Charging simulation method and device, computer device and readable storage medium

Abstract

The application relates to a charging simulation method, comprising the following steps: acquiring, at each simulation time in each simulation period, bicycle attribute information of each new energy bicycle in a power supply area corresponding to the simulation time and entity attribute information of each charging station; determining, from a charging object simulation body corresponding to each new energy bicycle, a first simulation body with a charging simulation demand according to the bicycle attribute information, a simulation time period to which the simulation time belongs, and simulation weather information of the power supply area at the simulation time; determining, from a charging station simulation body corresponding to each charging station, a second simulation body based on building attribute information of a building to which the first simulation body belongs and the entity attribute information; obtaining sub-simulation data of the power supply area at the simulation time in response to a charging simulation event of the second simulation body to the first simulation body; and obtaining charging simulation data of the power supply area in the simulation period based on the sub-simulation data.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a charging simulation method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the acceleration of urbanization, power supply areas in core urban areas are facing unique challenges: high building density, dense population, and widespread use of electric bicycles and other new energy vehicles for transportation. Electricity demand in these areas continues to rise, while outdated planning has led to a severe shortage of standardized charging facilities, resulting in widespread "flying wire charging" (charging via extension cords). This disorderly charging method not only poses fire safety hazards but also injects highly random and unevenly distributed impact loads into the power distribution network within the supply area, seriously affecting power quality and equipment safety. To quantitatively assess the specific impact of such impact loads on the power supply area, regional load simulation methods are needed.

[0003] In traditional technologies, regional load simulation is mostly based on historical total load data, using time series analysis or machine learning methods for trend extrapolation. However, this macroscopic analysis method results in low accuracy of simulation results, which cannot support refined distribution network operation and maintenance, safe charging facility layout, and effective fire safety management within the power supply area. Summary of the Invention

[0004] Therefore, it is necessary to provide a charging simulation method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of charging simulation results in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a charging simulation method, including:

[0006] At each simulation moment within each simulation cycle, acquire the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to the simulation moment;

[0007] Based on the attribute information of each vehicle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, the first simulation object with charging simulation needs is determined from the charging object simulation objects corresponding to each of the new energy vehicles.

[0008] Based on the building attribute information of the building to which the first simulation object belongs, and the attribute information of each entity, a second simulation object is determined from the charging station simulation objects corresponding to each of the multiple charging stations.

[0009] In response to the charging simulation event of the second simulation object to the first simulation object, sub-simulation data of the power supply area at the simulation time is obtained;

[0010] Based on the sub-simulation data, the charging simulation data of the power supply area in the simulation cycle is obtained.

[0011] In one embodiment, determining the first simulation object with charging simulation needs from the charging object simulation objects corresponding to each of the new energy bicycles, based on the attribute information of each bicycle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, includes:

[0012] The remaining battery power of each new energy bicycle is obtained by parsing the attribute information of each bicycle.

[0013] From the plurality of new energy bicycles, select the first bicycle whose remaining battery power is less than a first threshold.

[0014] For each of the first bicycles, a basic probability factor of battery power matching the remaining battery power of the first bicycle is determined; the basic probability factor of battery power has a negative correlation with the remaining battery power.

[0015] Determine the time modulation factor that matches the simulation time period to which the simulation time belongs, and the intention modulation factor that matches the simulation weather information;

[0016] The charging demand probability of the first bicycle is determined based on the basic probability factor of the battery power corresponding to the first bicycle, as well as the time modulation factor and the willingness modulation factor.

[0017] Based on the charging demand probability of each of the first single vehicles, the first simulation object with charging simulation demand is determined from multiple charging object simulation objects.

[0018] In one embodiment, determining the charging demand probability of the first bicycle based on the basic probability factor of the battery level corresponding to the first bicycle, the time modulation factor, and the willingness modulation factor includes:

[0019] Determine the basic probability factor of the battery corresponding to the first bicycle, and the factor product between the time modulation factor and the intention modulation factor;

[0020] The probability of charging demand for the new energy vehicle is determined by multiplying the factors.

[0021] In one embodiment, determining the time modulation factor that matches the simulation time period to which the simulation time belongs includes:

[0022] When the simulation time belongs to a first preset time period, the preset maximum time modulation factor is determined to be the time modulation factor that matches the simulation time to which the simulation time belongs.

[0023] When the simulation time belongs to a second preset time period, the target time modulation factor is determined to be the time modulation factor that matches the simulation time to which the simulation time belongs; the target time modulation factor is less than the maximum time modulation factor, and the end time of the second preset time period is earlier than or equal to the start time of the first preset time period.

[0024] When the simulation time belongs to a third preset time period, a preset minimum time modulation factor is determined as the time modulation factor that matches the simulation time to which the simulation time belongs; the target time modulation factor is greater than the minimum time modulation factor, the start time of the third preset time period is later than or equal to the end time of the first preset time period, and the end time of the third preset time period is earlier than or equal to the start time of the second preset time period.

[0025] In one embodiment, the method further includes:

[0026] If, among the multiple new energy bicycles, there is a bicycle with remaining power less than the second threshold, and if there is no second simulation entity in the charging station simulation entity corresponding to each of the multiple charging stations, then the first simulation entity and the flying wire charging process between the first simulation entity and the building to which the first simulation entity belongs are simulated, and the charging simulation data is updated.

[0027] In one embodiment, determining the second simulation entity from the charging station simulation entities corresponding to each of the plurality of charging stations based on the building attribute information of the building to which the first simulation entity belongs and the attribute information of each entity includes:

[0028] The building attribute information of the building to which the first simulation object belongs is analyzed to obtain the object coordinates of the charging execution object corresponding to the first simulation object in the building to which the first simulation object belongs;

[0029] The attribute information of each entity is parsed to obtain the charging station coordinates of each charging station and the interface status of each of the multiple charging interfaces;

[0030] Using the object's coordinates as the center and a preset walking tolerance distance as the radius, determine the target coordinates located within the circle from multiple charging station coordinates;

[0031] If, among the multiple charging interfaces of the target charging station corresponding to the target coordinates, there is a target interface that is in an idle state, the charging station simulation object corresponding to the target charging station is determined as the second simulation object; the second simulation object performs charging simulation interaction with the first simulation object through the simulation interface corresponding to the target interface.

[0032] Secondly, this application provides a charging simulation device, the device comprising:

[0033] The acquisition module is used to acquire the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to each simulation time in each simulation cycle.

[0034] The first analysis module is used to determine the first simulation object with charging simulation needs from the charging object simulation objects corresponding to each of the new energy bicycles, based on the attribute information of each bicycle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time.

[0035] The second analysis module is used to determine the second simulation object from the charging station simulation objects corresponding to each of the multiple charging stations based on the building attribute information of the building to which the first simulation object belongs and the attribute information of each entity.

[0036] The simulation module is used to respond to a charging simulation event of the second simulation object to the first simulation object and obtain sub-simulation data of the power supply area at the simulation time.

[0037] The processing module is used to obtain the charging simulation data of the power supply area in the simulation cycle based on the sub-simulation data.

[0038] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0039] At each simulation moment within each simulation cycle, acquire the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to the simulation moment;

[0040] Based on the attribute information of each vehicle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, the first simulation object with charging simulation needs is determined from the charging object simulation objects corresponding to each of the new energy vehicles.

[0041] Based on the building attribute information of the building to which the first simulation object belongs, and the attribute information of each entity, a second simulation object is determined from the charging station simulation objects corresponding to each of the multiple charging stations.

[0042] In response to the charging simulation event of the second simulation object to the first simulation object, sub-simulation data of the power supply area at the simulation time is obtained;

[0043] Based on the sub-simulation data, the charging simulation data of the power supply area in the simulation cycle is obtained.

[0044] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0045] At each simulation moment within each simulation cycle, acquire the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to the simulation moment;

[0046] Based on the attribute information of each vehicle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, the first simulation object with charging simulation needs is determined from the charging object simulation objects corresponding to each of the new energy vehicles.

[0047] Based on the building attribute information of the building to which the first simulation object belongs, and the attribute information of each entity, a second simulation object is determined from the charging station simulation objects corresponding to each of the multiple charging stations.

[0048] In response to the charging simulation event of the second simulation object to the first simulation object, sub-simulation data of the power supply area at the simulation time is obtained;

[0049] Based on the sub-simulation data, the charging simulation data of the power supply area in the simulation cycle is obtained.

[0050] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0051] At each simulation moment within each simulation cycle, acquire the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to the simulation moment;

[0052] Based on the attribute information of each vehicle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, the first simulation object with charging simulation needs is determined from the charging object simulation objects corresponding to each of the new energy vehicles.

[0053] Based on the building attribute information of the building to which the first simulation object belongs, and the attribute information of each entity, a second simulation object is determined from the charging station simulation objects corresponding to each of the multiple charging stations.

[0054] In response to the charging simulation event of the second simulation object to the first simulation object, sub-simulation data of the power supply area at the simulation time is obtained;

[0055] Based on the sub-simulation data, the charging simulation data of the power supply area in the simulation cycle is obtained.

[0056] The aforementioned charging simulation method, apparatus, computer equipment, computer-readable storage medium, and computer program product, at each simulation moment within each simulation cycle, acquires the individual vehicle attribute information of multiple new energy bicycles and the entity attribute information of multiple charging stations within the power supply area corresponding to that simulation moment. This allows for the collection of real-time data at the individual level, replacing the reliance on historical total load data in traditional methods. This overcomes the limitation of traditional methods in distinguishing individual differences, laying a data foundation for improving simulation accuracy and contributing to the improvement of simulation results. By considering the attribute information of each bicycle, the simulation time period, and the simulation weather information of the power supply area at that moment, the first simulation entity with charging simulation needs is determined from the charging object simulation entities corresponding to each new energy bicycle. Thus, by combining the bicycle's own attributes with external influencing factors such as time period and weather, a refined judgment of the charging needs of each bicycle is achieved. This ensures that the charging demand judgment results at each simulation moment are close to the actual situation, rather than relying on trend extrapolation based on historical total load data as in traditional methods, thereby improving the accuracy of charging demand identification. By using the building attribute information and entity attribute information of the building to which the first simulation object belongs, a second simulation object is determined from the charging station simulation objects corresponding to multiple charging stations. Thus, by associating the building attribute information and the entity attribute information of the charging stations, a precise match between charging demand and charging facilities is achieved. This solves the problem that traditional methods cannot characterize spatial distribution differences, helps improve the realism of the spatiotemporal distribution simulation of charging load, and ensures that the simulation results can truly reflect the load differences on different buildings. This provides highly reliable simulation support for distribution network operation analysis and charging facility planning. By responding to the charging simulation events of the second simulation object to the first simulation object, sub-simulation data of the power supply area at that simulation moment is obtained, providing fine-grained data support for subsequent aggregation analysis. By using the sub-simulation data, the charging simulation data of the power supply area during the simulation period is obtained. Therefore, the obtained charging simulation results break through the limitation of traditional methods that treat the power supply area as a whole, enabling the simulation output to clearly reflect the charging load of different buildings within the power supply area, providing reliable data basis for refined distribution network operation and maintenance and safety management. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart illustrating a charging simulation method in one embodiment;

[0059] Figure 2 This is a flowchart illustrating the process of determining the first simulation object with charging simulation needs from the charging object simulation objects corresponding to each new energy bicycle, based on the attribute information of each bicycle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time.

[0060] Figure 3 This is a flowchart illustrating the process of determining the second simulation entity from the building attribute information of the building to which the first simulation entity belongs, as well as the attribute information of each entity, from the charging station simulation entities corresponding to multiple charging stations in one embodiment.

[0061] Figure 4 This is a flowchart illustrating the charging simulation method in another embodiment;

[0062] Figure 5 This is a flowchart illustrating the charging simulation method in another embodiment;

[0063] Figure 6 This is a structural block diagram of a charging simulation device in one embodiment;

[0064] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0065] 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.

[0066] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0067] In traditional technologies, regional load simulation is mostly based on historical total load data, using time series analysis or machine learning methods for trend extrapolation. However, this macroscopic analysis method treats the power supply area as a whole, failing to depict the spatial distribution differences of regional load within the area (such as different buildings or different phases), resulting in low accuracy of simulation results. Furthermore, for loads composed of a large number of discrete and highly random residential charging behaviors, macroscopic simulation methods struggle to accurately reflect their true spatiotemporal characteristics, and are even less able to identify the source and proportion of "legal charging" and "flying wire charging" loads.

[0068] In some cases, modeling and simulation of charging demand for electric vehicles or electric bicycles can improve the accuracy of simulation results. However, these models are often overly idealistic. Common assumptions include: charging demand occurs at fixed times (such as after work), charging decisions depend solely on battery capacity, and users always choose the nearest charging station. Therefore, these models fail to adequately consider the complex, multi-dimensional factors influencing user decisions, such as:

[0069] Geographic constraints: The user's actual tolerance for walking distance (e.g., 300 meters).

[0070] Time characteristics: differences in travel patterns between weekdays and weekends, and dynamic changes in charging willingness at different times of the day (e.g., low willingness in the early morning).

[0071] Environmental and climate impacts: The objective impact of temperature on battery performance (capacity, charging efficiency), and the subjective impact of weather (such as rainy days and hot days) on users' willingness to charge. In addition, traditional technologies often ignore the latter or assume a single-direction impact, which does not match the actual situation (for example, users may reduce going out to charge due to safety concerns on rainy days).

[0072] Resource competition: Queuing and substitution behaviors (switching to fly-wire charging) caused by the limited number of charging station interfaces in charging piles.

[0073] In practical applications, when planning the layout of charging stations or evaluating power distribution network upgrade schemes, there is a lack of simulation results that can accurately simulate the interaction process of "people-vehicle-charging pile-network". This makes it difficult for planners to foresee whether the construction of new charging stations will truly reduce the risk of illegal charging in specific buildings, or whether the three-phase imbalance of the load will be aggravated before the implementation of the scheme. This makes it impossible to build a "digital sand table" that corresponds one-to-one with the power supply area and integrates geographical information, facility resources and behavioral logic, thus making it impossible to conduct "hypothesis analysis" and scheme optimization.

[0074] In summary, traditional technologies for simulating charging loads in power supply areas oversimplify or aggregate the massive amounts of microscopic individual behaviors with complex spatiotemporal distributions and intelligent decision-making characteristics. This results in simulations that cannot support refined distribution network operation and maintenance, safe charging facility layout, and effective fire safety management. Therefore, to address the problems of insufficient spatial granularity, simplified behavioral models, and lack of high-fidelity simulation results in simulating the charging load of new energy vehicles in power supply areas using traditional technologies, there is an urgent need for a charging simulation method that can integrate multi-source data, simulate individual charging behaviors driven by multi-dimensional factors, and output high spatiotemporal resolution load distribution. This would enable charging load simulation from the overall power supply area level down to the building level and individual phases.

[0075] Based on the above analysis, this application provides a charging simulation method that can be applied to computer devices, which can be terminals or servers. For example, terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing cloud computing services.

[0076] In one exemplary embodiment, such as Figure 1 As shown, a charging simulation method is provided. Taking the application of this method to a computer device as an example, the method includes the following steps:

[0077] S10: At each simulation moment within each simulation cycle, acquire the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to the simulation moment.

[0078] The simulation cycle refers to the basic time step for discretizing the charging behavior of new energy vehicles within the power supply area. Each simulation cycle has a preset fixed duration, such as 15 minutes, 30 minutes, or other values. The simulation moment refers to the discrete sampling moment set within each simulation cycle. Specifically, each simulation cycle is divided into multiple time points according to a fixed sampling interval (such as 1 minute, 5 minutes, etc.) or a non-fixed sampling interval, and each time point is a simulation moment.

[0079] The power supply area refers to the geographical scope and service boundary of the power distribution network. Specifically, the power supply area can refer to the power supply area of ​​urban villages. Vehicle attribute information refers to data describing the physical state and charging behavior characteristics of the new energy vehicle itself, including but not limited to one or more of the following: battery capacity, current battery level, charging power, vehicle type, and service life. Entity attribute information refers to data describing the physical characteristics of the charging station itself, including but not limited to: charging station location, number of charging piles, charging interface type, total number of charging interfaces, and charging interface status. Charging interface status includes idle or non-idle.

[0080] In an optional embodiment, the computer device is communicatively connected to the information acquisition device. Specifically, the information acquisition device is used to collect the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to the simulation time, when the current time reaches the simulation time.

[0081] S20: Based on the attribute information of each vehicle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, determine the first simulation object with charging simulation needs from the charging object simulation objects corresponding to each new energy vehicle.

[0082] The simulation time period to which the simulation moment belongs refers to one of the time intervals to which the simulation moment belongs after dividing a day into at least two consecutive time intervals.

[0083] Simulated weather information refers to parameters used to characterize the weather conditions of the power supply area at the simulation time, including one or more of the following: weather type, temperature, precipitation, etc. For example, simulated weather information can refer to weather information actually collected at the simulation time, or it can refer to weather information preset for the simulation time.

[0084] A charging object simulation entity refers to a virtual entity object constructed for a new energy bicycle, used to simulate the bicycle's charging demand judgment, charging behavior decision-making, and load access process during the simulation. Each charging object simulation entity is associated with the bicycle's attribute information.

[0085] S30: Based on the building attribute information of the building to which the first simulation object belongs, and the attribute information of each entity, determine the second simulation object from the charging station simulation objects corresponding to each of the multiple charging stations.

[0086] The building attribute information refers to information related to the actual building to which the new energy bicycle corresponding to the first simulation object belongs, including but not limited to: building name and number, power supply phase (A / B / C phase) of the building, geographical coordinates of the building, total number of households in the building, total number of new energy bicycles in the building, and household information of the new energy bicycles. Household information refers to information related to the householder, such as the householder's name and identity information.

[0087] A charging station simulator is a virtual entity object constructed for a charging station to represent its service capabilities and real-time status. Each charging station simulator is associated with the entity attribute information of the charging station.

[0088] S40, in response to the charging simulation event of the second simulation object to the first simulation object, obtain the sub-simulation data of the power supply area at the simulation time.

[0089] In an optional embodiment, the method further includes: simulating a charging process between the second simulation object and the first simulation object according to a preset charging time. The preset charging time is determined based on the simulated ambient temperature.

[0090] Sub-simulation data refers to simulation output results related to the charging simulation event. For example, sub-simulation data includes, but is not limited to: the building charging load corresponding to each building within the power supply area, and the first total charging load within the power supply area. Specifically, the building charging load is the sum of the power generated by the first simulator (simulated charging within the building and the second simulator) during the simulated charging process, and the first total charging load is the sum of the building charging loads of all buildings.

[0091] S50, based on the sub-simulation data, obtains the charging simulation data of the power supply area during the simulation cycle.

[0092] Among them, charging simulation data refers to the simulation results obtained from the analysis of all sub-simulation data within the simulation period, which are used to reflect the overall charging load status of the power supply area. Charging simulation data includes each sub-simulation data, as well as the first analysis data obtained from the analysis of each sub-simulation data (such as the building charging load curve of each building, the first total load curve, etc.).

[0093] In an optional embodiment, the sub-simulation data includes the building charging load of each building within the power supply area, and the charging simulation data includes the building charging load curve of each building. Specifically, based on the sub-simulation data, charging simulation data for the power supply area is obtained, including: for each building, curve fitting is performed on the building charging load at each simulation time to obtain the building charging load curve.

[0094] In an optional embodiment, the sub-simulation data includes the charging load of each building within the power supply area, and the charging simulation data includes a first total load curve. Specifically, based on each sub-simulation data, charging simulation data for the power supply area is obtained, including: statistically analyzing the charging load of each building at each simulation time to obtain the total building load; and performing curve fitting on the total load of each building according to the time sequence of the simulation time to obtain the first total load curve.

[0095] In an optional embodiment, charging simulation data for the power supply area is obtained based on the sub-simulation data, including: charging station utilization rate, three-phase imbalance, and distribution map of hotspots for illegal charging via overhead wires, based on the sub-simulation data and / or the total load curve of the area. The charging simulation data includes charging station utilization rate, three-phase imbalance, and distribution map of hotspots for illegal charging via overhead wires.

[0096] In an optional embodiment, the method further includes: generating a charging simulation log containing information such as event time, building, charging load, and charging load curve based on the charging simulation event corresponding to each first simulation object in multiple simulation cycles.

[0097] Using the method described in the above embodiments, at each simulation moment within each simulation cycle, the individual attribute information of multiple new energy bicycles and the entity attribute information of multiple charging stations within the power supply area corresponding to that simulation moment are acquired. This allows for the collection of real-time data at the individual level, replacing the reliance on historical total load data in traditional methods. This overcomes the limitation of traditional methods in distinguishing individual differences, laying a data foundation for improving simulation accuracy and contributing to the improvement of simulation results. By considering the attribute information of each bicycle, the simulation time period to which the simulation moment belongs, and the simulation weather information of the power supply area at that simulation moment, the first simulation entity with charging simulation needs is determined from the charging object simulation entities corresponding to each new energy bicycle. Thus, by combining the bicycle's own attributes with external influencing factors such as time period and weather, a refined judgment of the charging needs of each bicycle is achieved. This makes the charging demand judgment results at each simulation moment closer to the actual situation, rather than relying on trend extrapolation based on historical total load data in traditional methods, thereby improving the accuracy of charging demand identification. By using the building attribute information and entity attribute information of the building to which the first simulation object belongs, a second simulation object is determined from the charging station simulation objects corresponding to multiple charging stations. Thus, by associating the building attribute information and the entity attribute information of the charging stations, a precise match between charging demand and charging facilities is achieved. This solves the problem that traditional methods cannot characterize spatial distribution differences, helps improve the realism of the spatiotemporal distribution simulation of charging load, and ensures that the simulation results can truly reflect the load differences on different buildings. This provides highly reliable simulation support for distribution network operation analysis and charging facility planning. By responding to the charging simulation events of the second simulation object to the first simulation object, sub-simulation data of the power supply area at that simulation moment is obtained, providing fine-grained data support for subsequent aggregation analysis. By using the sub-simulation data, the charging simulation data of the power supply area during the simulation period is obtained. Therefore, the obtained charging simulation results break through the limitation of traditional methods that treat the power supply area as a whole, enabling the simulation output to clearly reflect the charging load of different buildings within the power supply area, providing reliable data basis for refined distribution network operation and maintenance and safety management.

[0098] In one embodiment, such as Figure 2 As shown, based on the attribute information of each bicycle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, the first simulation object with charging simulation needs is determined from the charging object simulation objects corresponding to each new energy bicycle, including the following steps:

[0099] S201, analyze the attribute information of each bicycle to obtain the remaining power of each new energy bicycle.

[0100] S202: Select the first electric vehicle with a remaining battery power less than a first threshold from multiple new energy electric vehicles.

[0101] S203, for each first vehicle, determine the basic probability factor of the remaining power of the first vehicle; the basic probability factor of the remaining power has a negative correlation with the remaining power.

[0102] The basic probability factor of battery power is a quantitative parameter used to characterize the likelihood of a new energy vehicle needing to charge due to its current remaining battery power. Its value is negatively correlated with the remaining battery power. In other words, the lower the remaining battery power, the higher the value of the basic probability factor of battery power, and the greater the likelihood of the new energy vehicle needing to charge; conversely, the higher the remaining battery power, the lower the value of the basic probability factor of battery power, and the lower the likelihood of the new energy vehicle needing to charge.

[0103] S204, determine the time modulation factor that matches the simulation time period to which the simulation time belongs, and the intention modulation factor that matches the simulation weather information.

[0104] The time modulation factor refers to a coefficient preset according to the simulation time period to which the simulation time belongs.

[0105] In an optional embodiment, the computer device is communicatively connected to the charging simulation platform. Determining the time modulation factor matching the simulation time period includes: in response to an input operation on the charging simulation platform regarding the time modulation factor, obtaining the time modulation factor matching the simulation time period. Therefore, this method allows users to dynamically input the time modulation factor based on actual observation data or empirical knowledge, enabling the simulation process to more accurately reflect the actual charging behavior patterns in different power supply areas, thus improving the accuracy and reliability of the charging simulation.

[0106] In an optional embodiment, determining the time modulation factor matching the simulation period to which the simulation time belongs includes: when the simulation period to which the simulation time belongs is a first preset period, determining a preset maximum time modulation factor as the time modulation factor matching the simulation period to which the simulation time belongs; when the simulation period to which the simulation time belongs is a second preset period, determining a target time modulation factor as the time modulation factor matching the simulation period to which the simulation time belongs; the target time modulation factor is less than the maximum time modulation factor, and the end time of the second preset period is earlier than or equal to the start time of the first preset period; when the simulation period to which the simulation time belongs is a third preset period, determining a preset minimum time modulation factor as the time modulation factor matching the simulation period to which the simulation time belongs; the target time modulation factor is greater than the minimum time modulation factor, and the start time of the third preset period is later than or equal to the end time of the first preset period, and the end time of the third preset period is earlier than or equal to the start time of the second preset period.

[0107] The first preset time period refers to the period when users' charging needs are most concentrated. For example, the first preset time period refers to the evening period after users return home from get off work, such as 18:00 to 23:00.

[0108] The second preset time period refers to the period during which the user's charging demand is at a moderate level. For example, the second preset time period refers to the user's daytime work hours or outdoor activity hours, such as 06:00 to 18:00.

[0109] The third preset time period refers to the time period when users' charging demand is lowest. For example, the third preset time period refers to the user's rest period from late at night to early morning, such as 23:00 on the same day to 6:00 the next day.

[0110] In the above embodiments, the segmented configuration method allows the time modulation factor to dynamically change according to the different time periods of the simulation, achieving differentiated configuration of the time modulation factor for different simulation times. This accurately reflects the significant differences in residents' charging behavior across different time periods. This segmented differentiated configuration method makes the simulation process closer to the real-world pattern of charging demand changing over time, significantly improving the accuracy and rationality of the charging simulation.

[0111] The willingness modulation factor is used to characterize the degree to which different weather conditions affect the charging willingness of new energy bicycle users. Specifically, determining the willingness modulation factor that matches the simulated weather information can be achieved in the following exemplary ways:

[0112] In an optional embodiment, the simulated weather information includes weather type. Determining the desired modulation factor that matches the simulated weather information includes: when the weather type represents rainy days, determining values ​​smaller than a first baseline desired modulation factor as desired modulation factors that match the simulated weather information. The first baseline desired modulation factor refers to the desired modulation factor set for a sunny weather type.

[0113] In an optional embodiment, the simulated weather information includes weather type. Determining the desired modulation factor that matches the simulated weather information includes: when the weather type represents high temperature weather, determining values ​​smaller than a second baseline desired modulation factor as desired modulation factors that match the simulated weather information. The second baseline desired modulation factor refers to the desired modulation factor set for a cloudy weather type.

[0114] Understandably, in rainy or extremely hot weather conditions, by setting the corresponding willingness modulation factor to be lower than the corresponding baseline value (e.g., sunny / cloudy conditions), it can be used to characterize the user's reduced willingness to go out and charge due to safety concerns or discomfort.

[0115] By using the method described in the above embodiments, corresponding willingness modulation factors are set for different weather types, enabling the simulation process to quantify the impact of weather factors on users' charging willingness. This avoids simulation bias caused by ignoring weather factors, thereby improving the accuracy of charging simulation under different weather conditions and helping to improve the accuracy of simulation results.

[0116] S205, based on the basic probability factor of the battery level corresponding to the first vehicle, as well as the time modulation factor and the willingness modulation factor, determine the charging demand probability of the first vehicle.

[0117] Among them, the charging demand probability refers to the quantitative value calculated by combining the basic probability factor of the first vehicle's power, the time modulation factor, and the willingness modulation factor, which is used to characterize the likelihood of the first vehicle generating a charging demand at the current simulation moment.

[0118] In an optional embodiment, the charging demand probability of each vehicle is determined based on the basic probability factor of the vehicle's remaining battery power, as well as the time modulation factor and the willingness modulation factor. This includes determining the factor product between the basic probability factor of the vehicle's remaining battery power, the time modulation factor, and the willingness modulation factor; and multiplying the factors to determine the charging demand probability of the vehicle. Thus, by multiplying and fusing the basic probability factor of the vehicle's remaining battery power, the time modulation factor reflecting the characteristics of the time period, and the willingness modulation factor reflecting the influence of weather, the charging demand probability can comprehensively characterize the combined effect of battery status, time patterns, and weather conditions on the user's charging decision. This multiplication method is simple and efficient, and the factors are independent of each other. Changes in any factor are sensitively reflected in the final probability, avoiding judgment bias caused by a single factor dominating or coupling between factors. This makes the charging demand determination result more comprehensive and reasonable, closely reflecting real user behavior, and helps improve the accuracy and rationality of charging simulation results.

[0119] S206. Based on the charging demand probability of each first vehicle, determine the first simulation object with charging simulation demand from multiple charging object simulation objects.

[0120] The number of first simulation entities is at least one. Specifically, based on the charging demand probability of each first vehicle, the first simulation entity with charging simulation demand is determined from multiple charging object simulation entities. This can be done in the following exemplary manner:

[0121] In an optional embodiment, based on the charging demand probability of each first bicycle, the first simulation object with charging simulation demand is determined from multiple charging object simulation objects, including: determining the charging object simulation object corresponding to the first bicycle with the highest charging demand probability among the multiple charging object simulation objects as the first simulation object with charging simulation demand.

[0122] In an optional embodiment, based on the charging demand probability of each first bicycle, a first simulation object with charging simulation demand is determined from multiple charging object simulation objects, including: if the charging demand probability of the first bicycle is greater than or equal to a preset threshold, the charging object simulation object corresponding to the first bicycle is determined as the first simulation object with charging simulation demand.

[0123] By employing the method described in the above embodiments, new energy bicycles with potential charging needs are screened based on their remaining battery power. This avoids performing ineffective simulations on all new energy bicycles, helping to reduce the computational overhead and resource consumption of the charging simulation platform, and reducing redundant charging demand judgment operations. This improves overall simulation efficiency while ensuring simulation accuracy. By establishing a negative correlation between the basic probability factor of battery power and the remaining battery power, bicycles with lower battery power receive a higher basic charging probability. This accurately reflects the objective law that users generate charging needs due to insufficient battery power, helping to improve the logical realism and accuracy of charging demand judgment. It avoids misjudgments or omissions caused by ignoring battery power status, enabling the simulation process to accurately identify bicycles with genuine charging needs. This provides a reliable input basis for subsequent charging load simulations, contributing to improved accuracy of simulation results. By introducing a time modulation factor to reflect the charging behavior patterns at different times and a willingness modulation factor to quantify the impact of weather on charging willingness, charging demand judgment considers both time and environmental factors. This helps improve the multidimensional comprehensiveness and scenario adaptability of charging demand simulation, avoiding simulation bias caused by relying solely on battery status while ignoring external factors. The simulation process can more realistically reproduce users' actual charging decision-making behavior under different times and weather conditions, thereby enhancing the credibility and practical value of the simulation results. By multiplying the basic probability factor of battery status, the time modulation factor, and the willingness modulation factor, the combined influence of battery status, time period characteristics, and weather conditions on charging decisions is comprehensively quantified, resulting in a comprehensive and reasonable charging demand probability. This helps improve the comprehensiveness and accuracy of charging demand judgment, avoiding judgment bias caused by a single factor dominating or coupling between factors. It allows various influencing factors to work synergistically to the final probability result, realistically reflecting users' actual charging decision-making behavior under multiple conditions, thus providing a reliable probabilistic basis for subsequent charging simulation event triggering and improving the accuracy of simulation results. By screening simulation subjects with charging simulation needs based on the calculated charging demand probability, it ensures that vehicles meeting the probability conditions are included in the subsequent charging simulation process, improving the relevance and accuracy of the simulation.

[0124] In one embodiment, the method further includes: when there are multiple new energy bicycles with remaining battery power less than a second threshold, if there is no second simulation entity in the charging station simulation entity corresponding to each of the multiple charging stations, then simulating the first simulation entity and the flying wire charging process between the first simulation entity and the building to which the first simulation entity belongs, and updating the charging simulation data. The second threshold is less than the first threshold; for example, the first threshold is 40% and the second threshold is 18%, or it can be set to other values.

[0125] In an optional embodiment, the method further includes: during the flying wire charging simulation process, setting the flying wire charging power to be constant until the new energy vehicle corresponding to the simulated first simulation object is in a fully charged state.

[0126] The updated charging simulation data includes sub-simulation data, first analysis data derived from the sub-simulation data (such as the building charging load curve and the first total load curve for each building), the fly-wire charging load for each building within the power supply area, second analysis data derived from the fly-wire charging load analysis (such as the building fly-wire load curve and the second total load curve for each building), and the total regional charging load. The total regional charging load is the sum of the first and second total charging loads, and the second total charging load is the sum of the fly-wire charging loads of each building. The fly-wire charging load of a building is the product of the number of simulated fly-wire charging units within the building and the rated power of a single vehicle.

[0127] In an optional embodiment, for each building, the flying wire charging load of the building at each simulation time is curve fitted to obtain the building flying wire load curve.

[0128] In an optional embodiment, the building charging load and the fly-wire charging load corresponding to each building at each simulation time are statistically analyzed to obtain the statistical load of the building at each simulation time; according to the time sequence of the simulation time, the statistical load of each building is curve-fitted to obtain the second total load curve.

[0129] By adopting the method of the above embodiments, a flying wire charging simulation mechanism is introduced to simulate the real-world behavior of users choosing non-standard charging methods when regular charging stations are unavailable or too far away. This allows the simulation process to cover both "legal charging" and "flying wire charging" scenarios, avoiding the loss of load data due to ignoring flying wire charging. This helps to accurately depict the spatiotemporal distribution characteristics of flying wire charging loads in the power supply area and their impact on the distribution network, providing a quantitative simulation basis for subsequent flying wire charging management and safety hazard investigation.

[0130] In one embodiment, such as Figure 3As shown, based on the building attribute information of the building to which the first simulation object belongs, and the attribute information of each entity, the second simulation object is determined from the charging station simulation objects corresponding to multiple charging stations, including the following steps:

[0131] S301, parse the building attribute information of the building to which the first simulation object belongs, and obtain the object coordinates of the charging execution object corresponding to the first simulation object in the building to which the first simulation object belongs.

[0132] Here, the charging execution object refers to the user who performs the actual charging behavior on the new energy bicycle corresponding to the first simulation object. The object coordinates refer to the spatial location of the charging execution object within the building, used to characterize the specific location of the house where the charging execution takes place.

[0133] S302, parse the attribute information of each entity to obtain the charging station coordinates of each charging station and the interface status of each of the multiple charging interfaces.

[0134] S303 uses the object's coordinates as the center and the preset walking tolerance distance as the radius to determine the target coordinates within the circle from multiple charging station coordinates.

[0135] The preset walking tolerance distance refers to the maximum acceptable distance that the charging device can walk from its house location to the charging station.

[0136] S304, if among the multiple charging interfaces of the target charging station corresponding to the target coordinates, there is a target interface with an idle state, the charging station simulation object corresponding to the target charging station is determined as the second simulation object; the second simulation object performs charging simulation interaction with the first simulation object through the simulation interface corresponding to the target interface.

[0137] By employing the method described in the above embodiments, the specific building location coordinates of the charging execution object within the building are accurately obtained by parsing the building attribute information of the building to which the first simulation object belongs. This provides a precise spatial positioning benchmark for subsequent charging station accessibility analysis, ensuring the accuracy of the simulation spatial dimension. This helps improve the spatial accuracy of charging station matching and the real reliability of simulation results, avoiding errors in charging station accessibility judgment due to missing or incorrect location information. It also accurately identifies available charging facilities within the user's walking range, providing a reliable spatial basis for distinguishing between legal charging and illegal charging. By parsing the entity attribute information of each charging station, the geographical coordinates of each charging station and the real-time occupancy status of each charging interface are obtained. This provides a complete data foundation for charging station matching and interface allocation, supporting subsequent charging station screening and availability judgment. This helps improve the accuracy of charging station matching and the rationality of charging resource allocation, avoiding matching unavailable or occupied charging interfaces due to a lack of charging station location or interface status information. It ensures that each charging demand during the simulation process accurately corresponds to available charging resources, thereby improving the success rate and credibility of charging simulation events. By constructing a circular search area centered on the user's house location and with a preset walking tolerance distance as the radius, charging stations within the user's acceptable walking range are selected. This simulates the user's actual behavior of choosing the nearest charging station for convenience, avoiding simulation distortion caused by matching charging stations that are too far away. This helps improve the realism of charging station matching and the credibility of user behavior simulation, making the simulation results closer to the user's objective requirements for charging convenience in real-world scenarios. At the same time, it reduces false judgments of "flying wire charging" triggered by matching unreachable charging stations, thereby improving the rationality and accuracy of the overall simulation logic. By identifying an idle interface among the multiple charging ports of the target charging station as the second simulation entity, and then conducting charging simulation interaction with the first simulation entity through the idle interface, this step ensures the feasibility of charging matching, avoids invalid simulation events caused by overload or interface occupancy, ensures the reasonable and orderly charging simulation process, helps improve the simulation realism of charging resource allocation and the success rate of event execution, avoids load data distortion caused by forcibly triggering charging events due to interface state mismatch, and enables the simulation process to accurately reflect the impact of the actual service capacity of the charging station on user charging behavior, providing reliable event-level data support for subsequent load statistics.

[0138] In summary, this application can construct a digital sandbox simulation environment corresponding to the power supply area on the charging simulation platform. The digital sandbox simulation environment includes multiple charging object simulation objects corresponding to each new energy bicycle, multiple charging station simulation objects corresponding to each charging station, and multiple charging interfaces corresponding to each charging interface in each charging station. Therefore, based on the constructed digital sandbox simulation environment, the charging simulation of the power supply area can be realized using the method provided in this application, thereby enabling pre-quantitative evaluation and optimization of planning schemes such as power distribution network transformation and charging facility layout.

[0139] In summary, such as Figure 4 As shown, a schematic diagram of a charging simulation method is provided, based on Figure 5 The content shown is as follows: Figure 5 As shown, a charging simulation method is provided. Taking the application of this method to a computer device as an example, the method includes the following steps:

[0140] S501 acquires the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to each simulation time in each simulation cycle.

[0141] S502 parses the attribute information of each bicycle to obtain the remaining power of each new energy bicycle.

[0142] S503 selects the first electric bicycle with a remaining battery level less than a first threshold from multiple electric bicycles.

[0143] S504, for each first vehicle, determine the basic probability factor of the remaining power of the first vehicle; the basic probability factor of the remaining power has a negative correlation with the remaining power.

[0144] S505, determine the time modulation factor that matches the simulation time period to which the simulation time belongs, and the intention modulation factor that matches the simulation weather information.

[0145] S506, based on the basic probability factor of the battery level corresponding to the first vehicle, as well as the time modulation factor and the willingness modulation factor, determine the charging demand probability of the first vehicle.

[0146] S507: Based on the charging demand probability of each first vehicle, determine the first simulation object with charging simulation demand from multiple charging object simulation objects.

[0147] S508, based on the building attribute information of the building to which the first simulation object belongs, and the attribute information of each entity, determine the second simulation object from the charging station simulation objects corresponding to each of the multiple charging stations.

[0148] S509, in response to the charging simulation event of the second simulation object to the first simulation object, obtain the sub-simulation data of the power supply area at the simulation time.

[0149] S510, based on the sub-simulation data, obtains the charging simulation data of the power supply area during the simulation cycle.

[0150] The specific contents of S501 to S510 can be found in the aforementioned description and will not be repeated here.

[0151] In summary, this application achieves accurate prediction and source tracing of charging load at the building, phase, and type (legal / illegal) levels through a micro-modeling method of "one file per building, one policy per vehicle," solving the problem of insufficient spatial resolution in traditional macro-prediction methods. The prediction results can be directly used to guide precise operation and maintenance. Furthermore, it innovatively couples and models multiple dynamic factors such as geographical accessibility, time-of-use behavioral preferences, the objective impact of climate on equipment, and the subjective impact of weather on users. In particular, it clarifies the behavioral logic that users' charging intentions may be suppressed under severe weather conditions such as rain and high temperatures, making the model more consistent with real-world human decision-making psychology and improving simulation reliability. Furthermore, the constructed "digital sandbox" is essentially a computable digital twin of a distribution substation. It allows managers to conduct various "what-if" analyses at extremely low cost before physical system modifications. For example, it can quantitatively assess the effect of adding charging stations on reducing the risk of illegal charging in specific areas; and simulate the effectiveness of different orderly charging strategies in smoothing load curves and improving three-phase balance. This provides a scientific and quantitative basis for distribution network planning, facility layout, and operational strategy formulation. Therefore, the method provided in this application is modular, facilitating the integration of richer real-world data (such as IoT-collected data) for model calibration. It can also be extended to modeling other distributed resources such as electric vehicles and distributed energy storage, laying a core foundation for building a more complete digital twin platform for urban distribution substations oriented towards new power systems.

[0152] Furthermore, this application uses actual data for simulation, rather than relying on trend extrapolation or theoretical assumptions based on historical total load data. Specifically, this application acquires, in real-time or periodically, the attribute information of each new energy vehicle within the power supply area (such as battery capacity, current remaining power, and charging power), the attribute information of each building (such as building location, power supply phase, and number of households), and the entity attribute information of each charging station (such as charging station coordinates, number of charging piles, and interface occupancy status) through information acquisition equipment. This actual collected data is used as the input parameters of the simulation model. Based on this, at each simulation moment within each simulation cycle, the charging demand of each vehicle is dynamically determined based on the actual data, available charging stations are matched, charging events are simulated, and sub-simulation data is recorded, ultimately outputting periodic-level charging simulation data. Since the data used in the simulation process comes from real systems or collectable measured data, the simulation results can objectively reflect the actual charging behavior characteristics of new energy vehicles in the power supply area, the spatiotemporal distribution pattern of load, and the composition ratio of legal charging and illegal charging. This avoids simulation deviations caused by using theoretical assumptions or aggregating historical data, and provides a real and reliable quantitative basis for power distribution network operation and maintenance, charging facility planning, and fire safety management.

[0153] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0154] Based on the same inventive concept, this application also provides a charging simulation device for implementing the charging simulation method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more charging simulation device embodiments provided below can be found in the limitations of the charging simulation method described above, and will not be repeated here.

[0155] In one exemplary embodiment, such as Figure 6As shown, a charging simulation device is provided, including: an acquisition module 601, a first analysis module 602, a second analysis module 603, a simulation module 604, and a processing module 605, wherein:

[0156] The acquisition module 601 is used to acquire, at each simulation moment within each simulation cycle, the individual vehicle attribute information of multiple new energy bicycles and the entity attribute information of multiple charging stations within the power supply area corresponding to the simulation moment; the first analysis module 602 is used to determine, based on the individual vehicle attribute information, the simulation time period to which the simulation moment belongs, and the simulation weather information of the power supply area at the simulation moment, a first simulation entity with charging simulation needs from the charging object simulation entities corresponding to each of the new energy bicycles; the second analysis module 603 is used to determine, based on the building attribute information of the building to which the first simulation entity belongs and the entity attribute information, a second simulation entity from the charging station simulation entities corresponding to each of the multiple charging stations; the simulation module 604 is used to obtain sub-simulation data of the power supply area at the simulation moment in response to the charging simulation event of the second simulation entity to the first simulation entity; and the processing module 605 is used to obtain the charging simulation data of the power supply area in the simulation cycle based on the sub-simulation data.

[0157] In one embodiment, the first analysis module 602 is further configured to: parse the attribute information of each of the bicycles to obtain the remaining power of each of the new energy bicycles; select a first bicycle from the plurality of new energy bicycles whose remaining power is less than a first threshold; for each first bicycle, determine a basic power probability factor that matches the remaining power of the first bicycle; the basic power probability factor has a negative correlation with the remaining power; determine a time modulation factor that matches the simulation period to which the simulation time belongs, and a willingness modulation factor that matches the simulation weather information; determine the charging demand probability of the first bicycle based on the basic power probability factor corresponding to the first bicycle, as well as the time modulation factor and the willingness modulation factor; and determine a first simulation object with charging simulation demand from the plurality of charging object simulation objects based on the charging demand probability of each of the first bicycles.

[0158] In one embodiment, the first analysis module 602 is further configured to: determine the basic probability factor of the battery power corresponding to the first bicycle, and the factor product between the factor product and the time modulation factor and the willingness modulation factor; and determine the charging demand probability of the new energy bicycle by multiplying the factor product.

[0159] In one embodiment, the first analysis module 602 is further configured to: when the simulation time period to which the simulation time belongs is a first preset time period, determine a preset maximum time modulation factor as a time modulation factor matching the simulation time period to which the simulation time belongs; when the simulation time period to which the simulation time belongs is a second preset time period, determine a target time modulation factor as a time modulation factor matching the simulation time period to which the simulation time belongs; the target time modulation factor is less than the maximum time modulation factor, and the end time of the second preset time period is earlier than or equal to the start time of the first preset time period; when the simulation time period to which the simulation time belongs is a third preset time period, determine a preset minimum time modulation factor as a time modulation factor matching the simulation time period to which the simulation time belongs; the target time modulation factor is greater than the minimum time modulation factor, the start time of the third preset time period is later than or equal to the end time of the first preset time period, and the end time of the third preset time period is earlier than or equal to the start time of the second preset time period.

[0160] In one embodiment, the processing module 605 is further configured to: when there is a bicycle among the plurality of new energy bicycles with a remaining power of less than a second threshold, if there is no second simulation body in the charging station simulation body corresponding to each of the plurality of charging stations, then simulate the first simulation body and the flying wire charging process between the first simulation body and the building to which the first simulation body belongs, and update the charging simulation data.

[0161] In one embodiment, the second analysis module 603 is further configured to: parse the building attribute information of the building to which the first simulation object belongs, and obtain the object coordinates of the charging execution object corresponding to the first simulation object in the building to which the first simulation object belongs; parse the attribute information of each entity, and obtain the charging station coordinates of each charging station and the interface status of each of the multiple charging interfaces; determine the target coordinates located within the circle from the multiple charging station coordinates with the object coordinates as the center and a preset walking tolerance distance as the radius; if there is a target interface with an idle state among the multiple charging interfaces of the target charging station corresponding to the target coordinates, determine the charging station simulation object corresponding to the target charging station as the second simulation object; the second simulation object performs charging simulation interaction with the first simulation object through the simulation interface corresponding to the target interface.

[0162] Each module in the aforementioned charging simulation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0163] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a charging simulation method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0164] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0165] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0166] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0167] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0168] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0169] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0170] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0171] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A charging simulation method, characterized in that, The method includes: At each simulation moment within each simulation cycle, acquire the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to the simulation moment; Based on the attribute information of each vehicle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, the first simulation object with charging simulation needs is determined from the charging object simulation objects corresponding to each of the new energy vehicles. Based on the building attribute information of the building to which the first simulation object belongs, and the attribute information of each entity, a second simulation object is determined from the charging station simulation objects corresponding to each of the multiple charging stations. In response to the charging simulation event of the second simulation object to the first simulation object, sub-simulation data of the power supply area at the simulation time is obtained; Based on the sub-simulation data, the charging simulation data of the power supply area in the simulation cycle is obtained.

2. The method according to claim 1, characterized in that, The step of determining the first simulation object with charging simulation needs from the charging object simulation objects corresponding to each of the new energy bicycles, based on the attribute information of each bicycle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time, includes: The remaining battery power of each new energy bicycle is obtained by parsing the attribute information of each bicycle. From the plurality of new energy bicycles, select the first bicycle whose remaining battery power is less than a first threshold. For each of the first bicycles, a basic probability factor of battery power matching the remaining battery power of the first bicycle is determined; the basic probability factor of battery power has a negative correlation with the remaining battery power. Determine the time modulation factor that matches the simulation time period to which the simulation time belongs, and the intention modulation factor that matches the simulation weather information; The charging demand probability of the first bicycle is determined based on the basic probability factor of the battery power corresponding to the first bicycle, as well as the time modulation factor and the willingness modulation factor. Based on the charging demand probability of each of the first single vehicles, the first simulation object with charging simulation demand is determined from multiple charging object simulation objects.

3. The method according to claim 2, characterized in that, The step of determining the charging demand probability of the first bicycle based on the basic probability factor of the battery level corresponding to the first bicycle, the time modulation factor, and the willingness modulation factor includes: Determine the basic probability factor of the battery corresponding to the first bicycle, and the factor product between the time modulation factor and the intention modulation factor; The probability of charging demand for the new energy vehicle is determined by multiplying the factors.

4. The method according to claim 2, characterized in that, The determination of the time modulation factor matching the simulation time period to which the simulation time belongs includes: When the simulation time period to which the simulation time belongs is a first preset time period, the preset maximum time modulation factor is determined to be the time modulation factor that matches the simulation time period to which the simulation time belongs. When the simulation time belongs to a second preset time period, the target time modulation factor is determined to be the time modulation factor that matches the simulation time to which the simulation time belongs; the target time modulation factor is less than the maximum time modulation factor, and the end time of the second preset time period is earlier than or equal to the start time of the first preset time period. When the simulation time belongs to a third preset time period, a preset minimum time modulation factor is determined as the time modulation factor that matches the simulation time to which the simulation time belongs; the target time modulation factor is greater than the minimum time modulation factor, the start time of the third preset time period is later than or equal to the end time of the first preset time period, and the end time of the third preset time period is earlier than or equal to the start time of the second preset time period.

5. The method according to claim 2, characterized in that, The method further includes: If, among the multiple new energy bicycles, there is a bicycle with remaining power less than the second threshold, and if there is no second simulation entity in the charging station simulation entity corresponding to each of the multiple charging stations, then the first simulation entity and the flying wire charging process between the first simulation entity and the building to which the first simulation entity belongs are simulated, and the charging simulation data is updated.

6. The method according to any one of claims 1 to 5, characterized in that, The step of determining the second simulation entity from the charging station simulation entities corresponding to each of the plurality of charging stations based on the building attribute information of the building to which the first simulation entity belongs and the attribute information of each entity includes: The building attribute information of the building to which the first simulation object belongs is analyzed to obtain the object coordinates of the charging execution object corresponding to the first simulation object in the building to which the first simulation object belongs; The attribute information of each entity is parsed to obtain the charging station coordinates of each charging station and the interface status of each of the multiple charging interfaces; Using the object's coordinates as the center and a preset walking tolerance distance as the radius, the target coordinates located within the circle are determined from multiple charging station coordinates. If, among the multiple charging interfaces of the target charging station corresponding to the target coordinates, there is a target interface that is in an idle state, the charging station simulation object corresponding to the target charging station is determined as the second simulation object; the second simulation object performs charging simulation interaction with the first simulation object through the simulation interface corresponding to the target interface.

7. A charging simulation device, characterized in that, The device includes: The acquisition module is used to acquire the individual vehicle attribute information of multiple new energy bicycles and the individual entity attribute information of multiple charging stations within the power supply area corresponding to each simulation time in each simulation cycle. The first analysis module is used to determine the first simulation object with charging simulation needs from the charging object simulation objects corresponding to each of the new energy bicycles, based on the attribute information of each bicycle, the simulation time period to which the simulation time belongs, and the simulation weather information of the power supply area at the simulation time. The second analysis module is used to determine the second simulation object from the charging station simulation objects corresponding to each of the multiple charging stations based on the building attribute information of the building to which the first simulation object belongs and the attribute information of each entity. The simulation module is used to respond to a charging simulation event of the second simulation object to the first simulation object and obtain sub-simulation data of the power supply area at the simulation time. The processing module is used to obtain the charging simulation data of the power supply area in the simulation cycle based on the sub-simulation data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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