatching method based on a large fire model, and the system includes:

[0042] The battery characteristic analysis module performs text summarization based on the power battery parameter data through a large language model, extracts the material composition and design structure information of various batteries, analyzes the heat release and combustion rate of various power batteries, identifies the combustion characteristics of various batteries, and generates combustion characteristic analysis results;

[0043] The environmental simulation analysis module generates environmental factor assessment information by simulating the influence of differentiated environmental factors on fire spread, including oxygen content and humidity, based on the combustion characteristic analysis results;

[0044] The fire alarm assessment module evaluates the urgency and priority of the fire alarm based on the environmental factor assessment information and combines the historical alarm data to generate a disaster urgency calculation result;

[0045] The fire resource analysis module performs real-time fire situation and resource status analysis based on the disaster urgency calculation result, adjusts the resource dispatching sequence of police force and equipment, and generates response priority evaluation results;

[0046] The disaster response scheduling module analyzes the required number of firefighters, vehicles and equipment based on the response priority assessment results, combined with the real-time fire geographical location, fire level and environmental conditions, calculates resource scheduling parameters and response paths, and generates alarm handling scheduling parameters.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by utilizing a large language model to perform text summarization tasks, the combustion characteristics of various types of new energy batteries are analyzed, the combustion characteristics of various batteries are identified, and the fire diffusion and spread processes under various external environments are simulated to predict the flame propagation path and the characteristics of battery fires, and the diffusion trend is identified. By analyzing historical alarm data, the urgency of various fire alarms is evaluated and priority scores are calculated, alarm priorities are sorted and resource requirements are identified, the efficiency and effectiveness of resource scheduling are optimized, and the speed of alarm response is improved. By analyzing the geographical location, fire level and environmental conditions of real-time fires, the required quantity and type of resources are predicted, fire-fighting resources and equipment are accurately mobilized, and efficient deployment of fire-fighting forces is achieved. The accuracy and speed of alarm response are enhanced, the waste of fire-fighting forces and resource redundancy are reduced, and police forces are quickly mobilized in actual fires to reduce casualties and property losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions 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 orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] Embodiment 1

[0060] See also Figure 1 The present invention provides a technical solution, a method for dispatching power battery alarm handling force based on a large fire model, comprising the following steps:

[0061] S1: Based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the heat released, combustion rate, and combustion characteristics of various types of new energy batteries during combustion, and generate a power battery combustion model;

[0062] S2: Based on the power battery combustion model, simulate the diffusion and spread process of battery fire under various external environments, analyze the flame propagation path and characteristics of battery fire, and generate diffusion trend analysis results;

[0063] S3: Based on the results of diffusion trend analysis and combined with historical alarm data, analyze various fire alarms and evaluate the urgency, calculate the priority score, and generate an alarm priority ranking model;

[0064] S4: Based on the alarm priority sorting model, analyze the real-time fire situation and police resource status, identify the urgency and resource requirements of the real-time alarm situation, adjust the police resource dispatch order, and generate a real-time alarm response sequence;

[0065] S5: Based on the real-time alarm response sequence, analyze the geographical location, fire level and environmental conditions of the real-time fire situation, and analyze the number of firefighters, vehicle types and equipment required by comparing with historical fire information to obtain resource demand forecast information;

[0066] S6: Based on resource demand forecast information, analyze real-time firefighting force deployment and equipment inventory, calculate firefighting resource mobilization parameters and firefighting response paths, and generate emergency response dispatch parameters.

[0067] The power battery combustion model specifically includes the thermal response characteristics of the battery material, the heat release rate, and the battery design structure. The diffusion trend analysis results include the flame propagation speed, the flame propagation path, and the affected battery structure characteristics. The alarm priority sorting model specifically includes the fire hazard rating, historical response time, and resource input statistics. The real-time alarm response sequence includes alarm time information, police force availability information, and fire location information. The resource demand forecast information includes the expected number of firefighters, fire truck types and quantity information, and estimated equipment type information. The alarm handling and dispatching parameters specifically include the mobilization time of the fire brigade, the response path optimization plan, and the equipment deployment strategy.

[0068] See also Figure 2 Based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the heat released, combustion rate, and combustion characteristics of various types of new energy batteries during combustion, and generate a power battery combustion model in the following steps:

[0069] S101: Based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the material composition and design structure of various batteries, identify the characteristics and combustion characteristics of various batteries, and generate basic data on battery combustion characteristics. The specific process is as follows;

[0070] In sub-step S101, based on the power battery parameter data, a text summary is performed through a large language model. The collected technical documents and research reports on the power battery are input into the model, and the key information, including the material composition, design structure, electrochemical performance, etc. of the battery, is extracted using natural language processing technology. The extracted data is classified and sorted, and the combination and structure of various battery materials and the physical and chemical properties, including energy density, charge and discharge speed, and temperature stability, are analyzed through algorithms, and the correlation with the combustion characteristics of the battery is analyzed. The formula is R ij = func(C i ,S j ), where R ij represents the combustion characteristics of the ith battery under the jth condition, C i is the material composition of the ith battery, S j Based on the design structural characteristics of batteries, the combustion risks of various batteries under differentiated operating conditions and environmental factors are identified, and basic data on battery combustion characteristics are generated.

[0071] S102: Based on the basic data of battery combustion characteristics, the Monte Carlo simulation method is used to simulate the temperature and oxygen concentration in the combustion environment to calculate the heat release rate of various types of batteries. The specific process of generating battery heat release and rate information is as follows;

[0072] In sub-step S102, based on the basic data of battery combustion characteristics, a Monte Carlo simulation method is used to establish a computational model that simulates the battery combustion environment. In the simulated environment, differentiated temperature and oxygen concentration parameters are set, and the heat release and combustion rate of the battery under differentiated environmental conditions are analyzed. The thermal energy output of the battery under simulated combustion conditions is quantified, and the heat release characteristics and combustion rates of various types of batteries are evaluated, generating battery heat release and rate information.

[0073] Monte Carlo simulation method, through the formula:

[0074]

[0075] Calculate the heat release rate of the battery in a simulated combustion environment, where Q′ is the total heat release rate, which is a comprehensive value calculated based on the weighted average, ∑ represents the sum, which is used to calculate the sum of all simulation results from 1 to N, i is an index variable, which is used to indicate the order of the current simulation, from 1 to N, N is the total number of simulations, indicating how many simulation calculations have been performed, and w i is the weight coefficient of the ith simulation, which is dynamically adjusted according to the influence of environmental variables to reflect the influence of the actual situation of the target simulation on the final result. i is the heat release rate obtained from the ith simulation, which is a parameter directly calculated in the simulation. i is the temperature during the ith simulation, reflecting the temperature conditions of the simulated environment. i is the oxygen concentration during the i-th simulation, indicating the oxygen condition in the target environment, E i is the energy state of the battery at the i-th simulation, indicating the energy level of the battery at the beginning of the simulation.

[0076] The specific execution process of the formula is as follows:

[0077] By considering the effects of temperature, oxygen concentration and battery energy state on heat release, the correlation parameter T is determined based on historical data and experiments. i , O i , and E i The value of , adjust the weight w i , to ensure that the simulation results reflect the actual combustion environment, and to use experimental and historical data to set the weight coefficient w i, reflecting the actual influence of multiple parameters on the heat release rate under the target environmental conditions, predicting the contribution of multiple environmental variables to the heat release rate based on historical data, and analyzing the results of multiple simulations by weighted average method to obtain the heat release rate Q′, which reflects the thermal response characteristics of the battery under differentiated environmental conditions.

[0078] S103: Based on the battery heat release and rate information, the combustion behaviors of various batteries under the same conditions are analyzed, the combustion modes and characteristics of the batteries are identified, and the specific process of generating a power battery combustion model is as follows;

[0079] In sub-step S103, based on the battery heat release and rate information, a multi-battery combustion behavior analysis is performed under unified experimental conditions. By setting the same external combustion conditions, including temperature, pressure and oxygen concentration, high-speed photography and thermal imaging technology are used to record the battery combustion process, and the combustion duration and heat release pattern of multiple batteries are analyzed and compared. The formula is:

[0080]

[0081] Among them, R represents the combustion rate, ΔQ represents the heat change, and Δt represents the time interval. The combustion efficiency and characteristics of various batteries under the same conditions are compared to generate a power battery combustion model.

[0082] See also Figure 3 Based on the power battery combustion model, the diffusion and spread process of battery fires under various external environments is simulated, the flame propagation path and the characteristics of battery fires are analyzed, and the steps to generate the diffusion trend analysis results are as follows:

[0083] S201: Based on the power battery combustion model, simulate the flame propagation process under differentiated external conditions, including oxygen content, humidity, temperature, and space size. The specific process of generating the environmental factor influence model is as follows;

[0084] In sub-step S201, based on the power battery combustion model, differentiated external conditions of the simulation experiment are set, including oxygen content, humidity, temperature and space size, to simulate the flame propagation process under various conditions, and the behavior changes of the flame are monitored by changing the oxygen concentration and ambient humidity. The formula is F = α·pO2+β·pH2O+γ·T+δ·V, wherein F represents the flame propagation rate, pO2 represents the partial pressure of oxygen, pH2O represents the influence coefficient of humidity, T represents the ambient temperature, V represents the space volume, α is the weight coefficient of the oxygen factor, β is the weight coefficient of the humidity factor, γ is the weight coefficient of the ambient temperature, and δ is the weight coefficient of the space volume factor. The influence of differentiated environmental factors on the flame propagation rate is quantified to generate an environmental factor influence model.

[0085] S202: Based on the environmental factor impact model, the fire spread rate and path of various battery types under different external environments are analyzed. Combined with the influence of battery arrangement and installation position, the specific process of generating the fire spread path simulation result is as follows;

[0086] In sub-step S202, based on the environmental factor impact model, the fire spread rate and path of various battery types in the target external environment are analyzed. Combined with the arrangement and installation position of the battery, the three-dimensional space modeling technology is used to simulate the process of flame spread in the differentiated layout. The formula is S = k·

[0087] (r·F+s·D), where S represents the fire spread speed, F is the aforementioned flame propagation rate, D represents the density and arrangement of the battery, r and s are the coefficients of the sensitivity and response speed of flame propagation to the battery arrangement, respectively, and k is the adjustment coefficient. The spread path of battery fire under differentiated arrangements and environmental conditions is analyzed to generate fire spread path simulation results.

[0088] S203: Based on the fire spread path simulation results, the characteristics and spread trends of various power battery fires are analyzed, the required fire extinguisher types and fire extinguishing steps are evaluated, and the specific process of generating the spread trend analysis results is as follows;

[0089] In sub-step S203, based on the fire spread path simulation results, the characteristics and spread trends of various power battery fires are analyzed and identified, the effectiveness of various types of fire extinguishers in various fire scenarios is evaluated, and corresponding fire extinguishing steps are formulated. The formula is:

[0090]

[0091] Where E represents the total fire extinguishing efficiency, w i represents the utility weight of the i-th fire extinguisher, t i Represents the response time of the fire extinguisher under the specified fire type, P i represents the fire extinguishing effect, n is the number of fire extinguisher types, and the overall fire extinguishing efficiency of various types of fire extinguishers under various battery fire situations is calculated and evaluated to generate diffusion trend analysis results.

[0092] See also Figure 4 ,Based on the results of diffusion trend analysis, combined with historical alarm data,,analyze multiple fire alarms and evaluate the urgency, calculate the priority score, and generate the alarm priority ranking model,the specific steps are:

[0093] S301: Based on the diffusion trend analysis results, the historical alarm data is analyzed to identify the data associated with the fire type, including the duration and fire intensity data, and the specific process of generating the fire type data classification is as follows;

[0094] In sub-step S301, based on the results of the diffusion trend analysis, historical alarm data are analyzed, including the duration, intensity and diffusion rate of the fire. Key information is extracted from batch alarm records using data mining technology, and the clustering algorithm is applied to classify fire events by type. The formula is: Among them, D i Represents the difference between the i-th type of fire and the average fire data, t i represents the duration, f i Represents fire data, is the average duration of all fire data, The average fire intensity data of the fire data is used to identify and classify the characteristics of differentiated fire types, and generate fire type data classification.

[0095] S302: Based on the classification of fire type data, the specific process of calculating the urgency of various power battery fires by evaluating the frequency and response time of various fire types and generating fire urgency assessment scores is as follows;

[0096] In sub-step S302, based on the classification of fire type data, the occurrence frequency and response time of various fires are evaluated, the urgency of various types of fires is determined through statistical analysis, and resource allocation and response strategies are optimized. The formula is:

[0097]

[0098] Among them, U i represents the urgency assessment score of the i-th fire, f i is the occurrence frequency of target type fire, r i is the average response time, α and β are the weight coefficients of frequency and response time respectively, and the urgency of various fire types is calculated to generate a fire urgency assessment score.

[0099] S303: Based on the fire urgency assessment scores, the response priorities of various types of fires are ranked, and the specific process of generating the alarm priority ranking model is as follows in combination with resource requirements and availability;

[0100] In sub-step S303, based on the fire urgency assessment scores, the response priorities of various types of fires are ranked, and the resource requirements of various fires and currently available resources are evaluated to determine the optimal response sequence. The formula is:

[0101]

[0102] Among them, P i represents the response priority of the i-th fire, U i is the urgency assessment score, R iThe resource demand index for the corresponding type of fire is used to rank the fire handling priorities by considering the ratio of urgency to resource demand, and a warning priority ranking model is generated.

[0103] See also Figure 5 ,Based on the alarm priority sorting model, the real-time fire situation and police resource status are analyzed, the urgency and resource requirements of the real-time alarm situation are identified, the police resource scheduling order is adjusted, and the steps of generating the real-time alarm response sequence are as follows:

[0104] S401: Based on the alarm priority ranking model, the fire situation and fire resource status are monitored in real time, including the location of the fire brigade, available vehicles and equipment, and the specific process of generating fire resource assessment information is as follows;

[0105] In sub-step S401, based on the alarm priority ranking model, a real-time monitoring system is set up to track the development of the fire and the status of firefighting resources, including the use of geographic information systems and real-time data streams to track the location of the fire brigade, the status of available vehicles and equipment, and the formula is Among them, R total represents the total available resource evaluation value, r j represents the availability of the jth resource unit, s j The strategic importance weight of resource units is expressed, the score of real-time available firefighting resources is calculated, and firefighting resource assessment information is generated.

[0106] S402: Based on the fire resource assessment information, the matching degree between the fire resource configuration and the disaster demand is analyzed, the priority of resource allocation is identified, and the specific process of generating the resource scheduling priority sequence is as follows;

[0107] In sub-step S402, based on the fire resource assessment information, the resource allocation and current fire demand are analyzed, and the optimization algorithm is used to identify the resource allocation strategy in an emergency situation. The formula is:

[0108]

[0109] Among them, M i represents the resource matching degree of the i-th fire, d i represents the fire demand index, R total The total available resources are evaluated, the matching degree between the resource demand of various types of fires and the current resource status is analyzed, and the resource scheduling priority sequence is generated.

[0110] S403: Based on the resource scheduling priority sequence, the specific process of adjusting police resources and optimizing the deployment plan to match the real-time fire situation and generating a real-time alarm response sequence is as follows;

[0111] In sub-step S403, based on the resource scheduling priority sequence, police resources are adjusted, and the deployment plan is optimized to match the real-time fire situation. The dynamic scheduling system is used to reallocate resources and adjust the deployment strategy to ensure maximum response speed and efficiency. The formula is:

[0112]

[0113] Among them, A i represents the adjusted response priority of the i-th fire, P i is the original priority, M i is the resource matching degree, T i To estimate arrival time, optimize police resource deployment and generate real-time police response sequences.

[0114] See also Figure 6 ,Based on the real-time alarm response sequence, analyze the geographical location, fire level and environmental conditions of the real-time fire, and analyze the number of firefighters, vehicle types and equipment required by comparing with historical fire information. The specific steps to obtain resource demand forecast information are as follows:

[0115] S501: Based on the real-time alarm response sequence, the historical dispatch records are compared with the real-time fire parameters, including the geographical location, fire level and environmental conditions of the fire, combined with the number of firefighters, vehicle type and equipment list, and the specific process of generating real-time disaster analysis results is as follows;

[0116] In sub-step S501, based on the real-time alarm response sequence, the historical dispatch records are compared with the real-time parameters of the current fire situation, including the analysis of the geographical location, fire level and environmental conditions of the fire situation, while taking into account the number of firefighters, vehicle type and equipment availability. The formula is:

[0117]

[0118] Where C represents the total scheduling difference cost, v k Indicates the current fire parameters, v k0 Indicates the corresponding parameter in the historical scheduling record, ω k is the importance weight of the parameter, m is the number of parameters, and the matching degree between the historical dispatch scheme and the current fire demand is evaluated to generate real-time disaster analysis results.

[0119] S502: Based on the real-time disaster analysis results, by analyzing the dispatch information of firefighters, vehicles and equipment for similar fire situations in historical records, the quantity and type of resources required for various fire situations are calculated, and the specific process of generating a dispatch resource evaluation list is as follows;

[0120] In sub-step S502, based on the real-time disaster analysis results, the dispatch information of firefighters, vehicles and equipment for similar fires in the historical records is analyzed to calculate the number and type of resources required for differentiated types of fires, and optimize resource allocation and response efficiency. The formula is R needed =α·N+β·V+γ·E, where, R needed represents the total number of resources required, N, V ​​and E represent the number of firefighters, vehicles and equipment required, respectively. α, β and γ are the demand coefficients of various resource types. The required resources are estimated based on the real-time fire situation and historical data to generate a dispatch resource evaluation list.

[0121] S503: Based on the dispatch resource assessment list, combined with the real-time fire development, including fire level and environmental conditions, the specific process of analyzing the number of firefighters, vehicle types and equipment requirements to generate resource demand forecast information is as follows;

[0122] In sub-step S503, based on the dispatch resource evaluation list and combined with the real-time fire development situation, including the fire level and environmental conditions, the required number of firefighters, vehicle types and equipment are analyzed. The formula is:

[0123]

[0124] Among them, D forevast Represents the predicted resource demand, R needed is the total amount of resources required, S is the current fire level, H is the environmental condition index, and δ is the adjustment factor. The required amount of resources under the target environment and fire level is predicted to generate resource demand prediction information.

[0125] See also Figure 7 ,Based on the resource demand forecast information, the real-time firefighting force deployment and equipment inventory are analyzed, the firefighting resource mobilization parameters and firefighting response paths are calculated, and the steps for generating the alarm handling dispatch parameters are as follows:

[0126] S601: Based on the resource demand forecast information, the specific process of analyzing the real-time firefighting force deployment and equipment inventory, including the available firefighter positions, vehicles and equipment status, and generating real-time firefighting resource information is as follows;

[0127] In sub-step S601, based on the resource demand forecast information, the current deployment of firefighting forces and the inventory status of equipment are analyzed, including monitoring the location of firefighters, the operating status of vehicles, and the availability of equipment. The formula is Among them, R available represents the total firefighting resources currently available, p i represents the number of firefighters available at the i-th fire station, q i For the strategic value of its location, v irepresents the number of vehicles available at the i-th fire station, w i is the strategic value of the vehicle, n is the number of fire stations, the overall status of real-time fire-fighting resources is evaluated, and real-time fire-fighting resource information is generated.

[0128] S602: Based on the real-time fire resource information, combined with the fire level, geographical location and current environment, the firefighter deployment, vehicle driving routes and equipment configuration of multiple fire stations are calculated, and the specific process of generating fire resource configuration parameters is as follows;

[0129] In sub-step S602, based on real-time firefighting resource information, combined with fire level, geographical location and current environment, the deployment of firefighters, vehicle routes and equipment configuration of multiple fire stations are planned. The formula is

[0130] Among them, P represents the optimization parameter of fire resource allocation, l j represents the distance from the jth fire station to the fire location, s j The resource supply capacity for the site, e j is the environmental impact coefficient, m is the number of fire stations involved, the resource allocation efficiency of multiple fire stations is calculated, and the fire resource allocation parameters are generated.

[0131] S603: Based on the fire resource configuration parameters, by matching the vehicle dispatch sequence and the fire station location, the fire response path is calculated and the specific process of generating the alarm handling dispatch parameters is as follows;

[0132] In sub-step S603, based on the fire resource configuration parameters, the vehicle dispatch sequence and the fire station location are matched to optimize the fire response path. The formula is: Among them, C represents the total scheduling cost, d k represents the distance from the kth fire station to the fire scene, t k is the travel time of the route, o is the number of fire stations and vehicles involved in the dispatch, the lowest cost response path is calculated, and the dispatch parameters for emergency handling are generated.

[0133] See also Figure 8 , A power battery alarm handling force dispatching system based on a large fire model, a power battery alarm handling force dispatching system based on a large fire model is used to execute the above-mentioned power battery alarm handling force dispatching method based on a large fire model, and the system includes:

[0134] The battery characteristic analysis module uses a large language model to perform text summarization based on power battery parameter data, extract material composition and design structure information of various batteries, analyze the heat release and combustion rate of various power batteries, identify the combustion characteristics of various batteries, and generate combustion characteristic analysis results;

[0135] The environmental simulation analysis module generates environmental factor assessment information based on the combustion characteristic analysis results by simulating the impact of differentiated environmental factors on fire spread, including oxygen content and humidity;

[0136] The fire alarm assessment module evaluates the urgency and priority of fire alarms based on environmental factor assessment information and historical alarm data, and generates disaster urgency calculation results;

[0137] The fire resource analysis module conducts real-time fire situation and resource status analysis based on the disaster urgency calculation results, adjusts the resource dispatch order of police force and equipment, and generates response priority assessment results;

[0138] The disaster response dispatch module analyzes the required number of firefighters, vehicles and equipment based on the response priority assessment results, combined with the real-time fire geographical location, fire level and environmental conditions, calculates the resource dispatch parameters and response path, and generates the alarm handling dispatch parameters.

[0139] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for dispatching power battery alarm handling forces based on a large fire model, characterized in that: The following steps are involved: Based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the heat released, combustion rate, and combustion characteristics of various types of new energy batteries during combustion, and generate a power battery combustion model; Based on the power battery combustion model, the diffusion and spread process of battery fire under various external environments is simulated, the flame propagation path and characteristics of battery fire are analyzed, and the diffusion trend analysis results are generated; Based on the diffusion trend analysis results, combined with historical alarm data, multiple fire alarms are analyzed and the urgency is evaluated, priority scores are calculated, and an alarm priority ranking model is generated; Based on the alarm priority ranking model, the real-time fire situation and police resource status are analyzed, the urgency and resource requirements of the real-time alarm situation are identified, the police resource dispatching sequence is adjusted, and a real-time alarm response sequence is generated; Based on the real-time alarm response sequence, the geographical location, fire level and environmental conditions of the real-time fire are analyzed, and by comparing with historical fire information, the number of firefighters, vehicle types and equipment required are analyzed to obtain resource demand forecast information; Based on the resource demand forecast information, the real-time firefighting force deployment and equipment inventory are analyzed, the firefighting resource mobilization parameters and firefighting response paths are calculated, and the alarm handling dispatch parameters are generated.

2. The method for dispatching power battery alarm handling forces based on a large fire model according to claim 1 is characterized in that: The power battery combustion model specifically includes the thermal response characteristics of the battery material, the heat release rate, and the battery design structure. The diffusion trend analysis results include the flame propagation speed, the flame propagation path, and the affected battery structure characteristics. The alarm priority ranking model specifically includes the fire hazard rating, historical response time, and resource input statistics. The real-time alarm response sequence includes alarm time information, police force availability information, and fire location information. The resource demand forecast information includes the expected number of firefighters, fire truck types and quantity information, and estimated equipment type information. The alarm handling and dispatching parameters specifically include the mobilization time of the fire brigade, the response path optimization plan, and the equipment deployment strategy.

3. The method for dispatching power battery alarm handling forces based on a large fire model according to claim 1 is characterized in that: Based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the heat released, combustion rate, and combustion characteristics of various types of new energy batteries during combustion, and generate a power battery combustion model in the following specific steps: Based on the power battery parameter data, a large language model is used to perform text summarization, extract the material composition and design structure information of various batteries, analyze the material composition and design structure of various batteries, identify the characteristics and combustion characteristics of various batteries, and generate basic data on battery combustion characteristics; Based on the basic data of battery combustion characteristics, a Monte Carlo simulation method is used to calculate the heat release rates of various types of batteries by simulating the temperature and oxygen concentration in the combustion environment, and generate battery heat release and rate information; Based on the battery heat release and rate information, the combustion behaviors of various batteries under the same conditions are analyzed, the combustion modes and characteristics of the batteries are identified, and a power battery combustion model is generated.

4. The method for dispatching power battery alarm handling forces based on a large fire model according to claim 3 is characterized in that: The Monte Carlo simulation method is based on the formula: Calculate the heat release rate of the battery in a simulated combustion environment, where Q′ is the total heat release rate, ∑ represents the sum, i is the index variable, N is the total number of simulations, and w i is the weight coefficient of the ith simulation, q i is the heat release rate obtained from the ith simulation, T i is the temperature during the ith simulation, O i is the oxygen concentration in the ith simulation, E i is the energy state of the battery during the i-th simulation.

5. The method for dispatching power battery alarm handling forces based on a large fire model according to claim 1 is characterized in that: Based on the power battery combustion model, the diffusion and spread process of battery fire under various external environments is simulated, the flame propagation path and characteristics of battery fire are analyzed, and the steps for generating diffusion trend analysis results are as follows: Based on the power battery combustion model, simulate the flame propagation process under differentiated external conditions, including oxygen content, humidity, temperature, and space size, and generate an environmental factor impact model; Based on the environmental factor impact model, the fire spread rate and path of various battery types in different external environments are analyzed, and the fire spread path simulation results are generated by combining the influence of battery arrangement and installation position; Based on the fire spread path simulation results, the characteristics and spread trends of various power battery fires are analyzed, the required fire extinguisher types and fire extinguishing steps are evaluated, and the spread trend analysis results are generated.

6. The method for dispatching power battery alarm handling forces based on a large fire model according to claim 1 is characterized in that: Based on the diffusion trend analysis results, combined with historical alarm data, multiple fire alarms are analyzed and the urgency is evaluated, priority scores are calculated, and the steps of generating an alarm priority ranking model are as follows: Based on the diffusion trend analysis results, historical alarm data are analyzed to identify data associated with fire types, including duration and fire intensity data, and generate fire type data classification; Based on the fire type data classification, by evaluating the frequency and response time of various fire types, calculating the urgency of various power battery fires, and generating a fire urgency assessment score; Based on the fire urgency assessment scores, the response priorities of various types of fires are ranked, and a warning priority ranking model is generated in combination with resource requirements and availability.

7. The method for dispatching power battery alarm handling forces based on a large fire model according to claim 1 is characterized in that: Based on the alarm priority ranking model, the steps of analyzing the real-time fire situation and police resource status, identifying the urgency and resource requirements of the real-time alarm situation, adjusting the police resource dispatching sequence, and generating the real-time alarm response sequence are as follows: Based on the alarm priority ranking model, real-time monitoring of fire conditions and firefighting resource status, including fire brigade locations, available vehicles and equipment, and generation of firefighting resource assessment information; Based on the fire resource assessment information, analyze the matching degree between fire resource configuration and disaster demand, identify the priority of resource allocation, and generate a resource scheduling priority sequence; Based on the resource scheduling priority sequence, police resources are adjusted and the deployment plan is optimized to match the real-time fire situation, generating a real-time alarm response sequence.

8. The method for dispatching power battery alarm handling forces based on a large fire model according to claim 1 is characterized in that: Based on the real-time alarm response sequence, the geographical location, fire level and environmental conditions of the real-time fire are analyzed, and by comparing with historical fire information, the required number of firefighters, vehicle types and equipment are analyzed to obtain resource demand forecast information. Specifically, the steps are as follows: Based on the real-time alarm response sequence, historical dispatch records are compared with real-time fire parameters, including the geographical location, fire level and environmental conditions of the fire, combined with the number of firefighters, vehicle types and equipment lists, to generate real-time disaster analysis results; Based on the real-time disaster analysis results, by analyzing the dispatch information of firefighters, vehicles and equipment for similar fire situations in historical records, the quantity and type of resources required for various fire situations are calculated, and a dispatch resource evaluation list is generated; Based on the dispatch resource assessment list, combined with real-time fire development, including fire level and environmental conditions, the number of firefighters, vehicle types and equipment requirements are analyzed to generate resource demand forecast information.

9. The method for dispatching power battery alarm handling forces based on a large fire model according to claim 1 is characterized in that: Based on the resource demand forecast information, the steps of analyzing the real-time firefighting force deployment and equipment inventory, calculating the firefighting resource mobilization parameters and the firefighting response path, and generating the alarm handling dispatch parameters are as follows: Based on the resource demand forecast information, analyze the real-time firefighting force deployment and equipment inventory, including the available firefighter locations, vehicles and equipment status, and generate real-time firefighting resource information; Based on the real-time fire resource information, combined with the fire level, geographical location and current environment, the firefighter deployment, vehicle driving routes and equipment configuration of multiple fire stations are calculated to generate fire resource configuration parameters; Based on the fire resource configuration parameters, the fire response path is calculated by matching the vehicle dispatch sequence and the fire station location, and the alarm handling dispatch parameters are generated.

10. The power battery alarm handling force dispatching system based on the fire model is characterized by: According to any one of claims 1 to 9, the method for dispatching power battery alarm handling forces based on a large fire model comprises: The battery characteristic analysis module performs text summarization based on the power battery parameter data through a large language model, extracts the material composition and design structure information of various batteries, analyzes the heat release and combustion rate of various power batteries, identifies the combustion characteristics of various batteries, and generates combustion characteristic analysis results; The environmental simulation analysis module generates environmental factor assessment information by simulating the influence of differentiated environmental factors on fire spread, including oxygen content and humidity, based on the combustion characteristic analysis results; The fire alarm assessment module evaluates the urgency and priority of the fire alarm based on the environmental factor assessment information and combines the historical alarm data to generate a disaster urgency calculation result; The fire resource analysis module performs real-time fire situation and resource status analysis based on the disaster urgency calculation result, adjusts the resource dispatching sequence of police force and equipment, and generates response priority evaluation results; The disaster response scheduling module analyzes the required number of firefighters, vehicles and equipment based on the response priority assessment results, combined with the real-time fire geographical location, fire level and environmental conditions, calculates resource scheduling parameters and response paths, and generates alarm handling scheduling parameters.

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