Highway energy management method, device, equipment and medium based on photovoltaic storage and charging

Abstract

The present application relates to the field of electric energy storage control technology, and provides a highway energy management method, device, equipment and medium based on photoelectric storage and charging. The method includes: obtaining historical traffic flow data and historical energy consumption data of 5G communication base stations in a target highway section; predicting the installation quantity of photovoltaic power generation equipment based on historical traffic flow data and historical energy consumption data of 5G communication base stations; obtaining real-time power generation data of installed photovoltaic power generation equipment, real-time power storage data of power storage equipment, first energy consumption data of vehicle charging equipment and second energy consumption data of 5G communication base stations; making priority predictions on the power demand of vehicle charging equipment and 5G communication base stations based on the real-time power generation data, real-time power storage data, first energy consumption data and second energy consumption data to obtain priority prediction results; based on the priority prediction results, efficiently coordinating the use of photovoltaic power generation, energy storage, vehicle charging facilities and 5G communication base stations.

Description

Technical Field

[0001] The present application relates to the field of electric energy storage control technology, and more specifically, to a highway energy management method, device, equipment and medium based on photovoltaic storage and charging. Background Art

[0002] With the popularization of new energy technologies and electric vehicles, energy demand along highways has increased significantly, especially the large-scale deployment of 5G (fifth-generation mobile communication technology) communication base stations has further increased the electricity load.

[0003] The photovoltaic storage and charging system is a comprehensive energy management solution system that integrates photovoltaic power generation, energy storage and electric vehicle charging. At present, although some highway service areas have begun to introduce photovoltaic storage and charging systems, the application scale of these systems is limited and lacks intelligent monitoring and scheduling methods. In addition, the centralized charging of a large number of electric vehicles may cause excessive load on the local power grid, affecting the stability of power supply; most of the existing car charging stations do not have intelligent management systems, and cannot monitor the status of equipment in real time, predict power demand or optimize the user charging experience; the high energy consumption demand of 5G base stations further exacerbates the pressure on power supply, and traditional power supply methods are difficult to meet their continuous and stable power demand. The existing photovoltaic storage and charging system is difficult to efficiently coordinate the use of photovoltaic power generation, energy storage, car charging facilities and 5G communication base stations, which leads to energy waste, imbalance between supply and demand and increased pressure on the power grid.

[0004] Therefore, how to efficiently coordinate the use of photovoltaic power generation, energy storage, vehicle charging facilities and 5G communication base stations is an urgent problem that needs to be solved. Summary of the invention

[0005] In view of the above problems existing in the prior art, the purpose of this application is to propose a highway energy management method, device, equipment and medium based on photo-storage and charging, so as to at least solve the technical problem of how to efficiently coordinate the use of photovoltaic power generation, energy storage, vehicle charging facilities and 5G communication base stations.

[0006] To achieve the above objectives and other related objectives, the present application provides a highway energy management method based on photovoltaic storage and charging, the method comprising:

[0007] Obtain historical traffic flow data and 5G communication base station historical energy consumption data within the target highway section;

[0008] Based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station, predict the number of photovoltaic power generation equipment to be installed, wherein the number of photovoltaic power generation equipment to be installed is used to indicate the installation of the photovoltaic power generation equipment, and part of the electric energy of the installed photovoltaic power generation equipment is stored in a pre-configured electric energy storage device, and the electric energy storage device is used to provide electric energy to the vehicle charging device and the 5G communication base station;

[0009] Acquire the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the vehicle charging equipment, and the second energy consumption data of the 5G communication base station;

[0010] According to the real-time power generation data, the real-time electric energy storage data, the first energy consumption data and the second energy consumption data, priority prediction is performed on the power demand of the vehicle charging device and the 5G communication base station to obtain a priority prediction result;

[0011] Based on the priority prediction result, the electric energy of the photovoltaic power generation device and the electric energy storage device is allocated.

[0012] In some embodiments, the predicting the number of photovoltaic power generation equipment to be installed based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station includes:

[0013] Obtaining historical weather data and geographic information corresponding to the target highway section, wherein the historical traffic flow data includes vehicle traffic volume, vehicle type distribution, and vehicle speed; the historical energy consumption data of the 5G communication base station includes power consumption, peak power, and average power; the historical weather data includes sunshine duration, solar radiation intensity, temperature, and humidity; and the geographic information includes the installation location of the photovoltaic power generation equipment, the area of ​​the installation location, and the inclination angle of the installation location;

[0014] Extracting traffic flow characteristics from the historical traffic flow data, 5G communication base station energy consumption characteristics from the 5G communication base station historical energy consumption data, weather impact characteristics from the historical weather data, and photovoltaic power generation location characteristics from the geographic information;

[0015] Respectively calculate the joint probability distribution between the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, the photovoltaic power generation location characteristics and the photovoltaic power generation demand, determine the first marginal probability distribution corresponding to the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, and the photovoltaic power generation location characteristics, determine the second marginal probability distribution of the photovoltaic power generation demand, and determine multiple mutual information between the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, the photovoltaic power generation location characteristics and the photovoltaic power generation demand based on the joint probability distribution, the first marginal probability distribution and the second marginal probability distribution;

[0016] According to the multiple mutual information and the preset mutual information threshold, determine the target feature from the traffic flow feature, the 5G communication base station energy consumption feature, the weather impact feature, and the photovoltaic power generation location feature;

[0017] The target feature is transmitted to a pre-trained photovoltaic power generation equipment prediction model to obtain the installation quantity of the photovoltaic power generation equipment.

[0018] In some embodiments, before acquiring the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the car charging equipment, and the second energy consumption data of the 5G communication base station, the method further includes:

[0019] Transmitting the traffic flow characteristics and 5G communication base station energy consumption characteristics to a pre-trained time series prediction model to obtain a power load demand prediction result;

[0020] Transmitting the weather impact characteristics to a pre-trained weather prediction model to obtain a weather prediction result;

[0021] Determine the minimum power cost and the maximum energy storage utilization rate according to the power load demand forecast result and the weather forecast result;

[0022] According to the minimization of electricity cost and maximization of energy storage utilization, the storage of electric energy from the photovoltaic power generation equipment to the electric energy storage equipment is adjusted.

[0023] In some embodiments, the priority prediction of the power demand of the vehicle charging device and the 5G communication base station is performed according to the real-time power generation data, the real-time power storage data, the first energy consumption data, and the second energy consumption data to obtain a priority prediction result, including:

[0024] Determining the power supply capacity according to the real-time power generation data and the real-time power storage data;

[0025] determining a total electric energy demand according to the first energy consumption data and the second energy consumption data;

[0026] Determining a power supply and demand difference based on the power supply capacity and the total power demand;

[0027] When the power supply and demand difference is greater than or equal to zero, determining that the priority prediction result is that the priorities of the power demands of the vehicle charging device and the 5G communication base station are equal;

[0028] When the difference between the supply and demand of electric energy is less than zero, priority prediction is performed on the power demand of the vehicle charging equipment and the 5G communication base station to obtain a priority prediction result.

[0029] In some embodiments, when the power supply and demand difference is less than zero, the priority prediction of the power demand of the vehicle charging device and the 5G communication base station includes:

[0030] Obtaining a first power demand weight and a charging urgency level corresponding to the vehicle charging device, and a second power demand weight corresponding to the 5G communication base station, wherein the first power demand weight is less than the second power demand weight;

[0031] Determining a first priority score corresponding to the vehicle charging device according to the first energy consumption data, the first power demand weight, and the charging urgency level;

[0032] Determine a second priority score corresponding to the 5G communication base station according to the second energy consumption data and the second power demand weight;

[0033] Based on the first priority score and the second priority score, the priorities of the vehicle charging equipment and the 5G communication base station are arranged in descending order, and the descending order result is used as the priority prediction result.

[0034] In some embodiments, allocating electric energy to the photovoltaic power generation device and the electric energy storage device based on the priority prediction result includes:

[0035] When the priority prediction result shows that the priorities of the power demands of the vehicle charging device and the 5G communication base station are equal, the electric energy of the photovoltaic power generation device and the electric energy storage device is simultaneously distributed to the vehicle charging device and the 5G communication base station;

[0036] When the priority prediction result is the descending order result, according to the descending order result, part of the electric energy of the photovoltaic power generation equipment and the electric energy storage equipment is allocated to the first device in the descending order result, and the remaining electric energy of the photovoltaic power generation equipment and the electric energy storage equipment is allocated to the devices in other positions in the descending order result.

[0037] In some embodiments, after allocating the electric energy to the photovoltaic power generation device and the electric energy storage device based on the priority prediction result, the method further includes:

[0038] Acquire a first distributed electric energy distributed to the vehicle charging device and a second distributed electric energy distributed to the 5G communication base station;

[0039] determining a total distributed electric energy according to the first distributed electric energy and the second distributed electric energy, and determining an electric energy supply capacity according to the real-time power generation data and the real-time electric energy storage data;

[0040] Comparing the total allocated electric energy with the electric energy supply capacity to obtain electric energy utilization rate, comparing the first allocated electric energy with the total allocated electric energy to obtain a first allocation ratio, and comparing the second allocated electric energy with the total allocated electric energy to obtain a second allocation ratio;

[0041] The electric energy utilization rate, the first allocation ratio, and the second allocation ratio are visualized.

[0042] In one embodiment of the present application, a highway energy management device based on solar energy storage and charging is also provided, the device comprising:

[0043] The first data acquisition unit is used to acquire historical traffic flow data and historical energy consumption data of 5G communication base stations in a target highway section;

[0044] A first prediction unit is used to predict the number of photovoltaic power generation equipment installed based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station, wherein the number of photovoltaic power generation equipment installed is used to indicate the installation of the photovoltaic power generation equipment, and part of the electric energy of the installed photovoltaic power generation equipment is stored in a pre-configured electric energy storage device, and the electric energy storage device is used to provide electric energy to the vehicle charging device and the 5G communication base station;

[0045] A second data acquisition unit is used to acquire the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the car charging equipment and the second energy consumption data of the 5G communication base station;

[0046] A second prediction unit is used to perform priority prediction on the power demand of the vehicle charging device and the 5G communication base station according to the real-time power generation data, the real-time power storage data, the first energy consumption data and the second energy consumption data to obtain a priority prediction result;

[0047] An electric energy distribution unit is used to distribute electric energy to the photovoltaic power generation equipment and the electric energy storage equipment based on the priority prediction result.

[0048] In one embodiment of the present application, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored computer program, wherein the computer program executes the above-mentioned highway energy management method based on solar energy storage and charging when running.

[0049] In one embodiment of the present application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-mentioned highway energy management method based on solar energy storage and charging through the computer program.

[0050] Beneficial effects of the present invention:

[0051] First, historical traffic flow data and historical energy consumption data of 5G communication base stations in the target expressway section are obtained; then, based on the historical traffic flow data and the historical energy consumption data of the 5G communication base stations, the number of installed photovoltaic power generation equipment is predicted, wherein the number of installed photovoltaic power generation equipment is used to indicate the installation of the photovoltaic power generation equipment, and part of the electric energy of the installed photovoltaic power generation equipment is stored in a pre-configured electric energy storage device, and the electric energy storage device is used to provide electric energy to the car charging equipment and the 5G communication base station; then, real-time power generation data of the installed photovoltaic power generation equipment, real-time electric energy storage data of the electric energy storage device, first energy consumption data of the car charging equipment and second energy consumption data of the 5G communication base station are obtained; then, according to the real-time power generation data, the real-time electric energy storage data, the first energy consumption data and the second energy consumption data, priority prediction is performed on the power demand of the car charging equipment and the 5G communication base station to obtain a priority prediction result; finally, based on the priority prediction result, the electric energy of the photovoltaic power generation equipment and the electric energy storage device is allocated. In this application, historical traffic flow data and historical energy consumption data of 5G communication base stations are combined to predict the installation quantity of photovoltaic power generation equipment. The multi-source data-driven prediction method can more accurately estimate future electricity demand, ensure that the installation scale of photovoltaic power generation equipment matches actual demand, and avoid over-installation or under-installation; by monitoring real-time power generation data, power storage data, vehicle charging equipment energy consumption data and 5G communication base station energy consumption data, it is possible to understand the current power supply and demand situation in real time, adjust the power distribution strategy in time, and ensure flexibility and adaptability; through priority prediction, the vehicle charging equipment and 5G communication base stations can be dynamically adjusted according to the real-time power supply and demand situation. The power demand priority, especially in the case of tight power supply, can give priority to guaranteeing the power demand of 5G communication base stations, ensure the stable operation of the communication network, and flexibly adjust the power supply strategy of vehicle charging equipment to avoid excessive impact on the power grid. Under different power demand conditions, it can respond flexibly and maximize the use of existing power resources to ensure that the power needs of key facilities and users are met first; based on the priority prediction results, this application scheme intelligently allocates the power of photovoltaic power generation equipment and energy storage equipment, and can reasonably allocate the power generation of photovoltaic power generation equipment and the remaining power of energy storage equipment according to the current power supply and demand situation, to ensure efficient use of power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0053] Figure 1 is a schematic diagram of an application environment of a highway energy management method based on photovoltaic storage and charging, shown in an exemplary embodiment of the present application;

[0054] Figure 2 It is a flowchart of a highway energy management method based on photovoltaic storage and charging shown in an exemplary embodiment of the present application;

[0055] Figure 3 is a schematic diagram of a highway energy management device based on photovoltaic storage and charging, shown in an exemplary embodiment of the present application;

[0056] Figure 4 It is a schematic diagram of the structure of an electronic device shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0058] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0059] In one embodiment of the present application, a highway energy management method based on photovoltaic storage and charging is provided. Optionally, as an optional implementation, the above-mentioned highway energy management method based on photovoltaic storage and charging can be applied to, but not limited to, Figure 1 in the environment shown. Figure 1 is a schematic diagram of an application environment of a highway energy management method based on solar energy storage and charging, shown in an exemplary embodiment of the present application. Figure 1, the highway energy management terminal 101 can communicate with the data acquisition terminal 102, the photovoltaic power generation equipment 103, the electric energy storage equipment 104, the car charging equipment 105, and the 5G communication base station 106 through the network, and the highway energy management terminal 101 can perform operations on the database, such as writing data operations or reading data operations. The above-mentioned highway energy management terminal 101 can include, but is not limited to, a human-computer interaction screen, a processor, and a memory. The above-mentioned human-computer interaction screen can be used to display the results of electric energy distribution, but is not limited to. The above-mentioned processor can be used to respond to the above-mentioned human-computer interaction operation, perform corresponding operations, or generate corresponding instructions, and send the generated instructions to the photovoltaic power generation equipment 103, the electric energy storage equipment 104, the car charging equipment 105, and the 5G communication base station 106. The above-mentioned memory is used to store relevant storage data, such as historical traffic flow data, 5G communication base station historical energy consumption data, and electric energy distribution results.

[0060] As an optional method, data can be collected through the data collection terminal 102, such as collecting and preprocessing historical traffic flow data in the target highway section and historical energy consumption data of 5G communication base stations.

[0061] As an optional method, the following steps in the highway energy management method based on solar energy storage and charging may be performed on the data collection terminal 102:

[0062] Obtain historical traffic flow data and 5G communication base station historical energy consumption data within the target highway section;

[0063] Based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station, predict the number of photovoltaic power generation equipment to be installed, wherein the number of photovoltaic power generation equipment to be installed is used to indicate the installation of the photovoltaic power generation equipment, and part of the electric energy of the installed photovoltaic power generation equipment is stored in a pre-configured electric energy storage device, and the electric energy storage device is used to provide electric energy to the vehicle charging device and the 5G communication base station;

[0064] Acquire the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the vehicle charging equipment, and the second energy consumption data of the 5G communication base station;

[0065] According to the real-time power generation data, the real-time electric energy storage data, the first energy consumption data and the second energy consumption data, priority prediction is performed on the power demand of the vehicle charging device and the 5G communication base station to obtain a priority prediction result;

[0066] Based on the priority prediction result, the electric energy of the photovoltaic power generation device and the electric energy storage device is allocated.

[0067] Optionally, in this embodiment, the network may include but is not limited to: a wireless network, wherein the wireless network includes: Bluetooth, WIFI and other networks that implement wireless communication. The highway energy management terminal 101 may be a single server, or a server cluster consisting of multiple servers, or a cloud server. The above is only an example, and this embodiment does not make any limitation to this.

[0068] As an optional example, this embodiment does not limit the execution subject of the above-mentioned highway energy management method based on solar energy storage and charging. Some or all steps of the above-mentioned highway energy management method based on solar energy storage and charging can be executed on the highway energy management terminal 101.

[0069] The above method combines historical traffic flow data and historical energy consumption data of 5G communication base stations to predict the installation quantity of photovoltaic power generation equipment. The multi-source data-driven prediction method can more accurately estimate future electricity demand, ensure that the installation scale of photovoltaic power generation equipment matches actual demand, and avoid over-installation or under-installation. By monitoring real-time power generation data, power storage data, vehicle charging equipment energy consumption data and 5G communication base station energy consumption data, it is possible to understand the current power supply and demand situation in real time, adjust the power distribution strategy in time, and ensure flexibility and adaptability. Through priority prediction, the vehicle charging equipment and 5G communication base stations can be dynamically adjusted according to the real-time power supply and demand situation. The power demand priority, especially in the case of tight power supply, can give priority to guaranteeing the power demand of 5G communication base stations, ensure the stable operation of the communication network, and flexibly adjust the power supply strategy of vehicle charging equipment to avoid excessive impact on the power grid. Under different power demand conditions, it can respond flexibly and maximize the use of existing power resources to ensure that the power needs of key facilities and users are met first; based on the priority prediction results, this application scheme intelligently allocates the power of photovoltaic power generation equipment and energy storage equipment, and can reasonably allocate the power generation of photovoltaic power generation equipment and the remaining power of energy storage equipment according to the current power supply and demand situation, to ensure efficient use of power resources.

[0070] In one embodiment of the present application, a highway energy management method based on photovoltaic storage and charging is provided. Figure 2 This is a flowchart of a highway energy management method based on photovoltaic storage and charging, shown in an exemplary embodiment of the present application. Figure 2 The highway energy management method based on photovoltaic storage and charging includes the following steps S210 to S250:

[0071] In step S210, historical traffic flow data and historical energy consumption data of 5G communication base stations in the target highway section are obtained.

[0072] Among them, the target highway section refers to a specific highway section, which is usually delineated according to geographical areas, traffic flow characteristics or energy management needs. The section can be a service area, a section of highway, or a connecting section between multiple service areas. The scope of energy management is clarified to ensure the targeted data collection and analysis. There may be significant differences in traffic flow, 5G communication base station distribution, photovoltaic power generation potential, etc. in different sections, so customized energy management is required for specific sections. By defining the target section, the power demand and photovoltaic power generation potential within the section can be more accurately evaluated, thereby optimizing the installation location and scale of photovoltaic power generation equipment and ensuring the effective use of resources. For example, the section between service area A and service area B on a highway can be used as a target section, which is equipped with multiple electric vehicle charging stations and 5G communication base stations.

[0073] Among them, historical traffic flow data refers to the record of vehicle traffic in the target highway section over a period of time in the past (such as the past few months or years). These data usually include but are not limited to the following: vehicle traffic volume, which is the number of vehicles passing through the section every day and every hour; vehicle type distribution, which is the proportion of different types of vehicles (such as cars, trucks, buses, etc.); vehicle speed, which is the average driving speed of vehicles in the section; time period distribution, which is the change in traffic flow in different time periods (such as peak hours and valley hours); holiday effect, which is the difference in traffic flow between holidays and weekdays. By analyzing historical traffic flow data, future traffic flow trends can be predicted, and then the changes in future electric vehicle charging demand can be inferred. For example, a large traffic flow during peak hours may cause more electric vehicles to need to be charged; while a small traffic flow during valley hours has a relatively low charging demand. Traffic flow data can also help evaluate the installation location and scale of photovoltaic power generation equipment. For example, a service area with a large traffic flow may have more electric vehicle charging needs, so more photovoltaic power generation equipment is needed to meet the power demand. Assume that in the past year, the traffic flow in a service area reached its peak at 8:00-10:00 and 17:00-19:00 every day, when the demand for electric vehicle charging is relatively large. By analyzing this data, it is possible to prioritize the power supply to electric vehicle charging stations during peak hours, and store excess power during off-peak hours.

[0074] Among them, the historical energy consumption data of 5G communication base stations refers to the record of power consumption of 5G communication base stations in the target highway section in the past period of time (such as the past few months or years). These data usually include but are not limited to the following: power consumption, which is the power consumption of each 5G base station every day and every hour; peak power, which is the maximum power demand of 5G base stations during peak periods; average power, which is the daily average power demand of 5G base stations; time period distribution, which is the change of energy consumption in different time periods (such as daytime and nighttime); seasonal changes, which are the energy consumption differences in different seasons (such as summer and winter). By analyzing the historical energy consumption data of 5G communication base stations, the future trend of power demand changes can be predicted. For example, the energy consumption of 5G base stations is usually higher during the day and lower at night, especially during the peak period of network use (such as working hours on weekdays), the energy consumption will increase further. As a key infrastructure, the power demand of 5G communication base stations should be prioritized. Through the analysis of historical energy consumption data, the power demand of 5G base stations can be estimated in advance, and when the power supply is tight, priority will be given to powering them to ensure the stable operation of the communication network. Assuming that the average power of a 5G base station is 10kW, but the peak power may reach 20kW during 9:00-17:00 on weekdays, by analyzing this data, 5G base stations can be given priority power supply during peak hours to ensure the stable operation of the communication network.

[0075] In step S220, the installed number of photovoltaic power generation equipment is predicted based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station, wherein the installed number of photovoltaic power generation equipment is used to indicate the installation of the photovoltaic power generation equipment, and part of the electric energy of the installed photovoltaic power generation equipment is stored in a pre-configured energy storage device, and the energy storage device is used to provide electric energy to the vehicle charging equipment and the 5G communication base station.

[0076] Among them, predicting the number of photovoltaic power generation equipment to be installed refers to predicting the number and scale of photovoltaic power generation equipment required in the target highway section based on historical traffic flow data, historical energy consumption data of 5G communication base stations, and other relevant factors (such as weather conditions, geographical location, etc.). These devices include photovoltaic panels, inverters, brackets, etc. By predicting the number of photovoltaic power generation equipment to be installed, it is possible to ensure that the photovoltaic power generation capacity matches the future power demand. Too much installation will lead to waste of resources, while too little installation may lead to insufficient power supply. Therefore, reasonable prediction can maximize the power generation potential of photovoltaic power generation equipment and reduce dependence on the main grid. Accurate prediction helps control the cost of project construction. Too much installation will increase the initial investment, while too little installation may lead to increased subsequent expansion costs. Through reasonable prediction, construction and operation costs can be minimized while meeting power demand. Assuming that the traffic flow in a service area is large and the energy consumption of 5G base stations is high, it is predicted that the service area needs to install 100kW of photovoltaic power generation equipment to meet the power demand in the next five years. Through reasonable prediction, over-installation or under-installation can be avoided to ensure that the power generation capacity of photovoltaic power generation equipment matches the actual demand.

[0077] Among them, energy storage equipment refers to energy storage equipment used to store the power generated by photovoltaic power generation equipment. Common energy storage equipment includes lithium batteries, lead-acid batteries, supercapacitors, etc. These devices can store excess power and release it during peak power demand periods or when photovoltaic power generation is insufficient to ensure the stability of power supply. Energy storage equipment can store excess power during low power demand periods (such as at night or when traffic is light) and release it during peak power demand periods (such as during the day or when traffic is heavy), smoothing the grid load and reducing electricity costs. Under extreme weather conditions (such as rain, blizzards, etc.), the power generation of photovoltaic power generation equipment may be insufficient. At this time, energy storage equipment can be used as an emergency backup power supply to ensure the normal operation of 5G communication base stations and electric vehicle charging stations. Energy storage equipment can also be used in combination with electric vehicles with V2G (Vehicle-to-Grid) functions, allowing electric vehicles to reverse power to the grid during peak hours, further enhancing flexibility and scalability. Assume that the photovoltaic power generation equipment in a service area generates a large amount of power during the day, but generates zero power at night. By installing a 500kWh energy storage device, excess electricity can be stored during the day and used to power 5G base stations and electric vehicle charging stations at night, ensuring the continuity of power supply.

[0078] Among them, car charging equipment refers to facilities used to provide charging services for electric vehicles. Common car charging equipment includes fast charging piles, slow charging piles, wireless charging equipment, etc. These devices can provide charging services of different powers according to different charging needs and vehicle types. Car charging equipment provides convenient charging services for electric vehicle users, ensuring that they can replenish their power in time when traveling long distances or short distances. Especially in highway service areas, the layout and service quality of charging equipment directly affect the user's travel experience. Through intelligent management of car charging equipment, the charging power can be dynamically adjusted according to the real-time power supply and demand situation, to avoid charging congestion during peak hours and improve user experience. For example, when the power supply is tight, the charging power can be appropriately reduced, or users can be guided to charge during off-peak hours. Assuming that a service area is equipped with 10 fast charging piles and 20 slow charging piles, the charging power of each charging pile can be dynamically adjusted according to the real-time power supply and demand situation. During peak hours, fast charging services can be provided to vehicles that are in urgent need of charging, while during off-peak hours, slow charging services can be provided to more vehicles to make full use of cheap electricity.

[0079] In step S230, the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the car charging equipment and the second energy consumption data of the 5G communication base station are obtained.

[0080] In step S240, priority prediction is performed on the power demand of the vehicle charging equipment and the 5G communication base station based on the real-time power generation data, the real-time power storage data, the first energy consumption data and the second energy consumption data to obtain a priority prediction result.

[0081] Among them, the priority prediction of the power demand of the car charging equipment and the 5G communication base station refers to dynamically evaluating the power demand of the two according to the real-time power generation data, power storage data, energy consumption data of the car charging equipment and energy consumption data of the 5G communication base station, and determining their power supply priority. Through priority prediction, the power demand of key facilities and users can be prioritized when power resources are limited. For example, as a key infrastructure, the power demand of 5G communication base stations should be prioritized; and the power supply of car charging equipment can be flexibly adjusted according to the charging urgency of users. Priority prediction is a dynamic process based on real-time data, and the priority can be adjusted in real time according to the current power supply and demand situation to ensure flexibility and adaptability. For example, when the power supply is sufficient, the priority of car charging equipment and 5G communication base stations can be equal; when the power supply is tight, the 5G base station can be powered first, and the charging request of some electric vehicles can be temporarily postponed. Assume that the total power generation of photovoltaic power generation equipment and power storage equipment in a service area is 150kW, and the current power demand is 180kW. At this time, it can be determined through priority prediction that the power demand priority of 5G communication base stations is higher than that of car charging equipment. Priority will be given to powering 5G base stations to ensure the stable operation of the communication network. At the same time, the charging power of electric vehicles will be appropriately reduced to avoid excessive impact on the power grid.

[0082] In step S250, the electric energy of the photovoltaic power generation device and the electric energy storage device is distributed based on the priority prediction result.

[0083] Among them, allocating the electric energy of the photovoltaic power generation equipment and the electric energy storage equipment based on the priority prediction result means that the electric energy of the photovoltaic power generation equipment and the electric energy storage equipment is reasonably allocated according to the result of the priority prediction to ensure the efficient use of electric power resources. The process of electric energy allocation can dynamically adjust the power supply order and power supply amount of each device according to the priority score. Through reasonable electric energy allocation, the power generation of photovoltaic power generation equipment and the storage capacity of electric energy storage equipment can be maximized, the dependence on the main grid can be reduced, and the power cost can be reduced. For example, when the power supply is sufficient, photovoltaic power generation equipment can be used to power the load first; when the power supply is tight, electric energy storage equipment can be used to power key facilities first. Electric energy allocation is a dynamic process based on priority prediction. According to the real-time power supply and demand situation, the electric energy allocation strategy can be flexibly adjusted to ensure flexibility and adaptability. For example, when the power supply is tight, 5G base stations can be powered first, temporarily postponing the charging requests of some electric vehicles; when the power supply is sufficient, the charging service of electric vehicles can be restored.

[0084] The above-mentioned embodiment provided by the present application combines historical traffic flow data and historical energy consumption data of 5G communication base stations to predict the installation quantity of photovoltaic power generation equipment. The multi-source data-driven prediction method can more accurately estimate future electricity demand, ensure that the installation scale of photovoltaic power generation equipment matches actual demand, and avoid over-installation or under-installation. By monitoring real-time power generation data, power storage data, energy consumption data of vehicle charging equipment, and energy consumption data of 5G communication base stations, it is possible to understand the current power supply and demand situation in real time, adjust the power distribution strategy in time, and ensure flexibility and adaptability. Through priority prediction, the priority of vehicle charging equipment and 5G communication base stations can be dynamically adjusted according to the real-time power supply and demand situation. The power demand priority of 5G communication base stations, especially in the case of tight power supply, can give priority to the power demand of 5G communication base stations, ensure the stable operation of the communication network, and flexibly adjust the power supply strategy of automobile charging equipment to avoid excessive impact on the power grid. Under different power demand conditions, it can respond flexibly and maximize the use of existing power resources to ensure that the power needs of key facilities and users are met first; this application scheme is based on the priority prediction results, and intelligently allocates the power of photovoltaic power generation equipment and energy storage equipment. According to the current power supply and demand situation, it can reasonably allocate the power generation of photovoltaic power generation equipment and the remaining power of energy storage equipment to ensure efficient use of power resources.

[0085] In one embodiment of the present application, the predicting the number of photovoltaic power generation equipment to be installed based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station includes:

[0086] Obtaining historical weather data and geographic information corresponding to the target highway section, wherein the historical traffic flow data includes vehicle traffic volume, vehicle type distribution, and vehicle speed; the historical energy consumption data of the 5G communication base station includes power consumption, peak power, and average power; the historical weather data includes sunshine duration, solar radiation intensity, temperature, and humidity; and the geographic information includes the installation location of the photovoltaic power generation equipment, the area of ​​the installation location, and the inclination angle of the installation location;

[0087] Extracting traffic flow characteristics from the historical traffic flow data, 5G communication base station energy consumption characteristics from the 5G communication base station historical energy consumption data, weather impact characteristics from the historical weather data, and photovoltaic power generation location characteristics from the geographic information;

[0088] Respectively calculate the joint probability distribution between the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, the photovoltaic power generation location characteristics and the photovoltaic power generation demand, determine the first marginal probability distribution corresponding to the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, and the photovoltaic power generation location characteristics, determine the second marginal probability distribution of the photovoltaic power generation demand, and determine multiple mutual information between the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, the photovoltaic power generation location characteristics and the photovoltaic power generation demand based on the joint probability distribution, the first marginal probability distribution and the second marginal probability distribution;

[0089] According to the multiple mutual information and the preset mutual information threshold, determine the target feature from the traffic flow feature, the 5G communication base station energy consumption feature, the weather impact feature, and the photovoltaic power generation location feature;

[0090] The target feature is transmitted to a pre-trained photovoltaic power generation equipment prediction model to obtain the installation quantity of the photovoltaic power generation equipment.

[0091] Among them, historical weather data refers to the weather condition records of the target highway section in the past period of time (such as the past few months or years). These data usually include but are not limited to the following: sunshine duration, which is the sunshine time per day; solar radiation intensity, which is the solar radiation energy received per unit area; temperature, which is the ambient temperature; humidity, which is the water vapor content in the air.

[0092] Geographic information refers to geographic data related to the installation location of photovoltaic power generation equipment, including but not limited to the following: installation location, which is the specific installation location of the photovoltaic power generation equipment; the area of ​​the installation location, which is the total area occupied by the photovoltaic power generation equipment; the inclination angle of the installation location, which is the installation angle of the photovoltaic power generation equipment, affecting the angle at which it receives solar radiation. The roof area of ​​a service area is large, and the installation angle is the optimal inclination angle of 30 degrees. Based on this geographic information, photovoltaic power generation equipment can be installed here to ensure that it can receive solar radiation energy to the maximum extent and improve power generation efficiency.

[0093] Among them, traffic flow characteristics are key features extracted from historical traffic flow data, which are used to describe the changing patterns of traffic flow. Common traffic flow characteristics include but are not limited to: the changing trend of vehicle traffic volume, which is the daily, weekly, and monthly changes in vehicle traffic volume; the changing trend of vehicle type distribution, which is the changing proportion of different types of vehicles; the changing trend of vehicle speed, which is the average speed of vehicles in different time periods. Historical data from a service area shows that 9:00-17:00 on weekdays is the peak traffic flow period, when there is a large demand for electric vehicle charging. By extracting these traffic flow characteristics, future weekday peaks can be predicted, and more electricity can be allocated to electric vehicle charging stations in advance to avoid insufficient power supply.

[0094] Among them, the energy consumption characteristics of 5G communication base stations are key features extracted from the historical energy consumption data of 5G communication base stations, which are used to describe the power consumption patterns of 5G base stations. Common energy consumption characteristics of 5G communication base stations include but are not limited to: the trend of changes in power consumption, which is the change in power consumption on a daily, weekly, and monthly basis; the trend of changes in peak power, which is the change in the maximum power demand of 5G base stations in different time periods; the trend of changes in average power, which is the change in the daily average power demand of 5G base stations. The historical data of a 5G base station shows that its average power is 10kW, but during the period of 9:00-17:00 on weekdays, the peak power may reach 20kW. By extracting these energy consumption characteristics of 5G communication base stations, 5G base stations can be powered first during peak hours to ensure the stable operation of the communication network.

[0095] Among them, weather impact features are key features extracted from historical weather data, which are used to describe the impact of weather conditions on the power generation capacity of photovoltaic power generation equipment. Common weather impact features include but are not limited to: the trend of sunshine duration, which is the change of sunshine duration per day, week, and month; the trend of solar radiation intensity, which is the change of solar radiation energy received per unit area; the trend of temperature and humidity, which is the change of ambient temperature and humidity.

[0096] Among them, photovoltaic power generation location features are key features extracted from geographic information, which are used to describe the installation location of photovoltaic power generation equipment and its impact on power generation capacity. Common photovoltaic power generation location features include but are not limited to: the area of ​​the installation location, which is the total area occupied by the photovoltaic power generation equipment; the inclination angle of the installation location, which is the installation angle of the photovoltaic power generation equipment and affects the angle at which it receives solar radiation; the geographical location of the installation location, which is the specific installation location of the photovoltaic power generation equipment and affects the intensity of solar radiation it receives.

[0097] Among them, the joint probability distribution refers to the probability distribution between multiple random variables, reflecting the correlation between these variables. There is a certain correlation between traffic flow characteristics, 5G communication base station energy consumption characteristics, weather impact characteristics and photovoltaic power generation location characteristics. By calculating the joint probability distribution of these characteristics, the future photovoltaic power generation demand can be predicted more accurately and power generation plans can be made in advance.

[0098] Among them, the marginal probability distribution refers to the probability distribution of a single random variable, which reflects the probability distribution of the variable itself. Traffic flow characteristics, 5G communication base station energy consumption characteristics, weather impact characteristics and photovoltaic power generation location characteristics each have different marginal probability distributions. By calculating the marginal probability distribution of these characteristics, the impact of each feature on photovoltaic power generation demand can be analyzed separately and the synergistic effect between them can be evaluated.

[0099] Among them, mutual information refers to the degree of dependence between two random variables, reflecting the amount of relevant information between them. Assume that the mutual information values ​​between the traffic flow characteristics, 5G communication base station energy consumption characteristics, weather impact characteristics, photovoltaic power generation location characteristics and photovoltaic power generation demand in a certain service area are 0.8, 0.7, 0.9 and 0.6 respectively. By calculating these mutual information values, the correlation between traffic flow characteristics, 5G communication base station energy consumption characteristics, weather impact characteristics, photovoltaic power generation location characteristics and photovoltaic power generation demand can be measured.

[0100] In this embodiment, by analyzing the historical traffic flow data, the future traffic flow change trend can be predicted, and then the future changes in electric vehicle charging demand can be inferred. By analyzing the historical energy consumption data of 5G communication base stations, the future power demand change trend can be predicted. As a key infrastructure, the power demand of 5G communication base stations should be prioritized. Through the analysis of historical energy consumption data, the power demand of 5G base stations can be estimated in advance, and when the power supply is tight, priority can be given to power supply to ensure the stable operation of the communication network. Weather data can help evaluate the power generation potential of photovoltaic power generation equipment and can also help formulate peak-cutting and valley-filling strategies. Geographic information can help evaluate whether the installation location of photovoltaic power generation equipment is suitable for photovoltaic power generation. By reasonably selecting the installation location, the power generation potential of photovoltaic power generation equipment can be maximized, the dependence on the main grid can be reduced, and the power cost can be reduced. By extracting traffic flow characteristics, the future traffic flow change trend can be more accurately predicted, and then the future electric vehicle charging demand can be inferred. By extracting the energy consumption characteristics of 5G communication base stations, the future power demand change trend can be more accurately predicted. By extracting weather impact characteristics, the power generation potential of photovoltaic power generation equipment can be more accurately evaluated. By extracting photovoltaic power generation location characteristics, the power generation potential of photovoltaic power generation equipment can be more accurately evaluated. By calculating the joint probability distribution, the mutual influence between each feature can be quantified to help more accurately predict the demand for photovoltaic power generation. The joint probability distribution can also help optimize the installation location and scale of photovoltaic power generation equipment to ensure that photovoltaic power generation capacity matches future electricity demand. By calculating the marginal probability distribution, the probability distribution of each feature can be analyzed separately to help understand the impact of each feature on photovoltaic power generation demand. The marginal probability distribution can also be used as a benchmark to evaluate the synergy in the joint probability distribution. By calculating the mutual information, the correlation between each feature and photovoltaic power generation demand can be quantified to help select the most relevant features. Mutual information can also help optimize the installation location and scale of photovoltaic power generation equipment to ensure that photovoltaic power generation capacity matches future electricity demand.

[0101] In one embodiment of the present application, before acquiring the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the car charging equipment, and the second energy consumption data of the 5G communication base station, it also includes:

[0102] Transmitting the traffic flow characteristics and 5G communication base station energy consumption characteristics to a pre-trained time series prediction model to obtain a power load demand prediction result;

[0103] Transmitting the weather impact characteristics to a pre-trained weather prediction model to obtain a weather prediction result;

[0104] Determine the minimum power cost and the maximum energy storage utilization rate according to the power load demand forecast result and the weather forecast result;

[0105] According to the minimization of electricity cost and maximization of energy storage utilization, the storage of electric energy from the photovoltaic power generation equipment to the electric energy storage equipment is adjusted.

[0106] Among them, the time series prediction model is a machine learning or statistical model used to predict future data trends. It captures the laws of data changes over time by modeling the time series of historical data, and predicts future values ​​based on these laws. In this embodiment, the time series prediction model is used to predict future power load demand. By inputting traffic flow characteristics and 5G communication base station energy consumption characteristics, the power consumption in different time periods can be predicted.

[0107] Among them, the traffic flow characteristics and 5G communication base station energy consumption characteristics are transmitted to the pre-trained time series prediction model to obtain the power load demand prediction result, which means that the characteristics extracted from the historical traffic flow data and the historical energy consumption data of the 5G communication base station (such as vehicle traffic volume, vehicle type distribution, vehicle speed, 5G base station power consumption, peak power, etc.) are input into the pre-trained time series prediction model to predict future power load demand. By predicting the power load demand in advance, the best preparation can be made in advance before obtaining real-time data, ensuring that the power cost is minimized and the energy storage utilization rate is maximized while meeting the power demand.

[0108] Among them, the weather prediction model is a model used to predict future weather conditions, which is usually trained based on historical weather data (such as sunshine duration, solar radiation intensity, temperature, humidity, etc.). The weather prediction model can help understand future weather changes in advance, especially the impact on photovoltaic power generation equipment. The weather prediction model can predict future sunshine duration and solar radiation intensity and evaluate the power generation potential of photovoltaic power generation equipment.

[0109] Among them, minimizing electricity costs means reducing electricity procurement costs through reasonable electricity distribution strategies. Electricity costs usually include the cost of purchasing electricity from the main grid and the cost of charging and discharging losses of energy storage equipment. Maximizing energy storage utilization means ensuring that the electricity of energy storage equipment is effectively utilized and avoiding energy waste through reasonable energy storage management. By minimizing electricity costs, operating costs can be reduced while meeting electricity demand. For example, during off-peak hours when electricity prices are low, the main grid electricity can be used to charge energy storage equipment first; and during peak hours when electricity prices are high, the electricity of energy storage equipment can be used to power the load first, reducing the cost of purchasing electricity from the main grid. By maximizing energy storage utilization, it is possible to ensure that the electricity of energy storage equipment is effectively utilized and avoid energy waste. For example, when the weather forecast shows that it will be cloudy in the next few days, excess electricity can be stored in advance for emergency use; and when the weather forecast shows that it will be sunny in the next few days, photovoltaic power generation equipment can be used to power the load first, reducing dependence on energy storage equipment.

[0110] For example, the electricity price in a service area is lower at night and higher during the day. Through the strategy of minimizing electricity costs, the main grid electricity can be used to charge the energy storage device at night; and the energy storage device's electricity can be used to power the load during the day to reduce the cost of purchasing electricity from the main grid. At the same time, through the strategy of maximizing energy storage utilization, the energy of the energy storage device can be effectively used to avoid energy waste.

[0111] Among them, determining the minimization of electricity cost and maximization of energy storage utilization rate according to the electricity load demand forecast results and the weather forecast results means formulating the optimal electricity distribution strategy according to the electricity load demand forecast results and the weather forecast results to ensure that the electricity cost is minimized and the energy storage utilization rate is maximized under the premise of meeting the electricity demand. By combining the electricity load demand forecast results and the weather forecast results, multiple optimization objectives can be considered at the same time, such as minimizing electricity cost and maximizing energy storage utilization rate. If the electricity load demand forecast results show that the electricity demand in the next few days is large, and the weather forecast results show that it will be sunny in the next few days, photovoltaic power generation equipment can be used to power the load first to reduce the cost of purchasing electricity from the main grid; at the same time, excess electricity can be stored for emergency use. This step is a dynamic process, and the electricity distribution strategy can be flexibly adjusted according to the real-time electricity supply and demand situation and weather changes to ensure flexibility and adaptability.

[0112] In one embodiment of the present application, the priority prediction of the power demand of the vehicle charging device and the 5G communication base station is performed according to the real-time power generation data, the real-time power storage data, the first energy consumption data and the second energy consumption data to obtain a priority prediction result, including:

[0113] Determining the power supply capacity according to the real-time power generation data and the real-time power storage data;

[0114] determining a total electric energy demand according to the first energy consumption data and the second energy consumption data;

[0115] Determining a power supply and demand difference based on the power supply capacity and the total power demand;

[0116] When the power supply and demand difference is greater than or equal to zero, determining that the priority prediction result is that the priorities of the power demands of the vehicle charging device and the 5G communication base station are equal;

[0117] When the difference between the supply and demand of electric energy is less than zero, priority prediction is performed on the power demand of the vehicle charging equipment and the 5G communication base station to obtain a priority prediction result.

[0118] Among them, the power supply capacity refers to the total amount of electricity that can be provided by the current photovoltaic power generation equipment and power storage equipment. The determination of this capacity is based on the following two main factors: real-time power generation data, which refers to the actual power generation of the current photovoltaic power generation equipment, usually in kilowatt-hours (kWh). The power generation of photovoltaic power generation equipment is affected by weather conditions (such as sunshine duration, solar radiation intensity, etc.), so the real-time power generation data reflects the current solar power generation potential; real-time power storage data, which refers to the remaining power of the current power storage equipment, usually in kilowatt-hours (kWh). The remaining power of the power storage equipment reflects the available power of the power storage equipment, which can supplement the power demand when photovoltaic power generation is insufficient.

[0119] For example, the current power generation of the photovoltaic power generation equipment in a service area is 50kW, and the remaining power of the energy storage equipment is 200kWh. At this time, the power supply capacity is 50kW (real-time power generation) + 200kWh (remaining power of energy storage), that is, 250kWh. This means that there is currently 250kWh of electricity available to meet the load demand.

[0120] Among them, the total power demand refers to the total power consumption of all current loads (including car charging equipment and 5G communication base stations). The determination of this demand is based on the following two main factors: the first energy consumption data refers to the current power consumption of car charging equipment, usually in kilowatt-hours (kWh), and the energy consumption of car charging equipment depends on factors such as the number of electric vehicles currently being charged, the battery capacity of each vehicle, and the charging power; the second energy consumption data refers to the current power consumption of 5G communication base stations, usually in kilowatt-hours (kWh), and the energy consumption of 5G communication base stations depends on factors such as their operating status, network usage, and peak power.

[0121] For example, the current power consumption of the car charging equipment in a service area is 150kW, and the power consumption of the 5G communication base station is 30kW. At this time, the total power demand is 150kW (car charging equipment) + 30kW (5G communication base station), that is, 180kW. This means that 180kW of power is currently required to meet the needs of all loads.

[0122] Among them, the electricity supply and demand difference refers to the difference between the current electricity supply capacity and the total electricity demand. The electricity supply and demand difference = electricity supply capacity - total electricity demand. If the electricity supply and demand difference is greater than or equal to zero, it means that the current electricity supply capacity is sufficient to meet the total electricity demand, and there is even surplus electricity; if the electricity supply and demand difference is less than zero, it means that the current electricity supply capacity is insufficient to meet the total electricity demand and there is a power gap.

[0123] For example, the power supply capacity of a service area is 250kWh, and the total power demand is 180kW. At this time, the power supply and demand difference is 250kWh - 180kW = 70kWh, which means that the current power supply capacity is sufficient to meet the total power demand, and there is 70kWh of surplus power.

[0124] For example, the power supply capacity of a service area is 200kWh, and the total power demand is 250kW. At this time, the power supply and demand difference is 200kWh - 250kW = -50kWh, indicating that the current power supply capacity is insufficient to meet the total power demand, and there is a 50kWh power gap.

[0125] When the difference between power supply and demand is less than zero, it means that the current power supply capacity is insufficient to meet the total power demand and there is a power gap. At this time, it is necessary to prioritize the power demand of vehicle charging equipment and 5G communication base stations to determine which loads should be powered first and which loads can be appropriately delayed or reduced in power supply.

[0126] For example, the difference between electricity supply and demand in a service area is -50kWh, which means that the current electricity supply capacity is insufficient to meet the total electricity demand. At this time, it is necessary to prioritize the power demand of vehicle charging equipment and 5G communication base stations. The power demand of 5G communication base stations has a higher priority, and they will be powered first to ensure the stable operation of the communication network. For vehicle charging equipment, the charging power can be appropriately reduced according to the user's charging urgency, or users can be guided to charge during off-peak hours to avoid excessive impact on the power grid.

[0127] In this embodiment, by combining real-time power generation data and power storage data, the current power supply capacity can be accurately evaluated. This helps to understand how much power is currently available to meet the load demand, thereby providing a basis for subsequent priority prediction and power distribution. The power supply capacity is a dynamically changing value that changes with the power generation of photovoltaic power generation equipment and the charging and discharging of power storage equipment. It is necessary to monitor these data in real time to ensure that the power distribution strategy can be flexibly adjusted under different power supply and demand conditions. By combining the energy consumption data of car charging equipment and 5G communication base stations, the current total power demand can be accurately evaluated. This helps to understand how much power demand needs to be met at present, thereby providing a basis for subsequent priority prediction and power distribution. The total power demand is a dynamically changing value that changes with the use of car charging equipment and the operating status of 5G communication base stations. It is necessary to monitor these data in real time to ensure that the power distribution strategy can be flexibly adjusted under different power supply and demand conditions. By calculating the difference in power supply and demand, it is possible to evaluate whether the current power supply and demand is balanced. This helps to determine whether further measures are needed, such as adjusting the power distribution strategy, starting the emergency backup power supply, etc. The difference between power supply and demand is an important basis for priority forecasting. If the difference between power supply and demand is greater than or equal to zero, it can be considered that the power supply is sufficient and no special adjustment of priority is required; if the difference between power supply and demand is less than zero, it is necessary to prioritize the power demand of different loads to ensure that the power demand of key facilities and users is met first.

[0128] In one embodiment of the present application, when the difference between the power supply and demand is less than zero, the priority prediction of the power demand of the vehicle charging device and the 5G communication base station includes:

[0129] Obtaining a first power demand weight and a charging urgency level corresponding to the vehicle charging device, and a second power demand weight corresponding to the 5G communication base station, wherein the first power demand weight is less than the second power demand weight;

[0130] Determining a first priority score corresponding to the vehicle charging device according to the first energy consumption data, the first power demand weight, and the charging urgency level;

[0131] Determine a second priority score corresponding to the 5G communication base station according to the second energy consumption data and the second power demand weight;

[0132] Based on the first priority score and the second priority score, the priorities of the vehicle charging equipment and the 5G communication base station are arranged in descending order, and the descending order result is used as the priority prediction result.

[0133] Among them, the power demand weight refers to the relative importance coefficient assigned to the power demand of different devices. The car charging equipment and 5G communication base station correspond to different power demand weights, where the first power demand weight is used for the car charging equipment and the second power demand weight is used for the 5G communication base station. Generally speaking, the power demand weight of the 5G communication base station is higher, indicating that it has a higher priority; while the power demand weight of the car charging equipment is lower, indicating that it has the second highest priority.

[0134] Among them, the charging urgency level refers to the urgency level of electric vehicle users when requesting charging. According to the actual needs of users, the charging urgency level can be divided into multiple levels, such as high, medium, and low. Users with a higher urgency level usually need to complete charging as soon as possible, perhaps because the vehicle is low on power or has a tight schedule; while users with a lower urgency level can complete charging at a later time period.

[0135] Among them, determining the first priority score corresponding to the vehicle charging device according to the first energy consumption data, the first power demand weight and the charging urgency level means calculating the priority score of the vehicle charging device according to the energy consumption data of the vehicle charging device (i.e., the current charging demand), the power demand weight and the charging urgency level. The priority score reflects the power supply priority of the device under the current power supply and demand situation.

[0136] Among them, determining the second priority score corresponding to the 5G communication base station according to the second energy consumption data and the second power demand weight means calculating the priority score of the 5G communication base station according to the energy consumption data (i.e., current power consumption) and power demand weight of the 5G communication base station. The priority score reflects the power supply priority of the device under the current power supply and demand conditions.

[0137] Among them, based on the first priority score and the second priority score, the priorities of the car charging equipment and the 5G communication base station are arranged in descending order, and the descending order result is used as the priority prediction result, which means that the power supply priorities of the car charging equipment and the 5G communication base station are arranged in descending order according to the calculated priority score. The equipment with higher priority scores is arranged in front and gets power supply first; the equipment with lower priority scores is arranged in the back and gets power supply later. The final descending order result is used as the priority prediction result to guide the subsequent power allocation strategy.

[0138] Exemplarily, it is detected that the current power supply and demand difference is less than zero, that is, the power supply is insufficient to meet the power demand of all devices. At this time, it is necessary to prioritize the power demand of the car charging equipment and the 5G communication base station to reasonably allocate power. The power demand weights of the car charging equipment and the 5G communication base station are obtained. Assume that the power demand weight of the 5G communication base station is 0.8, indicating that it has a higher priority; and the power demand weight of the car charging equipment is 0.6, indicating that it has the second highest priority. At the same time, the charging urgency level of each electric vehicle is also obtained. Assume that there are two electric vehicles in the service area: the battery power of vehicle A is 10% and the charging urgency level is "high"; the battery power of vehicle B is 50% and the charging urgency level is "low". For vehicle A, based on its energy consumption data (charging power is 50kW), power demand weight (0.6) and charging urgency level ("high"), its priority score is calculated to be 0.8. For vehicle B, based on its energy consumption data (charging power is 30kW), power demand weight (0.6) and charging urgency level ("low"), its priority score is calculated to be 0.4. For the 5G communication base station, based on its energy consumption data (power consumption is 10kW) and power demand weight (0.8), its priority score is calculated to be 0.9. The devices are arranged in descending order according to the priority score, and the results are as follows: 5G communication base station, vehicle A, vehicle B. Based on this arrangement, power is supplied to devices with higher scores first.

[0139] In this embodiment, by assigning a higher power demand weight to the 5G communication base station, it can be ensured that it can still operate normally when the power supply is tight, and the stability of the communication network can be guaranteed. This not only improves reliability, but also guarantees the user's communication experience. By introducing the charging emergency level, personalized charging services can be provided to users. For users who are in urgent need of charging, higher-power charging piles can be assigned to them first, and the charging speed can be accelerated; for users who are not in a hurry to charge, charging services can be provided to them when the power supply is sufficient. This personalized service improves the user experience and reduces the user's waiting time. By calculating the priority score and arranging the devices in descending order, electric energy can be reasonably allocated when power resources are limited, ensuring that the power needs of key facilities and users are met first. This not only improves energy utilization efficiency, but also reduces electricity costs and improves economy.

[0140] In one embodiment of the present application, allocating electric energy to the photovoltaic power generation device and the electric energy storage device based on the priority prediction result includes:

[0141] When the priority prediction result shows that the priorities of the power demands of the vehicle charging device and the 5G communication base station are equal, the electric energy of the photovoltaic power generation device and the electric energy storage device is simultaneously distributed to the vehicle charging device and the 5G communication base station;

[0142] When the priority prediction result is the descending order result, according to the descending order result, part of the electric energy of the photovoltaic power generation equipment and the electric energy storage equipment is allocated to the first device in the descending order result, and the remaining electric energy of the photovoltaic power generation equipment and the electric energy storage equipment is allocated to the devices in other positions in the descending order result.

[0143] Among them, when the priority prediction results show that the power demand priorities of the car charging equipment and the 5G communication base station are equal, the power of the photovoltaic power generation equipment and the energy storage equipment will be allocated to the two devices at the same time. This means that the two will share the available power resources equally. By allocating power at the same time, the power generation and storage capacity of the photovoltaic power generation equipment and the energy storage equipment can be maximized while meeting the power needs of the two devices.

[0144] Among them, when the priority prediction results show that the priorities of car charging equipment and 5G communication base stations are not equal, according to the results of descending order, power is allocated to devices with higher scores first. Specifically, part of the power will be allocated to the device ranked first first, and then the remaining power will be allocated to devices ranked in other positions. This method ensures that high-priority devices can get power support first, while low-priority devices will get power later. By allocating power according to the results of descending order, the power needs of high-priority devices can be prioritized. For example, as a critical infrastructure, the power demand of 5G communication base stations should be prioritized; while the power supply of car charging equipment can be flexibly adjusted according to the user's charging urgency. This method ensures that the power needs of critical facilities and users are met first.

[0145] In this embodiment, by allocating electric energy according to the results of descending order, the power demand of high-priority devices can be prioritized. By giving priority to powering car charging devices with higher scores, faster charging services can be provided to users who are in urgent need of charging, reducing the waiting time of users. In particular, for users with higher emergency levels, higher-power charging piles can be allocated to them first to ensure that they can complete charging in a short time. By reasonably allocating electric energy, the power generation and storage capacity of photovoltaic power generation equipment and electric energy storage equipment can be maximized while meeting the power needs of different devices. This not only improves energy utilization efficiency, but also reduces electricity costs and improves economic efficiency.

[0146] In one embodiment of the present application, after allocating the electric energy to the photovoltaic power generation device and the electric energy storage device based on the priority prediction result, the method further includes:

[0147] Acquire a first distributed electric energy distributed to the vehicle charging device and a second distributed electric energy distributed to the 5G communication base station;

[0148] determining a total distributed electric energy according to the first distributed electric energy and the second distributed electric energy, and determining an electric energy supply capacity according to the real-time power generation data and the real-time electric energy storage data;

[0149] Comparing the total allocated electric energy with the electric energy supply capacity to obtain electric energy utilization rate, comparing the first allocated electric energy with the total allocated electric energy to obtain a first allocation ratio, and comparing the second allocated electric energy with the total allocated electric energy to obtain a second allocation ratio;

[0150] The electric energy utilization rate, the first allocation ratio, and the second allocation ratio are visualized.

[0151] The first allocated power refers to the power actually allocated to the car charging device after the power is allocated based on the priority prediction results. This power is provided to the car charging device based on the current power demand and power supply capacity. By recording the power allocated to the car charging device, its actual power supply can be quantified to ensure that each electric vehicle can obtain sufficient power support.

[0152] The second allocated power refers to the power actually allocated to the 5G communication base station after the power is allocated based on the priority prediction result. This power is provided to the 5G communication base station based on the current power demand and power supply capacity. By recording the power allocated to the 5G communication base station, it can be ensured that its power demand is met first and the stable operation of the communication network is guaranteed.

[0153] The total distributed electric energy refers to the total electric energy obtained by adding the first distributed electric energy (the electric energy distributed to the car charging equipment) and the second distributed electric energy (the electric energy distributed to the 5G communication base station). It reflects the actual amount of electric energy distributed in a certain period of time.

[0154] Among them, power supply capacity refers to the total power generation and available power of power storage equipment within a certain period of time. It includes the real-time power generation data of photovoltaic power generation equipment and the real-time power storage data of power storage equipment. Power supply capacity reflects the maximum amount of power that can be provided.

[0155] Among them, the power utilization rate refers to the ratio of the total allocated power to the power supply capacity, reflecting the efficiency of power use in a certain period of time. The higher the power utilization rate, the more fully the power is used. The first allocation ratio refers to the proportion of the first allocated power (the power allocated to the car charging equipment) to the total allocated power, reflecting the proportion of the car charging equipment in the total power distribution. The second allocation ratio refers to the proportion of the second allocated power (the power allocated to the 5G communication base station) to the total allocated power, reflecting the proportion of the 5G communication base station in the total power distribution. Visual display refers to the intuitive display of the power utilization rate, the first allocation ratio and the second allocation ratio in the form of charts, dashboards, etc., to help users and managers understand the operating status in real time.

[0156] In this embodiment, by visually displaying the power utilization rate, the first allocation ratio and the second allocation ratio, intuitive data support is provided for users and managers, and transparency and operability are improved. Users can understand the operating status in real time, and managers can make more informed decisions based on these data to optimize the power allocation strategy. Through real-time monitoring of the power utilization rate, the first allocation ratio and the second allocation ratio, potential problems can be discovered in time and corresponding measures can be taken. By comparing the total distributed power and the power supply capacity, the current power utilization rate can be evaluated to ensure that the power distribution matches the power supply capacity. This not only improves energy utilization efficiency, but also reduces electricity costs and improves economy.

[0157] It can be seen from the above embodiments that by combining historical traffic flow data and historical energy consumption data of 5G communication base stations to predict the installation quantity of photovoltaic power generation equipment, the multi-source data-driven prediction method can more accurately estimate future electricity demand, ensure that the installation scale of photovoltaic power generation equipment matches actual demand, and avoid over-installation or under-installation; by monitoring real-time power generation data, power storage data, energy consumption data of vehicle charging equipment and energy consumption data of 5G communication base stations, it is possible to understand the current power supply and demand situation in real time, adjust the power distribution strategy in time, and ensure flexibility and adaptability; through priority prediction, the allocation of vehicle charging equipment and 5G communication base stations can be dynamically adjusted according to the real-time power supply and demand situation. The power demand priority of the station, especially in the case of tight power supply, can give priority to the power demand of 5G communication base stations, ensure the stable operation of the communication network, and flexibly adjust the power supply strategy of vehicle charging equipment to avoid excessive impact on the power grid. Under different power demand conditions, it can respond flexibly and maximize the use of existing power resources to ensure that the power needs of key facilities and users are met first; based on the priority prediction results, this application scheme intelligently allocates the power of photovoltaic power generation equipment and energy storage equipment, and can reasonably allocate the power generation of photovoltaic power generation equipment and the remaining power of energy storage equipment according to the current power supply and demand situation, to ensure efficient use of power resources.

[0158] In one embodiment of the present application, a highway energy management device based on solar storage and charging is also provided. Figure 3 is a schematic diagram of a highway energy management device based on solar energy storage and charging, shown in an exemplary embodiment of the present application. Figure 3 , the device comprises:

[0159] The first data acquisition unit 301 is used to acquire historical traffic flow data and historical energy consumption data of 5G communication base stations in a target highway section;

[0160] A first prediction unit 302 is used to predict the number of photovoltaic power generation equipment installed based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station, wherein the number of photovoltaic power generation equipment installed is used to indicate the installation of the photovoltaic power generation equipment, and part of the electric energy of the installed photovoltaic power generation equipment is stored in a pre-configured electric energy storage device, and the electric energy storage device is used to provide electric energy to the vehicle charging device and the 5G communication base station;

[0161] The second data acquisition unit 303 is used to acquire the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the car charging equipment and the second energy consumption data of the 5G communication base station;

[0162] A second prediction unit 304 is used to perform priority prediction on the power demand of the vehicle charging device and the 5G communication base station according to the real-time power generation data, the real-time power storage data, the first energy consumption data and the second energy consumption data to obtain a priority prediction result;

[0163] The electric energy distribution unit 305 is used to distribute the electric energy to the photovoltaic power generation equipment and the electric energy storage equipment based on the priority prediction result.

[0164] The highway energy management device based on photovoltaic storage and charging in the embodiment of the present application combines historical traffic flow data and historical energy consumption data of 5G communication base stations to predict the installation quantity of photovoltaic power generation equipment. The multi-source data-driven prediction method can more accurately estimate future electricity demand, ensure that the installation scale of photovoltaic power generation equipment matches actual demand, and avoid over-installation or under-installation. By monitoring real-time power generation data, power storage data, energy consumption data of vehicle charging equipment, and energy consumption data of 5G communication base stations, it is possible to understand the current power supply and demand in real time, adjust the power distribution strategy in time, and ensure flexibility and adaptability. Through priority prediction, the vehicle charging can be dynamically adjusted according to the real-time power supply and demand. The power demand priority of electrical equipment and 5G communication base stations, especially in the case of tight power supply, can give priority to the power demand of 5G communication base stations to ensure the stable operation of the communication network, and flexibly adjust the power supply strategy of automobile charging equipment to avoid excessive impact on the power grid. Under different power demand conditions, it can respond flexibly and maximize the use of existing power resources to ensure that the power needs of key facilities and users are met first; this application scheme is based on the priority prediction results, and intelligently allocates the power of photovoltaic power generation equipment and energy storage equipment. According to the current power supply and demand situation, it can reasonably allocate the power generation of photovoltaic power generation equipment and the remaining power of energy storage equipment to ensure efficient use of power resources.

[0165] The specific embodiments of the highway energy management device based on solar energy storage and charging in the present application can refer to the examples shown in the above-mentioned highway energy management method based on solar energy storage and charging, which will not be repeated here in this example.

[0166] In one embodiment of the present application, an electronic device for implementing the above-mentioned highway energy management method based on solar energy storage and charging is also provided. The electronic device includes a memory and a processor, the memory stores a computer program, and the processor is configured to execute the above-mentioned highway energy management method based on solar energy storage and charging through the computer program.

[0167] See also Figure 4 , Figure 44 is a schematic diagram of the structure of an electronic device shown in an exemplary embodiment of the present application. The computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In RAM 403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0168] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0169] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0170] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier, which carries a computer-readable computer program. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0171] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a unit, a program segment, or a part of the code, and the above-mentioned unit, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0172] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0173] Another aspect of the present application further provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein the computer program executes the above-mentioned highway energy management method based on solar energy storage and charging when it is run. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0174] Another aspect of the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the highway energy management method based on solar energy storage and charging provided in the above-mentioned embodiments.

[0175] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A highway energy management method based on solar energy storage and charging, characterized in that: The method comprises: Obtain historical traffic flow data and 5G communication base station historical energy consumption data within the target highway section; Based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station, predict the number of photovoltaic power generation equipment to be installed, wherein the number of photovoltaic power generation equipment to be installed is used to indicate the installation of the photovoltaic power generation equipment, and part of the electric energy of the installed photovoltaic power generation equipment is stored in a pre-configured electric energy storage device, and the electric energy storage device is used to provide electric energy to the vehicle charging device and the 5G communication base station; Obtaining real-time power generation data of the installed photovoltaic power generation equipment, real-time power storage data of the power storage equipment, first energy consumption data of the vehicle charging equipment, and second energy consumption data of the 5G communication base station; According to the real-time power generation data, the real-time electric energy storage data, the first energy consumption data and the second energy consumption data, priority prediction is performed on the power demand of the vehicle charging device and the 5G communication base station to obtain a priority prediction result; Based on the priority prediction result, allocating electric energy to the photovoltaic power generation device and the electric energy storage device; The predicting the installation quantity of photovoltaic power generation equipment based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station includes: Obtaining historical weather data and geographic information corresponding to the target highway section; Extracting traffic flow characteristics from the historical traffic flow data, 5G communication base station energy consumption characteristics from the 5G communication base station historical energy consumption data, weather impact characteristics from the historical weather data, and photovoltaic power generation location characteristics from the geographic information; Respectively calculate the joint probability distribution between the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, the photovoltaic power generation location characteristics and the photovoltaic power generation demand, determine the first marginal probability distribution corresponding to the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, and the photovoltaic power generation location characteristics, determine the second marginal probability distribution of the photovoltaic power generation demand, and determine multiple mutual information between the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, the photovoltaic power generation location characteristics and the photovoltaic power generation demand based on the joint probability distribution, the first marginal probability distribution and the second marginal probability distribution; According to the multiple mutual information and the preset mutual information threshold, determine the target feature from the traffic flow feature, the 5G communication base station energy consumption feature, the weather impact feature, and the photovoltaic power generation location feature; The target feature is transmitted to a pre-trained photovoltaic power generation equipment prediction model to obtain the installation quantity of the photovoltaic power generation equipment.

2. The highway energy management method based on photovoltaic storage and charging as claimed in claim 1 is characterized in that: Before acquiring the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the vehicle charging equipment and the second energy consumption data of the 5G communication base station, the method further includes: Transmitting the traffic flow characteristics and 5G communication base station energy consumption characteristics to a pre-trained time series prediction model to obtain a power load demand prediction result; Transmitting the weather impact characteristics to a pre-trained weather prediction model to obtain a weather prediction result; Determine the minimum power cost and the maximum energy storage utilization rate according to the power load demand forecast result and the weather forecast result; According to the minimization of electricity cost and maximization of energy storage utilization, the storage of electric energy from the photovoltaic power generation equipment to the electric energy storage equipment is adjusted.

3. The highway energy management method based on photovoltaic storage and charging as claimed in claim 1 is characterized in that: The priority prediction of the power demand of the vehicle charging device and the 5G communication base station is performed according to the real-time power generation data, the real-time power storage data, the first energy consumption data and the second energy consumption data to obtain a priority prediction result, including: Determining the power supply capacity according to the real-time power generation data and the real-time power storage data; determining a total electric energy demand according to the first energy consumption data and the second energy consumption data; Determining a power supply and demand difference based on the power supply capacity and the total power demand; When the power supply and demand difference is greater than or equal to zero, determining that the priority prediction result is that the priorities of the power demands of the vehicle charging device and the 5G communication base station are equal; When the difference between the supply and demand of electric energy is less than zero, priority prediction is performed on the power demand of the vehicle charging equipment and the 5G communication base station to obtain a priority prediction result.

4. The highway energy management method based on solar energy storage and charging as claimed in claim 3 is characterized in that: When the difference between the power supply and demand is less than zero, the priority prediction of the power demand of the vehicle charging device and the 5G communication base station includes: Obtaining a first power demand weight and a charging urgency level corresponding to the vehicle charging device, and a second power demand weight corresponding to the 5G communication base station, wherein the first power demand weight is less than the second power demand weight; Determining a first priority score corresponding to the vehicle charging device according to the first energy consumption data, the first power demand weight, and the charging urgency level; Determine a second priority score corresponding to the 5G communication base station according to the second energy consumption data and the second power demand weight; Based on the first priority score and the second priority score, the priorities of the vehicle charging equipment and the 5G communication base station are arranged in descending order, and the descending order result is used as the priority prediction result.

5. The highway energy management method based on photovoltaic storage and charging as claimed in claim 4 is characterized in that: The allocating electric energy to the photovoltaic power generation equipment and the electric energy storage equipment based on the priority prediction result includes: When the priority prediction result shows that the priorities of the power demands of the vehicle charging device and the 5G communication base station are equal, the electric energy of the photovoltaic power generation device and the electric energy storage device is simultaneously distributed to the vehicle charging device and the 5G communication base station; When the priority prediction result is the descending order result, according to the descending order result, part of the electric energy of the photovoltaic power generation equipment and the electric energy storage equipment is allocated to the first device in the descending order result, and the remaining electric energy of the photovoltaic power generation equipment and the electric energy storage equipment is allocated to the devices in other positions in the descending order result.

6. The highway energy management method based on photovoltaic storage and charging as claimed in claim 1 is characterized in that: After allocating the electric energy to the photovoltaic power generation equipment and the electric energy storage equipment based on the priority prediction result, the method further includes: Acquire a first distributed electric energy distributed to the vehicle charging device and a second distributed electric energy distributed to the 5G communication base station; determining a total distributed electric energy according to the first distributed electric energy and the second distributed electric energy, and determining an electric energy supply capacity according to the real-time power generation data and the real-time electric energy storage data; Comparing the total allocated electric energy with the electric energy supply capacity to obtain electric energy utilization rate, comparing the first allocated electric energy with the total allocated electric energy to obtain a first allocation ratio, and comparing the second allocated electric energy with the total allocated electric energy to obtain a second allocation ratio; The electric energy utilization rate, the first allocation ratio, and the second allocation ratio are visualized.

7. A highway energy management device based on solar energy storage and charging, characterized in that: The device comprises: The first data acquisition unit is used to acquire historical traffic flow data and historical energy consumption data of 5G communication base stations in a target highway section; A first prediction unit is used to predict the number of photovoltaic power generation equipment installed based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station, wherein the number of photovoltaic power generation equipment installed is used to indicate the installation of the photovoltaic power generation equipment, and part of the electric energy of the installed photovoltaic power generation equipment is stored in a pre-configured electric energy storage device, and the electric energy storage device is used to provide electric energy to the vehicle charging device and the 5G communication base station; A second data acquisition unit is used to acquire the real-time power generation data of the installed photovoltaic power generation equipment, the real-time power storage data of the power storage equipment, the first energy consumption data of the car charging equipment and the second energy consumption data of the 5G communication base station; A second prediction unit is used to perform priority prediction on the power demand of the vehicle charging device and the 5G communication base station according to the real-time power generation data, the real-time power storage data, the first energy consumption data and the second energy consumption data to obtain a priority prediction result; an electric energy distribution unit, configured to distribute electric energy to the photovoltaic power generation device and the electric energy storage device based on the priority prediction result; The predicting the installation quantity of photovoltaic power generation equipment based on the historical traffic flow data and the historical energy consumption data of the 5G communication base station includes: Obtaining historical weather data and geographic information corresponding to the target highway section; Extracting traffic flow characteristics from the historical traffic flow data, 5G communication base station energy consumption characteristics from the 5G communication base station historical energy consumption data, weather impact characteristics from the historical weather data, and photovoltaic power generation location characteristics from the geographic information; Respectively calculate the joint probability distribution between the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, the photovoltaic power generation location characteristics and the photovoltaic power generation demand, determine the first marginal probability distribution corresponding to the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, and the photovoltaic power generation location characteristics, determine the second marginal probability distribution of the photovoltaic power generation demand, and determine multiple mutual information between the traffic flow characteristics, the 5G communication base station energy consumption characteristics, the weather impact characteristics, the photovoltaic power generation location characteristics and the photovoltaic power generation demand based on the joint probability distribution, the first marginal probability distribution and the second marginal probability distribution; According to the multiple mutual information and the preset mutual information threshold, determine the target feature from the traffic flow feature, the 5G communication base station energy consumption feature, the weather impact feature, and the photovoltaic power generation location feature; The target feature is transmitted to a pre-trained photovoltaic power generation equipment prediction model to obtain the installation quantity of the photovoltaic power generation equipment.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein the computer program, when running, executes the highway energy management method based on photovoltaic storage and charging as described in any one of claims 1 to 6.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to execute the highway energy management method based on solar energy storage and charging according to any one of claims 1 to 6 through the computer program.

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