A Method for Predicting and Optimizing Electric Load Based on Mobile Energy Storage and Charging Robots

Abstract

The present invention relates to the technical field of electric load prediction and optimization, and specifically discloses an electric load prediction and optimization method based on a mobile storage and charging robot. The method includes: predicting the required electricity consumption, calculating the actual remaining electricity, confirming the available stored electricity, and optimizing the mobile storage and charging transfer; by predicting the required electricity consumption corresponding to each charging area in the next charging cycle, calculating the actual remaining electricity corresponding to each charging area in the current charging cycle, and confirming the actual available stored electricity corresponding to each mobile storage and charging robot in the next charging cycle, the present invention finally optimizes the storage and charging of each mobile storage and charging robot in the next charging cycle, improves the accuracy and reliability of the prediction of the required electricity consumption, the calculation of the actual remaining electricity, and the confirmation of the available stored electricity, provides an effective data support basis for the subsequent optimization of the mobile storage and charging transfer, and avoids unreasonable resource allocation and charging inconvenience for vehicle owners.

Description

Technical Field

[0001] The present invention relates to the technical field of electric load prediction and optimization. Specifically, it relates to a method for electric load prediction and optimization based on a mobile energy storage and charging robot. Background Art

[0002] With the rapid development of smart grid and new energy technologies, load prediction and optimal scheduling play an increasingly important role in the power system. The accuracy of load prediction directly affects the power generation planning, resource allocation, and power supply reliability of the power system. As an important part of the smart grid, the flexibility and intelligence of mobile energy storage and charging robots provide new ideas for load prediction and optimization.

[0003] For example, the patent with the publication number CN116316599A discloses an intelligent electric load prediction method, including: obtaining a data set required for electric load prediction in a target area; preprocessing the load, time, and climate data sets, and selecting data with strong characteristics for model training and testing; dividing the target area, and performing anomaly processing on the historical electric load data of each region for load prediction; obtaining a multi-layer random forest algorithm model optimized by the MPSO algorithm, inputting the training data set into the model after reinforcement learning for training and testing to obtain an electric load prediction model; evaluating the prediction performance of the electric load prediction model, inputting the historical electric load data after regional division processing into the load prediction model for electric load prediction, and outputting the prediction result. This intelligent electric load prediction method constructs a three-layer random forest algorithm model to improve the accuracy of the data source for prediction and the accuracy of the model for load prediction.

[0004] The following problems still exist in the above prior art: 1. In terms of predicting the required electricity consumption, the charging behavior of charging vehicles in the underground parking lot of the community is not combined to analyze the charging trend of the charging vehicles, and the charging situation of the charging vehicles cannot be intuitively and dynamically displayed, reducing the accuracy of predicting the required electricity consumption in the next cycle, which may lead to unreasonable resource allocation and inconvenience for vehicle owners to charge.

[0005] 2. In terms of calculating the actual remaining power, the actual remaining power of the charging area is not comprehensively analyzed by combining the power situation of the charging area and the health status such as the charging speed and charging energy consumption of the charging piles, reducing the reliability of the calculation of the actual remaining power, resulting in incomplete data sources for the prediction of the remaining power, and thus affecting the prediction accuracy.

[0006] 3. In terms of confirming the available stored power, the charging and discharging information of the mobile energy storage and charging robot during the charging and discharging process is not monitored, reducing the accuracy and reasonableness of confirming the actual available stored power corresponding to the mobile energy storage and charging robot in the next charging cycle, and unable to provide an effective data support basis for the subsequent optimization of mobile energy storage and charging scheduling. Summary of the Invention

[0007] In view of this, to solve the problems proposed in the above background art, a method for predicting and optimizing the electricity load based on a mobile storage and charging robot is proposed.

[0008] The object of the present invention can be achieved by the following technical solutions: The present invention provides a method for predicting and optimizing the electricity load based on a mobile storage and charging robot, including the following steps: S1. Prediction of required electricity consumption: Extract the remaining electricity in each charging area in the current charging cycle in the underground parking lot of the target community, the number of charging times corresponding to each charging vehicle, and the charging time points and charging amounts for each charging, and predict the required electricity consumption corresponding to each charging area in the next charging cycle.

[0009] S2. Calculation of actual remaining electricity: Collect the access current and output current corresponding to each charging area in the underground parking lot of the target community at each monitoring time period, and collect the health status information of each charging pile in each charging area, and calculate the actual remaining electricity corresponding to each charging area in the current charging cycle.

[0010] S3. Confirmation of available storage electricity: Extract the charge and discharge information corresponding to each mobile storage and charging robot in each historical charge and discharge in the underground parking lot of the target community, and extract the remaining storage electricity corresponding to each mobile storage and charging robot in the current charging cycle, analyze the charge and discharge efficiency anomaly index of each mobile storage and charging robot, and confirm the actual available storage electricity corresponding to each mobile storage and charging robot in the next charging cycle.

[0011] S4. Optimization of mobile storage and charging transfer: Optimize the storage and charging of each mobile storage and charging robot in the next charging cycle.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) By combining the number of charging times corresponding to each charging vehicle and the charging time points and charging amounts for each charging, the present invention predicts the required electricity consumption corresponding to each charging area in the next charging cycle, intuitively and dynamically displays the charging situation of the charging vehicles, improves the accuracy of predicting the required electricity consumption in the next cycle, and avoids unreasonable resource allocation and charging inconvenience for the vehicle owners.

[0013] (2) By comprehensively analyzing the actual remaining electricity of the charging area in combination with the power stability of the charging area and the health status such as the charging speed and charging energy consumption of the charging pile, the present invention improves the reliability of calculating the actual remaining electricity and the integrity of the data source for predicting the remaining electricity.

[0014] (3) By monitoring the charging and discharging information of the mobile storage and charging robot during the charging and discharging process, analyzing the abnormal index of the charging and discharging efficiency of each mobile storage and charging robot, and confirming the actual available storage power corresponding to each mobile storage and charging robot in the next charging cycle, the accuracy and rationality of confirming the actual available storage power corresponding to each mobile storage and charging robot in the next charging cycle are improved, providing an effective data support basis for the subsequent optimization of mobile storage and charging scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic flowchart of the method steps of the present invention.

[0017] Figure 2 It is a schematic diagram of the loss power deviation curve of the charging pile of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] Please refer to Figure 1 As shown, the present invention provides a method for predicting and optimizing the electrical load based on a mobile storage and charging robot, including: S1. Prediction of required power consumption: Extract the remaining power corresponding to each charging area in the current charging cycle in the underground parking lot of the target community, the number of charging times corresponding to each charging vehicle, and the charging time points and charging amounts of each charging, and predict the required power consumption corresponding to each charging area in the next charging cycle.

[0020] It should be noted that the remaining power corresponding to each charging area in the current charging cycle is obtained by extracting from the power management background of each charging area, and the number of charging times corresponding to each charging vehicle, and the charging time points and charging amounts of each charging are all obtained by extracting from the charging vehicle data management background.

[0021] In a specific embodiment of the present invention, the specific process of predicting the required power consumption of each charging area in the next charging cycle is as follows: A1. Based on the charging time points and charging amounts of each charging of each charging vehicle corresponding to each charging area in the underground parking lot of the target community in the current charging cycle, calculate the charging tendency index β of each charging vehicle in each charging area ij , where i represents the number of the charging area, i = 1, 2,..., n, and j represents the number of the charging vehicle, j = 1, 2,..., m.

[0022] In a specific embodiment of the present invention, the specific process of calculating the charging tendency index of each charging vehicle in each charging area is as follows: B1. Subtract the charging time points of each charging of each charging vehicle corresponding to each charging area to obtain the interval duration of each charging of each charging vehicle, and extract the maximum value from the interval durations of each charging as the charging interval duration corresponding to each charging vehicle in each charging area, denoted as ΔT ij .

[0023] B2. Denote the number of chargings of each charging vehicle corresponding to each charging area as μ ij .

[0024] B3. Calculate the charging tendency index β of each charging vehicle in each charging area ij , where ΔT′ and μ′ respectively represent the set reference charging interval duration and the number of chargings, and a1 and a2 respectively represent the weights of the charging tendency evaluation corresponding to the set charging interval duration and the number of chargings, and a1 + a2 = 1.

[0025] In a specific embodiment of the present invention, the set value of a1 is 0.5, and the set value of a2 is 0.5. When calculating the charging tendency index, both the charging interval duration and the number of chargings are important factors to be considered. They respectively reflect different aspects of the vehicle's charging behavior and jointly constitute a complete portrait of the charging tendency.

[0026] A2. If the charging tendency index of a certain charging vehicle is greater than or equal to the set reference charging tendency index, then determine that the charging vehicle is a fixed charging vehicle; if the charging tendency index of a certain charging vehicle is less than the set reference charging tendency index, then determine that the charging vehicle is a variable charging vehicle.

[0027] A3. Multiply the number of chargings and the charging amount of each charging of each fixed charging vehicle corresponding to each charging area to obtain the total charging amount corresponding to each fixed charging vehicle, and multiply it by the number of fixed charging vehicles to obtain the total charging amount corresponding to the fixed charging vehicles in each charging area, denoted as

[0028] A4. Multiply the number of charging times corresponding to each variable charging vehicle in each charging area by the charging amount of each charging, to obtain the total charging amount corresponding to each variable charging vehicle, and calculate the average value thereof, to obtain the total charging amount corresponding to the variable charging vehicles in each charging area, denoted as

[0029] A5. Predict the required power consumption corresponding to each charging area in the next charging cycle

[0030] In the embodiment of the present invention, by combining the number of charging times corresponding to each charging vehicle, as well as the charging time point and charging amount of each charging, the required power consumption corresponding to each charging area in the next charging cycle is predicted, intuitively and dynamically displaying the charging situation of the charging vehicles, improving the accuracy of predicting the required power consumption in the next cycle, and avoiding unreasonable resource allocation and inconvenient charging for vehicle owners.

[0031] S2. Calculation of actual remaining power: Collect the access current and output current corresponding to each charging area in the underground parking lot of the target community in each monitoring time period, and collect the health status information of each charging pile in each charging area, and calculate the actual remaining power corresponding to each charging area in the current charging cycle.

[0032] It should be noted that the access current and output current corresponding to each charging area in each monitoring time period are respectively collected by watt-hour meters installed at the access end and output end of each charging area.

[0033] In a specific embodiment of the present invention, the health status information includes the discharge speed, required discharge amount, and actual discharge amount corresponding to each discharge process.

[0034] It should be noted that the discharge speed, required discharge amount, and actual discharge amount corresponding to each discharge process are all extracted from the charging pile management app.

[0035] In a specific embodiment of the present invention, the specific process of calculating the actual remaining power corresponding to each charging area in the current charging cycle is: C1. Based on the access current and output current corresponding to each charging area in the underground parking lot of the target community in each monitoring time period, calculate the power stability χ of each charging area i 。

[0036] It should be noted that the specific process of calculating the power stability of each charging area is: Subtract the access current and output current corresponding to each charging area in the underground parking lot of the target community in each monitoring time period to obtain the output current deviation corresponding to each charging area in each monitoring time period, and calculate the average value thereof to obtain the output current deviation corresponding to each charging area, denoted as ΔI i 。

[0037] Calculate the power stability χ of each charging area i , where ΔI′ represents the output current deviation of the set reference.

[0038] C2. Extract the corresponding discharge speed, required discharge amount, and actual discharge amount during each discharge process from the health status information of each charging pile in each charging area, and calculate the charging pile health status index δ of each charging area i .

[0039] In a specific embodiment of the present invention, the specific process of calculating the charging pile health status index of each charging area is as follows: D1. Subtract the corresponding discharge speed during each discharge process of each charging pile in each charging area from the discharge speed provided by the charging pile manufacturer stored in the database to obtain the corresponding discharge speed deviation during each discharge process of each charging pile, and extract the maximum value therefrom, denoted as Δv ij .

[0040] D2. Calculate the charging pile discharge speed abnormality index of each charging area where Δv′ represents the set reference discharge speed deviation, m represents the number of charging vehicles, and e represents the natural constant.

[0041] D3. Subtract the corresponding actual discharge amount and required discharge amount during each discharge process of each charging pile in the charging area to obtain the corresponding power loss amount during each discharge process of each charging pile, and calculate the charging pile power loss abnormality index θ of each charging area i .

[0042] Please refer to Figure 2 As shown, it should be noted that the specific process of calculating the charging pile power loss abnormality index of each charging area is as follows: Taking the number of discharge times as the abscissa and the power loss amount as the ordinate, construct the power loss deviation curve of each charging pile, and locate the slope value from the curve as the power loss growth rate of each charging pile, denoted as K i .

[0043] Calculate the charging pile power loss abnormality index θ of each charging area i , where K′ represents the set reference power loss growth rate.

[0044] D4. Calculate the charging pile health status index δ of each charging area i , where a3 and a4 respectively represent the weights of the charging pile charging speed abnormality index and the charging pile power loss abnormality index corresponding to the charging pile health status assessment, and a3 + a4 = 1.

[0045] In a specific embodiment of the present invention, the set value of a3 is 0.4, and the set value of a4 is 0.6. When calculating the health state index of charging piles in each charging area, the charging speed anomaly index and the power loss anomaly index of the charging piles should be comprehensively considered. Among them, the power loss anomaly directly affects the energy efficiency of the charging piles. Therefore, different weights need to be assigned to them to more comprehensively reflect the health state of the charging piles. C3: Extract the power loss corresponding to the unit power stability deviation and the power loss corresponding to the unit charging pile health state deviation from the database, and record them as ε′ and ε″ respectively.

[0046] C4: Record the remaining power corresponding to each charging area in the underground parking lot of the target community during the current charging cycle as

[0047] C5: Calculate the actual remaining power corresponding to each charging area during the current charging cycle Among them, χ′ and δ′ respectively represent the set reference power stability and the charging pile health state index.

[0048] The embodiment of the present invention comprehensively analyzes the actual remaining power of the charging area by combining the power stability of the charging area and the health states such as the charging speed and charging energy consumption of the charging piles, improves the reliability of the actual remaining power calculation, and improves the integrity of the data source for the remaining power prediction.

[0049] S3: Confirmation of available stored power: Extract the charge and discharge information corresponding to each mobile storage and charging robot in the underground parking lot of the target community during each historical charge and discharge, and extract the remaining stored power corresponding to each mobile storage and charging robot during the current charging cycle. Analyze the charge and discharge efficiency anomaly index of each mobile storage and charging robot to confirm the actual available stored power of each mobile storage and charging robot corresponding to the next charging cycle.

[0050] In a specific embodiment of the present invention, the charge and discharge information includes charging current, charging time to full charge, average charging voltage, discharge current, discharge time to cut-off voltage, and discharge cut-off voltage.

[0051] It should be noted that the charging current, charging time to full charge, average charging voltage, discharge current, discharge time to cut-off voltage, discharge cut-off voltage, and remaining stored power are all extracted from the work management platform of the mobile storage and charging robot.

[0052] In a specific embodiment of the present invention, the specific process of analyzing the charging and discharging efficiency anomaly index of each mobile storage and charging robot is as follows: E1. Extract the charging current, charging time to full charge, average charging voltage, discharging current, discharging time to cut-off voltage, and discharging cut-off voltage from the charging and discharging information corresponding to each mobile storage and charging robot in each historical charging and discharging in the underground parking lot of the target community, and denote them respectively as and where g represents the number of the mobile storage and charging robot, g = 1, 2,..., p, and r represents the number of each historical charging and discharging, r = 1, 2,..., q.

[0053] E2. Calculate the charging and discharging efficiency of each mobile storage and charging robot in each historical charging and discharging

[0054] E3. Calculate the charging and discharging efficiency anomaly index ω of each mobile storage and charging robot g , where represents the set reference charging and discharging efficiency, and q represents the number of historical charging and discharging times.

[0055] In a specific embodiment of the present invention, the specific method for confirming the actual available storage power corresponding to each mobile storage and charging robot in the next charging cycle is as follows: F1. Denote the remaining storage power corresponding to each mobile storage and charging robot in the current charging cycle as

[0056] F2. Extract the storage power consumed corresponding to the unit charging and discharging efficiency anomaly deviation from the database, and denote it as τ0.

[0057] F3. Set the actual available storage power corresponding to each mobile storage and charging robot in the next charging cycle where ω′ represents the set reference charging and discharging efficiency anomaly index.

[0058] By monitoring the charging and discharging information of the mobile storage and charging robot during the charging and discharging process, analyzing the charging and discharging efficiency anomaly index of each mobile storage and charging robot, and confirming the actual available storage power corresponding to each mobile storage and charging robot in the next charging cycle, the embodiment of the present invention improves the accuracy and rationality of confirming the actual available storage power corresponding to the mobile storage and charging robot in the next charging cycle, and provides an effective data support basis for the subsequent optimization of mobile storage and charging transfer.

[0059] S4. Optimization of mobile storage and charging transfer: Optimize the storage and charging of each mobile storage and charging robot in the next charging cycle.

[0060] In a specific embodiment of the present invention, the specific process of optimizing the storage and charging of each mobile storage and charging robot in the next charging cycle is as follows: G1. Compare the actual remaining power corresponding to each charging area in the current charging cycle with the required power consumption corresponding to each charging area in the next charging cycle. If the actual remaining power of a certain charging area is greater than its required power consumption corresponding to the next charging cycle, it indicates that the charging area does not require a mobile storage and charging robot to supplement power. Otherwise, mark the remaining charging areas as target charging areas.

[0061] G2. Subtract the actual remaining power corresponding to each target charging area in the current charging cycle from the required power consumption corresponding to each target charging area in the next charging cycle to obtain the supplementary power corresponding to each target charging area.

[0062] G3. Extract the target charging area corresponding to the minimum value from the supplementary powers corresponding to each target charging area, and mark it as the reference charging area. Subtract the supplementary power corresponding to the reference charging area from the actual available storage power corresponding to each mobile storage and charging robot in the next charging cycle, and extract the mobile storage and charging robot corresponding to the minimum difference from the differences, and use it as the mobile storage and charging robot corresponding to the reference charging area.

[0063] G4. Similarly select the mobile storage and charging robots corresponding to the remaining target charging areas in the same way as the selection method of the mobile storage and charging robot corresponding to the reference charging area. Among them, if the supplementary power corresponding to a certain target charging area is greater than the actual available storage power corresponding to the mobile storage and charging robot in the next charging cycle, it indicates that the mobile storage and charging robot cannot supplement power to the target charging area, and feedback is performed.

[0064] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements or use similar methods to replace the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for predicting and optimizing electricity load based on a mobile charging and storage robot, characterized in that, It includes the following steps: S1. Prediction of required electricity consumption: Extract the remaining electricity in each charging area in the underground parking lot of the target community during the current charging cycle, the number of charging times of each charging vehicle, and the charging time points and charging amounts of each charging, and predict the required electricity consumption of each charging area in the next charging cycle; Predicting the required power consumption of each charging area in the next charging cycle requires calculating the charging trend index of each charging vehicle in each charging area , where represents the number of the charging area, , represents the number of the charging vehicle, ; The specific process of calculating the charging trend index of each charging vehicle in each charging area is as follows: B1. Subtract the charging time points of each charging of each charging vehicle in each charging area to obtain the interval duration of each charging of each charging vehicle, and extract the maximum value from the interval durations of each charging as the charging interval duration corresponding to each charging vehicle in each charging area, denoted as ; B2. Record the number of charging times corresponding to each charging vehicle in each charging area as ; B3. Calculate the charging tendency index of each charging vehicle in each charging area , , where and respectively represent the set reference charging interval duration and charging times, and respectively represent the charging tendency evaluation proportion weights corresponding to the set charging interval duration and charging times, ; S2. Calculation of actual remaining electricity: Collect the access current and output current of each charging area in the underground parking lot of the target community during each monitoring time period, and collect the health status information of each charging pile in each charging area, and calculate the actual remaining electricity of each charging area during the current charging cycle; S3. Confirmation of available stored electricity: Extract the charge and discharge information of each mobile storage and charging robot in the underground parking lot of the target community during each historical charge and discharge, and extract the remaining stored electricity of each mobile storage and charging robot during the current charging cycle, analyze the charge and discharge efficiency anomaly index of each mobile storage and charging robot, and confirm the actual available stored electricity of each mobile storage and charging robot in the next charging cycle; S4. Optimization of mobile storage and charging transfer: Optimize the storage and charging transfer of each mobile storage and charging robot in the next charging cycle.

2. The method for predicting and optimizing electrical load based on a mobile charging and storage robot according to claim 1, wherein: The specific process of predicting the required electricity consumption of each charging area in the next charging cycle is as follows: A1. Calculate the charging trend index of each charging vehicle in each charging area based on the charging time points and charging amounts of each charging of each charging vehicle corresponding to each charging area in the underground parking lot of the target cell during the current charging cycle. , where represents the number of the charging area, , represents the number of the charging vehicle, ; A2. If the charging trend index of a certain charging vehicle is greater than or equal to the set reference charging trend index, it is determined that the charging vehicle is a fixed charging vehicle; if the charging trend index of a certain charging vehicle is less than the set reference charging trend index, it is determined that the charging vehicle is a variable charging vehicle; A3. Multiply the number of charging times of each stationary charging vehicle in each charging area by the charging amount of each charging, to obtain the total charging amount corresponding to each stationary charging vehicle, and multiply it by the number of stationary charging vehicles to obtain the total charging amount corresponding to the stationary charging vehicles in each charging area, denoted as ; A4. Multiply the number of charging times corresponding to each variable charging vehicle in each charging area by the charging amount of each charging time to obtain the total charging amount corresponding to each variable charging vehicle, and perform an average calculation on it to obtain the total charging amount corresponding to the variable charging vehicles in each charging area, denoted as ; A5. Predict the required power consumption for each charging area in the next charging cycle , .

3. The method for predicting and optimizing the electrical load based on a mobile charging and storage robot according to claim 2, wherein: The health status information includes the discharge speed, required discharge amount, and actual discharge amount during each discharge process.

4. The method for predicting and optimizing the electrical load based on a mobile charging and storage robot according to claim 3, wherein: The specific process of calculating the actual remaining electricity of each charging area during the current charging cycle is as follows: C1. Calculate the power stability of each charging area based on the access current and output current corresponding to each charging area in the underground parking lot of the target cell during each monitoring time period ; C2. Extract the corresponding discharge speed, required discharge amount, and actual discharge amount during each discharge process from the health status information of each charging pile in each charging area, and calculate the health status index of the charging piles in each charging area. ; C3. Extract the power loss corresponding to the unit power stability deviation and the power loss corresponding to the unit charging pile health state deviation from the database, and denote them as and ; C4. Record the remaining power corresponding to each charging area in the underground parking lot of the target cell during the current charging cycle as ; C5. Calculate the actual remaining power corresponding to each charging area during the current charging cycle , , where and respectively represent the power stability set for reference and the charging pile health status index.

5. The method for predicting and optimizing the electrical load based on a mobile storage and charging robot according to claim 4, wherein: The specific process of calculating the charging pile health status index of each charging area is as follows: D1. Subtract the discharge speed corresponding to each discharge process of each charging pile in each charging area from the discharge speed provided by the charging pile manufacturer stored in the database to obtain the discharge speed deviation corresponding to each discharge process of each charging pile, and extract the maximum value therefrom, denoted as ; D2. Calculate the abnormal index of the charging pile discharge speed in each charging area , , where represents the discharge speed deviation set for reference, represents the number of charging vehicles, represents the natural constant; D3. Subtract the actual discharge amount corresponding to each discharge process of each charging pile in the charging area from the required discharge amount to obtain the power loss amount corresponding to each discharge process of each charging pile, and calculate the abnormal index of the power loss of the charging piles in each charging area ; D4. Calculate the charging pile health status index for each charging area , , where and respectively represent the corresponding proportion weights of the abnormal charging speed index and abnormal power loss index of the charging pile in the evaluation of the charging pile health status .

6. The method for predicting and optimizing the electrical load based on a mobile charging and storage robot according to claim 4, wherein: The charge and discharge information includes charging current, charging time to full charge, average charging voltage, discharge current, discharge time to cut-off voltage, and discharge cut-off voltage.

7. The method for predicting and optimizing the electricity load based on a mobile storage and charging robot according to claim 6, wherein: The specific process of analyzing the charge and discharge efficiency anomaly index of each mobile storage and charging robot is as follows: E1. Extract the charging current, charging time to full charge, average charging voltage, discharging current, discharging time to cut-off voltage, and discharging cut-off voltage from the charging and discharging information corresponding to each mobile storage and charging robot in the underground parking lot of the target cell during each historical charging and discharging, and denote them as , , , , and , respectively, where represents the number of the mobile storage and charging robot, , represents the number of each historical charging and discharging, ; E2. Calculate the charge-discharge efficiency of each mobile storage and charging robot in each historical charge-discharge process , ; E3. Calculate the charge-discharge efficiency anomaly index of each mobile charge and storage robot , , where represents the charge-discharge efficiency set as a reference, represents the historical number of charge and discharge times.

8. A method for predicting and optimizing electricity load based on a mobile storage and charging robot according to claim 7, characterized in that: The specific method of confirming the actual available stored electricity of each mobile storage and charging robot in the next charging cycle is as follows: F1. Denote the remaining stored power corresponding to each mobile storage and charging robot during the current charging cycle as ; F2. Extract the consumed and stored electricity corresponding to the abnormal deviation of the unit charge-discharge efficiency from the database and record it as ; F3. Set the actual available storage power of each mobile storage and charging robot for the next charging cycle , , where represents the abnormal index of charge-discharge efficiency for setting reference 9. The method for predicting and optimizing the electrical load based on a mobile charging and storage robot according to claim 8, wherein: The specific process of optimizing the storage and charging transfer of each mobile storage and charging robot in the next charging cycle is as follows: G1. Compare the actual remaining electricity of each charging area during the current charging cycle with the required electricity consumption of each charging area in the next charging cycle. If the actual remaining electricity of a certain charging area is greater than its required electricity consumption in the next charging cycle, it means that the charging area does not require a mobile storage and charging robot to supplement electricity; otherwise, mark the remaining charging areas as target charging areas; G2. Subtract the required electricity consumption of each target charging area in the next charging cycle from its actual remaining electricity during the current charging cycle to obtain the supplementary electricity of each target charging area; G3. Extract the target charging area corresponding to the minimum value from the supplementary power corresponding to each target charging area, and record it as the reference charging area. Subtract the actual available stored power corresponding to each mobile storage and charging robot in the next charging cycle from the supplementary power corresponding to the reference charging area, and extract the mobile storage and charging robot corresponding to the minimum difference from the differences, and use it as the mobile storage and charging robot corresponding to the reference charging area; G4. Similarly select the mobile storage and charging robots corresponding to the remaining target charging areas according to the selection method of the mobile storage and charging robot corresponding to the reference charging area. Among them, if the supplementary power corresponding to a certain target charging area is greater than the actual available stored power corresponding to the mobile storage and charging robot in the next charging cycle, it indicates that the mobile storage and charging robot cannot supplement the power of the target charging area, and feedback is made.

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