Mobile shared storage and charging robot for new energy vehicles

Abstract

The invention relates to the technical field of shared storage and charging, in particular to a new energy automobile mobile shared storage and charging robot which comprises a shared storage and charging management platform, a user side, a scheduling division unit, a scheduling planning analysis unit, a site scheduling unit, a return planning unit and a scheduling management unit. According to the method, analysis is performed preliminarily from the state angle of the storage and charging robots, namely, storage and charging robot division scheduling analysis is performed on the state data, so that on one hand, the state condition of each storage and charging robot can be known, and on the other hand, reasonable scheduling planning can be performed on the storage and charging robots meeting the requirements; analysis is carried out from two points of full power and return of the storage and charging robot in an information feedback mode, so that reasonable storage and charging robot scheduling processing is carried out on the generated storage and charging task, meanwhile, a user can know the position of the storage and charging robot all the time in an information display mode, the operation experience of the user is improved, and the user experience is improved. And rapid mobile energy charging and scheduling management and control effects are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of shared storage and charging technology, and particularly relates to a mobile shared storage and charging robot for new energy vehicles. BACKGROUND

[0002] With the rapid popularization of the new energy electric vehicle industry, how to quickly and conveniently charge has become a bottleneck factor restricting the rapid and sustainable development of the entire new energy industry, and many discussions on resonance and technical exploration have been generated in the industry.

[0003] The common charging pile in the market is mainly a fixed charging pile form with one pile and one parking space. Due to the limitation of the single charging machine covering the parking space, the actual use efficiency of the charging machine is low, the cost per parking space is high, and there are many adverse effects such as oil car occupation, overtime occupation, and limited total power capacity of the power grid. With the gradual increase of the penetration rate of new energy vehicles, the disadvantages of fixed charging piles are more obvious in some commercial intensive areas and transportation hub areas with high charging demand. However, in the prior art, the mobile shared storage and charging robot cannot be reasonably scheduled, which leads to chaos and non-compliance with the storage and charging conditions, resulting in low storage and charging scheduling efficiency and low reliability of the mobile shared storage and charging robot.

[0004] In view of the above technical defects, a solution is proposed. SUMMARY

[0005] The present application aims to provide a mobile shared storage and charging robot for new energy vehicles to solve the above technical defects. The present application preliminarily analyzes the state of the storage and charging robot, i.e. divides and analyzes the state data of the storage and charging robot. On the one hand, it helps to understand the state of each storage and charging robot, and on the other hand, it helps to reasonably schedule the storage and charging robot that meets the requirements to improve the intelligent scheduling efficiency of the storage and charging robot. Through the information feedback mode, the full charge and return of the storage and charging robot are analyzed to reasonably schedule the generated storage and charging task. At the same time, through the information display mode, the user can always know the position and arrival time of the storage and charging robot, which helps to improve the operation experience of the user and realizes the effect of fast mobile charging and scheduling control.

[0006] The purpose of the present application can be achieved by the following technical solution: a mobile shared storage and charging robot for new energy vehicles, comprising a shared storage and charging management platform, a user terminal, a scheduling division unit, a scheduling planning analysis unit, a station scheduling unit, a return planning unit and a scheduling management unit.

[0007] When the shared charging management platform receives the application use instruction sent by the user terminal on the user operation page, the state data of each charging robot is called, and the state data is sent to the scheduling division unit;

[0008] After receiving the state data, the scheduling division unit immediately performs charging robot division scheduling analysis on the state data to obtain the priority allocation robot and the to-be-allocated robot;

[0009] The scheduling planning analysis unit is used to call the positioning information of the priority allocation robot, and perform scheduling intelligent planning feedback analysis on the positioning information to obtain an allocation optimization signal or a feedback verification signal;

[0010] When the feedback verification signal is generated, the return planning unit is used to collect the state working condition information of the charging robot corresponding to the feedback verification signal, and perform in-depth evaluation scheduling analysis on the state working condition information, compare the obtained energy utilization value, and obtain a first recommended execution signal or a return signal;

[0011] When the allocation optimization signal is generated, the site scheduling unit is used to collect the energy basic data of the charging robot in the full-charging standby state, and perform charging evaluation sorting and unlocking feedback analysis on the energy basic data, set the to-be-allocated robot corresponding to the first sorting position as an unlocked robot, and feedback output.

[0012] Preferably, the charging robot division scheduling analysis process is as follows:

[0013] The state data of each charging robot is obtained, the state data includes the in-station state and the out-station state, and the state data is subjected to discrimination processing:

[0014] If the state data is a full-charging standby state or a return state, the corresponding charging robot is set as a priority allocation robot; if the state data is an energy storage state or an energy charging state, the corresponding charging robot is set as a to-be-allocated robot; the in-station state includes the full-charging standby state and the energy storage state, and the out-station state includes the return state and the energy charging state.

[0015] Preferably, the scheduling intelligent planning feedback analysis process is as follows:

[0016] S1: Obtain the positioning information of the user and each priority allocation robot, the positioning information represents the positioning coordinates, generate the optimal planning route based on the positioning information of the user and each priority allocation robot, and obtain the planning feasible value corresponding to the optimal planning route of each priority allocation robot;

[0017] The optimal planning route represents the planning route corresponding to the minimum value of the planning feasible value in the multiple planning routes generated between the priority allocation robot and the user, and the planning feasible value represents the product value obtained by multiplying the corresponding values of the driving time length and the driving energy consumption;

[0018] S2: Sort the planning feasible values ​​in ascending order, and set the first priority allocation robot corresponding to the sorted planning feasible value as the planning robot;

[0019] S3: Perform discrimination analysis on the planned robot. If the planned robot is in a fully charged standby state, corresponding to a storage and charging robot, a distribution priority signal is generated; if the planned robot is in a return state, corresponding to a storage and charging robot, a feedback verification signal is generated.

[0020] Preferably, the in-depth evaluation and scheduling analysis process is as follows:

[0021] When a feedback verification signal is generated, the storage and charging robot corresponding to the return state is set as the target return robot, and the state and working condition information of the target return robot is obtained. The state and working condition information includes normal return and fault return. The state and working condition information of the target return robot is judged and processed:

[0022] If the target return robot returns normally, a feedback instruction is generated;

[0023] If the target return robot is a fault return, a recursive instruction is generated. When the recursive instruction is generated, the S2 operation is performed again, and the next priority allocation robot is selected as the planning robot;

[0024] Preferably, when generating the feedback instruction, the energy storage utilization value of the target return robot is obtained, the energy storage utilization value represents the current remaining charging power value of the target return robot, and the energy storage utilization value is compared and analyzed with the preset energy storage utilization value threshold:

[0025] If the energy storage utilization value is less than or equal to the preset energy storage utilization value threshold, a return signal is generated. When the return signal is generated, the S2 operation is performed again, and the next priority allocation robot is selected as the planning robot;

[0026] If the energy storage utilization value is greater than the preset energy storage utilization value threshold, a first-push execution signal is generated. After the first-push execution signal is generated, the optimal planned route of the robot corresponding to the first-push execution signal is retrieved.

[0027] Preferably, the storage and charging evaluation ranking unlocking feedback analysis process is as follows:

[0028] Set the storage and charging robot corresponding to the fully charged standby state as the robot to be assigned;

[0029] The energy storage basic data of each to-be-assigned robot is acquired, the energy storage basic data including a battery actual energy storage total value, a battery evaluation coefficient and a charging risk coefficient, a preset correction weight factor corresponding to the energy storage basic data is acquired, a product value of the battery evaluation coefficient and the charging risk coefficient is divided by the battery actual energy storage total value, and then multiplied by a value obtained by multiplying the preset correction weight factor to set the value as a mobile charging sorting coefficient;

[0030] The mobile charging sorting coefficient of each to-be-assigned robot is sorted in ascending order, a to-be-assigned robot corresponding to a first sorting position after sorting is acquired, and the to-be-assigned robot corresponding to the first sorting position after sorting is set as an unlocked robot.

[0031] Preferably, the battery evaluation coefficient represents a product value obtained by multiplying a decay coefficient of the to-be-assigned robot and a basic coefficient corresponding value, the decay coefficient represents a value obtained by subtracting a preset decay rate from an actual decay rate of a total length of time between a time when the to-be-assigned robot is put into use and a current time, and the basic coefficient represents a product value obtained by multiplying a total charging number and a charging time corresponding value, the charging time representing a sum of lengths of time between a time when the new energy automobile is charged and a time when the charging is ended.

[0032] The analysis process of the charging risk coefficient is as follows: the historical charging information of the to-be-assigned robot is acquired, the historical charging information including a total charging number and a charging performance value, the charging performance value representing an over-temperature time length that an operating temperature of a historical single charging period of the to-be-assigned robot exceeds a preset operating temperature, a rectangular coordinate system is established with the charging number as an X axis and the over-temperature time length as a Y axis, and then an over-temperature time length curve is drawn, and a total sum of numbers of rising line segments in the over-temperature time length curve and corresponding to horizontal line segments connected to upper ends of the rising line segments is set as the charging risk coefficient.

[0033] The beneficial effects of the present application are as follows:

[0034] (1) The present application preliminarily analyzes from the state of the charging robot, that is, the state data is analyzed for charging robot division and scheduling, which is helpful for understanding the state of each charging robot, and is helpful for reasonable scheduling planning of the charging robot meeting the requirements, so as to improve the intelligent scheduling efficiency of the charging robot.

[0035] (2) The application analyzes from the full power and return two points of the storage and charging robot through the information feedback mode, so as to reasonably schedule the generated storage and charging task of the robot, if the planning robot is in the return state corresponding to the storage and charging robot, whether the storage and charging robot meets the ability to continue to execute other charging tasks is analyzed from the perspective of return, so as to reasonably schedule the storage and charging robot, if the planning robot is in the full power standby state corresponding to the storage and charging robot, the storage and charging evaluation sorting unlocking feedback analysis is carried out on the basic data of the storage and charging, so as to reasonably schedule the storage and charging robot, and through the information display mode, the user can know the position and arrival time of the storage and charging robot at any time, which helps to improve the operation experience of the user, and realizes the effect of quick movement charging and scheduling control. BRIEF DESCRIPTION OF DRAWINGS

[0036] The application will be further described below with reference to the drawings.

[0037] Fig. 1 is a system flow chart of the application;

[0038] Fig. 2 is a local analysis reference diagram of the application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0040] Embodiment one:

[0041] Please refer to Figs. 1-2 The application is a new energy automobile mobile shared storage and charging robot, which comprises a shared storage and charging management platform, a user end, a scheduling division unit, a scheduling planning analysis unit, a station scheduling unit, a return planning unit and a scheduling management unit. The user end is in bidirectional communication connection with the shared storage and charging management platform. The shared storage and charging management platform is in unidirectional communication connection with the scheduling division unit. The scheduling division unit is in unidirectional communication connection with the scheduling planning analysis unit and the scheduling management unit. The scheduling planning analysis unit is in unidirectional communication connection with the station scheduling unit and the return planning unit. The station scheduling unit and the return planning unit are in unidirectional communication connection with the scheduling management unit.

[0042] When the shared charging management platform receives the application use instruction sent by the user terminal on the user operation page, the state data of each charging robot is called, and the state data is sent to the scheduling division unit. After receiving the state data, the scheduling division unit immediately performs charging robot division scheduling analysis. On the one hand, it helps to understand the state of each charging robot, and on the other hand, it helps to reasonably schedule the charging robots that meet the requirements, so as to improve the intelligent scheduling efficiency of the charging robots. The specific charging robot division scheduling analysis process is as follows:

[0043] The state data of each charging robot is obtained, and the state data includes the in-station state and the out-station state, and the state data is discriminated and processed:

[0044] If the state data is a full-power standby state or a return state, the corresponding charging robot is set as a priority allocation robot;

[0045] If the state data is a storage energy state or a charging energy state, the corresponding charging robot is set as a standby allocation robot, and the priority allocation robot and the standby allocation robot are sent to the scheduling management unit. After receiving the priority allocation robot and the standby allocation robot, the scheduling management unit immediately divides the charging robot, so as to reasonably schedule the subsequent charging robot;

[0046] In the embodiment of the application, the in-station state includes a full-power standby state and a storage energy state, and the out-station state includes a return state and a charging energy state;

[0047] The scheduling planning analysis unit is used to call the positioning information of the priority allocation robot, and to perform scheduling intelligent planning feedback analysis on the positioning information, so as to analyze from two angles of planning route and live state, so as to reasonably schedule the priority allocation robot. The specific scheduling intelligent planning feedback analysis process is as follows:

[0048] S1: Obtain the positioning information of the user and each priority allocation robot, the positioning information represents the positioning coordinates, generate the optimal planning route based on the positioning information of the user and each priority allocation robot, and obtain the planning feasible value corresponding to the optimal planning route of each priority allocation robot;

[0049] In the embodiment of the application, the optimal planning route represents a planning route corresponding to the minimum value of the planning feasible value in the multiple planning routes generated between the priority allocation robot and the user, and the planning feasible value represents the product value obtained by multiplying the driving time length and the driving energy consumption corresponding value;

[0050] S2: the planning feasible value is sorted in ascending order, the first priority assigned robot corresponding to the sorted planning feasible value is set as a planning robot, and the first priority assigned robot corresponding to the sorted planning feasible value is sequentially analyzed to accurately select the optimal and most suitable planning robot;

[0051] S3: the planning robot is analyzed, if the planning robot is a full-electric standby state corresponding to a storage and charging robot, a distribution optimization signal is generated, and the distribution optimization signal is sent to a site scheduling unit;

[0052] If the planning robot is a return state corresponding to a storage and charging robot, a feedback verification signal is generated, and the feedback verification signal is sent to a return planning unit.

[0053] Embodiment two:

[0054] When the feedback verification signal is generated, the return planning unit is used to collect the state condition information of the storage and charging robot corresponding to the feedback verification signal, and the state condition information is deeply evaluated and analyzed, that is, whether the storage and charging robot meets the ability to continue to perform other energy charging tasks is analyzed from the perspective of return, so as to reasonably schedule the storage and charging robot. The specific deep evaluation and analysis process is as follows:

[0055] When the feedback verification signal is generated, the return state corresponding to the storage and charging robot is set as a target return robot, and the state condition information of the target return robot is obtained, including normal return and fault return.

[0056] In the embodiment of the application, the normal return indicates that the target return robot normally returns after completing the energy charging task; the fault return indicates that the target return robot returns after completing the energy charging task with a defect charging feature, and the defect charging feature includes abnormal sound, no charging voltage, etc.

[0057] The state condition information of the target return robot is discriminated and processed:

[0058] If the target return robot is a normal return, a feedback instruction is generated;

[0059] If the target return robot is a fault return, a recursive instruction is generated, and when the recursive instruction is generated, the S2 operation is performed again, and the next priority assigned robot is selected as a planning robot.

[0060] When the feedback instruction is generated, the energy storage utilization value of the target return robot is obtained, the energy storage utilization value represents the current remaining energy charging power value of the target return robot, and the energy storage utilization value is compared and analyzed with a preset energy storage utilization value threshold:

[0061] If the energy storage utilization value is less than or equal to the preset energy storage utilization value threshold, a return signal is generated, and when the return signal is generated, the S2 operation is performed again to select the next priority allocation robot as the planning robot;

[0062] If the energy storage utilization value is greater than the preset energy storage utilization value threshold, a first push execution signal is generated, and when the first push execution signal is generated, the optimal planning route of the planning robot corresponding to the first push execution signal is called, the optimal planning route is sent to the dispatch management unit, and the dispatch management unit immediately displays the optimal planning route on the user operation page, so that the user can know the position and arrival time of the planning robot at any time, thereby improving the operation experience of the user;

[0063] When the allocation optimization signal is generated, the site dispatch unit is used to collect the energy storage basic data of the storage and charging robot in the full power standby state, and the energy storage basic data is subjected to storage and charging evaluation sorting and unlocking feedback analysis.

[0064] The storage and charging robot in the full power standby state is set as a to-be-allocated robot.

[0065] The energy storage basic data of each to-be-allocated robot is obtained, the energy storage basic data includes the actual total value of the battery, the battery evaluation coefficient and the charging risk coefficient, the preset correction weight factor corresponding to the energy storage basic data is obtained, the product value of the battery evaluation coefficient and the charging risk coefficient is divided by the actual total value of the battery, and then multiplied by the preset correction weight factor to obtain a value, which is set as a mobile charging sorting coefficient.

[0066] The mobile charging sorting coefficient of each to-be-allocated robot is obtained, the mobile charging sorting coefficient is sorted in ascending order, the to-be-allocated robot corresponding to the first sorting position after sorting is obtained, and the to-be-allocated robot corresponding to the first sorting position after sorting is set as an unlocking robot. The unlocking robot is sent to the dispatch management unit, and the dispatch management unit immediately displays the number and positioning of the unlocking robot on the user operation page, so that the user can quickly find the corresponding unlocking robot, and flexible scheduling is facilitated, so as to realize the effects of fast mobile charging and dispatch control.

[0067] In the embodiment of the application, the battery evaluation coefficient represents the product value obtained by multiplying the decay coefficient of the to-be-allocated robot with the basic coefficient corresponding value, the decay coefficient represents the actual decay rate of the total length of time from the time when the to-be-allocated robot is put into use to the current time minus the preset decay rate, and the basic coefficient represents the product value obtained by multiplying the total number of charging times with the charging time corresponding value. The charging time represents the sum of the time length between the time when the new energy automobile is charged and the time when the charging is completed. It should be noted that the larger the value of the battery evaluation coefficient, the greater the risk of abnormal charging performance of the to-be-allocated robot.

[0068] In the embodiment of the present application, the analysis process of the charging risk coefficient is as follows:

[0069] The historical charging information of the to-be-assigned robot is acquired, the historical charging information includes the total number of charging and a charging performance value, the charging performance value represents the over-temperature duration when the running temperature of the historical single charging period of the to-be-assigned robot exceeds the preset running temperature, a rectangular coordinate system is established with the charging number as the X axis and the over-temperature duration as the Y axis, and then an over-temperature duration curve is drawn, and the sum of the number of the rising line segment in the over-temperature duration curve and the number of the horizontal line segment connected to the upper end of the rising line segment is set as the charging risk coefficient, it should be noted that the greater the value of the charging risk coefficient is, the greater the temperature abnormal risk of the charging period of the to-be-assigned robot is, and the greater the charging efficiency risk is;

[0070] To sum up, the present application preliminarily analyzes from the state of the storage and charging robot, that is, the state data is analyzed for the storage and charging robot, which helps to understand the state of each storage and charging robot and helps to reasonably schedule the storage and charging robot that meets the requirements, so as to improve the intelligent scheduling efficiency of the storage and charging robot;

[0071] The present application analyzes from the full charge and return of the storage and charging robot through information feedback, so as to reasonably schedule the generated storage and charging task, if the planning robot is the return state corresponding to the storage and charging robot, whether the storage and charging robot meets the ability to continue to execute other charging tasks is analyzed from the return angle, so as to reasonably schedule the storage and charging robot, if the planning robot is the full charge standby state corresponding to the storage and charging robot, the storage and charging evaluation sorting unlocking feedback analysis is performed on the basic data, so as to reasonably schedule the storage and charging robot, and through the information display, the user can know the position and arrival time of the storage and charging robot at any time, which helps to improve the operation experience of the user and realizes the effect of fast moving charging and scheduling control.

[0072] The size of the threshold is set for comparison, and the size of the threshold depends on the number of sample data and the number of base set by the person skilled in the art for each group of sample data, as long as the proportional relationship between the parameter and the quantized value is not affected.

[0073] The size of the coefficient is to obtain a specific value by quantizing each parameter for subsequent comparison, and the size of the coefficient depends on the number of sample data and the corresponding running coefficient preliminarily set by the person skilled in the art for each group of sample data, as long as the proportional relationship between the parameter and the quantized value is not affected.

[0074] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A mobile shared storage and charging robot for new energy vehicles, characterized in that: It includes a shared storage and charging management platform, a user terminal, a dispatching division unit, a dispatching planning and analysis unit, a site dispatching unit, a return trip planning unit, and a dispatching management unit; When the shared storage and charging management platform receives the application instruction sent by the user terminal on the user operation page, it retrieves the status data of each storage and charging robot and sends the status data to the scheduling division unit; After receiving the status data, the scheduling division unit immediately performs storage and charging robot division and scheduling analysis on the status data to obtain the priority assigned robots and the robots to be assigned; The scheduling planning and analysis unit is used to retrieve the positioning information of the priority assigned robot, and perform scheduling intelligent planning feedback analysis on the positioning information to obtain the allocation priority signal or feedback verification signal; When a feedback verification signal is generated, the return planning unit is used to collect the status and working condition information of the storage and charging robot corresponding to the feedback verification signal, and at the same time conduct in-depth evaluation and scheduling analysis on the status and working condition information, compare and analyze the obtained energy storage utilization value, and obtain the first execution signal or return signal; When the allocation priority signal is generated, the site scheduling unit is used to collect the basic energy storage data of the storage and charging robots corresponding to the fully charged standby state, and at the same time perform storage and charging evaluation, sorting, unlocking and feedback analysis on the basic energy storage data, and set the robot to be allocated corresponding to the first place in the sorting as the unlocked robot and output feedback.

2. A new energy vehicle mobile shared storage and charging robot according to claim 1, characterized in that: The storage and charging robot division scheduling analysis process is as follows: Obtain the status data of each storage and charging robot, including the status inside and outside the station, and perform discrimination processing on the status data: If the status data is a fully charged standby state or a return state, the corresponding storage and charging robot will be set as the priority allocation robot; if the status data is an energy storage state or a charging state, the corresponding storage and charging robot will be set as the robot to be allocated; the status within the station includes the fully charged standby state and the energy storage state, and the status outside the station includes the return state and the charging state.

3. A new energy vehicle mobile shared storage and charging robot according to claim 1, characterized in that: The scheduling intelligent planning feedback analysis process is as follows: S1: Obtain the positioning information of the user and each priority-assigned robot, where the positioning information represents the positioning coordinates, generate the optimal planning route based on the positioning information of the user and each priority-assigned robot, and obtain the planning feasibility value corresponding to the optimal planning route of each priority-assigned robot; The optimal planned route means giving priority to the planned route with the minimum feasible value among the multiple planned routes generated between the robot and the user. The feasible value is the product of the driving time and the corresponding values ​​of the driving energy consumption. S2: Sort the planning feasible values ​​in ascending order, and set the first priority allocation robot corresponding to the sorted planning feasible value as the planning robot; S3: Perform discrimination analysis on the planned robot. If the planned robot is in a fully charged standby state, corresponding to a storage and charging robot, a distribution priority signal is generated; if the planned robot is in a return state, corresponding to a storage and charging robot, a feedback verification signal is generated.

4. A new energy vehicle mobile shared storage and charging robot according to claim 1, characterized in that: The in-depth evaluation and scheduling analysis process is as follows: When a feedback verification signal is generated, the storage and charging robot corresponding to the return state is set as the target return robot, and the state and working condition information of the target return robot is obtained. The state and working condition information includes normal return and fault return. The state and working condition information of the target return robot is judged and processed: If the target return robot returns normally, a feedback instruction is generated; If the target return robot is a fault return, a recursive instruction is generated. When the recursive instruction is generated, the S2 operation is performed again, and the next priority assigned robot is selected as the planning robot.

5. A new energy vehicle mobile shared storage and charging robot according to claim 4, characterized in that: When a feedback instruction is generated, the energy storage utilization value of the target return robot is obtained. The energy storage utilization value represents the current remaining charging power value of the target return robot. The energy storage utilization value is compared and analyzed with the preset energy storage utilization value threshold: If the energy storage utilization value is less than or equal to the preset energy storage utilization value threshold, a return signal is generated. When the return signal is generated, the S2 operation is performed again, and the next priority allocation robot is selected as the planning robot; If the energy storage utilization value is greater than the preset energy storage utilization value threshold, a first-push execution signal is generated. After the first-push execution signal is generated, the optimal planned route of the robot corresponding to the first-push execution signal is retrieved.

6. A new energy vehicle mobile shared storage and charging robot according to claim 1, characterized in that: The storage and charging evaluation ranking unlocking feedback analysis process is as follows: Set the storage and charging robot corresponding to the fully charged standby state as the robot to be assigned; Obtain the basic energy storage data of each robot to be assigned, including the total actual energy storage value of the battery, the battery evaluation coefficient, and the charging risk coefficient. Obtain the preset correction weight factor corresponding to the basic energy storage data. Divide the product of the battery evaluation coefficient and the charging risk coefficient by the total actual energy storage value of the battery, and then multiply the product by the preset correction weight factor to set the value as the mobile charging ranking coefficient; Obtain the mobile charging sorting coefficients of each robot to be assigned, sort the mobile charging sorting coefficients in ascending order, obtain the robot to be assigned corresponding to the first position after sorting, and set the robot to be assigned corresponding to the first position after sorting as the unlocked robot.

7. A new energy vehicle mobile shared storage and charging robot according to claim 6, characterized in that: The battery evaluation coefficient represents the product of the attenuation coefficient of the robot to be assigned and the corresponding value of the basic coefficient. The attenuation coefficient represents the value obtained by subtracting the preset attenuation rate from the actual attenuation rate of the total time between the time the robot to be assigned is put into use and the current time. The basic coefficient represents the product of the total number of charging times and the corresponding value of the charging time. The charging time represents the sum of the time between the time the new energy vehicle is charged and the time the charging ends. The analysis process of the charging risk coefficient is as follows: historical charging information of the robot to be assigned is obtained, the historical charging information includes the total number of charging times and the charging performance value, the charging performance value indicates that the operating temperature of the robot to be assigned in a historical single charging period exceeds the over-temperature duration corresponding to the preset operating temperature, a rectangular coordinate system is established with the number of charging times as the X-axis and the over-temperature duration as the Y-axis, and then an over-temperature duration curve is drawn, and the sum of the corresponding numbers of rising segments in the over-temperature duration curve and the horizontal segments connected to the upper end of the rising segments is set as the charging risk coefficient.

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