Battery dispatching method, device and equipment used between battery charging and replacing stations and medium

Abstract

The invention relates to the technical field of battery swap station battery dispatching prediction, in particular to a battery dispatching method, device and equipment used between battery charging and swap station points and a medium. The method comprises the steps of obtaining multiple groups of battery scheduling parameters, calculating demand satisfaction values of all stations of the multiple groups of battery scheduling parameters, and performing successive multiplication calculation to obtain demand satisfaction rates corresponding to the multiple groups of battery scheduling parameters, and carrying out battery scheduling processing on the first station and the second station according to the battery scheduling parameter with the highest demand satisfaction rate. The problem that the new energy automobile battery replacement cannot be met due to the fact that station battery supply and demand are not matched is solved, and the use experience of a new energy automobile user battery charging and replacing station is further improved.

Description

Technical Field

[0001] This application relates to the field of battery scheduling analysis technology for battery swapping stations, and in particular to a battery scheduling method, apparatus, equipment and medium for battery scheduling between charging and swapping stations. Background Technology

[0002] With the rapid development of the electric vehicle market, battery swapping stations have emerged as a solution for quickly replacing batteries, enabling electric vehicles to complete charging or battery replacement in a short time. However, due to the dynamic changes in battery demand and battery inventory at charging and swapping stations, there may be situations where the batteries provided by these stations cannot meet the demand, directly impacting the usability of electric vehicles. Summary of the Invention

[0003] In view of this, one of the technical problems solved by the embodiments of this application is to provide a battery scheduling method, apparatus, equipment and medium for charging and swapping stations, so as to solve the problem of supply and demand balance between charging and swapping stations.

[0004] According to a first aspect of the embodiments of this application, a battery scheduling method for charging and swapping stations is provided, the method comprising:

[0005] Obtain multiple sets of battery scheduling parameters, including a first value of the number of full batteries transferred from the first station to each of the multiple second stations and a second value of the number of empty batteries transferred from the first station to each of the multiple second stations.

[0006] Calculate the demand satisfaction value of each station for each of the multiple sets of battery scheduling parameters and multiply them together to obtain the demand satisfaction rate corresponding to each of the multiple sets of battery scheduling parameters. The demand satisfaction value of any station includes the ratio of the sum of the first values ​​of the number of full batteries transferred from each of the multiple second stations to the number of full batteries missing at the first station.

[0007] Battery scheduling is performed on the first and second sites based on the battery scheduling parameters that maximize demand fulfillment.

[0008] A second aspect of this application discloses a battery scheduling device for charging and swapping stations, the device comprising:

[0009] The scheduling parameter acquisition module is used to acquire multiple sets of battery scheduling parameters. The battery scheduling parameters include a first value of the number of full batteries transferred from the first station to the first station by each of the multiple second stations and a second value of the number of empty batteries transferred from the first station to the multiple second stations respectively.

[0010] The demand satisfaction rate calculation module is used to calculate the demand satisfaction value of each station for each of the multiple sets of battery scheduling parameters and perform a multiplication calculation to obtain the demand satisfaction rate corresponding to each of the multiple sets of battery scheduling parameters. The demand satisfaction value of any station includes the ratio of the sum of the first values ​​of the number of full batteries transferred from each of the multiple second stations to the number of full batteries missing at the first station.

[0011] The battery scheduling determination module is used to perform battery scheduling processing on the first and second sites based on the battery scheduling parameters with the highest demand fulfillment rate.

[0012] A third aspect of this application discloses an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0013] A fourth aspect of this application discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0014] The embodiments of this application obtain multiple sets of battery scheduling parameters, including a first value for a full battery transferred from a first station to multiple second stations and a second value for an empty battery transferred from the first station to multiple second stations. The demand satisfaction values ​​for each station within each set of battery scheduling parameters are then calculated and multiplied together to obtain the demand satisfaction rate corresponding to each set of battery scheduling parameters. Battery scheduling is then performed on the first and second stations based on the battery scheduling parameter with the highest demand satisfaction rate. This method of scheduling full and empty batteries at different stations to maximize the demand satisfaction rate solves the problem of insufficient battery swapping capacity for new energy vehicles due to mismatched battery supply and demand at stations, further improving the user experience for new energy vehicle users at battery swapping stations. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] Figure 1 This is a flowchart illustrating a battery scheduling method between charging and swapping stations according to an embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the structure of a battery scheduling device between charging and swapping stations provided in one embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0021] According to one embodiment of this application, a battery scheduling method for charging and swapping stations is provided, such as... Figure 1 As shown, the method includes steps S101, S102 and S103.

[0022] Step S101: Obtain multiple sets of battery scheduling parameters. The battery scheduling parameters include a first value of the number of full batteries transferred from the first station to each of the multiple second stations and a second value of the number of empty batteries transferred from the first station to the multiple second stations.

[0023] In this embodiment, both the first site and the second site are charging and swapping sites. Specifically, the charging and swapping site can be a swapping station or a photovoltaic power station. More specifically, among a plurality of charging and swapping sites, any one site can serve as the first site, and the rest as the second site.

[0024] Specifically, different stations have different numbers of fully charged batteries. Due to factors such as geographical location and customer traffic, different stations have different battery swapping needs. Therefore, when the number of fully charged batteries at a certain station cannot meet the battery swapping needs, batteries need to be allocated from other stations.

[0025] Step S102: Calculate the demand satisfaction value of each station for each of the multiple sets of battery scheduling parameters and perform a multiplication calculation to obtain the demand satisfaction rate corresponding to each of the multiple sets of battery scheduling parameters. The demand satisfaction value of any station includes the ratio of the sum of the first values ​​of the number of full batteries transferred from each of the multiple second stations to the number of full batteries missing from the first station.

[0026] In this embodiment, the full battery shortage number is used to characterize the difference between the battery demand and the remaining quantity of full batteries. In application, the number of full batteries transferred from all second stations to the first station is first summed to obtain S1. Then, the full battery shortage number S2 of the first station is calculated. Therefore, the demand satisfaction rate of the first station is the ratio of S1 to S2.

[0027] Step S103: Perform battery scheduling processing on the first and second sites based on the battery scheduling parameters with the highest demand satisfaction rate.

[0028] Specifically, the demand fulfillment values ​​of each station in multiple sets of battery scheduling parameters can be sorted in descending order to obtain the battery scheduling parameter with the highest demand fulfillment rate. Based on this battery scheduling parameter, the first value for the number of full batteries transferred from the first station to each second station can be determined. And a second value, which includes the number of empty batteries transferred from the first station to each second station. The first value can be used... Let i represent the first station and j represent the second station. This represents the number of fully charged batteries transferred from the second station j to the first station i; the second value can be used... Let i represent the first station and j represent the second station. This represents the number of empty batteries transferred from the first station i to the second station j.

[0029] This application embodiment obtains multiple sets of battery scheduling parameters, including a first value for a full battery transferred from a first station to multiple second stations and a second value for an empty battery transferred from the first station to multiple second stations. It then calculates the demand satisfaction value for each station according to each set of battery scheduling parameters and multiplies them together to obtain the demand satisfaction rate corresponding to each set of battery scheduling parameters. Based on the battery scheduling parameter with the highest demand satisfaction rate, it performs battery scheduling processing on the first and second stations. This method of scheduling full and empty batteries at different stations to maximize the demand satisfaction rate solves the problem of insufficient battery swapping capacity for new energy vehicles due to mismatch between battery supply and demand at stations, further improving the user experience of new energy vehicle users at charging and swapping stations.

[0030] In some embodiments, the step of obtaining multiple sets of battery scheduling parameters further includes:

[0031] Based on the charging and swapping station network, obtain battery-related information for the first station and multiple second stations. The battery-related information includes the remaining number of fully charged batteries, the number of empty batteries and the number of available charging slots, the station's battery installed capacity, and the projected power generation and battery demand for a future predetermined period.

[0032] Based on the preset battery allocation algorithm, the battery-related information of the first site and multiple second sites is calculated to obtain multiple sets of battery scheduling parameters to be determined;

[0033] Multiple sets of battery scheduling parameters to be determined are filtered using predefined scheduling constraints to obtain multiple sets of battery scheduling parameters.

[0034] Specifically, the predicted power generation and battery demand within a predetermined time period can be determined using a preset algorithm. In this embodiment, the predicted power generation is used to characterize whether a site has photovoltaic power generation capacity and the magnitude of that capacity. In application, the predicted power generation within a predetermined time period is determined based on power generation data from different past time periods, such as determining the power generation of each site in different past time periods as the predicted power generation for the corresponding predetermined time period in the future.

[0035] Specifically, multiple application scenarios can be pre-configured, and when a trigger condition for any application scenario (such as a specific time period) is detected, a data collection command is sent to each station to obtain battery-related information for the first station and multiple second stations. During application, the remaining number of fully charged batteries, the number of empty batteries, the number of available charging slots, and the battery capacity of each station can be determined through the information uploaded by each station.

[0036] Specifically, battery-related information includes:

[0037] Remaining battery capacity Electricity generation Battery quantity required: Number of empty batteries Number of available charging slots Installed capacity Where, the installed capacity is the number of battery cabinet compartments (i.e., the maximum number of batteries that can be placed), m is the number of battery swapping stations, and the subscript 0 indicates a photovoltaic station. The battery demand of the photovoltaic station is... In application, after sending data acquisition requests to each site, battery-related information can be determined based on the feedback information from each site's response to the data acquisition requests. Specifically, the remaining number of fully charged batteries, empty batteries, and available charging slots at each site, as well as the site's installed battery capacity, can be obtained directly by filtering based on the feedback information. The power generation and battery demand of each site in the future predetermined time period can be determined using a pre-configured calculation model, or it can be determined based on the power generation and battery demand in the same past time period (e.g., the previous day, or the same time period of the previous day). For example, it can be determined based on the average demand, maximum demand, or highest frequency of demand in the past predetermined time period.

[0038] Specifically, battery allocation algorithms can be categorized into genetic algorithms, differential evolution algorithms, annealing algorithms, etc. In application, the suitability of each algorithm can be used to calculate multiple sets of battery scheduling parameters.

[0039] Specifically, the scheduling constraints include at least one of the following:

[0040] Limitations on the number of empty batteries transferred from the same site;

[0041] Limitations on the number of empty batteries at the same site;

[0042] Adjust the full battery limit condition when the site is a photovoltaic site;

[0043] Limitations on the total number of batteries at the same site;

[0044] Restrictions on the import and export of empty batteries at the same site;

[0045] Restrictions on bringing in and out full batteries from the same site;

[0046] Limitations on the transport of empty batteries between the two sites;

[0047] Restrictions on the transport of fully charged batteries between the two stations.

[0048] When applied, scheduling constraints include:

[0049] The number of empty batteries transferred from the i-site is less than the remaining available rechargeable slots: ;

[0050] The number of empty batteries at site i must be greater than the number of rechargeable photovoltaic batteries at that point: ,in, This indicates the number of empty batteries at site i. This indicates the number of empty batteries transferred from station i to station j;

[0051] When scheduling between sites, priority is given to satisfying the needs of the current site: Where j=0 indicates that the number of fully charged batteries transported out of the photovoltaic station does not exceed the number of remaining fully charged batteries at the photovoltaic station;

[0052] The total number of batteries at the first site is less than the installed capacity of that site: ;

[0053] An empty battery will not be loaded and unloaded simultaneously at the same site. ;

[0054] A full battery cannot be loaded and unloaded simultaneously from the same website. ;

[0055] The two stations will not simultaneously transport fully charged batteries to each other. , This means that either a full battery is shipped from station i to station j, or a full battery is shipped from station j to station i.

[0056] Empty batteries will not be transported between the two stations simultaneously. , This means either an empty battery is run from station i to station j, or an empty battery is transported from station j to station i.

[0057] Among the above constraints, This indicates the number of batteries remaining at site i when it is fully charged. This represents the projected power generation of site i over a future scheduled period. This indicates the number of batteries required at site i. This indicates the number of empty batteries at site i. This indicates the number of available charging stations at station i. This indicates the installed capacity of site i. This indicates the number of empty batteries transferred from station i to station j. This represents the number of empty batteries transferred from station j (from other stations besides station i) to station i. This represents the number of full batteries transported from station i to station j. This represents the number of full batteries transported from station j to station i. The above scheduling constraints prevent stations from exceeding their capacity when incoming or outgoing batteries.

[0058] In some embodiments, obtaining the power generation forecasts for a first site and multiple second sites includes: determining a first power generation forecast result for each of the first site and multiple second sites based on historical power generation data and meteorological data for each of the multiple sites; determining a second power generation forecast result for each of the first site and multiple second sites based on photovoltaic cell physical information related to each of the first site and multiple second sites; and performing a weighted sum calculation on the first power generation forecast result and the second power generation forecast result to obtain the power generation forecast for each of the first site and multiple second sites.

[0059] Specifically, the physical information related to photovoltaic cells can include climatic factors such as solar radiation, humidity, and temperature.

[0060] Specifically, the weighted sum of the first power generation forecast result and the second power generation forecast result can be calculated using the following formula: ,in, These are weighting coefficients. This is the first power generation forecast result. This is the second power generation prediction result. The first power generation prediction result is corrected by weighting and summing to ensure the prediction conforms to physical constraints and improves prediction robustness. In application, a lookup table can be set to determine the weighting coefficients; for example, the value is M1 for climate temperature N1, and M2 for climate temperature N2.

[0061] Specifically, a trained machine learning model (such as an LSTM model) can be used to predict the historical power generation data and meteorological data of multiple stations; a machine learning model such as a convolutional neural network (CNN) can be used to determine the second power generation prediction result.

[0062] In the above embodiments, the step of obtaining the power generation forecast of the first site and the multiple second sites further includes: calculating the average power generation of the power generation corresponding to multiple historical time periods based on historical power generation records, and using the average power generation as the power generation in a future predetermined time period.

[0063] In some embodiments, obtaining the battery demand quantity of the first site and the plurality of second sites includes: performing a fusion analysis based on the battery swapping demand information of the plurality of sites to obtain the battery demand quantity of the first site and the plurality of second sites.

[0064] Specifically, information related to battery swapping demand can include time-series data of historical battery swapping demand, which shows the dynamic changes in battery swapping demand at different times. This time-series data can also include other factors influencing battery swapping demand, such as traffic flow and weather conditions. In application, the time-series data, along with traffic flow and weather conditions, can be processed to obtain the battery swapping demand at different points in time and the corresponding traffic flow and climate data. This data is then input into a pre-defined model, which may include a structure combining multiple layers of Long Short-Term Memory (LSTM) networks and multiple fully connected layers. For example, each set of data can be processed using an LSTM network and fully connected layers, or the time-series data can be sequentially input into an LSTM network and then processed by the fully connected layers. Through processing by the LSTM layers and fully connected layers, the model learns more complex and accurate mapping relationships, thereby improving the accuracy of the prediction results. Finally, the model outputs the number of batteries needed in different future time periods.

[0065] In some embodiments, based on the charging and swapping station network, obtaining battery-related information of the first station and multiple second stations respectively, further includes: when a prompt message indicating that any station in the charging and swapping station network is fault-closed is detected, re-obtaining battery-related information of the first station and multiple second stations respectively.

[0066] Specifically, the charging and swapping station network is adjusted by using fault shutdown prompts, thereby adjusting battery allocation.

[0067] One embodiment of this application provides a battery scheduling device for charging and swapping stations, such as... Figure 2 As shown, the device 20 includes: a scheduling parameter acquisition module 201, a satisfaction rate calculation module 202, and a battery scheduling determination module 203.

[0068] The scheduling parameter acquisition module 201 is used to acquire multiple sets of battery scheduling parameters. The battery scheduling parameters include a first value of the number of full batteries transferred from the first station to the first station by each of the multiple second stations and a second value of the number of empty batteries transferred from the first station to the multiple second stations respectively.

[0069] The demand satisfaction rate calculation module 202 is used to calculate the demand satisfaction value of each station for each of the multiple sets of battery scheduling parameters and perform multiplication calculation to obtain the demand satisfaction rate corresponding to each of the multiple sets of battery scheduling parameters. The demand satisfaction value of any station includes the ratio of the sum of the first values ​​of the number of full batteries transferred from each of the multiple second stations to the number of full batteries missing in the first station.

[0070] The battery scheduling determination module 203 is used to perform battery scheduling processing on the first and second sites based on the battery scheduling parameters with the highest demand satisfaction rate.

[0071] This application embodiment obtains multiple sets of battery scheduling parameters, including a first value for a full battery transferred from a first station to multiple second stations and a second value for an empty battery transferred from the first station to multiple second stations. It then calculates the demand satisfaction value for each station according to each set of battery scheduling parameters and multiplies them together to obtain the demand satisfaction rate corresponding to each set of battery scheduling parameters. Based on the battery scheduling parameter with the highest demand satisfaction rate, it performs battery scheduling processing on the first and second stations. This method of scheduling full and empty batteries at different stations to maximize the demand satisfaction rate solves the problem of insufficient battery swapping capacity for new energy vehicles due to mismatch between battery supply and demand at stations, further improving the user experience of new energy vehicle users at charging and swapping stations.

[0072] Furthermore, the scheduling parameter acquisition module includes:

[0073] The information acquisition submodule is used to acquire battery-related information for the first site and multiple second sites based on the charging and swapping site network. The battery-related information includes the remaining number of fully charged batteries, the number of empty batteries and the number of available charging slots, the installed battery capacity of the site, and the predicted power generation and battery demand in a future predetermined time period.

[0074] The data allocation determination submodule is used to calculate the battery-related information of the first site and multiple second sites according to the preset battery allocation algorithm, and obtain multiple sets of battery scheduling parameters to be determined.

[0075] The data filtering submodule is used to filter multiple sets of battery scheduling parameters to be determined using predefined scheduling constraints, thereby obtaining multiple sets of battery scheduling parameters.

[0076] Furthermore, the scheduling constraints include at least one of the following:

[0077] Limitations on the number of empty batteries transferred from the same site;

[0078] Limitations on the number of empty batteries at the same site;

[0079] Adjust the full battery limit condition when the site is a photovoltaic site;

[0080] Limitations on the total number of batteries at the same site;

[0081] Restrictions on the import and export of empty batteries at the same site;

[0082] Restrictions on bringing in and out full batteries from the same site;

[0083] Limitations on the transport of empty batteries between the two sites;

[0084] Restrictions on the transport of fully charged batteries between the two stations.

[0085] Furthermore, the information acquisition submodule includes:

[0086] The first power generation result determination submodule is used to determine the first power generation prediction result for the first site and multiple second sites based on the historical power generation data and meteorological data of multiple sites.

[0087] The second power generation result determination submodule is used to determine the second power generation prediction result for each of the first site and the multiple second sites based on the photovoltaic cell physical information related to each of the first site and the multiple second sites.

[0088] The first power generation forecast result determination submodule is used to perform weighted sum calculation on the first power generation forecast result and the second power generation forecast result to obtain the power generation forecast of the first site and multiple second sites respectively.

[0089] Furthermore, the information acquisition submodule includes:

[0090] The second power generation forecast result determination submodule is used to calculate the average power generation corresponding to multiple historical time periods based on historical power generation records, and use the average power generation as the power generation in the future predetermined time period.

[0091] Furthermore, the information acquisition submodule includes:

[0092] The battery demand determination submodule is used to perform fusion analysis based on the battery swapping demand information of multiple sites to obtain the battery demand quantity of the first site and multiple second sites.

[0093] Furthermore, the information acquisition submodule also includes:

[0094] The information update unit is used to reacquire the battery-related information of the first station and multiple second stations when a prompt message indicating that any station in the charging and swapping station network has failed to shut down is detected.

[0095] The battery scheduling device for charging and swapping stations in this embodiment can execute the battery scheduling method for charging and swapping stations shown in the embodiments of this application. The implementation principle is similar, and will not be described again here.

[0096] Another embodiment of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0097] Specifically, the processor can be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0098] Specifically, the processor connects to the memory via a bus, which may include a path for transmitting information. The bus can be a PCI bus or an EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc.

[0099] The memory may be ROM or other types of static storage devices that can store static information and instructions, RAM or other types of dynamic storage devices that can store information and instructions, or EEPROM, CD-ROM or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0100] Optionally, the memory stores the code of a computer program that executes the scheme of this application, and the execution is controlled by a processor. The processor executes the application code stored in the memory to implement the operation of the apparatus provided in the above embodiments.

[0101] Another embodiment of this application provides a computer-readable storage medium storing computer-executable instructions for performing the methods provided in the above embodiments.

[0102] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0103] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0104] The above is a detailed description of the preferred embodiments of this application. However, this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A battery scheduling method for charging and swapping stations, characterized in that, include: Multiple sets of battery scheduling parameters are obtained. The battery scheduling parameters include a first value of the number of full batteries that are each transferred to the first station from multiple second stations and a second value of the number of empty batteries that are transferred from the first station to the multiple second stations respectively. Calculate the demand satisfaction value of each station for each of the multiple sets of battery scheduling parameters and multiply them together to obtain the demand satisfaction rate corresponding to each of the multiple sets of battery scheduling parameters. The demand satisfaction value of any station includes the ratio of the sum of the first values ​​of the number of full batteries transferred from each of the multiple second stations to the number of full batteries missing from the first station. Battery scheduling is performed on the first site and the second site based on the battery scheduling parameters that have the highest demand satisfaction rate.

2. The method according to claim 1, characterized in that, The acquisition of multiple sets of battery scheduling parameters includes: Based on the charging and swapping station network, battery-related information of the first station and multiple second stations is obtained. The battery-related information includes the remaining number of fully charged batteries, the number of empty batteries and the number of available charging slots, the station's battery installed capacity, and the predicted power generation and battery demand in a future predetermined time period. Based on a preset battery allocation algorithm, the battery-related information of the first site and multiple second sites is calculated to obtain multiple sets of battery scheduling parameters to be determined. Multiple sets of battery scheduling parameters to be determined are filtered using predefined scheduling constraints to obtain multiple sets of battery scheduling parameters.

3. The method according to claim 2, characterized in that, The scheduling constraints include at least one of the following: Limitations on the number of empty batteries transferred from the same site; Limitations on the number of empty batteries at the same site; Adjust the full battery limit condition when the site is a photovoltaic site; Limitations on the total number of batteries at the same site; Restrictions on the import and export of empty batteries at the same site; Restrictions on bringing in and out full batteries from the same site; Limitations on the transport of empty batteries between the two sites; Restrictions on the transport of fully charged batteries between the two stations.

4. The method according to claim 2, characterized in that, Obtaining the predicted power generation for each of the first site and multiple second sites includes: Based on the historical power generation data and meteorological data of multiple stations, the first power generation prediction results of the first station and multiple second stations are determined respectively; Based on the photovoltaic cell physical information of the first site and the multiple second sites, the second power generation prediction results of the first site and the multiple second sites are determined; The first power generation forecast result and the second power generation forecast result are weighted and summed to obtain the power generation forecast amount for the first site and the plurality of second sites respectively.

5. The method according to claim 2, characterized in that, Obtaining the predicted power generation for each of the first site and multiple second sites includes: Based on historical power generation records, the average power generation corresponding to multiple historical time periods is calculated, and the average power generation is used as the power generation for a future predetermined time period.

6. The method according to claim 2, characterized in that, Obtaining the battery demand quantities for the first site and multiple second sites respectively includes: Based on the fusion analysis of battery swapping demand information from multiple sites, the required number of batteries for the first site and multiple second sites is obtained.

7. The method according to claim 2, characterized in that, The method of obtaining battery-related information for the first station and multiple second stations based on the charging and swapping station network also includes: When a prompt message indicating that any station in the charging and swapping station network has failed to shut down is detected, the battery-related information of the first station and multiple second stations is reacquired.

8. A battery scheduling device for use between charging and swapping stations, characterized in that, include: The scheduling parameter acquisition module is used to acquire multiple sets of battery scheduling parameters. The battery scheduling parameters include a first value of the number of full batteries transferred from the first station to the first station by each of the multiple second stations and a second value of the number of empty batteries transferred from the first station to the multiple second stations respectively. The satisfaction rate calculation module is used to calculate the demand satisfaction value of each station for each of the multiple sets of battery scheduling parameters and perform multiplication calculation to obtain the demand satisfaction rate corresponding to each of the multiple sets of battery scheduling parameters. The demand satisfaction value of any station includes the ratio of the sum of the first values ​​of the number of full batteries transferred from each of the multiple second stations to the first station to the number of full batteries missing at the first station. The battery scheduling determination module is used to perform battery scheduling processing on the first site and the second site based on the battery scheduling parameters that have the highest demand satisfaction rate.

9. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing computer-readable instructions, and the processor being configured to execute the computer-readable instructions, wherein the computer-readable instructions, when executed, perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions for performing the method according to any one of claims 1 to 7.

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