Electric vehicle operation optimization management method and system, storage medium and electronic equipment

Abstract

The invention provides an electric vehicle operation optimization management method and system, a storage medium and electronic equipment, and relates to the technical field of electric vehicle operation optimization management, and the method comprises the steps: planning a next operation optimization stage and an optimization target thereof based on an operation map of an electric vehicle; before entering the next operation optimization stage, obtaining the tendency upper limit of the vehicle personnel to the optimization target in a non-inductive manner; and when entering the next operation optimization stage, performing operation optimization on the electric vehicle in an optimal game manner based on the optimization target and the tendency upper limit. Before the next operation optimization stage is entered, the tendency upper limit of the vehicle personnel to the optimization target is obtained in a non-inductive mode, the vehicle personnel can complete obtaining without any feeling, and when the next operation optimization stage is entered, operation optimization is conducted on the electric vehicle in an optimal game mode based on the optimization target and the tendency upper limit. And while the operation of the vehicle is optimized, the experience of all people in the vehicle is ensured to the greatest extent, and unnecessary interference to the people is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle operation optimization management, and in particular to an electric vehicle operation optimization management method, system, storage medium and electronic equipment. Background Art

[0002] Currently, in order to ensure that electric vehicles always maintain the best condition during operation, they must be continuously optimized.

[0003] However, the implementation of certain optimization measures inevitably impacts the driver's experience. For example, battery management, powertrain adjustments, or the activation of energy-saving modes can cause delayed vehicle response, reduce ride comfort, and even affect comfort settings like interior temperature and acoustics, impacting the overall driver experience. Existing technologies typically employ two approaches to these optimizations: default execution, or allowing drivers to choose whether to implement them.

[0004] In the case of direct default execution, although it can ensure that the vehicle is always in the best operating condition, this approach may have a long-term impact on the experience of vehicle personnel, especially when the optimization measures interfere with the driving experience or ride comfort, user satisfaction may be greatly reduced, and the user cost will be high in the long run.

[0005] In the mode where occupants are required to choose whether to perform optimization operations themselves, although this allows vehicle occupants to decide whether to perform optimization operations based on their personal needs, in actual use, especially for the driver, this requires making additional decisions during the busy driving task, which undoubtedly interrupts the driver's focus. For passengers, if the operation involves selecting optimization measures, it may cause additional trouble or interrupt their rest.

[0006] Therefore, how to ensure that the vehicle operation is optimized while maximizing the experience of all people in the car and avoiding unnecessary interference has become an important challenge in the optimization of electric vehicle technology. Summary of the Invention

[0007] One of the purposes of the present invention is to provide an electric vehicle operation optimization management method to solve the technical problems in the background technology.

[0008] An embodiment of the present invention provides an electric vehicle operation optimization management method, comprising:

[0009] Before entering the next operation optimization phase, the upper limit of the vehicle personnel's preference for the optimization target is obtained in a non-sensical manner;

[0010] When entering the next operation optimization stage, the operation of electric vehicles is optimized in an optimal game based on the optimization target and the upper limit of the tendency.

[0011] Optionally, the planning steps for the next optimization phase and optimization target include:

[0012] Extracting the next stage operation situation from the operation map of the electric vehicle;

[0013] Match the next stage operation situation with multiple standard situations respectively;

[0014] When the match is met, the next operation optimization stage and its optimization target are planned based on the planning knowledge package corresponding to the standard situation that meets the match.

[0015] Optional, non-sensing acquisition steps towards the upper limit include:

[0016] Determining, from the next operation optimization stage, a plurality of first partial stages and their respective impact categories that affect vehicle occupant experience due to operation optimization for achieving the optimization goal;

[0017] Traverse each first local stage in turn, and each time it is traversed:

[0018] Trying to determine a second local stage having a first value condition with the traversed first local stage from the remaining running stages before entering the next running optimization stage;

[0019] If the attempt is successful, the optimization constraints are generated based on the first generated template and the first behavioral response information of the vehicle personnel when entering the second local stage; otherwise, the third local stage that has the second value condition most recently between the traversed first local stage and the historical operation stage is determined;

[0020] Based on the second generated template, generating optimization constraints according to the second behavioral response information of the vehicle occupants when entering the third local stage;

[0021] At the end of the sequential traversal:

[0022] Determine the upper limit of the tendency based on the optimization constraints generated at each traversal;

[0023] Among them, the first value condition, the first generation template, the second value condition and the second generation template each correspond to the impact category of the first local stage traversed; the second value condition is more stringent than the first value condition.

[0024] Optionally, the steps for determining the upper limit of the tendency include:

[0025] The optimization constraints generated during each traversal are classified into multiple constraint sets according to the constraint target type;

[0026] The optimization constraint with the highest degree of constraint in each constraint set is taken in turn, and the optimization objective is constrained successively to finally obtain the upper limit of the tendency.

[0027] Optionally, the steps of optimizing the operation of the electric vehicle in an optimal game-playing manner include:

[0028] When the real-time optimization progress approaches the upper limit of the tendency, the optimization operation is continued so that the upper limit exceeds the real-time cost and is always equal to the cost threshold.

[0029] Optionally, the steps for determining the upper limit beyond the real-time cost include:

[0030] Quantify in real time the impact of the next operational optimization measure on the actual experience of vehicle personnel, and obtain an upper limit that exceeds the real-time cost.

[0031] Optionally, the steps of determining the cost threshold include:

[0032] The degree to which the upper limit of the real-time quantitative tendency is confirmed when entering the next operation optimization stage, and the corresponding value of the quantitative result in the threshold table is used as the cost threshold.

[0033] An embodiment of the present invention provides an electric vehicle operation optimization management system, comprising:

[0034] The acquisition module is used to obtain the upper limit of the vehicle personnel's tendency towards the optimization target before entering the next operation optimization stage;

[0035] The optimization module is used to optimize the operation of the electric vehicle in an optimal game based on the optimization target and the upper limit of the tendency when entering the next operation optimization stage.

[0036] An embodiment of the present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and a processor executes the computer program to implement any of the methods described above.

[0037] An embodiment of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the methods described above.

[0038] The present invention has achieved the following beneficial effects:

[0039] Before entering the next operation optimization stage, the present invention obtains the vehicle occupants' tendency upper limit for the optimization target in a non-sensical manner, so that the vehicle occupants complete the acquisition without any feeling. When entering the next operation optimization stage, the electric vehicle is optimized for operation based on the optimization target and the tendency upper limit in an optimal game manner. While ensuring that the vehicle operation is optimized, the experience of all occupants in the vehicle is maximized to avoid unnecessary interference to them, thus overcoming the disadvantages of the prior art of direct execution by default and the vehicle occupants choosing whether to execute the execution on their own.

[0040] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0041] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0043] Figure 1 Schematic diagram of an electric vehicle operation optimization management method according to an embodiment of the present invention;

[0044] Figure 2 Schematic diagram of an electric vehicle operation optimization management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0046] Example 1:

[0047] The embodiment of the present invention provides an electric vehicle operation optimization management method, such as Figure 1 Shown, including:

[0048] S1. Before entering the next operation optimization phase, obtain the upper limit of the vehicle personnel's preference for the optimization target in a non-sensing manner;

[0049] S2. When entering the next operation optimization stage, the operation of the electric vehicle is optimized in an optimal game based on the optimization target and the upper limit of the tendency.

[0050] The next operation optimization phase refers to the next phase of operation optimization of the electric vehicle.

[0051] The optimization goal is to optimize the operation of the electric vehicle in the next operation optimization stage.

[0052] The tendency upper limit refers to the optimization upper limit that the vehicle personnel tend to for the optimization target. It is obtained in a senseless manner so that the vehicle personnel can complete the acquisition without feeling anything.

[0053] When optimizing the operation of electric vehicles in an optimal game, the optimal balance between the optimization target and the upper limit of the tendency is maintained in real time, and the operation optimization is carried out under this premise.

[0054] The working principle and beneficial effects of the above technical solution are:

[0055] Specifically, for example, the next operational optimization phase involves improving the range of electric vehicles entering highways. Two optimization methods are used: reducing power response sensitivity and limiting maximum speed. The inductively captured tendency upper limit is simply that the driver of the vehicle is concerned about power response sensitivity at low speeds and desires a higher sensitivity. Therefore, when optimizing the operation of electric vehicles, the optimal strategy involves continuously limiting the maximum speed, reducing power response sensitivity on high-speed sections and increasing it on low-speed sections (such as those entering service areas or making emergency stops).

[0056] Before entering the next operation optimization stage, the present invention obtains the vehicle occupants' tendency upper limit for the optimization target in a non-sensical manner, so that the vehicle occupants complete the acquisition without any feeling. When entering the next operation optimization stage, the electric vehicle is optimized for operation based on the optimization target and the tendency upper limit in an optimal game manner. While ensuring that the vehicle operation is optimized, the experience of all occupants in the vehicle is maximized to avoid unnecessary interference to them, thus overcoming the disadvantages of the prior art of direct execution by default and the vehicle occupants choosing whether to execute the execution on their own.

[0057] Example 2:

[0058] In an embodiment of the present invention, the planning steps for the next operation optimization phase and the optimization target include:

[0059] Extracting the next stage operation situation from the operation map of the electric vehicle;

[0060] Match the next stage operation situation with multiple standard situations respectively;

[0061] When the match is met, the next operation optimization stage and its optimization target are planned based on the planning knowledge package corresponding to the standard situation that meets the match.

[0062] The working principle and beneficial effects of the above technical solution are:

[0063] The operation map of an electric vehicle includes the future driving path of the electric vehicle (obtained based on the driver's map navigation operation) and the environmental information along the path (such as road type, passing scene type, weather conditions and traffic conditions during the future passage).

[0064] The operation map can reflect the stage-by-stage situation of the electric vehicle's future driving, so the operation situation of the next stage can be extracted from it.

[0065] Multiple sets of one-to-one corresponding standard scenarios and planning knowledge packages are pre-set. The standard scenario is a stage operation scenario representing the stage operation optimization. The planning knowledge package indicates how to plan the next operation optimization stage and its optimization target under the corresponding standard scenario. Specifically, for example, the standard scenario is that electric vehicles enter the highway. The corresponding planning knowledge package is to take the stage of electric vehicles entering the highway as the next operation optimization stage, and to take the endurance improvement optimization as the optimization target.

[0066] When the next phase of operation matches the standard scenario, the next phase of operation optimization and its optimization target are planned directly based on the planning knowledge package corresponding to the matching standard scenario. This improves the accuracy and efficiency of the planning for the next phase of operation optimization and its optimization target.

[0067] Example 3:

[0068] In an embodiment of the present invention, the step of obtaining the tendency upper limit in a non-sensical manner includes:

[0069] Determining, from the next operation optimization stage, a plurality of first partial stages and their respective impact categories that affect vehicle occupant experience due to operation optimization for achieving the optimization goal;

[0070] Traverse each first local stage in turn, and each time it is traversed:

[0071] Attempt to determine a second local stage having a first value condition between the traversed first local stage and the remaining operating stages before entering the next operating optimization stage; the remaining operating stages are stages that the electric vehicle has not yet operated before entering the next operating optimization stage;

[0072] When the attempt is successful, based on the first generated template, the optimization constraints are generated according to the first behavioral response information of the vehicle personnel when entering the second local stage; otherwise, the third local stage that has the second value condition most recently between the traversed first local stage and the historical operation stage is determined; the historical operation stage is the stage that the electric vehicle has operated in the past;

[0073] Based on the second generated template, generating optimization constraints according to the second behavioral response information of the vehicle occupants when entering the third local stage;

[0074] At the end of the sequential traversal:

[0075] Determine the upper limit of the tendency based on the optimization constraints generated at each traversal;

[0076] Among them, the first value condition, the first generation template, the second value condition and the second generation template each correspond to the impact category of the first local stage traversed; the second value condition is more stringent than the first value condition.

[0077] The working principle and beneficial effects of the above technical solution are:

[0078] In order to achieve the optimization goal, the system will automatically plan a series of operation optimization measures. When these measures are implemented, they will affect the vehicle personnel experience in the first local stage. The impact category refers to the category that affects the vehicle personnel experience.

[0079] The first value condition is a condition representing that the vehicle and personnel reactions in the second local stage have a value for determining the upper limit of the vehicle and personnel's tendency towards the optimization target, which corresponds to the impact category. Specifically, for example: the first local stage is the stage of reducing the power response sensitivity in low-speed driving sections, and the impact category is affecting the power response experience of vehicle and personnel. The first value condition is that the speed limit of the road section involved in the second local stage is the same as that in the first local stage. In this case, the vehicle and personnel reactions in the second local stage can be used to determine whether they have high requirements for power response sensitivity.

[0080] The first generation template is a template for generating optimization constraints based on the first behavioral response information of the vehicle occupant upon entering the second partial phase. This template also corresponds to the impact category. Specifically, for example, if the impact category is affecting the vehicle occupant's power response experience and the first behavioral response information is frequent acceleration, the generated optimization constraint is a high requirement for power response sensitivity. The second value condition is a condition representing the value of the vehicle occupant's response in the third partial phase, which is used to determine the upper limit of the vehicle occupant's tendency toward the optimization objective. This condition corresponds to the impact category and is more stringent than the first value condition. Specifically, for example, if the first partial phase is a phase of reducing power response sensitivity on low-speed driving sections and the impact category is affecting the vehicle occupant's power response experience, the second value condition is that the speed limit of the road section involved in the third partial phase is the same as that in the first partial phase, and the similarity between the vehicle occupant's driving state (total continuous driving time, number of passengers, etc.) before entering the third partial phase and before the first partial phase exceeds 90%. Because the second value condition is used in the historical operating phase, which is less referenceable than the remaining operating phases, setting this second value condition allows the vehicle occupant's response in the third partial phase to be accurately used to determine whether the vehicle occupant has a high requirement for power response sensitivity.

[0081] The second generation template is a template for generating optimization constraints based on the second behavioral response information of the vehicle occupants when entering the third local stage. It also corresponds to the impact category. Specifically, for example: the impact category is affecting the dynamic response experience of the vehicle occupants, and the second behavioral response information is that the user's acceleration frequency is low, then the generated optimization constraint is a low requirement for the dynamic response sensitivity.

[0082] Identify the first local stages affected by the vehicle occupant experience and their respective impact categories, and traverse each first local stage in sequence. During each traversal, first use the first value condition to attempt to identify a second local stage in which the occupant's response during that stage has a value that can be used to determine the upper limit of the occupant's propensity toward the optimization objective. If the attempt is successful, it indicates that first behavioral response information with that value has recently been generated, and this information is prioritized and optimized based on the first generation template. Otherwise, it indicates that first behavioral response information with that value has not recently been generated. Using the more stringent second value condition, select a third local stage from the historical operating stage in which the occupant's response during that stage has a value that can be used to determine the upper limit of the occupant's propensity toward the optimization objective. Based on the second generation template, generate optimization constraints based on the occupant's second behavioral response information when the third local stage enters. Finally, determine the upper limit of the propensity based on the optimization constraints generated during each traversal.

[0083] When seamlessly acquiring a propensity cap, this embodiment of the present invention uses a first value condition to attempt to determine the first behavioral response information. If successful, it directly generates optimization constraints. Otherwise, it uses a second value condition to determine the second behavioral response information and generate optimization constraints. Finally, based on the optimization constraints generated at each traversal, the propensity cap is determined. This process requires no action perceptible to the vehicle occupants, achieving truly seamless acquisition. Furthermore, the first value condition, the first generation template, the second value condition, and the second generation template each correspond to the impact category of the first local phase traversed. This allows for targeted and precise identification of phases within which the vehicle occupant response possesses value for determining the vehicle occupant's propensity cap for the optimization objective, as well as targeted and precise generation of optimization constraints. This significantly improves the accuracy and applicability of seamless acquisition of a propensity cap. Furthermore, setting the second value condition to be more stringent than the first value condition ensures that the second behavioral response information from the third local phase, determined during the historical phase, can be accurately used to determine the optimization constraints. Overall, this approach achieves highly customized and precise optimization constraint generation while ensuring seamless acquisition.

[0084] Example 4:

[0085] In an embodiment of the present invention, the step of determining the upper limit of the tendency includes:

[0086] The optimization constraints generated during each traversal are classified into multiple constraint sets according to the constraint target type;

[0087] The optimization constraint with the highest degree of constraint in each constraint set is taken in turn, and the optimization objective is constrained successively to finally obtain the upper limit of the tendency.

[0088] The working principle and beneficial effects of the above technical solution are:

[0089] The constraint objective type refers to the type of constraint objective for the optimization constraint. For example, if the optimization constraint requires a higher level of dynamic response sensitivity, the constraint objective is to improve dynamic response sensitivity. The constraint degree refers to the degree of constraint for the optimization constraint objective.

[0090] The optimization constraints generated at each traversal are classified into multiple constraint sets according to the constraint target type. The optimization constraint with the highest degree of constraint in each constraint set is taken, and the optimization targets are constrained in succession to finally obtain the tendency upper limit.

[0091] The embodiment of the present invention enables the determined upper limit of tendency to conform to the tendencies of all vehicle occupants, and the accuracy of the determination of the upper limit of tendency is improved.

[0092] Example 5:

[0093] In an embodiment of the present invention, the steps of optimizing the operation of an electric vehicle in an optimal game-playing manner include:

[0094] When the real-time optimization progress approaches the upper limit of the tendency, the optimization operation is continued so that the upper limit exceeds the real-time cost and is always equal to the cost threshold.

[0095] The working principle and beneficial effects of the above technical solution are:

[0096] The real-time optimization progress refers to the real-time progress of the operation optimization of the electric vehicle. Approaching the upper limit of the tendency means that there is still a preset distance from reaching the upper limit of the tendency.

[0097] The upper limit exceeding the real-time cost refers to the cost caused by the real-time optimization progress exceeding the tendency upper limit. The cost threshold is the maximum threshold of the cost. When the real-time optimization progress approaches the tendency upper limit, the operation optimization is continued to make the upper limit exceeding the real-time cost always equal to the cost threshold. Under the premise of ensuring the operation optimization effect, the upper limit exceeding the real-time cost is always equal to its maximum threshold, achieving the optimal game between the optimization goal and the tendency upper limit.

[0098] Example 6:

[0099] In an embodiment of the present invention, the step of determining whether the upper limit exceeds the real-time cost includes:

[0100] Quantify in real time the impact of the next operational optimization measure on the actual experience of vehicle personnel, and obtain an upper limit that exceeds the real-time cost.

[0101] The working principle and beneficial effects of the above technical solution are:

[0102] The actual impact of the next operational optimization measure on the vehicle occupants' experience refers to, for example, the impact on their perceived power response, if the next operational optimization measure is to further reduce power response sensitivity, but the vehicle occupants have previously repeatedly stepped deeply on the accelerator, indicating a desire to improve power response sensitivity. During quantification, a technician can pre-set a quantification table of the upper limit of real-time cost overruns corresponding to different actual experience impacts (the greater the actual experience impact in the table, the greater the quantified upper limit of real-time cost overruns). The upper limit of real-time cost overruns can be determined by looking up the table. Improving the upper limit of real-time cost overruns enhances accuracy and efficiency.

[0103] Example 7:

[0104] In this embodiment of the present invention, the step of determining the cost threshold includes:

[0105] The degree to which the upper limit of the real-time quantitative tendency is confirmed when entering the next operation optimization stage, and the corresponding value of the quantitative result in the threshold table is used as the cost threshold.

[0106] The working principle and beneficial effects of the above technical solution are:

[0107] The degree of confirmation refers to the degree to which the vehicle crew's upper limit of preference for the optimization target is actually achieved by the vehicle crew when entering the next operational optimization phase. For example, if the upper limit of preference is a driver's concern for power response sensitivity at low speeds, and the driver frequently steps on the accelerator when entering the next operational optimization phase, this is confirmed, with a confirmation degree of 100% and a quantization result of 1. The threshold table pre-sets cost thresholds corresponding to different quantization results (the larger the quantization result in the table, the higher the cost threshold). This improves the accuracy and efficiency of cost threshold determination.

[0108] Example 8:

[0109] The embodiment of the present invention provides an electric vehicle operation optimization management system, such as Figure 2 Shown, including:

[0110] Acquisition module 1 is used to obtain the upper limit of the vehicle personnel's tendency towards the optimization target before entering the next operation optimization stage;

[0111] The optimization module 2 is used to optimize the operation of the electric vehicle in an optimal game based on the optimization target and the upper limit of the tendency when entering the next operation optimization stage.

[0112] Example 9:

[0113] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. A processor executes the computer program to implement any of the above methods.

[0114] Example 10:

[0115] An embodiment of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the methods described above.

[0116] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for optimizing the operation of an electric vehicle, characterized in that: include: Before entering the next operation optimization phase, the upper limit of the vehicle personnel's preference for the optimization target is obtained in a non-sensical manner; When entering the next operation optimization stage, the operation of electric vehicles is optimized in an optimal game based on the optimization target and the upper limit of the tendency.

2. The electric vehicle operation optimization management method according to claim 1, characterized in that: The planning steps for the next operational optimization phase and optimization goals include: Extracting the next stage operation situation from the operation map of the electric vehicle; Match the next stage operation situation with multiple standard situations respectively; When the match is met, the next operation optimization stage and its optimization target are planned based on the planning knowledge package corresponding to the standard situation that meets the match.

3. The electric vehicle operation optimization management method according to claim 1, characterized in that: The steps for obtaining the upper limit of the tendency without any feeling include: Determining, from the next operation optimization stage, a plurality of first partial stages and their respective impact categories that affect vehicle occupant experience due to operation optimization for achieving the optimization goal; Traverse each first local stage in turn, and each time it is traversed: Trying to determine a second local stage having a first value condition with the traversed first local stage from the remaining running stages before entering the next running optimization stage; If the attempt is successful, the optimization constraints are generated based on the first generated template and the first behavioral response information of the vehicle personnel when entering the second local stage; otherwise, the third local stage that has the second value condition most recently between the traversed first local stage and the historical operation stage is determined; Based on the second generated template, generating optimization constraints according to the second behavioral response information of the vehicle occupants when entering the third local stage; At the end of the sequential traversal: Determine the upper limit of the tendency based on the optimization constraints generated at each traversal; Among them, the first value condition, the first generation template, the second value condition and the second generation template each correspond to the impact category of the first local stage traversed; the second value condition is more stringent than the first value condition.

4. The electric vehicle operation optimization management method according to claim 3, characterized in that: The steps to determine the upper limit of the propensity include: The optimization constraints generated during each traversal are classified into multiple constraint sets according to the constraint target type; The optimization constraint with the highest degree of constraint in each constraint set is taken in turn, and the optimization objective is constrained successively to finally obtain the upper limit of the tendency.

5. The electric vehicle operation optimization management method according to claim 1, characterized in that: The steps to optimize the operation of electric vehicles in an optimal game include: When the real-time optimization progress approaches the upper limit of the tendency, the optimization operation is continued so that the upper limit exceeds the real-time cost and is always equal to the cost threshold.

6. The electric vehicle operation optimization management method according to claim 5, characterized in that: The steps to determine the upper limit beyond real-time cost include: Quantify in real time the impact of the next operational optimization measure on the actual experience of vehicle personnel, and obtain an upper limit that exceeds the real-time cost.

7. The electric vehicle operation optimization management method according to claim 5, characterized in that: The steps to determine the cost threshold include: The degree to which the upper limit of the real-time quantitative tendency is confirmed when entering the next operation optimization stage, and the corresponding value of the quantitative result in the threshold table is used as the cost threshold.

8. An electric vehicle operation optimization management system, characterized in that: include: The acquisition module is used to obtain the upper limit of the vehicle personnel's tendency towards the optimization target before entering the next operation optimization stage; The optimization module is used to optimize the operation of the electric vehicle in an optimal game based on the optimization target and the upper limit of the tendency when entering the next operation optimization stage.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

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