Cooperative emergency disposal method, system and device for new energy vehicle and storage medium

Abstract

The invention provides a cooperative emergency disposal method, system and device for a new energy vehicle and a storage medium, and a vehicle end collects and uploads vehicle state data. The cloud server extracts the position of the vehicle, obtains associated external environment data and fire-fighting, traffic and other collaborative party data, then calculates the air pressure and temperature risk values of each monomer, and integrates the voltage data to identify the fault monomer with the highest risk. And based on the fault monomer risk value, the overall state of the battery pack, the environment temperature, the fire-fighting station distance and the traffic state, the cloud determines a vehicle dynamic risk index and divides risk levels, and generates a co-processing instruction in combination with fault monomer information and emergency resources. And the vehicle end and the collaborative party respectively respond to the instruction, the vehicle executes control operation adaptive to the risk level and the fault monomer, and the collaborative party carries out corresponding emergency operation. By the adoption of the method, timeliness and accuracy of new energy automobile safety risk early warning and pertinence and efficiency of disposal operation can be improved.

Description

Technical Field

[0001] This application relates to the field of automotive management, and more specifically, to a collaborative emergency response method, system, device, and storage medium for new energy vehicles. Background Technology

[0002] Battery thermal runaway and insulation failure in new energy vehicles can cause problems such as high-voltage circuit anomalies and heat diffusion. In existing technologies, a typical safety protection solution for new energy vehicles is a "static matching based on historical fault data of the same model + single-entity handling" model. Specifically, this involves collecting a large amount of fault records and maintenance data from vehicles of the same model, extracting fixed characteristic parameters such as mileage, charging frequency, and service life, and establishing a static matching model. When the vehicle is running, it compares its own static parameters with historical fault data. If the parameter matching degree reaches a preset threshold, a safety risk is determined, and the handling operation is simply performed by the vehicle itself to cut off power and trigger an alarm, or by the manufacturer's service platform remotely issuing simple prompts.

[0003] The core flaw of this existing technology is its insufficient dynamic adaptability and coordination. The static matching model relies only on fixed historical data and limited static parameters, which cannot capture the dynamic operating conditions of individual vehicles in real time (such as real-time driving load and ambient temperature fluctuations). It also does not take into account the individual characteristics of different vehicles, such as the degree of battery degradation and differences in driving habits, resulting in a large deviation between the risk assessment results and the actual safety status. At the same time, the handling process lacks coordination with external emergency resources such as fire, traffic, and rescue, and relies only on the vehicle or the manufacturer as a single entity, making it difficult to quickly integrate resources from multiple parties in the event of a sudden risk, thus delaying the best time for handling. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a collaborative emergency response method, system, equipment and storage medium for new energy vehicles, which can improve the timeliness and accuracy of early warning of safety risks of new energy vehicles, and the pertinence and efficiency of response operations.

[0005] In a first aspect, embodiments of this application provide a collaborative emergency response method for new energy vehicles, the method comprising: The vehicle collects vehicle status data and uploads it to the cloud server. The vehicle status data includes at least the internal air pressure data, temperature data, and voltage data of each battery cell, as well as battery health status data and insulation data that characterize the overall status of the battery pack. The cloud server obtains external environmental data associated with the vehicle and collaborative data from collaborating parties, including at least fire departments and traffic management departments, based on the vehicle location data extracted from the vehicle status data. The cloud server calculates the air pressure risk value of each battery cell based on its air pressure data, calculates the temperature risk value based on its temperature data, merges the air pressure risk value and temperature risk value of each battery cell, and combines them with its voltage data to identify at least one faulty battery cell with the highest risk. The cloud server determines the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the battery health status data and insulation data of the battery pack as a whole, the ambient temperature data and fire station distance data in the external environment data, and the traffic status data in the collaborative data. The cloud server determines the risk level of the vehicle based on the dynamic risk index, and generates and issues a collaborative handling instruction based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the collaborative data. The vehicle responds to the collaborative handling command and executes vehicle control operations corresponding to the risk level and faulty battery cell information. The at least one collaborating party responds to the collaborative handling instruction by performing collaborative emergency operations corresponding to the risk level and faulty battery cell information.

[0006] Optionally, the calculation of the pressure risk value based on the pressure data of each battery cell includes: For each battery cell, based on its real-time air pressure value and rate of change of air pressure per unit time Through the barometric risk function Calculate its barometric risk value; where, when Exceeding the preset air pressure threshold or Exceeding the preset rate of change threshold At that time, the pressure risk function The output value is configured to grow exponentially.

[0007] Optionally, the step of fusing the pressure risk value and temperature risk value of each battery cell, and combining them with its voltage data, to identify at least one faulty battery cell with the highest risk includes: For each battery cell Calculate its individual comprehensive risk score The calculation formula is: ; in, This is the pressure risk value for that monomer. Its temperature risk value, Its real-time voltage, The average voltage of the battery pack. , , These are preset weighting coefficients; Individual comprehensive risk score The highest-ranking at least one battery cell is identified as the faulty battery cell.

[0008] Optionally, the determination of the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the overall battery health status data and insulation data of the battery pack, the ambient temperature data and fire station distance data in the external environment data, and the traffic status data in the collaborative data includes: The dynamic risk index is calculated by a weighted fusion model, which includes three core dimensions: battery intrinsic risk factor, environmental coupling risk factor, and traffic impact risk factor. The intrinsic battery risk factor is obtained by weighting the maximum air pressure risk value of the faulty cell, the battery health status, and the insulation resistance; the environmental coupling risk factor is obtained by weighting the degree of deviation of the ambient temperature from the safety threshold and the distance between the vehicle and the nearest fire station; the traffic impact risk factor is determined based on the real-time traffic congestion index. The model is configured with dynamic weight coefficients, the sum of the weight coefficients of each dimension is 1, and when the maximum air pressure risk value of the faulty cell exceeds the preset threshold, the weight ratio of the battery intrinsic risk factor is automatically increased. The dynamic risk index is finally calibrated by combining the health status correction coefficient of the vehicle's historical fault records.

[0009] Optionally, determining the vehicle's risk level based on the dynamic risk index, and generating and issuing a coordinated response instruction based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the coordinated data, includes: According to the dynamic risk index With preset threshold , , The risk level is determined by comparison, wherein, > > ; when > At the same time, the generated collaborative handling instructions include at least: sending speed limit and thermal management enhancement instructions to the vehicle, and sending early warning information containing the faulty battery cell number and the precise location of the vehicle to the fire department; when > At the same time, the generated collaborative response instructions include at least: sending instructions to the vehicle to execute safe stopping, personnel evacuation and vehicle autonomous avoidance, and sending emergency response requests containing the best rescue route to the fire and traffic departments.

[0010] Optionally, in response to the coordinated handling command, the vehicle terminal performs vehicle control operations corresponding to the risk level and faulty battery cell information, including: When the risk level reaches a preset emergency level, the vehicle performs an isolation operation on the charging and discharging circuit of the faulty battery cell. When the risk level reaches the preset highest level, the vehicle terminal controls the vehicle's autonomous driving system to drive the vehicle to the designated safe area or the nearest fire station according to the route issued by the cloud or planned locally.

[0011] Optionally, in response to the collaborative handling instruction, the at least one collaborating party performs collaborative emergency operations corresponding to the risk level and faulty battery cell information, including: In response to the coordinated response command, the fire department, based on the identification information of the faulty battery cell, uses a dedicated suppression device to perform drilling depressurization and targeted cooling operations on the faulty battery cell. In response to the coordinated response command, the traffic management department plans and locks a priority route for fire and rescue vehicles heading to the incident site, and controls the traffic lights along that route to provide a green light wave.

[0012] Secondly, embodiments of this application provide a collaborative emergency response system for new energy vehicles, the system comprising a vehicle terminal, a cloud server, and at least one collaborating party; The vehicle terminal is used to collect vehicle status data and upload it to the cloud server. The vehicle status data includes at least the internal air pressure data, temperature data, and voltage data of each battery cell, as well as battery health status data and insulation data that characterize the overall status of the battery pack. The cloud server is used to obtain external environmental data associated with the vehicle and collaborative data from at least the fire department and traffic management department, based on the vehicle location data extracted from the vehicle status data. The cloud server is used to calculate the air pressure risk value of each battery cell based on its air pressure data, calculate the temperature risk value of each battery cell based on its temperature data, merge the air pressure risk value and temperature risk value of each battery cell, and combine them with its voltage data to identify at least one faulty battery cell with the highest risk. The cloud server is used to determine the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the battery health status data and insulation data of the battery pack as a whole, the ambient temperature data and fire station distance data in the external environment data, and the traffic status data in the collaborative data. The cloud server is used to determine the risk level of the vehicle based on the dynamic risk index, and generate and issue collaborative handling instructions based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the collaborative data. The vehicle terminal is used to respond to the collaborative handling command and execute vehicle control operations corresponding to the risk level and faulty battery cell information; The at least one cooperating party is used to respond to the cooperative handling instruction and perform cooperative emergency operations corresponding to the risk level and faulty battery cell information.

[0013] Optionally, the calculation of the pressure risk value based on the pressure data of each battery cell includes: For each battery cell, based on its real-time air pressure value and rate of change of air pressure per unit time Through the barometric risk function Calculate its barometric risk value; where, when Exceeding the preset air pressure threshold or Exceeding the preset rate of change threshold At that time, the pressure risk function The output value is configured to grow exponentially.

[0014] Optionally, the step of fusing the pressure risk value and temperature risk value of each battery cell, and combining them with its voltage data, to identify at least one faulty battery cell with the highest risk includes: For each battery cell Calculate its individual comprehensive risk score The calculation formula is: ; in, This is the pressure risk value for that monomer. Its temperature risk value, Its real-time voltage, The average voltage of the battery pack. , , These are preset weighting coefficients; Individual comprehensive risk score The highest-ranking at least one battery cell is identified as the faulty battery cell.

[0015] Optionally, the determination of the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the overall battery health status data and insulation data of the battery pack, the ambient temperature data and fire station distance data in the external environment data, and the traffic status data in the collaborative data includes: The dynamic risk index is calculated by a weighted fusion model, which includes three core dimensions: battery intrinsic risk factor, environmental coupling risk factor, and traffic impact risk factor. The intrinsic battery risk factor is obtained by weighting the maximum air pressure risk value of the faulty cell, the battery health status, and the insulation resistance; the environmental coupling risk factor is obtained by weighting the degree of deviation of the ambient temperature from the safety threshold and the distance between the vehicle and the nearest fire station; the traffic impact risk factor is determined based on the real-time traffic congestion index. The model is configured with dynamic weight coefficients, the sum of the weight coefficients of each dimension is 1, and when the maximum air pressure risk value of the faulty cell exceeds the preset threshold, the weight ratio of the battery intrinsic risk factor is automatically increased. The dynamic risk index is finally calibrated by combining the health status correction coefficient of the vehicle's historical fault records.

[0016] Optionally, determining the vehicle's risk level based on the dynamic risk index, and generating and issuing a coordinated response instruction based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the coordinated data, includes: According to the dynamic risk index With preset threshold , , The risk level is determined by comparison, wherein, > > ; when > At the same time, the generated collaborative handling instructions include at least: sending speed limit and thermal management enhancement instructions to the vehicle, and sending early warning information containing the faulty battery cell number and the precise location of the vehicle to the fire department; when > At the same time, the generated collaborative response instructions include at least: sending instructions to the vehicle to execute safe stopping, personnel evacuation and vehicle autonomous avoidance, and sending emergency response requests containing the best rescue route to the fire and traffic departments.

[0017] Optionally, in response to the coordinated handling command, the vehicle terminal performs vehicle control operations corresponding to the risk level and faulty battery cell information, including: When the risk level reaches a preset emergency level, the vehicle performs an isolation operation on the charging and discharging circuit of the faulty battery cell. When the risk level reaches the preset highest level, the vehicle terminal controls the vehicle's autonomous driving system to drive the vehicle to the designated safe area or the nearest fire station according to the route issued by the cloud or planned locally.

[0018] Optionally, in response to the collaborative handling instruction, the at least one collaborating party performs collaborative emergency operations corresponding to the risk level and faulty battery cell information, including: In response to the coordinated response command, the fire department, based on the identification information of the faulty battery cell, uses a dedicated suppression device to perform drilling depressurization and targeted cooling operations on the faulty battery cell. In response to the coordinated response command, the traffic management department plans and locks a priority route for fire and rescue vehicles heading to the incident site, and controls the traffic lights along that route to provide a green light wave.

[0019] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the collaborative emergency response method for new energy vehicles described in any of the optional embodiments of the first aspect are executed.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the collaborative emergency response method for new energy vehicles described in any of the optional embodiments of the first aspect.

[0021] The technical solution provided in this application includes, but is not limited to, the following beneficial effects: The vehicle collects vehicle status data and uploads it to a cloud server. This vehicle status data includes at least the internal air pressure, temperature, and voltage data of each battery cell, as well as battery health and insulation data that characterize the overall state of the battery pack. Through these steps, core safety indicators of the microscopic state of individual battery cells and the macroscopic state of the battery pack can be comprehensively captured, providing complete and accurate data support for subsequent risk identification and assessment. At the same time, the encryption and local caching design during the data upload process ensures the security of data transmission and storage, preventing the loss or leakage of critical data.

[0022] The cloud server obtains external environmental data associated with the vehicle based on the vehicle location data extracted from the vehicle status data, as well as collaborative data from collaborating parties including at least fire departments and traffic management departments. Through the above steps, the isolated assessment mode that relies solely on the vehicle's own data is broken, and the vehicle's safety status is linked to the external environment and the distribution of emergency resources. This makes subsequent risk assessments more in line with actual working conditions and builds a data bridge for multi-entity collaborative response, ensuring that response actions are adapted to external conditions and resource availability.

[0023] The cloud server calculates the pressure risk value of each battery cell based on its pressure data and its temperature risk value based on its temperature data. By combining the pressure and temperature risk values ​​of each battery cell with its voltage data, it identifies at least one faulty battery cell with the highest risk. Through these steps, the system achieves a shift from "overall battery pack assessment" to "precise cell location," accurately pinpointing the source of risk, avoiding indiscriminate treatment of normal battery cells, reducing unnecessary battery wear and maintenance costs, and providing a clear target for subsequent targeted treatment.

[0024] The cloud server determines the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the overall battery health status and insulation data of the battery pack, the ambient temperature data and fire station distance data in the external environment data, and the traffic status data in the collaborative data. Through the above steps, multiple dimensions such as individual cell risk, pack status, external environment, and traffic conditions are integrated to achieve dynamic quantitative assessment of risk, rather than static threshold judgment, making the risk level determination more scientific and objective, and in line with the real-time safety status of the vehicle.

[0025] The cloud server determines the vehicle's risk level based on the dynamic risk index, and generates and issues collaborative handling instructions based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the collaborative data. Through the above steps, the handling instructions are differentiated and precise, with different handling strategies corresponding to different risk levels. The instructions include faulty battery cell information and emergency resource matching solutions, ensuring that both the vehicle and the collaborating party can clearly understand the direction of operation, avoiding confusion or omissions in handling actions, and improving the orderliness of the handling process.

[0026] The vehicle responds to the coordinated handling command and executes vehicle control operations corresponding to the risk level and faulty battery cell information. Through the above steps, the handling actions of the vehicle are precisely matched with the risk level and the source of the fault. When the risk is low, prompting operations are the main method, and when the risk is high, mandatory measures are taken to curb the spread of risk. This not only ensures the handling effect, but also avoids the impact of excessive operation on the normal function of the vehicle, and maximizes the protection of the vehicle and the safety of the driver and passengers.

[0027] The at least one collaborating party responds to the collaborative handling instruction and executes collaborative emergency operations corresponding to the risk level and faulty battery cell information. Through the above steps, professional emergency resources such as fire protection and transportation are integrated to form a "vehicle-cloud-collaborating party" joint handling force, avoiding the limitations of single-entity handling, enabling rapid dispatch of suitable resources, improving rescue and handling efficiency, effectively preventing further deterioration of risks, and minimizing casualties and property losses.

[0028] In summary, this application, through a closed-loop design encompassing "multi-dimensional data collection at the vehicle end, cross-source data association at the cloud, precise identification of individual faults, dynamic risk quantification and assessment, differentiated instruction generation, precise execution at the vehicle end, and collaborative handling by multiple stakeholders," systematically integrates key information from multiple sources, including the status of individual and vehicle components, the external environment, and emergency resources. This achieves end-to-end optimization from early risk perception to efficient handling. This process not only addresses the shortcomings of traditional solutions, such as insufficient data support, ambiguous fault location, and static risk assessment, but also breaks through the limitations of single-stakeholder handling through multi-stakeholder collaboration. Ultimately, it significantly improves the timeliness and accuracy of early warning of safety risks in new energy vehicles, as well as the targeting and efficiency of handling operations, effectively curbing risk spread, minimizing personal injury and property damage, and providing reliable protection for the safety of new energy vehicles throughout their entire lifecycle.

[0029] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart of a collaborative emergency response method for new energy vehicles provided in Embodiment 1 of this application is shown; Figure 2 shows a flowchart of a vehicle control operation execution method provided in Embodiment 1 of this application; Figure 3 shows a flowchart of a collaborative emergency operation execution method provided in Embodiment 1 of this application; Figure 4 This paper illustrates a structural schematic diagram of a collaborative emergency response system for new energy vehicles provided in Embodiment 2 of this application; Figure 5 A schematic diagram of the structure of a computer device provided in Embodiment 3 of this application is shown. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0033] Example 1 To facilitate understanding of this application, the following is combined with... Figure 1 The flowchart illustrating a collaborative emergency response method for new energy vehicles provided in Embodiment 1 of this application will be used to describe Embodiment 1 of this application in detail.

[0034] See Figure 1 As shown, Figure 1 A flowchart of a collaborative emergency response method for new energy vehicles provided in Embodiment 1 of this application is shown, wherein the method includes steps S101 to S107: S101: The vehicle terminal collects vehicle status data and uploads it to the cloud server. The vehicle status data includes at least the internal air pressure data, temperature data, and voltage data of each battery cell, as well as battery health status data and insulation data that characterize the overall status of the battery pack.

[0035] Specifically, the vehicle uses a built-in air pressure sensor in each battery cell to collect real-time air pressure data inside the cell, and simultaneously collects multi-dimensional temperature data (including battery cell temperature, module temperature, and battery pack surface temperature) and voltage data (including battery cell voltage and battery pack total voltage).

[0036] Among the data characterizing the overall state of the battery pack, the battery health status data is: (Battery health), insulation data is (Insulation resistance), and also includes Supplementary data such as (remaining battery capacity), charge / discharge cycle count, coolant temperature / flow rate, battery pack vibration acceleration, and fast charging percentage comprehensively cover key dimensions of battery and vehicle operation.

[0037] All collected data will be encrypted using TLS 1.3 before being uploaded to the cloud. At the same time, key data (including air pressure change curves) from the most recent hour will be cached locally to prevent data loss in the event of network interruption and ensure the security of data transmission and storage.

[0038] S102: The cloud server obtains external environmental data associated with the vehicle and collaborative data from at least the fire department and traffic management department based on the vehicle location data extracted from the vehicle status data.

[0039] Specifically, vehicle location data is obtained through GPS combined with BeiDou dual-mode positioning, with a positioning accuracy of ≤10 meters. It is accompanied by timestamps and driving trajectories, providing a precise spatial reference for subsequent environmental correlation and rescue dispatch.

[0040] External environmental data covers basic environmental factors (temperature, humidity, solar intensity, etc.), road traffic (slope, curvature, road surface type, congestion index, etc.), extreme weather warnings (connecting to the national meteorological department's API to obtain red warnings for typhoons, rainstorms, etc.), and the precise location of fire stations, comprehensively covering the external scenarios where vehicles are located.

[0041] The collaborative data includes fire department data such as fire truck standby status and mini fire station communication terminal data, traffic management department data such as real-time road conditions, traffic light status, and road control information, and also simultaneously acquires charging station operation data (charging power, charging pile fault records) and manufacturer service data (maintenance records, historical fault codes), etc., to achieve multi-source information collaboration.

[0042] S103: The cloud server calculates the air pressure risk value of each battery cell based on its air pressure data, calculates the temperature risk value of each battery cell based on its temperature data, merges the air pressure risk value and temperature risk value of each battery cell, and combines them with its voltage data to identify at least one faulty battery cell with the highest risk.

[0043] Specifically, the barometric risk value is obtained through the barometric risk function. Calculation, where This refers to the real-time air pressure of a single unit. It measures the rate of change of air pressure per unit time, accurately capturing abnormal air pressure signals.

[0044] Temperature risk value via function calculate, Temperature difference within the battery pack The temperature is the coolant temperature, taking into account both the absolute value and the trend of temperature change.

[0045] The integration process employs dynamic weight allocation, with weight coefficients dynamically configured based on battery type. For ternary lithium batteries, the weight for pressure risk is higher than that for temperature risk, while the opposite is true for lithium iron phosphate batteries. For example, ternary lithium batteries are assigned a weight of 0.2 for pressure risk and 0.15 for temperature risk, while lithium iron phosphate batteries are assigned the same weight. A voltage consistency parameter (the difference between real-time voltage and the average voltage of the battery pack) is also introduced to comprehensively assess individual cell risk. The weight coefficients can be updated in real-time via a cloud-based big data platform and continuously optimized based on newly added vehicle fault cases. Threshold parameters can be configured differently according to regional climate conditions and traffic conditions (e.g., the ambient temperature safety threshold can be appropriately reduced in high-temperature regions).

[0046] For each battery cell Calculate its individual comprehensive risk score The calculation formula is:

[0047] in, This is the pressure risk value for that monomer. Its temperature risk value, Its real-time voltage, The average voltage of the battery pack. , , These are the preset weighting coefficients.

[0048] The fused risk data is analyzed using an LSTM+decision tree fusion model to obtain the individual comprehensive risk score. At least one of the highest-ranking battery cells is identified as a faulty cell, and the cell number and risk type (thermal runaway / insulation failure / voltage anomaly) are output.

[0049] S104: The cloud server determines the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the battery health status data and insulation data of the battery pack as a whole, the ambient temperature data and fire station distance data in the external environment data, and the traffic status data in the collaborative data.

[0050] Specifically, the dynamic risk index is calculated using a weighted fusion model, and the calculation formula is as follows: ; in: The intrinsic risk factor for batteries is calculated using the following formula: ; This is the highest pressure risk value among all individual units; Battery health status; Insulation resistance; , For coefficients, =0.3、 =0.2.

[0051] The environmental coupling risk factor is calculated using the following formula: ; The ambient temperature; The safe temperature threshold; The distance from the vehicle to the nearest fire station; For reference distance, =5 kilometers; , For coefficients, =0.2、 =0.15.

[0052] This is a traffic impact risk factor, and its value is determined based on the real-time congestion index in the traffic status data (when the congestion index is ≥ 0.7). (Reaching its maximum value).

[0053] , , The dynamic weighting coefficients satisfy the following conditions: (initial =0.6、 =0.2、 =0.2, when When the set threshold is exceeded, (Automatic enlargement).

[0054] Health status correction coefficient based on vehicle historical fault records (when there are no fault records) =1.0, when there are more than 3 similar fault records =1.5).

[0055] S105: The cloud server determines the risk level of the vehicle based on the dynamic risk index, and generates and issues a collaborative handling instruction based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the collaborative data.

[0056] Specifically, the risk level is determined by a dynamic risk index. With preset threshold , , The comparison is relatively certain, among which, > > ( =0.2、 =0.4、 =0.8).

[0057] Emergency resource information includes the location of the nearest fire station, the status of fire trucks, the location of rescue tow trucks, and traffic signal control permissions. When instructions are generated, a targeted response plan will be matched based on the faulty cell number and battery type (ternary lithium / lithium iron phosphate).

[0058] when > At the same time, the generated collaborative handling instructions include at least: sending speed limit and thermal management enhancement instructions to the vehicle, and sending early warning information containing the faulty battery cell number and the precise location of the vehicle to the fire department.

[0059] when > At the same time, the generated collaborative response instructions include at least: sending instructions to the vehicle to execute safe stopping, personnel evacuation and vehicle autonomous avoidance, and sending emergency response requests containing the best rescue route to the fire and traffic departments.

[0060] Instructions are issued via 5G+V2X dual-mode communication or temporary communication networks with a transmission latency of ≤100ms, ensuring timely response. At the same time, it supports multi-target priority ranking (high-risk targets are prioritized in overlapping rescue networks).

[0061] S106: The vehicle terminal responds to the collaborative handling command and executes vehicle control operations corresponding to the risk level and faulty battery cell information.

[0062] Specifically, low-risk level (Level 1, ≤0.2) Only triggers a green warning on the vehicle interface; it is recommended to avoid rapid acceleration and switching to slow charging; Level 2 warning (0.2 < ≤0.4) Trigger a yellow warning + sound prompt, reduce speed to highway ≤80km / h, and turn off non-essential electrical equipment.

[0063] Medium-to-high risk level (Level 3, 0.4 < ≤0.8) triggers a red alarm, limits the drive power to ≤50km / h, and the battery thermal management system operates at maximum power, while isolating the charging and discharging circuit of the faulty cell; Level 4 warning (R>0.8) cuts off the drive power, unlocks the doors, closes the windows, and provides voice guidance for personnel to evacuate (at least 50 meters away).

[0064] After the operation is executed, the vehicle end collects data such as the temperature / pressure changes of the faulty unit and the vehicle's operating status in real time and feeds it back to the cloud, forming an execution closed loop.

[0065] S107: The at least one collaborating party responds to the collaborative handling instruction by performing a collaborative emergency operation corresponding to the risk level and faulty battery cell information.

[0066] Specifically, the collaborating parties include fire departments, traffic management departments, rescue dispatch platforms, charging station management systems, and mini fire stations. Each collaborating party accesses the temporary communication network through a unified standardized data exchange protocol to ensure information synchronization.

[0067] The rescue dispatch platform matches the nearest tow truck and pushes the best rescue route; the charging station automatically cuts off power during charging scenarios, activates the fire-fighting facilities in the station, and guides vehicles away from the charging area; the mini fire station dispatches personnel to direct traffic on key road sections and open the fire lane protective doors.

[0068] In an optional implementation, calculating the pressure risk value based on the pressure data of each battery cell includes: For each battery cell, based on its real-time air pressure value and rate of change of air pressure per unit time Through the barometric risk function Calculate its barometric risk value; where, when Exceeding the preset air pressure threshold or Exceeding the preset rate of change threshold At that time, the pressure risk function The output value is configured to grow exponentially.

[0069] Specifically, preset air pressure threshold =1.5atm, preset rate of change threshold =0.2 atm / min, when the real-time gas pressure of the unit >1.5 atm or pressure change rate per unit time When the air pressure is >0.2 atm / min, the barometric risk function The output value increases exponentially, rapidly amplifying the slight pressure anomalies in the early stages of thermal runaway.

[0070] When calculating the temperature risk value, the focus is on the temperature difference between the battery cell and the module, and the degree of deviation of the coolant temperature from the normal range (40℃±5℃). The greater the temperature difference and the higher the coolant temperature, the higher the temperature risk value, which is adapted to the thermal runaway temperature characteristics of different battery types.

[0071] In an optional implementation, the process of fusing the pressure risk value and temperature risk value of each battery cell, and combining them with its voltage data, to identify at least one faulty battery cell with the highest risk includes: For each battery cell Calculate its individual comprehensive risk score The calculation formula is: ; in, This is the pressure risk value for that monomer. Its temperature risk value, Its real-time voltage, The average voltage of the battery pack. , , These are the preset weighting coefficients.

[0072] Specifically, the weighting coefficients are dynamically configured based on the thermal runaway mechanism of the battery type: thermal runaway in ternary lithium batteries is usually triggered by rapid gas generation after the decomposition of the SEI film, and the gas pressure signal is an early core warning indicator, therefore, the weighting coefficients are set accordingly. =0.2 (highest weight) =0.15; Lithium iron phosphate batteries have stronger thermal stability, and most failures are caused by long-term temperature accumulation, therefore, it is set... =0.15、 =0.2; The value is uniformly set to 0.1 to balance the risk contribution of voltage anomalies and avoid misjudgment based on a single dimension.

[0073] Characterizing the deviation between individual cell voltage and the average voltage of the battery pack: Voltage deviation is a core characteristic of abnormal internal resistance and capacity decay in individual cells. A deviation of more than 0.2V is often accompanied by an increase in internal resistance of more than 30%. The contribution value of an item will double (e.g., a contribution of 0.02 becomes 0.04), highlighting the risk of poor voltage consistency; calculation At that time, the voltage anomalies (i.e., those that deviate from the average voltage of all individual cells by ±3 standard deviations) will be eliminated by the 3σ principle to avoid anomalies from skewing the average voltage and to ensure the accuracy of the calculation.

[0074] Individual comprehensive risk score The highest-ranking at least one battery cell is identified as the faulty battery cell.

[0075] Specifically, first, for all individual battery cells... The cells are sorted in descending order, and candidate faulty cells are selected based on the number of cells in the battery pack: Top 1 is selected for small battery packs (number of cells ≤ 100), Top 2 is selected for medium battery packs (100 < number of cells ≤ 200), and Top 3 is selected for large battery packs (number of cells > 200). This approach avoids missing high-risk cells and controls the number of candidates to improve the efficiency of subsequent verification.

[0076] The candidate cells are then validated a second time using historical data: An LSTM model is used to analyze the cell's time-series data for the past hour (sampling frequency 60 seconds / time for low-risk scenarios, 10 seconds / time for medium-risk scenarios, and 1 second / time for high-risk scenarios) to predict the pressure / temperature change trend for the next 5 minutes. Simultaneously, a decision tree model is used to overlay static features—charge / discharge cycle count (risk weight +0.1 when >3000 cycles), SOC range (risk weight +0.05 when SOC >90% or <20%), and historical fault records (risk weight +0.08 when pressure fluctuations have occurred in the past 3 months). Finally, a risk confidence score is output. When the risk confidence score is ≥95%, the cell is directly identified as faulty; when it is 80%~95%, the overall insulation resistance of the battery pack needs to be considered. Secondary confirmation is made using auxiliary parameters such as direct judgment and coolant flow rate (direct judgment when it is 30% below the normal range).

[0077] In an optional implementation, the determination of the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the overall battery health status data and insulation data of the battery pack, the ambient temperature data and fire station distance data in the external environmental data, and the traffic status data in the collaborative data includes: The dynamic risk index is calculated using a weighted fusion model, which includes three core dimensions: intrinsic battery risk factor, environmental coupling risk factor, and traffic impact risk factor. The intrinsic battery risk factor is obtained by weighting the maximum air pressure risk value of the faulty cell, the battery health status, and the insulation resistance. The environmental coupling risk factor is obtained by weighting the degree to which the ambient temperature deviates from the safety threshold and the distance between the vehicle and the nearest fire station. The traffic impact risk factor is determined based on the real-time traffic congestion index. The model is configured with dynamic weight coefficients, the sum of which is 1. When the maximum air pressure risk value of the faulty cell exceeds a preset threshold, the weight of the intrinsic battery risk factor is automatically increased. The dynamic risk index is finally calibrated by combining the health status correction coefficient of the vehicle's historical fault records.

[0078] Specifically, the weighted fusion model is constructed based on the evolutionary law of battery failure in new energy vehicles. It takes the risk transmission mechanism of "battery risk itself as the core internal factor and environmental and traffic conditions as key external factors" as its theoretical basis. Through the correlation analysis of multi-dimensional risk factors, it achieves a quantitative assessment of dynamic risk. The calculation and weighting rules for each factor are as follows: Weighted calculation rules for battery intrinsic risk factors: The maximum pressure risk value of the faulty cell, the state of health (SOH) of the battery, and the insulation resistance are selected as core parameters, and their weights are dynamically configured based on the battery type and fault history characteristics. Among them, the maximum pressure risk value is an early core precursor parameter of battery thermal runaway, and its weight is assigned higher priority than the other two parameters; the battery health status reflects the degree of battery aging, and the thermal runaway trigger threshold of aged batteries is lower, so the weight of this parameter is negatively correlated with the battery health status; the insulation resistance reflects the leakage risk of the battery pack, and the decline in insulation performance will exacerbate the spread of thermal runaway risk, so its weight is negatively correlated with the insulation resistance value.

[0079] The dynamic configuration of weights is based on training with real vehicle fault data from a cloud-based big data platform. It uses a gradient descent algorithm for iterative optimization, with the optimization goal of minimizing the false alarm and false alarm rates of risk warnings, ensuring that vehicles with different battery types and different aging levels can receive accurate risk assessments.

[0080] Weighted calculation rules for environmental coupling risk factors: The degree to which the ambient temperature deviates from the safety threshold and the distance between the vehicle and the nearest fire station were selected as core parameters, with weighting based on the climate characteristics and emergency resource distribution of the area where the vehicle is located. Specifically, the weight of the degree to which the ambient temperature deviates from the safety threshold is positively correlated with the historical battery failure rate under high / low temperatures in the area; the weight of the distance between the vehicle and the nearest fire station is positively correlated with the statistical data on rescue response time in the area, with a higher weight given to greater distances, thus reflecting the impact of rescue timeliness on risk level.

[0081] Quantification rules for traffic impact risk factors: The impact of real-time traffic congestion index on the travel time of rescue vehicles is quantified, and the congestion index is linearly positively correlated with the quantified value of this factor. Traffic congestion directly prolongs rescue response time and compresses the window of opportunity for risk management. Therefore, when the traffic congestion index reaches a preset threshold, the weight of this factor will be adaptively increased.

[0082] The adjustment mechanism for dynamic weighting coefficients: The sum of the weighting coefficients for the battery intrinsic risk factor, environmental coupling risk factor, and traffic impact risk factor is always 1. When the maximum air pressure risk value of a faulty cell exceeds a preset threshold, the system automatically increases the weighting of the battery intrinsic risk factor while decreasing the weighting of the other two factors. This adjustment mechanism is based on the fact that when the battery itself shows clear signs of impending thermal runaway, the battery's own risk becomes the dominant factor in risk evolution, and the assessment of the core risk source should be prioritized.

[0083] Calibration rules for health status correction coefficients: The health status correction coefficient is determined based on the vehicle's historical fault records, and its value is positively correlated with the number of times the same type of fault has occurred in the past. Specifically, the correction coefficient is the baseline value for vehicles with no similar fault records; for vehicles with 1-2 similar fault records, the correction coefficient is appropriately increased; and for vehicles with 3 or more similar fault records, the correction coefficient is significantly increased. This rule is based on a statistical model of fault recurrence probability. Experimental data shows that vehicles with repeated occurrences of the same type of fault have a higher risk evolution rate and severity than vehicles with a first-time fault.

[0084] Furthermore, through multi-scenario real-vehicle simulation experiments, the risk assessment scheme using this weighted fusion model has improved the early warning accuracy by more than 35% and shortened the rescue response time by more than 40% compared with the traditional static matching scheme, which fully demonstrates the effectiveness and feasibility of this scheme.

[0085] Furthermore, the dynamic risk index can also be calculated using a weighted fusion model, with the following formula:

[0086] in: Intrinsic risk factors for batteries:

[0087] This is the highest pressure risk value among all individual units; Battery health status; Insulation resistance; , For coefficients, =0.3、 =0.2; It is the ratio of the battery's current capacity to its rated capacity. The lower the value, the higher the degree of battery aging: such as When =0.8 (health level 80%), 1- =0.2, multiplied by =0.3 then contributes 0.06; if =0.6, then the contribution is 0.12, and the risk weight of aging batteries is significantly increased.

[0088] (unit: This refers to the insulation resistance between the positive and negative terminals of the battery pack and the vehicle body. The smaller the value, the worse the insulation performance: such as =0.5 hour, =2, multiplied by =0.2 then contributes 0.4; if =2 If the value is 0.1, then the risk of insulation failure is precisely quantified.

[0089] Environmentally coupled risk factors: ; The ambient temperature; For safe temperature threshold, =45℃; The distance from the vehicle to the nearest fire station; For reference distance, =5 kilometers; , For coefficients, =0.2、 =0.15; The risk contribution factor is ambient temperature: a risk only arises when the ambient temperature exceeds 45°C, such as... At 50℃, multiplied by =0.2 then contributes 1.0; if If the temperature is 40℃, then this part contributes 0, thus avoiding meaningless risk accumulation.

[0090] This is a risk contribution item to fire accessibility: the farther away from the fire station, the greater the rescue delay, such as... =10 km, multiplied by =0.15 then contributes 0.3; if =2 kilometers, then the contribution is 0.06, which directly reflects the impact of the difficulty of rescue on the risk.

[0091] This is a traffic impact risk factor, the value of which is determined based on the real-time congestion index in the traffic status data. , , The dynamic weighting coefficients satisfy the following conditions: ,when When the set threshold is exceeded, Automatically increases in size; This is a health status correction factor based on the vehicle's historical fault records.

[0092] It is linearly positively correlated with the congestion index: when the congestion index (value 0~1) = 0 =0, congestion index =0.5 =0.15, when the congestion index = 1 =0.3, accurately quantifying the impact of traffic congestion on rescue efficiency; setting a threshold of 1.5 atm, when When >1.5 atm, Increase from the initial 0.6 to 0.8, while Adjusted to 0.1 Adjusted to 0.1, while maintaining a weighted sum of 1, prioritizing the core impact of intrinsic battery risks.

[0093] This is a correction factor for historical faults: when the vehicle has no similar fault records. =1.0, with 1-2 similar fault records. =1.2, when there are more than 3 records of the same type of fault. =1.5, reflecting the actual logic that "vehicles with a higher probability of recurring faults are at higher risk".

[0094] Intrinsic risk factors of batteries: sudden pressure change is an early core feature of battery thermal runaway, and has a higher warning sensitivity than temperature and voltage parameters; battery state of health (SOH) directly reflects the degree of battery aging, and the probability of thermal runaway increases by more than 30% for aged batteries; a decrease in insulation resistance will cause leakage risk, which will further aggravate thermal runaway.

[0095] Environmental coupling risk factors: When the ambient temperature exceeds the safety threshold, the battery heat dissipation efficiency decreases by 50%, and the risk of thermal runaway increases exponentially; the distance to the fire station determines the rescue response time, and the farther the distance, the shorter the risk disposal window.

[0096] Traffic impact risk factors: Traffic congestion will lead to longer travel time for rescue vehicles. When the congestion index is ≥0.7, the rescue delay rate exceeds 40%.

[0097] The weighting coefficients are trained based on a large amount of real vehicle fault data and iteratively optimized using the gradient descent algorithm, with the goal of minimizing the false alarm rate and false alarm rate of risk warnings.

[0098] When the gas pressure risk value of a single battery cell exceeds the threshold, the weight of the intrinsic risk factor of the battery is increased because this parameter is a direct precursor to thermal runaway and should be given priority.

[0099] The weighting differences for different battery types are based on the different thermal runaway mechanisms of ternary lithium batteries and lithium iron phosphate batteries: ternary lithium batteries have a faster gas production rate and a higher gas pressure weighting; lithium iron phosphate batteries have stronger thermal stability and a higher temperature weighting.

[0100] In an optional implementation, determining the vehicle's risk level based on the dynamic risk index, and generating and issuing a coordinated response instruction based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the coordinated data, includes: According to the dynamic risk index With preset threshold , , The risk level is determined by comparison, wherein, > > .

[0101] Specifically, the preset threshold value is =0.2、 =0.4、 =0.8, the corresponding risk level and basic warning logic are as follows: ≤ (Level 1, Reminder) - The in-vehicle screen displays a green text message "Battery status needs attention," accompanied by a gentle beep. < ≤ (Level 2, Warning) - The in-vehicle screen displays a flashing yellow warning, indicating that fast charging is disabled and acceleration power is limited to 50%. < ≤ (Level 3, Emergency) - The vehicle screen displays a continuous red warning, accompanied by a high-frequency beep, limiting the drive power to ≤30kW; > (Level 4, Critical) - The vehicle screen displays a full-screen warning with red background and black text, cuts off the high-voltage circuit, and unlocks all doors.

[0102] when > At the same time, the generated collaborative handling instructions include at least: sending speed limit and thermal management enhancement instructions to the vehicle, and sending early warning information containing the faulty battery cell number and the precise location of the vehicle to the fire department.

[0103] Specifically, the speed limit command is dynamically adapted to different road sections: a maximum of 80 km / h is set on highways (to avoid aggravating battery load during high-speed driving), a maximum of 50 km / h is set on ordinary urban roads, and a maximum of 30 km / h is set on mountain roads; the thermal management enhancement command triggers the battery thermal management system to operate at maximum power - the coolant circulation rate is increased to 150% of the normal state, the cooling fan speed is maximized, and if a liquid cooling system is equipped, the auxiliary cooling module is activated to keep the battery pack temperature below 40°C.

[0104] The warning information sent to the fire department includes multi-dimensional key data: in addition to the faulty battery cell number and the vehicle's GPS+BeiDou dual-mode positioning (accuracy ≤5 meters), it also includes the battery type (ternary lithium / lithium iron phosphate), the current SOC value (e.g., SOC=92%), and the vehicle's operating status (driving / parking / charging), so that the fire department can prepare appropriate tools in advance—high boiling point flame retardant coolant for ternary lithium batteries and high-pressure water spray device for lithium iron phosphate batteries.

[0105] when > At the same time, the generated collaborative response instructions include at least: sending instructions to the vehicle to execute safe stopping, personnel evacuation and vehicle autonomous avoidance, and sending emergency response requests containing the best rescue route to the fire and traffic departments.

[0106] Specifically, the safe parking instruction prioritizes guiding vehicles to designated areas: in highway scenarios, the emergency lane is prioritized (avoiding curves and slopes); in urban scenarios, open parking lots or dead-end roads are prioritized; and in charging scenarios, vehicles are prioritized to leave the charging parking space. The personnel evacuation instruction is broadcast repeatedly via the vehicle's voice system (once every 5 seconds): "There is a serious risk to the battery. Please evacuate the vehicle immediately and move to a safe area at least 50 meters away." At the same time, the vehicle's speaker volume is turned up to 100 decibels to ensure that the people in the vehicle can clearly hear the instruction.

[0107] The optimal rescue route is a Class A traffic level route (traffic saturation ≤ 30%) throughout the entire route: when planning the route, avoid densely populated areas such as schools and hospitals, and set up "dedicated rescue lanes" in core sections (such as main roads within 2 kilometers), lock the lanes through the traffic control system, and prohibit social vehicles from entering; the route information includes real-time traffic light status and estimated travel time (accurate to the minute), so that fire trucks can plan their travel rhythm in advance.

[0108] In an optional implementation, referring to Figure 2, Figure 2 shows a flowchart of a vehicle control operation execution method provided in Embodiment 1 of this application, wherein the vehicle responds to the coordinated handling command and executes a vehicle control operation corresponding to the risk level and faulty battery cell information, including steps S201~S202: S201: When the risk level reaches a preset emergency level, the vehicle performs an isolation operation on the charging and discharging circuit of the faulty battery cell.

[0109] Specifically, the preset emergency level is level three ( < ≤ The vehicle-side operation is completed through a fault cell isolation unit: this unit consists of high-voltage DC relays, with each battery cell corresponding to an independent relay switch. Upon receiving a command, it only disconnects the charging and discharging circuit of the fault cell (without affecting other normal cells), preventing the fault from spreading to the battery pack. After isolation, the voltage and air pressure data of the fault cell are still sampled at a frequency of 1 second and continuously uploaded to the cloud.

[0110] After the isolation operation, the vehicle-side monitors the faulty individual unit in real time. Temperature data; if the parameters continue to deteriorate (judgment criteria: If the voltage rises by 0.3 atm within 5 minutes, or the temperature rises by 10°C, the associated operation of the Level 4 warning will be automatically triggered—cutting off the high-voltage circuit of the entire battery pack and activating the vehicle's hazard warning lights in hazard flash + strobe mode.

[0111] S202: When the risk level reaches the preset highest level, the vehicle terminal controls the vehicle's autonomous driving system to drive the vehicle to the designated safe area or the nearest fire station according to the route issued by the cloud or planned locally.

[0112] Specifically, the highest preset level is level four. > The vehicle first uses seat pressure sensors to detect evacuation: the pressure threshold for each seat is set to 5kg. If the pressure of all seats is less than 5kg for 3 consecutive seconds, it is determined that the personnel have been evacuated, and only then will the autonomous driving function be activated (to avoid danger caused by the vehicle moving when the personnel have not been evacuated).

[0113] The route prioritizes the route delivered from the cloud; if the network is interrupted, locally cached map data will be used: the locally cached map is automatically updated weekly, covering an area within 20 kilometers of the vehicle's current location, prioritizing fire stations within 5 kilometers; if there are no fire stations, an open area in the suburbs will be selected (standard: area ≥ 50㎡, distance from buildings ≥ 20 meters, no flammable or explosive materials); during the journey, avoidance warnings will be sent to surrounding vehicles via V2X (content: "New energy vehicle is in the process of avoiding a malfunction, please maintain a safe distance of more than 100 meters").

[0114] In an optional implementation, referring to Figure 3, Figure 3 shows a flowchart of a collaborative emergency operation execution method provided in Embodiment 1 of this application, wherein the at least one collaborating party, in response to the collaborative handling instruction, executes a collaborative emergency operation corresponding to the risk level and faulty battery cell information, including steps S301~S302: S301: In response to the coordinated response command, the fire department, based on the faulty battery cell identification information, uses a dedicated suppression device to perform drilling depressurization and targeted cooling operations on the faulty battery cell.

[0115] Specifically, the dedicated suppression device includes multi-dimensional precision tools: a multi-degree-of-freedom robotic arm (positioning accuracy ±2cm), a dedicated drilling mechanism with a diameter of 3mm (to avoid damaging adjacent cells), an explosion-proof valve puncture component, a vacuum suction head, and a directional coolant nozzle; the robotic arm uses the cell position mapping table of the battery pack BMS to accurately locate the physical coordinates of the faulty cell within the battery pack (e.g., "the 5th cell of the 2nd module, 15cm from the left side and 20cm from the top of the battery pack").

[0116] The operation process strictly follows the logic of "precise loss control": the drilling mechanism drills open the battery casing (depth controlled at 5mm, only penetrating the casing without damaging the cell) - the puncture component is aligned with the explosion-proof valve position (force controlled at 50N to avoid the explosion-proof valve fragments flying) - the vacuum suction head sucks away the explosion-proof valve membrane (to prevent fragments from entering the cell) - the directional coolant nozzle injects 500ml of water-based flame-retardant coolant into the faulty cell (flow rate controlled at 100ml / second to ensure pressure relief and cooling at the same time).

[0117] S302: In response to the coordinated response instruction, the traffic management department plans and locks a priority passage route for fire and rescue vehicles heading to the incident site, and controls the traffic lights on that route to provide a green light wave.

[0118] Specifically, the planning and locking logic for priority passage routes is as follows: the traffic control system selects routes with the "lowest traffic saturation and fewest intersections", sets the core sections of the routes (length ≥ 2 kilometers) as "rescue lanes", closes the entrances for social vehicles on these sections, and reminds vehicles already on the road to pull over via road monitoring broadcasts.

[0119] The green light traffic wave is dynamically adjusted based on the real-time location of the rescue vehicle: the traffic control system obtains the GPS data of the rescue vehicle in real time and updates the traffic light status every 10 seconds. When the rescue vehicle is 100 meters away from the intersection, the intersection is automatically switched to green light, and the green light duration is extended to 20 seconds (normal duration is 15 seconds). If the rescue vehicle's speed is ≥60km / h, the green light duration is further extended to 25 seconds, ensuring that the rescue vehicle does not need to wait for the red light, and the passage time is shortened by more than 40%.

[0120] To better illustrate the collaborative emergency response method for new energy vehicles provided in this application, this application also provides a specific application example (summer high-speed ternary lithium battery thermal runaway risk scenario).

[0121] The basic information of the scene is as follows: A certain brand of ternary lithium pure electric vehicle (battery consists of 120 cells, rated capacity 70kWh), with a mileage of 150,000 kilometers, battery health status. =0.78, charge / discharge cycle count 5000. At 2:30 PM on a summer afternoon, the vehicle was traveling on a long uphill section of highway (6% gradient, asphalt road), at a speed of 90 km / h. The current SOC was 92% (just finished fast charging 30 minutes ago), the ambient temperature was 40℃, and the solar radiation intensity was 1500 W / m². 2 The nearest fire station is 8 kilometers away, and the real-time congestion index of the road section is 0.3.

[0122] The entire process execution details include: Step S101: Vehicle-side data collection and uploading.

[0123] The vehicle-side collects data at a high-risk scenario sampling frequency (1 second / time) and uploads it encrypted with TLS 1.3. Battery cell data: Real-time air pressure of the target cell =1.6 atm, rate of change of air pressure per unit time =0.3 atm / min; target single cell temperature 48℃, average battery pack temperature 40℃ ( ); target unit voltage =3.72V, total battery pack voltage 444V ( ); Battery pack overall data: Insulation resistance =0.8 Coolant temperature 55℃, fast charging cycle count 45%; Auxiliary data: GPS + Beidou dual-mode positioning coordinates (accuracy 5 meters), driving trajectory is the K123+450 section of the expressway, seat pressure sensor detected 1 driver (pressure value 65kg).

[0124] 2. Step S102: Obtain cloud data association.

[0125] After retrieving the vehicle's location from the cloud, it is correlated with multi-source data: External environmental data: The national meteorological department issued a red alert for local high temperatures; road curvature was 0; there were no traffic incidents (no construction / accidents). Collaborating data: The fire department reported that there is one fire truck on standby at a fire station 8 kilometers away (status: ready to depart within 5 minutes); the transportation department reported that the traffic lights on this section of the road are in intelligent control mode, and there are 3 cloud electronic billboards; the charging station data shows that the vehicle's fast charging power this time was 80kW, with no fault records.

[0126] III. Step S103: Faulty Individual Unit Identification.

[0127] 1. Calculate the individual risk parameters: Target unit pressure risk value :because and , It increased exponentially to 0.6; Target monomer temperature risk value : Coolant temperature 55℃ (triggers additional stacking). ; Voltage deviation: (Not exceeding the 0.2V threshold).

[0128] 2. Individual Comprehensive Risk Score: Ternary lithium battery weight Substitute into the formula:

[0129] 3. Candidate monomer verification: All monomers After sorting in descending order, the target single-cell unit ranks Top 1. An LSTM model analyzes its time-series data for the past hour (air pressure continuously rises after fast charging) and predicts its performance in the next 5 minutes. The speed will reach 0.4 atm / min; the decision tree model superimposes the number of iterations (5000 times > 3000 times, weight +0.1) and SOC (92% > 90%, weight +0.05), outputting a risk confidence of 98%, determining the target unit as a faulty unit, and the risk type as "thermal runaway".

[0130] IV. Step S104: Calculation of dynamic risk index.

[0131] 1. Calculation of each risk factor: Intrinsic risk factors of batteries : =0.6 (target unit pressure risk value). =0.3、 =0.2, substitute into the formula:

[0132] Environmental coupling risk factors : ; ; =8 kilometers =5 kilometers =0.2、 =0.15, substitute into the formula:

[0133] Traffic impact risk factors The congestion index is 0.3, showing a linear positive correlation. =0.3 * 0.3 = 0.09; Dynamic weighting and health correction: Initial weighting of ternary lithium batteries =0.6、 =0.1、 =0.1, because =0.6 corresponds , Automatically increases to 0.8. =0.1、 =0.1; The vehicle has two abnormal air pressure warning records in the past three months. =1.2.

[0134] 2. Calculation of the dynamic risk index R:

[0135] V. Step S105: Risk level determination and instruction generation.

[0136] because The situation was assessed as a Level IV alert (critical), and a collaborative response instruction was generated in the cloud: For the vehicle: safely stop, evacuate personnel, automatically avoid hazards (drive to the nearest fire station), and cut off the high-voltage circuit; Provide the fire department with the following information: identification of the faulty unit (target unit ID), ternary lithium battery type, precise vehicle location (emergency lane at K123+450 on the highway), SOC=92%, and optimal rescue route (3 sections of Class A traffic level). Inform the transportation department to: identify the rescue route (emergency lane + one inner lane of the main lane on the K120-K128 section of the expressway), activate the green light wave, and broadcast avoidance warnings through cloud electronic billboards.

[0137] VI. Step S106: Vehicle-side control operation execution.

[0138] 1. Personnel evacuation detection: The seat pressure sensor detects pressure <5kg for 3 consecutive seconds (driver has evacuated), and the vehicle voice broadcasts "Personnel evacuation confirmed, the vehicle will automatically avoid danger, please stay at least 50 meters away"; 2. Safe parking and high-voltage cut-off: The vehicle automatically decelerates to 30km / h, smoothly stops in the emergency lane, turns on the hazard lights and strobe lights, cuts off the drive power and high-voltage circuit, and unlocks all doors; 3. Autonomous driving hazard avoidance: The autonomous driving system receives the best route from the cloud (avoiding congested sections and expected to reach the fire station in 12 minutes), sends a warning to surrounding vehicles via V2X that "new energy vehicle is in the process of avoiding hazard, maintain a safe distance of 100 meters", and drives autonomously along the emergency lane.

[0139] VII. Step S107: Emergency operation execution by the collaborating party.

[0140] 1. Fire department response: The fire truck, carrying a ternary lithium-ion battery-specific suppression device (multi-degree-of-freedom robotic arm + 3mm diameter drilling mechanism + water-based flame-retardant coolant), set off along the optimal rescue route. With the traffic department's green light policy, the fire truck encountered green lights at every intersection and arrived at the 8-kilometer journey in just 10 minutes. Handling procedure: The robotic arm locates the target cell (the 8th cell in the 3rd module, 18cm from the left and 22cm from the top of the battery pack) using the battery pack BMS mapping table - the drilling mechanism drills open the outer shell (5mm deep) - the puncture component punctures the explosion-proof valve (50N force) - the vacuum suction head removes the explosion-proof valve membrane - the directional nozzle injects 500ml of coolant (100ml / second flow rate); Results: After 10 minutes, the temperature of the target unit dropped to 38°C, the air pressure returned to 1.1 atm, and no smoke or open flame was produced.

[0141] 2. Transportation sector collaboration: Identify the core section of the rescue route, close the inner lane entrances of that section, and use road monitoring broadcasts to remind vehicles already in the section to pull over. The system tracks the location of the fire truck in real time, updates the traffic light status every 10 seconds, maintains a speed of 60km / h, and does not encounter any red lights during the entire journey. The cloud-based electronic billboard displays a loop of messages: "Rescue vehicles are about to pass (estimated in 2 minutes), please slow down and give way," guiding surrounding vehicles to give way in an orderly manner.

[0142] 8. Finally, conduct closed-loop feedback.

[0143] The fire department uploads the response results (the temperature and air pressure of the faulty unit have returned to normal, and there is no secondary risk). The cloud system then inputs the data (the air pressure / temperature change curve of the target unit and the response parameters) into the model training library to optimize the air pressure risk function. Threshold (when the gas pressure of the ternary lithium monomer exceeds 1.5 atm, (Growth coefficient increased by 10%), and the vehicle was updated. The value is 1.5 (with 3 similar fault records), completing the entire closed-loop process.

[0144] Example 2 See Figure 4 As shown, Figure 4 The diagram shows a structural schematic of a collaborative emergency response system for new energy vehicles provided in Embodiment 2 of this application, wherein the system includes a vehicle terminal 401, a cloud server 402, and at least one collaborating party 403; The vehicle terminal is used to collect vehicle status data and upload it to the cloud server. The vehicle status data includes at least the internal air pressure data, temperature data, and voltage data of each battery cell, as well as battery health status data and insulation data that characterize the overall status of the battery pack. The cloud server is used to obtain external environmental data associated with the vehicle and collaborative data from at least the fire department and traffic management department, based on the vehicle location data extracted from the vehicle status data. The cloud server is used to calculate the air pressure risk value of each battery cell based on its air pressure data, calculate the temperature risk value of each battery cell based on its temperature data, merge the air pressure risk value and temperature risk value of each battery cell, and combine them with its voltage data to identify at least one faulty battery cell with the highest risk. The cloud server is used to determine the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the battery health status data and insulation data of the battery pack as a whole, the ambient temperature data and fire station distance data in the external environment data, and the traffic status data in the collaborative data. The cloud server is used to determine the risk level of the vehicle based on the dynamic risk index, and generate and issue collaborative handling instructions based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the collaborative data. The vehicle terminal is used to respond to the collaborative handling command and execute vehicle control operations corresponding to the risk level and faulty battery cell information; The at least one cooperating party is used to respond to the cooperative handling instruction and perform cooperative emergency operations corresponding to the risk level and faulty battery cell information.

[0145] In an optional implementation, calculating the pressure risk value based on the pressure data of each battery cell includes: For each battery cell, based on its real-time air pressure value and rate of change of air pressure per unit time Through the barometric risk function Calculate its barometric risk value; where, when Exceeding the preset air pressure threshold or Exceeding the preset rate of change threshold At that time, the pressure risk function The output value is configured to grow exponentially.

[0146] In an optional implementation, the process of fusing the pressure risk value and temperature risk value of each battery cell, and combining them with its voltage data, to identify at least one faulty battery cell with the highest risk includes: For each battery cell Calculate its individual comprehensive risk score The calculation formula is: ; in, This is the pressure risk value for that monomer. Its temperature risk value, Its real-time voltage, The average voltage of the battery pack. , , These are preset weighting coefficients; Individual comprehensive risk score The highest-ranking at least one battery cell is identified as the faulty battery cell.

[0147] In an optional implementation, the determination of the vehicle's dynamic risk index based on the risk value of the faulty battery cell, the overall battery health status data and insulation data of the battery pack, the ambient temperature data and fire station distance data in the external environmental data, and the traffic status data in the collaborative data includes: The dynamic risk index is calculated by a weighted fusion model, which includes three core dimensions: battery intrinsic risk factor, environmental coupling risk factor, and traffic impact risk factor. The intrinsic battery risk factor is obtained by weighting the maximum air pressure risk value of the faulty cell, the battery health status, and the insulation resistance; the environmental coupling risk factor is obtained by weighting the degree of deviation of the ambient temperature from the safety threshold and the distance between the vehicle and the nearest fire station; the traffic impact risk factor is determined based on the real-time traffic congestion index. The model is configured with dynamic weight coefficients, the sum of the weight coefficients of each dimension is 1, and when the maximum air pressure risk value of the faulty cell exceeds the preset threshold, the weight ratio of the battery intrinsic risk factor is automatically increased. The dynamic risk index is finally calibrated by combining the health status correction coefficient of the vehicle's historical fault records.

[0148] In an optional implementation, determining the vehicle's risk level based on the dynamic risk index, and generating and issuing a coordinated response instruction based on the risk level, the identification information of the faulty battery cell, and the emergency resource information in the coordinated data, includes: According to the dynamic risk index With preset threshold , , The risk level is determined by comparison, wherein, > > ; when > At the same time, the generated collaborative handling instructions include at least: sending speed limit and thermal management enhancement instructions to the vehicle, and sending early warning information containing the faulty battery cell number and the precise location of the vehicle to the fire department; when > At the same time, the generated collaborative response instructions include at least: sending instructions to the vehicle to execute safe stopping, personnel evacuation and vehicle autonomous avoidance, and sending emergency response requests containing the best rescue route to the fire and traffic departments.

[0149] In an optional implementation, the vehicle responds to the coordinated handling command by performing vehicle control operations corresponding to the risk level and faulty battery cell information, including: When the risk level reaches a preset emergency level, the vehicle performs an isolation operation on the charging and discharging circuit of the faulty battery cell. When the risk level reaches the preset highest level, the vehicle terminal controls the vehicle's autonomous driving system to drive the vehicle to the designated safe area or the nearest fire station according to the route issued by the cloud or planned locally.

[0150] In an optional implementation, the at least one collaborating party, in response to the collaborative handling instruction, performs collaborative emergency operations corresponding to the risk level and faulty battery cell information, including: In response to the coordinated response command, the fire department, based on the identification information of the faulty battery cell, uses a dedicated suppression device to perform drilling depressurization and targeted cooling operations on the faulty battery cell. In response to the coordinated response command, the traffic management department plans and locks a priority route for fire and rescue vehicles heading to the incident site, and controls the traffic lights along that route to provide a green light wave.

[0151] Example 3 Based on the same application concept, see [link / reference] Figure 5 As shown, Figure 5 This illustration shows a structural schematic diagram of a computer device provided in Embodiment 3 of this application, wherein, as shown... Figure 5 As shown, the computer device 500 provided in Embodiment 3 of this application includes: The computer device 500 includes a processor 501, a memory 502, and a bus 503. The memory 502 stores machine-readable instructions that can be executed by the processor 501. When the computer device 500 is running, the processor 501 and the memory 502 communicate through the bus 503. When the machine-readable instructions are executed by the processor 501, the steps of the collaborative emergency response method for new energy vehicles shown in Embodiment 1 are executed.

[0152] Example 4 Based on the same concept, this application also provides a computer-readable storage medium storing a computer program, which, when run by a processor, executes the steps of the collaborative emergency response method for new energy vehicles described in any of the above embodiments.

[0153] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0154] The computer program product for coordinated emergency response to new energy vehicles provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0155] The collaborative emergency response system for new energy vehicles provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The device provided in this application embodiment has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, units, and processes described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0156] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0157] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0158] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0159] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0161] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A cooperative emergency disposal method for new energy vehicles, characterized in that, The method comprises: The vehicle end collects vehicle state data and uploads to the cloud server, wherein the vehicle state data at least includes internal pressure data, temperature data, voltage data of each battery monomer, and battery health state data and insulation data representing the overall state of the battery pack; The cloud server obtains external environment data associated with the vehicle and collaborative data from at least one collaborative party including the fire department and the traffic management department according to the vehicle location data extracted from the vehicle state data; The cloud server calculates the pressure risk value of each battery monomer based on the pressure data of each battery monomer, calculates the temperature risk value of each battery monomer based on the temperature data of each battery monomer, fuses the pressure risk value and the temperature risk value of each battery monomer, and identifies at least one faulty battery monomer in combination with the voltage data; The cloud server determines the dynamic risk index of the vehicle based on the risk value of the faulty battery monomer, the battery health state data and the insulation data of the overall battery pack, the environmental temperature data and the fire station distance data in the external environment data, and the traffic state data in the collaborative data; The cloud server determines the risk level of the vehicle according to the dynamic risk index, and generates and issues a collaborative disposal instruction based on the risk level, the identification information of the faulty battery monomer, and the emergency resource information in the collaborative data; The vehicle end responds to the collaborative disposal instruction and performs vehicle control operations corresponding to the risk level and the faulty battery monomer information; The at least one collaborative party responds to the collaborative disposal instruction and performs collaborative emergency operations corresponding to the risk level and the faulty battery monomer information.

2. The method of claim 1, wherein, The calculation of the pressure risk value of each battery monomer based on the pressure data of each battery monomer comprises: For each battery cell, its real-time air pressure value and air pressure change rate per unit time , its air pressure risk value is calculated by an air pressure risk function ; wherein, when exceeds a preset air pressure threshold or exceeds a preset change rate threshold , the output value of the air pressure risk function is configured to grow exponentially.

3. The method according to claim 1 or 2, characterized in that, The fusion of the pressure risk value and the temperature risk value of each battery monomer, and the combination of the voltage data to identify at least one faulty battery monomer with the highest risk comprises: For each battery cell , its cell integrated risk score is calculated , the calculation formula is: ； wherein, is the gas pressure risk value for the monomer, is the temperature risk value thereof, is the real-time voltage thereof, is the average voltage of the battery pack, , , is a preset weight coefficient; Summarizing risk scores for monomers The highest at least one battery monomer is identified as the faulty battery monomer.

4. The method of claim 1, wherein, The determination of the dynamic risk index of the vehicle based on the risk value of the faulty battery monomer, the battery health state data and the insulation data of the overall battery pack, the environmental temperature data and the fire station distance data in the external environment data, and the traffic state data in the collaborative data comprises: The dynamic risk index is calculated by a weighted fusion model, and the weighted fusion model includes three core dimensions of battery intrinsic risk factor, environmental coupling risk factor, and traffic influence risk factor; The battery intrinsic risk factor is obtained by weighting the maximum pressure risk value of the faulty monomer, the battery health state, and the insulation resistance; the environmental coupling risk factor is obtained by weighting the degree of deviation of the environmental temperature from the safety threshold and the distance between the vehicle and the nearest fire station; and the traffic influence risk factor is determined based on the real-time traffic congestion index; The model is configured with dynamic weight coefficients, the sum of the weight coefficients of each dimension is 1, and when the maximum pressure risk value of the faulty monomer exceeds a preset threshold, the weight proportion of the battery intrinsic risk factor is automatically increased; The dynamic risk index is finally calibrated in combination with the health state correction coefficient of the vehicle historical fault record.

5. The method of claim 1, wherein, The risk level of the vehicle is determined according to the dynamic risk index, and a collaborative disposal instruction is generated and issued based on the risk level, identification information of the faulty battery monomer, and emergency resource information in the collaborative data, including: determining the risk level based on a comparison of the dynamic risk index with a pre-set threshold value , , , ;​​ When < / < / The generated cooperative treatment instruction at least includes: sending speed limit and thermal management strengthening instruction to the vehicle side, and sending early warning information containing the faulty battery cell number and the accurate position of the vehicle to the fire department. When < / < / The generated cooperative treatment instruction at least includes: sending the vehicle end to execute the instruction of safe parking, personnel evacuation and vehicle autonomous risk avoidance, and sending the emergency treatment request containing the best rescue route to the fire and traffic departments.

6. The method according to claim 1 or 5, characterized in that, The vehicle side executes vehicle control operations corresponding to the risk level and the faulty battery monomer information in response to the collaborative disposal instruction, including: When the risk level reaches a preset emergency level, the vehicle side executes a charge-discharge loop isolation operation on the faulty battery monomer; When the risk level reaches a preset highest level, the vehicle side controls the vehicle automatic driving system to drive the vehicle to a designated safe area or the nearest fire station according to the path issued by the cloud or planned locally.

7. The method according to claim 1 or 5, characterized in that, The at least one collaborative party executes collaborative emergency operations corresponding to the risk level and the faulty battery monomer information in response to the collaborative disposal instruction, including: The fire department executes drilling and pressure relief and spot cooling operations on the faulty battery monomer using a special suppression device in response to the collaborative disposal instruction according to the identification information of the faulty battery monomer; The traffic management department plans and locks a preferential passage for fire and rescue vehicles going to the incident site and controls the traffic signal lights on the path to provide a green light passage.

8. A cooperative emergency disposal system for new energy vehicles, characterized in that, The system includes a vehicle side, a cloud server, and at least one collaborative party; The vehicle side is configured to collect vehicle state data and upload the vehicle state data to the cloud server, wherein the vehicle state data at least includes internal pressure data, temperature data, and voltage data of each battery monomer, and battery health state data and insulation data representing the overall state of the battery pack; The cloud server is configured to obtain external environment data associated with the vehicle and collaborative data from at least one collaborative party including the fire department and the traffic management department according to vehicle location data extracted from the vehicle state data; The cloud server is configured to calculate a pressure risk value of each battery monomer based on the pressure data of the battery monomer, calculate a temperature risk value of each battery monomer based on the temperature data of the battery monomer, fuse the pressure risk value and the temperature risk value of each battery monomer, and identify at least one faulty battery monomer with the highest risk in combination with the voltage data of the battery monomer; The cloud server is configured to determine a dynamic risk index of the vehicle based on the risk value of the faulty battery monomer, the battery health state data and the insulation data of the overall battery pack, the environmental temperature data and the fire station distance data in the external environment data, and the traffic state data in the collaborative data; The cloud server is configured to determine a risk level of the vehicle according to the dynamic risk index, and generate and issue a collaborative disposal instruction based on the risk level, identification information of the faulty battery monomer, and emergency resource information in the collaborative data; The vehicle side is configured to execute vehicle control operations corresponding to the risk level and the faulty battery monomer information in response to the collaborative disposal instruction; The at least one cooperative party is configured to perform a cooperative emergency operation corresponding to the risk level and the fault battery cell information in response to the cooperative disposal instruction.

9. A computer device, comprising: The method comprises the following steps: A processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the computer equipment runs, the processor and the memory communicate through the bus, the machine readable instructions are executed by the processor and execute the steps of the cooperative emergency disposal method for new energy vehicles in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor and executes the steps of the cooperative emergency disposal method for new energy vehicles in any one of claims 1 to 7.

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