Charging management method and device for emergency energy vehicle and storage medium

Abstract

The present application relates to the technical field of emergency energy vehicle charging management, and particularly relates to a charging management method and device for an emergency energy vehicle and a storage medium, comprising: collecting vehicle data, environmental data and power grid data in real time; analyzing a time emergency coefficient of the emergency energy vehicle based on the vehicle data; storing the collected vehicle data and environmental data, and analyzing a slope correlation coefficient and an altitude correlation coefficient based on the stored data; constructing a priority decision model based on the vehicle data, the environmental data and the time emergency coefficient to analyze a charging priority, and updating the priority decision model according to the slope correlation coefficient and the altitude correlation coefficient; formulating a power grid power distribution rule, and using the power grid power distribution rule to perform adaptive charging power distribution according to the charging priority, the vehicle data and the environmental data. The present application realizes accurate management of charging of the emergency energy vehicle.

Description

Technical Field

[0001] This invention relates to the field of emergency energy vehicle charging management technology, and in particular to a charging management method, device and storage medium for emergency energy vehicles. Background Technology

[0002] In emergency scenarios, new energy vehicles need to perform critical tasks in complex environments, and their charging needs are dynamic, urgent, and resource-constrained.

[0003] However, existing charging management systems have the following drawbacks: Static priority allocation: Traditional methods allocate charging resources based on fixed rules, which cannot respond to real-time task changes; Poor environmental adaptability: The nonlinear effects of terrain slope and temperature on battery performance are not considered, resulting in low charging efficiency; Insufficient multi-vehicle coordination: Resource contention easily occurs when multiple vehicles are charging, and there is a lack of dynamic scheduling mechanisms based on task urgency. Summary of the Invention

[0004] The purpose of this invention is to provide a charging management method, device, and storage medium for emergency energy vehicles, so as to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A charging management method for emergency energy vehicles, comprising: Real-time collection of vehicle data, environmental data, and power grid data; The time urgency factor of emergency energy vehicles is analyzed based on vehicle data. The system stores the collected vehicle and environmental data and analyzes the slope and altitude correlation coefficients based on the stored data. A priority decision-making model is built based on vehicle data, environmental data, and time urgency coefficient to analyze charging priority, and the priority decision-making model is updated based on slope correlation coefficient and altitude correlation coefficient. Develop grid power allocation rules and use these rules to perform adaptive charging power allocation based on charging priority, vehicle data, and environmental data.

[0006] Furthermore, the time interval between the estimated completion time and the current time is used as the estimated interval duration, and the ratio of the vehicle location distance of emergency energy vehicles of different mission types to the estimated interval duration is used as the time urgency coefficient.

[0007] Furthermore, based on the stored terrain slope, battery health status, and battery internal resistance, the slope correlation coefficient is analyzed. The expression for the slope correlation coefficient is L1=Avg(tanθ(i)×ΔR(i) / SOH(i)), where L1 represents the slope correlation coefficient, i represents the task number, and i∈N. +θ(i) represents the terrain slope, ΔR(i) represents the change in battery internal resistance, SOH(i) represents the battery health status, and Avg() represents the average value of the data in parentheses; The altitude correlation coefficient is analyzed based on battery temperature, altitude, and completed task duration. The expression for the altitude correlation coefficient is L2=σ(ΔT(i)×lg[|H(i)|+1] / t(i)) / Avg(ΔT(i)×lg[|H(i)|+1] / t(i)), where L2 represents the altitude correlation coefficient, ΔT(i) represents the change in battery temperature, t(i) represents the completed task duration, H(i) represents the altitude, and σ() represents the standard deviation of the data in parentheses.

[0008] Furthermore, the difference between the ambient temperature and the preset temperature is used as the temperature difference factor, and the exponential relationship between the battery health state and the battery state of charge is used as the battery factor. The battery factor is set to the power of the battery health state and the battery state of charge.

[0009] Furthermore, dynamic weight allocation is performed on the model building factor and time urgency coefficient. Emergency weight, battery weight, and temperature difference weight are set according to the task type, and charging priority is calculated. The expression for the charging priority is: P=α1(k)×E(k)+α2(k) / W+α3(k) / ΔT; where P represents charging priority, α1(k) represents emergency weight, E(k) represents time urgency coefficient, α2(k) represents battery weight, W represents battery factor, α3(k) represents temperature difference weight, and ΔT represents temperature difference factor.

[0010] Furthermore, based on the analysis of the slope correlation coefficient, the dynamic weight allocation process is updated. When the slope correlation coefficient is greater than the slope correlation threshold, the expression for charging priority is updated to: P=α1(k)×E(k)+α2(k) / W+α3(k) / (ΔT×L1); The allocation ratios of emergency weight, battery weight, and temperature difference weight are updated based on the altitude correlation coefficient to further update the dynamic weight allocation process. When the altitude correlation coefficient is greater than or equal to the altitude correlation threshold, the temperature difference weight is increased while the emergency weight and battery weight are decreased. The increase in temperature difference weight is temperature difference weight × altitude correlation coefficient, and the decrease in emergency weight and battery weight is temperature difference weight × altitude correlation coefficient / 2.

[0011] Furthermore, the power grid allocation rule is set as follows: if the available power of the power grid is greater than or equal to the sum of the maximum charging power of all emergency energy vehicles waiting to be charged, then the charging power of each emergency energy vehicle is allocated to be equal to the maximum charging power; if the available power of the power grid is less than the sum of the maximum charging power of all emergency energy vehicles waiting to be charged, the power is allocated in descending order of charging priority until the available power of the power grid is exhausted.

[0012] Furthermore, adaptive charging power allocation is performed using grid power allocation rules based on charging priority, vehicle data, and environmental data. When the grid fluctuates, the charging power of emergency energy vehicles ranked Xth in the descending order of charging priority is reduced to maintain the charging power of emergency energy vehicles ranked 1-Xth in the descending order of charging priority. When the battery temperature is greater than or equal to the preset charging temperature, a stepped current reduction strategy is adopted to control the charging current.

[0013] On the other hand, the present invention also provides a charging management device for an emergency energy vehicle, comprising: The data acquisition module is used to collect vehicle data, environmental data, and power grid data in real time. The analysis module is used to analyze the time urgency factor of emergency energy vehicles based on vehicle data; The storage module is used to store the collected vehicle data and environmental data, and to analyze the slope correlation coefficient and altitude correlation coefficient based on the stored data; The module is used to build a priority decision model based on vehicle data, environmental data and time urgency coefficient to analyze charging priority, and update the priority decision model according to the slope correlation coefficient and altitude correlation coefficient. The allocation module is used to formulate grid power allocation rules and to perform adaptive charging power allocation based on charging priority, vehicle data, and environmental data using the grid power allocation rules.

[0014] On the other hand, the present invention also provides a storage medium storing instructions that, when run on a computer, cause the computer to execute the charging management method for emergency energy vehicles as described in any of the preceding claims.

[0015] The beneficial effects of this invention are as follows: By using real-time data-driven, dynamic priority adjustment, environmental adaptive strategies, and optimized allocation of power grid resources, the charging efficiency and reliability of emergency energy vehicles are improved, ensuring rapid response to emergency needs in complex environments. At the same time, it takes into account battery health and power grid stability, providing efficient energy support for emergency rescue missions. This improves the real-time performance of data processing and decision-making, optimizes charging priority ranking, dynamically adapts to environmental changes, protects battery health and extends battery life, and rationally allocates power grid resources to avoid overload, thereby comprehensively improving the charging management efficiency and system reliability of emergency energy vehicles. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the charging management method for emergency energy vehicles in this embodiment.

[0018] Figure 2 This is a flowchart of the data storage and analysis method in this embodiment.

[0019] Figure 3 This is a flowchart of the method for constructing the priority decision model in this embodiment.

[0020] Figure 4 This is a schematic diagram of the charging management device used in this embodiment for emergency energy vehicles. Detailed Implementation

[0021] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0022] It should be noted that although the terms first, second, third, etc., may be used in the embodiments of this application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of this application, first can also be referred to as second, and similarly, second can also be referred to as first.

[0023] Please see Figure 1 As shown, this is a charging management method for emergency energy vehicles in this embodiment, including: Step S1: Real-time collection of vehicle data, environmental data, and power grid data. The vehicle data includes vehicle location distance, task type, completed task duration, terrain slope, estimated completion time, and battery data. The vehicle location distance is the distance between the vehicle and the task location. The task type includes, but is not limited to, medical rescue and communication support. The terrain slope represents the terrain slope during the emergency vehicle's historical task executions; the terrain slope is the absolute value of the angle between the vehicle and the horizontal direction, taking a value less than 90°. The completed task duration represents the time taken for each task execution. The battery data includes battery health status, battery state of charge, battery temperature, battery internal resistance, and maximum charging power. The battery health status refers to a quantitative description of the battery's performance and aging degree under certain usage conditions compared to its brand-new state. The data is referred to as SOH. The vehicle data is acquired through an onboard terminal on the emergency energy vehicle. The onboard terminal is used to allocate and record vehicle task data and operational data. The environmental data includes altitude and ambient temperature, which are acquired through sensors. The power grid data includes grid load and available grid power, which are acquired through data import from the power management platform. The grid load is the total power load in the power system used for charging the emergency energy vehicle. The units for battery temperature and ambient temperature are degrees Celsius. The unit for vehicle location distance is kilometers. The unit for completed task duration is hours. The unit for battery internal resistance is ohms. The units for maximum charging power, grid load, and available grid power are watts. The unit for altitude is meters.

[0024] Specifically, in step S1 of this embodiment, by collecting multidimensional data in real time, the system can make dynamic decisions based on the latest information, reduce charging delays caused by data lag, and provide a reliable foundation for subsequent analysis using comprehensive data coverage.

[0025] Please continue reading. Figure 1 As shown, the charging management method for emergency energy vehicles further includes: Step S2: Analyze the time urgency coefficient of the emergency energy vehicle based on vehicle data.

[0026] Specifically, in step S2 of this embodiment, the time interval between the estimated completion time and the current time is taken as the estimated interval duration, and the time urgency coefficient is calculated based on the task type, vehicle location distance and estimated interval duration. The expression of the time urgency coefficient is: E(k)=D / Td, where E(k) represents the time urgency coefficient, k represents the task type, D represents the vehicle location distance and Td represents the estimated interval duration.

[0027] Specifically, in step S2 of this embodiment, the time urgency coefficient of the task is calculated to dynamically identify urgent tasks and prioritize the allocation of charging resources, ensuring that critical task vehicles can quickly complete charging and shorten emergency response time.

[0028] Please continue reading. Figure 1 As shown, the charging management method for emergency energy vehicles further includes: Step S3: Store the collected vehicle data and environmental data, and analyze the slope correlation coefficient and altitude correlation coefficient based on the stored data.

[0029] Please see Figure 2 As shown, it is a method for analyzing stored data, including: Step S31: Store the collected vehicle data and environmental data; Step S32: Analyze the slope correlation coefficient based on the stored terrain slope, battery health status, and battery internal resistance.

[0030] Specifically, in step S32 of this embodiment, the slope correlation coefficient is analyzed based on the stored terrain slope, battery health status, and battery internal resistance. The expression for the slope correlation coefficient is L1=Avg(tanθ(i)×ΔR(i) / SOH(i)), where L1 represents the slope correlation coefficient, i represents the task number, and i∈N. + θ(i) represents the terrain slope, ΔR(i) represents the change in battery internal resistance, SOH(i) represents the battery health status, and Avg() represents the average value of the data in parentheses. Let i be the number used to distinguish different missions performed by the emergency energy vehicle.

[0031] Specifically, in step S32 of this embodiment, the impact of terrain on battery wear is quantified by combining terrain slope, battery internal resistance, and health status, thereby reducing charging interruptions caused by battery failure.

[0032] Please continue reading. Figure 2 As shown, the method for analyzing the stored data further includes: Step S33: Analyze the altitude correlation coefficient based on battery temperature, altitude, and completed task duration.

[0033] Specifically, in step S33 of this embodiment, the altitude correlation coefficient is analyzed based on battery temperature, altitude, and completed task duration. The expression for the altitude correlation coefficient is L2=σ(ΔT(i)×lg[|H(i)|+1] / t(i)) / Avg(ΔT(i)×lg[|H(i)|+1] / t(i)), where L2 represents the altitude correlation coefficient, ΔT(i) represents the change in battery temperature, t(i) represents the completed task duration, H(i) represents the altitude, and σ() represents the standard deviation of the data in parentheses.

[0034] Specifically, in step S33 of this embodiment, the impact of high-altitude environment on battery performance is evaluated by combining altitude, battery temperature changes, and mission duration.

[0035] Please continue reading. Figure 1 As shown, the charging management method for emergency energy vehicles further includes: Step S4: Construct a priority decision model based on vehicle data, environmental data, and time urgency coefficient to analyze charging priority, and update the priority decision model based on slope correlation coefficient and altitude correlation coefficient.

[0036] Please see Figure 3 As shown, this is a method for constructing a priority decision-making model, including: Step S41: Construct factors based on the analysis model of ambient temperature, battery health status, and battery state of charge. The model construction factors include temperature difference factors and battery factors.

[0037] Specifically, in step S41 of this embodiment, the difference between the ambient temperature and the preset temperature is used as the temperature difference factor, the exponential relationship between the battery health state and the battery state of charge is used as the battery factor, the battery factor is set to the power of the battery health state and the battery state of charge, and W is set to SOH(i). SOC(i) W represents the battery factor, and SOC(i) represents the battery state of charge.

[0038] Specifically, in this embodiment, the preset temperature is set to 25°C. This embodiment does not impose specific limitations on the value of the preset temperature. Those skilled in the art can set it freely, as long as it meets the requirements for analyzing the temperature difference factor.

[0039] Please continue reading. Figure 3 As shown, the method for constructing the priority decision-making model further includes: Step S42: Dynamically assign weights to the model building factors and time urgency coefficient to analyze the charging priority.

[0040] Specifically, in step S42 of this embodiment, dynamic weight allocation is performed on the model construction factor and the time urgency coefficient. Emergency weight, battery weight and temperature difference weight are set according to the task type, and charging priority is calculated. The expression of the charging priority is: P=α1(k)×E(k)+α2(k) / W+α3(k) / ΔT; where P represents charging priority, α1(k) represents emergency weight, α2(k) represents battery weight, α3(k) represents temperature difference weight, α1(k)+α2(k)+α3(k)=1, and ΔT represents temperature difference factor.

[0041] Specifically, in step S42 of this embodiment, when setting the emergency weight, battery weight, and temperature difference weight, each weight is set according to the urgency of the task type. For example, if the urgency of medical rescue is higher than that of communication support, then the emergency weight for medical rescue is set to be greater than that for communication support, i.e., α1(medical rescue) > α1(communication support). In this embodiment, the emergency weight, battery weight, and temperature difference weight for medical rescue are set to α1(medical rescue) = 0.6, α2(medical rescue) = 0.3, and α3(medical rescue) = 0.1, respectively. The emergency weight, battery weight, and temperature difference weight for communication support are set to α1(communication support) = 0.5, α2(communication support) = 0.3, and α3(communication support) = 0.2, respectively. It is understood that this embodiment does not impose specific limitations on the setting of the emergency weight, battery weight, and temperature difference weight. Those skilled in the art can set them freely, as long as they meet the calculation of charging priority.

[0042] Specifically, in step S42 of this embodiment, emergency, battery, and temperature difference weights are flexibly assigned to task types to ensure that high-priority tasks charge quickly while balancing battery protection needs.

[0043] Please continue reading. Figure 3 As shown, the method for constructing the priority decision-making model further includes: Step S43: Update the dynamic weight allocation process based on the slope correlation coefficient analysis.

[0044] Specifically, in step S43 of this embodiment, the dynamic weight allocation process is updated based on the slope correlation coefficient. When the slope correlation coefficient is greater than the slope correlation threshold, the expression of charging priority is updated to: P=α1(k)×E(k)+α2(k) / W+α3(k) / (ΔT×L1); otherwise, the dynamic weight allocation process is not updated.

[0045] Specifically, in this embodiment, the slope association threshold is set to 0.1. This embodiment does not impose specific limitations on the value of the slope association threshold. Those skilled in the art can set it freely, as long as it satisfies the update of the dynamic weight allocation process. The value of the slope association threshold should satisfy the condition of being in the range of [0.07, 0.15].

[0046] Specifically, in step S43 of this embodiment, the influence of terrain factors on decision-making is strengthened by introducing the slope correlation coefficient into the charging priority formula.

[0047] Please continue reading. Figure 3 As shown, the method for constructing the priority decision-making model further includes: Step S44: Further update the dynamic weight allocation process based on the altitude correlation coefficient.

[0048] Specifically, in step S44 of this embodiment, the allocation ratio of emergency weight, battery weight, and temperature difference weight is updated according to the altitude correlation coefficient to further update the dynamic weight allocation process. When the altitude correlation coefficient is greater than or equal to the altitude correlation threshold, the temperature difference weight is increased and the emergency weight and battery weight are decreased. The increase in temperature difference weight is temperature difference weight × altitude correlation coefficient, and the decrease in emergency weight and battery weight is temperature difference weight × altitude correlation coefficient / 2. Otherwise, the dynamic weight allocation process is not further updated.

[0049] Specifically, in step S44 of this embodiment, the temperature difference weight is increased and the emergency weight is decreased according to the altitude correlation coefficient, so as to prioritize the response to temperature anomalies caused by high altitude and avoid charging failures caused by sudden environmental changes.

[0050] Please continue reading. Figure 1 As shown, the charging management method for emergency energy vehicles further includes: Step S5: Formulate grid power allocation rules and use the grid power allocation rules to perform adaptive charging power allocation based on charging priority, vehicle data, and environmental data.

[0051] Specifically, in step S5 of this embodiment, the power grid allocation rule is set as follows: if the available power of the power grid is greater than or equal to the sum of the maximum charging power of all emergency energy vehicles to be charged, then the charging power of each emergency energy vehicle is allocated to be equal to the maximum charging power; if the available power of the power grid is less than the sum of the maximum charging power of all emergency energy vehicles to be charged, the power is allocated in descending order of charging priority until the available power of the power grid is exhausted.

[0052] Specifically, in step S5 of this embodiment, adaptive charging power allocation is performed using the power grid allocation rules based on charging priority, vehicle data, and environmental data. When the power grid fluctuates, the charging power of the emergency energy vehicles ranked Xth in the descending order of charging priority is reduced to maintain the charging power of the emergency energy vehicles ranked 1-Xth in the descending order of charging priority. When the battery temperature is greater than or equal to the preset charging temperature, a stepped current reduction strategy is adopted to control the charging current. X represents the priority parameter.

[0053] Specifically, in step S5 of this embodiment, the grid fluctuations include, but are not limited to, abnormal situations such as voltage fluctuations, voltage transients, and frequency fluctuations in the grid, such as the voltage dropping below 90% of the rated value as the grid load increases, or the maximum transient of the output voltage of the power supply being greater than 10% of the rated voltage. In this embodiment, the preset charging temperature is set to 45°C and the priority parameter is set to 50%. In this embodiment, the values ​​of the preset charging temperature and priority parameter are not specifically limited. Those skilled in the art can freely set them as long as they meet the requirements for the allocation of charging power.

[0054] Specifically, in step S5 of this embodiment, the stepped current reduction strategy is to set different current reduction ranges according to the difference between the battery temperature and the preset charging temperature. For example, if the battery temperature is higher than the preset charging temperature, and the difference between the battery temperature and the preset charging temperature is in the range [0, 10), then the charging current is reduced by 20%; if the difference between the battery temperature and the preset charging temperature is in the range [10, 20), then the charging current is reduced by 30%, etc.

[0055] Specifically, in step S5 of this embodiment, the maximum power is allocated to all vehicles when the grid power is sufficient to maximize charging efficiency, and the power is allocated in descending order of priority when the power is insufficient to ensure that key vehicles are fully charged first and reduce task waiting time. In addition, the power of low-priority vehicles is dynamically reduced according to grid fluctuations to ensure that the charging of high-priority vehicles is not affected. A stepped current reduction strategy is used to prevent battery overheating and damage, extend battery life, reduce maintenance costs, and indirectly improve sustainability.

[0056] Please see Figure 4 As shown, this is a charging management device for emergency energy vehicles in this embodiment, including: The data acquisition module is used to collect vehicle data, environmental data, and power grid data in real time. The analysis module is used to analyze the time urgency factor of the emergency energy vehicle based on vehicle data. The analysis module is connected to the acquisition module. The storage module is used to store the collected vehicle data and environmental data, and to analyze the slope correlation coefficient and altitude correlation coefficient based on the stored data. The storage module is connected to the analysis module. The construction module is used to build a priority decision model based on vehicle data, environmental data and time urgency coefficient to analyze charging priority, and update the priority decision model according to the slope correlation coefficient and altitude correlation coefficient. The construction module is connected to the storage module. The allocation module is used to formulate grid power allocation rules and perform adaptive charging power allocation based on charging priority, vehicle data and environmental data using grid power allocation rules. The allocation module is connected to the construction module.

[0057] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the charging management method for emergency energy vehicles as described in the above method embodiments.

[0058] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable programs, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable programs, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0059] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A charging management method for emergency energy vehicles, characterized in that, include: Real-time collection of vehicle data, environmental data, and power grid data; The time urgency factor of emergency energy vehicles is analyzed based on vehicle data. The system stores the collected vehicle and environmental data and analyzes the slope and altitude correlation coefficients based on the stored data. A priority decision-making model is built based on vehicle data, environmental data, and time urgency coefficient to analyze charging priority, and the priority decision-making model is updated based on slope correlation coefficient and altitude correlation coefficient. Develop grid power allocation rules and use these rules to perform adaptive charging power allocation based on charging priority, vehicle data, and environmental data. Based on the stored terrain slope, battery health status, and battery internal resistance, the slope correlation coefficient is analyzed. The expression for the slope correlation coefficient is L1=Avg(tanθ(i)×ΔR(i) / SOH(i)), where L1 represents the slope correlation coefficient, i represents the task number, and i∈N. + θ(i) represents the terrain slope, ΔR(i) represents the change in battery internal resistance, SOH(i) represents the battery health status, and Avg() represents the average value of the data in parentheses; The altitude correlation coefficient is analyzed based on battery temperature, altitude, and completed task duration. The expression for the altitude correlation coefficient is L2=σ(ΔT(i)×lg[|H(i)|+1] / t(i)) / Avg(ΔT(i)×lg[|H(i)|+1] / t(i)), where L2 represents the altitude correlation coefficient, ΔT(i) represents the change in battery temperature, t(i) represents the completed task duration, H(i) represents the altitude, and σ() represents the standard deviation of the data in parentheses. The model building factors and time urgency coefficient are dynamically weighted. Urgency weight, battery weight, and temperature difference weight are set according to the task type, and the charging priority is calculated. The expression for the charging priority is: P=α1(k)×E(k)+α2(k) / W+α3(k) / ΔT; where P represents the charging priority, α1(k) represents the urgency weight, E(k) represents the time urgency coefficient, α2(k) represents the battery weight, W represents the battery factor, α3(k) represents the temperature difference weight, and ΔT represents the temperature difference factor. The dynamic weight allocation process is updated based on the slope correlation coefficient. When the slope correlation coefficient is greater than the slope correlation threshold, the expression for charging priority is updated to: P=α1(k)×E(k)+α2(k) / W+α3(k) / (ΔT×L1); The allocation ratios of emergency weight, battery weight, and temperature difference weight are updated based on the altitude correlation coefficient to further update the dynamic weight allocation process. When the altitude correlation coefficient is greater than or equal to the altitude correlation threshold, the temperature difference weight is increased while the emergency weight and battery weight are decreased. The increase in temperature difference weight is temperature difference weight × altitude correlation coefficient, and the decrease in emergency weight and battery weight is temperature difference weight × altitude correlation coefficient / 2.

2. The charging management method for emergency energy vehicles according to claim 1, characterized in that, The estimated interval duration is the time interval between the estimated completion time and the current time, and the ratio of the vehicle location distance of emergency energy vehicles of different mission types to the estimated interval duration is used as the time urgency coefficient.

3. The charging management method for emergency energy vehicles according to claim 1, characterized in that, The difference between ambient temperature and preset temperature is used as the temperature difference factor, and the exponential relationship between battery health status and battery state of charge is used as the battery factor. The battery factor is set to the power of battery health status and battery state of charge.

4. The charging management method for emergency energy vehicles according to claim 1, characterized in that, The power grid allocation rule is set as follows: if the available power of the power grid is greater than or equal to the sum of the maximum charging power of all emergency energy vehicles waiting to be charged, then the charging power allocated to each emergency energy vehicle is equal to the maximum charging power; if the available power of the power grid is less than the sum of the maximum charging power of all emergency energy vehicles waiting to be charged, the power is allocated in descending order of charging priority until the available power of the power grid is exhausted.

5. The charging management method for emergency energy vehicles according to claim 4, characterized in that, Adaptive charging power allocation is performed using grid power allocation rules based on charging priority, vehicle data, and environmental data. When the grid fluctuates, the charging power of emergency energy vehicles ranked Xth in the descending order of charging priority is reduced to maintain the charging power of emergency energy vehicles ranked 1-Xth in the descending order of charging priority. When the battery temperature is greater than or equal to the preset charging temperature, a stepped current reduction strategy is adopted to control the charging current. X represents the priority parameter.

6. A charging management device for an emergency energy vehicle, applied to the charging management method for an emergency energy vehicle as described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to collect vehicle data, environmental data, and power grid data in real time. The analysis module is used to analyze the time urgency factor of emergency energy vehicles based on vehicle data; The storage module is used to store the collected vehicle data and environmental data, and to analyze the slope correlation coefficient and altitude correlation coefficient based on the stored data; The module is used to build a priority decision model based on vehicle data, environmental data and time urgency coefficient to analyze charging priority, and update the priority decision model according to the slope correlation coefficient and altitude correlation coefficient. The allocation module is used to formulate grid power allocation rules and to perform adaptive charging power allocation based on charging priority, vehicle data, and environmental data using the grid power allocation rules.

7. A storage medium, characterized in that, The device stores instructions that, when executed on a computer, cause the computer to perform the charging management method for an emergency energy vehicle as described in any one of claims 1-5.

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