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

Abstract

The invention relates to the technical field of emergency energy vehicle charging management, in particular to a charging management method and device for an emergency energy vehicle and a storage medium, and the method comprises the steps: collecting vehicle data, environment 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 environment data, and analyzing a slope correlation coefficient and an altitude correlation coefficient based on the stored data; building a priority decision model based on the vehicle data, the environment data and the time emergency coefficient to analyze the charging priority, and updating the priority decision model according to the gradient correlation coefficient and the altitude correlation coefficient; and formulating a power grid power distribution rule, and performing adaptive charging power distribution by using the power grid power distribution rule according to the charging priority, the vehicle data and the environment data. According to the invention, accurate management of charging of the emergency energy vehicle is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency energy vehicle charging management, and in particular to a charging management method, device and storage medium for emergency energy vehicles. Background Art

[0002] In emergency scenarios, 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 defects: static priority allocation: traditional methods allocate charging resources based on fixed rules and cannot respond to real-time task changes; poor environmental adaptability: they do not consider the nonlinear effects of terrain slope and temperature on battery performance, resulting in low charging efficiency; insufficient multi-vehicle coordination: when multiple vehicles are charging, resource competition is prone to occur, and there is a lack of a dynamic scheduling mechanism based on the urgency of the task. Summary of the Invention

[0004] The object of the present invention is to provide a charging management method, device and storage medium for an emergency energy vehicle to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A charging management method for an emergency energy vehicle, comprising: Real-time collection of vehicle data, environmental data, and power grid data; Analyze the time emergency coefficient of emergency energy vehicles based on vehicle data; storing the collected vehicle data and environmental data, and analyzing the slope correlation coefficient and the altitude correlation coefficient based on the stored data; A priority decision model is built based on vehicle data, environmental data, and time urgency coefficients to analyze charging priorities, and the priority decision model is updated based on the slope correlation coefficient and altitude correlation coefficient; Formulate grid power allocation rules and use them to perform adaptive charging power allocation based on charging priority, vehicle data, and environmental data.

[0006] Furthermore, the time interval between the expected completion time and the current time is used as the expected interval time, and the ratio of the vehicle position distance and the expected interval time of the emergency energy vehicles of different task types is used as the time urgency coefficient.

[0007] Furthermore, the slope correlation coefficient is analyzed based on the stored terrain slope, battery health status, and battery internal resistance. The expression of the slope correlation coefficient is L1=Avg(tanθ(i)×ΔR(i) / SOH(i)), where L1 represents the slope correlation coefficient, i represents the mission 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 the brackets; The altitude correlation coefficient is analyzed based on the battery temperature, altitude, and completed mission 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 mission duration, H(i) represents the altitude, and σ() represents the standard deviation of the data in the brackets.

[0008] Furthermore, 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, and the battery factor is set to the battery health state raised to the power of the battery state of charge.

[0009] Furthermore, dynamic weight allocation is performed on the model construction factor and the time urgency coefficient, the urgency weight, battery weight and temperature difference weight are set according to the task type, and the 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 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.

[0010] Furthermore, the dynamic weight allocation process is updated based on the analysis of the slope correlation coefficient. When the slope correlation coefficient is greater than the slope correlation threshold, the expression of the charging priority is updated to: P = α1(k) × E(k) + α2(k) / W + α3(k) / (ΔT × L1); The distribution ratio of emergency weight, battery weight and temperature difference weight is updated according to the altitude correlation coefficient to further update the dynamic weight distribution 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 reduced. 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 grid power allocation rule is set as follows: if the grid available power is greater than or equal to the sum of the maximum charging powers of all emergency energy vehicles to be charged, the charging power allocated to each emergency energy vehicle is equal to the maximum charging power; if the grid available power is less than the sum of the maximum charging powers of all emergency energy vehicles to be charged, it is allocated in descending order of charging priority until the grid available power 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 the emergency energy vehicles in the last X order of charging priority is reduced to maintain the charging power of the emergency energy vehicles in the first 1-X order of charging priority. When the battery temperature is greater than or equal to the preset charging temperature, a step-by-step 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: Acquisition module, used to collect vehicle data, environmental data and power grid data in real time; An analysis module for analyzing the time emergency coefficient of the emergency energy vehicle based on vehicle data; A storage module, used to store the collected vehicle data and environmental data, and analyze the slope correlation coefficient and the altitude correlation coefficient based on the stored data; A 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 based on the slope correlation coefficient and the altitude correlation coefficient; The allocation module is used to 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.

[0014] On the other hand, the present invention also provides a storage medium storing instructions, which, when executed on a computer, enables the computer to execute any of the above-described charging management methods for emergency energy vehicles.

[0015] The beneficial effects of the present invention are as follows: through real-time data drive, dynamic priority adjustment, environmental adaptive strategy, and optimized allocation of power grid resources, the charging efficiency and reliability of emergency energy vehicles are improved, ensuring that they can quickly respond to emergency needs in complex environments, while taking into account battery health and power grid stability, providing efficient energy guarantees for emergency rescue missions, improving the real-time performance of data processing and decision-making, optimizing charging priority sorting, dynamically adapting to environmental changes, protecting battery health and extending battery life, and reasonably allocating power grid resources to avoid overload, thereby improving the charging management efficiency and system reliability of emergency energy vehicles as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a flow chart of the charging management method for emergency energy vehicles according to this embodiment.

[0018] Figure 2 This is a flow chart of the storage data analysis method of this embodiment.

[0019] Figure 3 Flowchart of the method for constructing the priority decision model of this embodiment.

[0020] Figure 4 This is a schematic diagram of the structure of the charging management device for emergency energy vehicles in this embodiment. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the following detailed description is illustrative rather than restrictive and should not be used to limit the scope of protection of the present invention.

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

[0023] See also Figure 1 As shown, this is a charging management method for emergency energy vehicles according to 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, expected completion time and battery data, the vehicle location distance is the distance between the vehicle and the task location, the task type is not limited to medical rescue and communication guarantee, the terrain slope represents the terrain slope during the historical execution of the emergency energy vehicle's tasks, the terrain slope is the absolute value of the angle between the vehicle and the horizontal direction, and takes a value less than 90°, the completed task duration represents the duration of each task execution, the battery data includes battery health status, battery charge status, battery temperature, battery internal resistance and maximum charging power, the battery health status shown refers to a quantitative description of the performance and aging degree of the battery under certain conditions of use relative to its new state, It is called SOH. The vehicle data is collected through the on-board terminal on the emergency energy vehicle. The on-board terminal is a terminal on the emergency energy vehicle used to distribute and record vehicle mission data and operation data. The environmental data includes altitude and ambient temperature. The environmental data is collected through sensors. The power grid data includes power grid load and power grid available power. The power grid data is collected by importing data from the power management platform. The power grid load is the total power load in the power system used to charge the emergency energy vehicle. The unit of the battery temperature and the ambient temperature is degrees Celsius. The unit of the vehicle location distance is kilometers. The unit of the completed task duration is hours. The unit of the battery internal resistance is ohm. The unit of the maximum charging power, power grid load and power grid available power is watt. The unit of the altitude is meter.

[0024] Specifically, in step S1 described in this embodiment, by collecting multi-dimensional data in real time, it is ensured that the system can make dynamic decisions based on the latest information, reduce charging delays caused by data lags, and use comprehensive data coverage to provide a reliable basis for subsequent analysis.

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

[0026] Specifically, in step S2 described in this embodiment, the time interval between the expected completion time and the current time is used as the expected interval duration, and the time urgency coefficient is calculated based on the task type, vehicle location distance and expected 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 expected interval duration.

[0027] Specifically, in step S2 of this embodiment, by calculating the time urgency coefficient of the task, urgent tasks are dynamically identified and charging resources are allocated preferentially, ensuring that mission-critical vehicles complete charging quickly and shortening the emergency response time.

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

[0029] See also Figure 2 As shown, it is a method for analyzing stored data, including: Step S31, storing 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 of 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 state of health, and Avg() represents the average value of the data in parentheses. i is defined as the number that distinguishes different emergency energy vehicle missions.

[0031] Specifically, in step S32 of this embodiment, the terrain slope, battery internal resistance, and health status are combined to quantify the impact of the terrain on battery loss, thereby reducing charging interruptions caused by battery failure.

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

[0033] Specifically, in step S33 described in this embodiment, the altitude correlation coefficient is analyzed based on the battery temperature, altitude, and completed task duration. The expression of 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 the calculation brackets.

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

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

[0036] See also Figure 3 As shown in FIG, a method for constructing a priority decision model includes: Step S41 : analyzing model construction factors based on ambient temperature, battery health status, and battery state of charge, wherein the model construction factors include a temperature difference factor and a battery factor.

[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, and the battery factor is set to the battery health state raised to the battery state of charge, and W=SOH(i) is set. SOC(i) , W represents the battery factor, and SOC(i) represents the battery state of charge.

[0038] Specifically, the preset temperature in this embodiment is set to 25° C. In this embodiment, there is no specific limitation on the value of the preset temperature, and those skilled in the art can freely set it as long as the analysis of the temperature difference factor is satisfied.

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

[0040] Specifically, in step S42 described in this embodiment, dynamic weight allocation is performed on the model construction factor and the time urgency coefficient, the emergency weight, battery weight and temperature difference weight are set according to the task type, and the 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 the charging priority, α1(k) represents the emergency weight, α2(k) represents the battery weight, α3(k) represents the temperature difference weight, α1(k)+α2(k)+α3(k)=1, and ΔT represents the temperature difference factor.

[0041] Specifically, in step S42 described in 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 in the task type is higher than the urgency of communication guarantee, the emergency weight of the task type of medical rescue is set to be greater than the emergency weight of the task type of communication rescue, that is, α1 (medical rescue) > α1 (communication guarantee). In this embodiment, the emergency weight, battery weight and temperature difference weight of the task type of medical rescue are set to α1 (medical rescue) = 0.6, α2 (medical rescue) = 0.3, and α3 (medical rescue) = 0.1, respectively, and the emergency weight, battery weight and temperature difference weight of the task type of communication guarantee are set to α1 (communication guarantee) = 0.5, α2 (communication guarantee) = 0.3, and α3 (communication guarantee) = 0.2, respectively. It can be understood that the setting of the emergency weight, battery weight and temperature difference weight is not specifically limited in this embodiment, and those skilled in the art can set them freely as long as the calculation of the charging priority is satisfied.

[0042] Specifically, in step S42 of this embodiment, by flexibly allocating emergency, battery, and temperature difference weights to task types, high-priority tasks are ensured to be quickly charged while balancing battery protection requirements.

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

[0044] Specifically, in step S43 described in this embodiment, the dynamic weight allocation process is updated based on the analysis of the slope correlation coefficient. When the slope correlation coefficient is greater than the slope correlation threshold, the expression of the 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. In this embodiment, there is no specific limitation on the value of the slope association threshold. Those skilled in the art can set it freely. It only needs to satisfy the update of the dynamic weight allocation process. The value of the slope association threshold should satisfy the range [0.07, 0.15].

[0046] Specifically, in step S43 of this embodiment, the slope correlation coefficient is introduced into the charging priority formula to enhance the influence of terrain factors in decision-making.

[0047] Please continue reading Figure 3 As shown, the method for constructing the priority decision model further includes: Step S44: further updating the dynamic weight allocation process according to the altitude correlation coefficient.

[0048] Specifically, in step S44 described in this embodiment, the distribution ratio of the emergency weight, battery weight and temperature difference weight is updated according to the altitude correlation coefficient to further update the dynamic weight distribution 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 reduced. The increase in the temperature difference weight is the temperature difference weight × the altitude correlation coefficient, and the decrease in the emergency weight and battery weight is the temperature difference weight × the altitude correlation coefficient / 2; otherwise, the dynamic weight distribution 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 give priority to dealing with 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 also 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 described in this embodiment, the grid power allocation rule is set as follows: if the available power of the grid is greater than or equal to the sum of the maximum charging powers of all emergency energy vehicles to be charged, the charging power allocated to each emergency energy vehicle is equal to the maximum charging power; if the available power of the grid is less than the sum of the maximum charging powers of all emergency energy vehicles to be charged, it is allocated in descending order of charging priority until the available power of the grid is exhausted.

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

[0053] Specifically, in step S5 described in this embodiment, the grid fluctuations include but are not limited to abnormal conditions such as voltage fluctuations, voltage transients and frequency fluctuations in the grid, such as the voltage being lower than 90% of the rated value as the grid load increases, and the maximum transient 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, there is no specific limitation on the values of the preset charging temperature and priority parameters, and those skilled in the art can freely set them as long as the allocation of charging power is satisfied.

[0054] Specifically, in step S5 described in this embodiment, the step-by-step current reduction strategy is to set different current reduction amplitudes according to the difference between the battery temperature and the preset charging temperature. For example, when the battery temperature is set to be higher than the preset charging temperature, if the difference between the battery temperature and the preset charging temperature is in the range of [0, 10), the charging current is reduced by 20%; if the difference between the battery temperature and the preset charging temperature is in the range of [10, 20), the charging current is reduced by 30%, and so on.

[0055] Specifically, in step S5 described in this embodiment, when the grid power is sufficient, maximum power is allocated to all vehicles to maximize charging efficiency, and when power is insufficient, power is allocated in descending order of priority to ensure that key vehicles are fully charged first, reducing 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 step-by-step current reduction strategy is used to prevent battery overheating and damage, extend battery life, reduce maintenance costs, and indirectly improve sustainability.

[0056] See also Figure 4 As shown, this is a charging management device for emergency energy vehicles according to this embodiment, including: Acquisition module, used to collect vehicle data, environmental data and power grid data in real time; An analysis module, configured to analyze the time urgency coefficient of the emergency energy vehicle based on vehicle data, the analysis module being connected to the acquisition module; a storage module for storing the collected vehicle data and environmental data, and analyzing the slope correlation coefficient and the altitude correlation coefficient based on the stored data, the storage module being connected to the analysis module; a construction module, configured to construct a priority decision model based on vehicle data, environmental data, and a time urgency coefficient to analyze charging priority, and update the priority decision model according to a slope correlation coefficient and an altitude correlation coefficient, the construction module being connected to the storage module; The allocation module is used to 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. The allocation module is connected to the construction module.

[0057] An embodiment of the present application also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the charging management method for emergency energy vehicles as described in the above method embodiment.

[0058] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as a computer-readable program, a data structure, a program module, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable programs, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0059] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection 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; Analyze the time emergency coefficient of emergency energy vehicles based on vehicle data; storing the collected vehicle data and environmental data, and analyzing the slope correlation coefficient and the altitude correlation coefficient based on the stored data; A priority decision model is built based on vehicle data, environmental data, and time urgency coefficients to analyze charging priorities, and the priority decision model is updated based on the slope correlation coefficient and altitude correlation coefficient; Formulate grid power allocation rules and use them to perform adaptive charging power allocation based on charging priority, vehicle data, and environmental data.

2. The charging management method for emergency energy vehicles according to claim 1, characterized in that: The time interval between the expected completion time and the current time is taken as the expected interval time, and the ratio of the vehicle position distance and the expected interval time of emergency energy vehicles of different task types is taken as the time urgency coefficient.

3. The charging management method for emergency energy vehicles according to claim 2, characterized in that: The slope correlation coefficient is analyzed based on the stored terrain slope, battery health status and battery internal resistance. The expression of the slope correlation coefficient is L1=Avg(tanθ(i)×ΔR(i) / SOH(i)), where L1 represents the slope correlation coefficient, i represents the mission 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 the brackets; The altitude correlation coefficient is analyzed based on the battery temperature, altitude, and completed mission 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 mission duration, H(i) represents the altitude, and σ() represents the standard deviation of the data in the brackets.

4. The charging management method for emergency energy vehicles according to claim 1, characterized in that: 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, and the battery factor is set to the battery health state raised to the battery state of charge.

5. The charging management method for emergency energy vehicles according to claim 4, characterized in that: Dynamic weight allocation is performed on the model construction factor and the time urgency coefficient. The urgency weight, battery weight, and temperature difference weight are set according to the task type, and the 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 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.

6. The charging management method for emergency energy vehicles according to claim 5, characterized in that: The dynamic weight allocation process is updated based on the analysis of the slope correlation coefficient. When the slope correlation coefficient is greater than the slope correlation threshold, the charging priority expression is updated to: P = α1(k) × E(k) + α2(k) / W + α3(k) / (ΔT × L1); The distribution ratio of emergency weight, battery weight and temperature difference weight is updated according to the altitude correlation coefficient to further update the dynamic weight distribution 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 reduced. 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.

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

8. The charging management method for emergency energy vehicles according to claim 7, 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 the emergency energy vehicles ranked last in descending charging priority is reduced to maintain the charging power of the emergency energy vehicles ranked first to X in descending charging priority. When the battery temperature is greater than or equal to the preset charging temperature, a step-by-step current reduction strategy is adopted to control the charging current.

9. A charging management device for an emergency energy vehicle, applied to the charging management method for an emergency energy vehicle according to any one of claims 1 to 8, characterized in that: include: Acquisition module, used to collect vehicle data, environmental data and power grid data in real time; An analysis module for analyzing the time emergency coefficient of the emergency energy vehicle based on vehicle data; A storage module, used to store the collected vehicle data and environmental data, and analyze the slope correlation coefficient and the altitude correlation coefficient based on the stored data; A 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 based on the slope correlation coefficient and the altitude correlation coefficient; The allocation module is used to 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.

10. A storage medium, characterized in that: Instructions are stored, which, when run on a computer, enable the computer to execute the charging management method for an emergency energy vehicle as described in any one of claims 1 to 8.

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