Intelligent wind-solar complementary charging control system

Abstract

The invention discloses an intelligent wind-solar complementary charging control system, which belongs to the technical field of charging control and comprises a state monitoring unit, a central control unit and a communication unit. The state monitoring unit is used for acquiring operation state data of the power generation module, the energy storage module and the charging module in real time; the central control unit is internally provided with an optimization control program, and the program is matched with the rectification circuit and can automatically adjust the operation mode of the charging equipment based on the data acquired by the state monitoring unit; the communication unit is used for realizing data interaction with an AI + control data cloud platform and other equipment and supporting data uploading and instruction receiving, the state monitoring unit is used for acquiring operation data of each module in real time, an energy distribution strategy is dynamically adjusted in combination with an optimization control program and an AI learning algorithm of the central control unit, and the energy distribution efficiency is improved. Electric energy is preferentially allocated according to electric quantity of the energy storage module and output power of the power generation module, thereby realizing high-efficiency utilization of wind-solar energy and reducing energy waste.

Description

Technical Field

[0001] This invention belongs to the field of charging control technology, specifically referring to an intelligent wind-solar hybrid charging control system. Background Technology

[0002] With the global trend of energy structure transitioning to clean energy, wind and solar energy, as important components of renewable energy, have become key directions for promoting sustainable energy development through their efficient utilization. Wind-solar hybrid charging technology has gained widespread attention in scenarios such as outdoor emergency power supply, power supplementation in remote areas, and mobile device charging due to its ability to integrate the advantages of both energy sources and improve power supply stability. However, existing wind-solar hybrid charging control systems still face many technical bottlenecks in practical applications, making it difficult to fully utilize the efficiency of wind-solar hybrid energy and meet the increasingly diversified charging needs.

[0003] The existing system has weak status monitoring capabilities and lacks an integrated real-time monitoring mechanism for power generation modules, energy storage modules, and charging modules. Traditional equipment can only detect some operating parameters and cannot accurately obtain key data such as the real-time capacity fluctuations of the power generation module (e.g., output changes of photovoltaic power generation affected by light intensity and angle, and power fluctuations of wind power generation caused by changes in wind speed and direction), the remaining power and health status of the energy storage module (e.g., battery degradation and charge / discharge cycle count), and the real-time load of the charging module (e.g., number of interfaces occupied and power requirements of charging equipment). This lag and one-sidedness in information acquisition prevents the system from adjusting its operating strategy in a timely manner according to the actual operating conditions of each module, often resulting in energy distribution imbalances. For example, when the power generation module has sufficient capacity, the energy storage module fails to store surplus energy in time, resulting in energy waste; while when charging demand surges, insufficient judgment on the remaining power of the energy storage module leads to power outages. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent wind-solar hybrid charging control system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent wind-solar hybrid charging control system, comprising a status monitoring unit, a central control unit, and a communication unit; the status monitoring unit is used to acquire real-time operating status data of the power generation module, energy storage module, and charging module; the central control unit has a built-in optimization control program, which, in conjunction with the rectifier circuit, can automatically adjust the operating mode of the charging equipment based on the data acquired by the status monitoring unit; the communication unit is used to realize data interaction with the AI+ control data cloud platform and other devices, supporting data uploading and command reception.

[0006] The operational status data acquired by the status monitoring unit includes: real-time power generation data of the power generation module, remaining power data of the energy storage module, and workload data of the charging module.

[0007] The optimized control program of the central control unit is configured to: when a charging demand is detected, prioritize the use of the energy storage module to power the load; if the power generation module is in operation at this time, and there is still surplus energy after meeting the current charging demand, control the surplus energy to supplement the energy storage module or charge the backup energy storage device.

[0008] The optimized control program of the central control unit can dynamically adjust the energy distribution strategy according to the remaining power of the energy storage module and the real-time output power of the power generation module: when the power of the energy storage module is lower than the preset threshold, the power generated by the power generation module and processed by the rectifier circuit is given priority to supplement the energy storage module; when the power of the energy storage module is higher than the preset threshold, the power generated by the power generation module is given priority to be used directly to power the charging module.

[0009] The central control unit's optimization control program integrates an AI learning algorithm, which can continuously optimize the charging mode adjustment strategy based on historical charging data, environmental parameter data, and equipment operation data to maximize energy utilization efficiency and adapt to the charging protocols of different brands of mobile phones in the charging module.

[0010] The communication unit is connected to the AI+ control data cloud platform and is used to upload the operating status data obtained by the status monitoring unit and the control command data of the central control unit to the cloud platform in real time, and to receive remote control commands issued by the cloud platform.

[0011] When the intelligent wind-solar hybrid charging control system is expanded to two or more sets, the communication units of each system can realize data interaction with each other through network technology, and complete the sharing of operating status and transmission of collaborative control commands among multiple systems.

[0012] It also includes an energy coordination unit, which is connected to the energy conversion module. When the system is expanded to two or more systems, the central control unit can control the energy conversion module through the energy coordination unit to convert and allocate the electrical energy of different systems, thereby realizing energy scheduling and sharing among multiple systems.

[0013] The central control unit can dynamically allocate power according to the real-time load of the wired and wireless charging units in the charging module, prioritizing power supply to high-priority or high-load charging units.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention acquires real-time operating data of each module through a status monitoring unit, and dynamically adjusts the energy allocation strategy by combining the optimized control program of the central control unit and the AI ​​learning algorithm. It prioritizes the allocation of electrical energy based on the power of the energy storage module and the output power of the power generation module, thereby achieving efficient utilization of wind and solar energy and reducing energy waste.

[0015] 2. This invention can adapt to the charging protocols of different brands of mobile phones by optimizing the control program, and can continuously optimize the charging mode based on historical data, environmental parameters, etc. At the same time, the communication unit connects with the cloud platform to support remote control, enabling the system to flexibly respond to diverse charging needs and improve intelligent management capabilities.

[0016] 3. When the system is expanded to two or more sets, the present invention realizes "network technology data connection" through the communication unit, and completes energy scheduling and sharing between multiple systems by combining the energy coordination unit and the energy conversion module, thereby improving the overall efficiency when multiple systems work together and meeting the needs of large-scale application. Attached Figure Description

[0017] Figure 1 This is the core operation flowchart of the intelligent wind-solar hybrid charging control system of the present invention; Figure 2 This is a flowchart of the energy distribution strategy of the intelligent wind-solar hybrid charging control system of the present invention; Figure 3 This is a flowchart illustrating the multi-system collaborative operation of the intelligent wind-solar hybrid charging control system of the present invention. Figure 4 This is a flowchart of the AI ​​learning algorithm optimization process for the intelligent wind-solar hybrid charging control system of the present invention. Figure 5 This is a flowchart illustrating the emergency mode operation of the intelligent wind-solar hybrid charging control system of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0019] Please see Figures 1-5As shown, this invention provides a technical solution: an intelligent wind-solar hybrid charging control system, including a status monitoring unit, a central control unit, and a communication unit; the status monitoring unit is used to acquire real-time operating status data of the power generation module, energy storage module, and charging module; the central control unit has a built-in optimization control program, which, in conjunction with the rectifier circuit, can automatically adjust the operating mode of the charging equipment based on the data acquired by the status monitoring unit; the communication unit is used to realize data interaction with the AI+ control data cloud platform and other devices, supporting data uploading and command reception.

[0020] It should also be understood that the status monitoring unit is equipped with a multi-dimensional environmental perception module, which can collect parameters such as light intensity, wind speed and direction, ambient temperature and humidity in real time. This provides environmental basis for the central control unit to judge the power generation potential of the power generation modules (photovoltaic panels, wind turbines), avoiding the control lag caused by relying solely on operational data. The central control unit has a built-in fault self-diagnosis and redundancy switching program. When an abnormality is detected in core components such as rectifier circuits and charging equipment, it can automatically switch to backup circuits or equipment. At the same time, it sends a fault warning to the cloud platform through the communication unit to ensure continuous power supply to the system. The communication unit supports 5G+LoRa dual-mode communication. In areas with good 5G signal coverage, high-speed 5G is used to transmit data first, while in remote or weak signal areas, it automatically switches to low-power LoRa mode to ensure uninterrupted data interaction and adapt to different application scenarios.

[0021] It should be noted that the sensor selection for the status monitoring unit is as follows: the power generation module is equipped with a light sensor (model BH1750, accuracy ±20 lux) and a wind speed sensor (model FC-2A, measurement range 0-60 m / s, accuracy ±0.1 m / s); the energy storage module uses a SOC sensor (model DS2784, measurement error ≤1%); the charging module is equipped with a current sensor (model ACS712, range 0-5A, accuracy ±0.05A) to achieve real-time monitoring of load current. The data acquisition frequency has been optimized: the power generation data acquisition frequency has been increased to once every 10 seconds to cope with rapid fluctuations in wind and solar power generation; the charging protocol identification data acquisition frequency has been set to once every 2 seconds to ensure timely adaptation to the charging needs of different brands of mobile phones.

[0022] The operational status data acquired by the status monitoring unit includes: real-time power generation data of the power generation module, remaining power data of the energy storage module, and workload data of the charging module.

[0023] It should also be understood that the operational status data now includes equipment health data, such as the aging degree of components in the power generation module (e.g., the degradation rate of photovoltaic panels, the wear coefficient of wind turbine bearings), the charge and discharge cycle count of the energy storage module, and the interface contact resistance of the charging module. This provides data support for system maintenance and lifespan prediction. Real-time production capacity data adds a predictive dimension, combining historical production capacity patterns with current environmental parameters (e.g., predicted sunshine duration, predicted wind force) to generate a production capacity forecast curve for the next 1-2 hours, helping the central control unit to formulate energy allocation strategies in advance. The workload data is refined into dynamic load and static load. Dynamic load refers to the real-time power demand fluctuations of the charging equipment, while static load refers to the standby power consumption of the charging module itself, enabling more accurate statistics and control of power consumption.

[0024] In addition, a PTC heater (50W power, start-up threshold -20℃) can be added to the energy storage module cabinet to prevent battery capacity degradation at low temperatures; the status monitoring unit adopts a wide-temperature sensor (operating temperature -40℃~85℃) to ensure normal data acquisition at low temperatures. In high-sunlight environments (Yili summer): the photovoltaic panel is equipped with an MPPT maximum power point tracking module (model CN3065) with a tracking accuracy ≥99%, improving solar energy utilization; the charging module shell is made of sun-proof material (ABS+PC alloy) to avoid interface aging caused by high temperatures.

[0025] The optimized control program of the central control unit is configured to: when a charging demand is detected, prioritize the use of the energy storage module to power the load; if the power generation module is in operation at this time, and there is still surplus energy after meeting the current charging demand, control the surplus energy to supplement the energy storage module or charge the backup energy storage device.

[0026] It should be noted that when power is supplied by the energy storage module, a new battery protection logic has been added. Based on the current temperature of the energy storage module (e.g., limiting discharge power at low temperatures and activating heat dissipation at high temperatures) and its health status, the discharge rate is adjusted to avoid over-discharge damaging the battery and extend the lifespan of the energy storage module. A priority setting has been added to the allocation of surplus power. When there are simultaneous needs for energy storage module replenishment and backup energy storage device charging, the system can automatically prioritize the backup devices based on their urgency (e.g., backup batteries for emergency lighting, backup power for medical equipment), ensuring that higher-priority devices are charged first. In the power quality detection stage, before surplus power is transmitted to the energy storage module or backup device, it is first processed by a filtering circuit and a voltage stabilization module to ensure that the voltage, frequency, and harmonic content of the output power meet standards, preventing damage to the energy storage device from inferior power.

[0027] It should also be understood that the central control unit's hardware configuration includes: an STM32H743ZI microcontroller (480MHz clock speed, 1MB RAM) supporting multi-task real-time processing; and an integrated 12-bit ADC module (500kSPS sampling rate) to improve the accuracy of voltage and current data acquisition. The rectifier circuit's collaborative logic includes: newly added overvoltage / overcurrent protection threshold parameters; when the rectifier circuit's output voltage > 56V or current > 10A, the central control unit triggers a relay to disconnect the input, protecting the energy storage module; and supplemented voltage regulation accuracy indicators, ensuring that the rectified output voltage fluctuation is controlled within ±0.3V.

[0028] The optimized control program of the central control unit can dynamically adjust the energy distribution strategy according to the remaining power of the energy storage module and the real-time output power of the power generation module: when the power of the energy storage module is lower than the preset threshold, the power generated by the power generation module and processed by the rectifier circuit is given priority to supplement the energy storage module; when the power of the energy storage module is higher than the preset threshold, the power generated by the power generation module is given priority to be used directly to power the charging module.

[0029] In addition, the preset threshold is set to a dynamically adjustable mode. The system can automatically adjust according to seasonal changes (such as lowering the threshold when the energy storage module capacity decreases in winter and raising the threshold when the capacity recovers in summer) and peak electricity consumption periods (such as lowering the threshold to reserve more power when charging demand is high during the day), improving the flexibility of threshold setting. The energy distribution strategy adds grid interaction logic. When the power generation module capacity far exceeds the system demand and the energy storage module is fully charged, the excess power can be connected to the public grid (subject to grid connection standards) to achieve reverse power transmission and improve the system's energy utilization benefits. The rectifier circuit processing stage adds energy efficiency optimization. According to the output characteristics of the power generation module (such as DC voltage fluctuations of photovoltaic panels and AC frequency changes of wind turbines), the switching frequency and topology of the rectifier circuit are automatically adjusted to reduce power conversion losses and improve rectification efficiency.

[0030] The central control unit's optimization control program integrates an AI learning algorithm, which can continuously optimize the charging mode adjustment strategy based on historical charging data, environmental parameter data, and equipment operation data to maximize energy utilization efficiency and adapt to the charging protocols of different brands of mobile phones in the charging module.

[0031] It should be noted that the AI ​​learning algorithm adds a user behavior analysis dimension, combining the usage frequency of charging devices in different time periods and areas (such as concentrated charging in office areas during the day and concentrated charging in residential areas at night) to optimize the time allocation of charging modes. For example, it can store energy in advance during high-frequency charging periods to reduce waiting time. When adapting to charging protocols, a protocol compatibility update mechanism is added. When new mobile phone charging protocols appear in the market (such as fast charging protocol upgrades or private protocol updates), the protocol data packets sent by the cloud platform can be received through the communication unit to automatically update the protocol adaptation library in the AI ​​algorithm without the need for manual hardware upgrades. Energy utilization efficiency optimization adds a multi-objective balance. In addition to maximizing efficiency, it also takes into account system noise control (such as the noise threshold of wind turbines when operating at high power) and carbon emission reduction (prioritizing the use of photovoltaic power to reduce the demand for fossil fuel substitution), achieving multi-dimensional optimization in terms of economy, environmental protection, and experience.

[0032] The communication unit is connected to the AI+ control data cloud platform and is used to upload the operating status data obtained by the status monitoring unit and the control command data of the central control unit to the cloud platform in real time, and to receive remote control commands issued by the cloud platform.

[0033] It should also be understood that data upload now includes edge computing preprocessing. The communication unit first locally filters the running status data and control command data (such as filtering duplicate data and compressing redundant information) before uploading them to the cloud platform, reducing data transmission volume and lowering network bandwidth usage and transmission latency. Remote control commands now have tiered permission management, distinguishing between administrator commands (such as system parameter reset and firmware upgrade), maintenance personnel commands (such as equipment inspection scheduling and troubleshooting), and ordinary user commands (such as charging reservation and load priority adjustment). Different permissions correspond to different operation scopes to ensure system security. A data encryption transmission mechanism has been added, using national cryptographic algorithms (such as SM4 symmetric encryption and SM2 asymmetric encryption) to encrypt uploaded and received data. At the same time, certificate authentication ensures the legitimacy of the identities between the cloud platform and the system, preventing data theft or tampering.

[0034] It should be noted that the network module parameters of the communication unit are as follows: The 4G module (model EC20CEHCLG) supports LTE Cat.4 with a downlink speed of 150Mbps, ensuring the stability of data transmission in remote areas; the WiFi module (model ESP8266-12F) supports the 802.11b / g / n protocol with a communication distance of ≤100m, meeting the data interaction requirements of multi-system LANs. For data transmission security, a new data encryption mechanism has been added, using the AES-128 algorithm to encrypt the running data uploaded to the AI+ control data cloud platform to prevent data leakage or tampering.

[0035] When the intelligent wind-solar hybrid charging control system is expanded to two or more sets, the communication units of each system can realize data interaction with each other through network technology, and complete the sharing of operating status and transmission of collaborative control commands among multiple systems.

[0036] Furthermore, the multi-system data interaction incorporates load balancing logic. When the charging module of one system is overloaded (e.g., devices are queuing for charging) while another system is underloaded, some charging demand can be automatically redirected to the underloaded system, achieving a balanced distribution of charging resources within the region and reducing user waiting time. The collaborative control command transmission adds an emergency linkage mechanism. When a system malfunctions (e.g., a generator module shuts down or an energy storage module is damaged), a support request can be sent to surrounding systems via data interaction. These surrounding systems, based on their remaining capacity and energy storage, allocate power to support the faulty system, ensuring continuous power supply in the region. Network technology adaptation enhances anti-interference capabilities. During multi-system data transmission, frequency hopping communication and data retransmission mechanisms are employed to address electromagnetic interference and signal obstruction issues in industrial environments, ensuring the stability and reliability of data interaction.

[0037] It also includes an energy coordination unit, which is connected to the energy conversion module. When the system is expanded to two or more systems, the central control unit can control the energy conversion module through the energy coordination unit to convert and allocate the electrical energy of different systems, thereby realizing energy scheduling and sharing among multiple systems.

[0038] It should be noted that the energy coordination unit has added multi-type energy adaptation functions. In addition to wind and solar power generation, it can connect to other distributed energy sources (such as small biomass generators and geothermal power generation equipment). Through the energy conversion module, different types of electrical energy (such as AC, DC, and different voltage levels) are uniformly converted into system-compatible electrical energy, realizing multi-energy coordinated dispatch. The power allocation adds cost accounting logic, combining the power generation costs of different systems (such as the operation and maintenance costs of wind and solar power generation and the fuel costs of biomass energy), and prioritizes the allocation of system power with lower costs to reduce the overall energy use cost and improve the system's economics. It also adds energy loss monitoring, which monitors the values ​​of line loss and conversion loss in real time during the power conversion and allocation process. Through AI algorithms, it optimizes the allocation path (such as selecting the transmission line with the lowest loss) and conversion parameters to reduce energy waste.

[0039] It should also be understood that emergency mode triggering logic can be added to the central control unit as needed: when a power grid failure is detected (monitored by a voltage sensor), the system automatically switches to "emergency priority" mode, prioritizing the charging of medical equipment (such as blood glucose meters and small ventilators) and cutting off power to non-essential equipment; in emergency mode, the minimum discharge threshold of the energy storage module is set to 10% to extend the emergency power supply time.

[0040] The central control unit can dynamically allocate power according to the real-time load of the wired and wireless charging units in the charging module, prioritizing power supply to high-priority or high-load charging units.

[0041] In addition, priority settings are adapted to different scenarios. For example, in emergency scenarios (such as earthquakes or power outages), priority is given to powering medical equipment charging units and emergency communication equipment charging units; in daily scenarios, priority is given to high-power fast charging units and multi-device charging units under high load, improving scenario adaptability. Power allocation adds dynamic power adjustment. When wired and wireless charging units are under high load at the same time, if the total system power is insufficient, the power supply of high-progress units can be appropriately reduced according to the charging progress of each unit (such as a device being charged to 80% and a device being charged to 20%), so as to prioritize meeting the basic charging needs of low-progress units. Charging safety monitoring is added. While allocating power, the temperature (such as interface temperature and device battery temperature) and current fluctuations of each charging unit are monitored in real time. If overload, short circuit, overheating or other safety hazards are detected, the power supply to the unit is immediately cut off and an alarm mechanism is activated to ensure charging safety.

[0042] Working Principle: The status monitoring unit acquires real-time production capacity data from the power generation module, remaining power data from the energy storage module, and workload data from the charging module, and transmits this data to the central control unit. The central control unit's built-in optimization control program, in conjunction with the rectifier circuit, automatically adjusts the charging equipment's operating mode based on the received data. When a charging demand is detected, it prioritizes using the energy storage module's power to supply the load. If the power generation module is already operating and has surplus power after meeting the current charging demand, it controls the surplus power to supplement the energy storage module or charge the backup energy storage device. Simultaneously, the optimization control program dynamically adjusts the energy allocation strategy based on the remaining power of the energy storage module and the real-time output power of the power generation module. When the energy storage module's power is below a preset threshold, it prioritizes using the power generated by the power generation module and processed by the rectifier circuit to supplement the energy storage module. When the energy storage module's power is above the preset threshold, it prioritizes using the power generated by the power generation module directly to power the charging module. Furthermore, this optimization control program integrates an AI learning algorithm, enabling it to analyze historical charging data... The charging mode adjustment strategy is continuously optimized based on electrical data, environmental parameter data, and equipment operation data to maximize energy utilization efficiency and adapt to the charging protocols of different brands of mobile phones in the charging module. At the same time, the communication unit realizes data interaction with the AI+ control data cloud platform and other devices, uploading the operation status data obtained by the status monitoring unit and the control command data of the central control unit to the cloud platform in real time, and receiving remote control commands issued by the cloud platform. When the system is expanded to two or more systems, the communication units of each system realize data interaction with each other through network technology, complete the sharing of operation status and the transmission of collaborative control commands between multiple systems, and realize "network technology data interconnection". The central control unit controls the energy conversion module connected to it through the energy coordination unit to convert and allocate the electrical energy of different systems, realizing energy scheduling and sharing between multiple systems. In addition, the central control unit can dynamically allocate electrical energy according to the real-time load of wired charging units and wireless charging units in the charging module, and prioritize power supply to high-priority or high-load charging units.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0044] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An intelligent wind-solar complementary charging control system, characterized in that, It comprises a state monitoring unit, a central control unit and a communication unit; the state monitoring unit is used to acquire the running state data of the power generation module, the energy storage module and the charging module in real time; the central control unit is internally provided with an optimization control program, which cooperates with the rectifier circuit and can automatically adjust the running mode of the charging equipment based on the data acquired by the state monitoring unit; the communication unit is used to realize data interaction with the AI+ control data cloud platform and other equipment, support data uploading and instruction receiving.

2. The intelligent wind-solar complementary charging control system according to claim 1, characterized in that: The running state data acquired by the state monitoring unit includes real-time power generation data of the power generation module, residual power data of the energy storage module and working load data of the charging module. 3.The intelligent wind-solar complementary charging control system according to claim 1, characterized in that: The optimization control program of the central control unit is configured to: when detecting a charging demand, preferentially calling the electric energy of the energy storage module to supply power to the load; if the power generation module is in a running state at this time, after meeting the current charging demand, the surplus electric energy is controlled to supplement the energy storage module or charge the standby energy storage equipment.

4. The intelligent wind-solar complementary charging control system according to claim 1, characterized in that: The optimization control program of the central control unit can dynamically adjust the energy distribution strategy according to the residual power of the energy storage module and the real-time output power of the power generation module: when the residual power of the energy storage module is lower than a preset threshold, the electric energy generated by the power generation module and processed by the rectifier circuit is preferentially supplemented to the energy storage module; when the residual power of the energy storage module is higher than the preset threshold, the electric energy generated by the power generation module is preferentially used for power supply of the charging module. 5.The intelligent wind-solar complementary charging control system according to claim 1, characterized in that: The optimization control program of the central control unit integrates an AI learning algorithm, which can continuously optimize the charging mode adjustment strategy based on historical charging data, environmental parameter data and equipment running data, maximize energy utilization efficiency, and adapt to the charging protocols of different brands of mobile phones in the charging module. 6.The intelligent wind-solar complementary charging control system according to claim 1, characterized in that: The communication unit is connected with the AI+ control data cloud platform, and is used to upload the running state data acquired by the state monitoring unit and the control instruction data of the central control unit to the cloud platform in real time, and receive the remote control instructions issued by the cloud platform. 7.The intelligent wind-solar complementary charging control system according to claim 6, characterized in that: When the intelligent wind-solar complementary charging control system is expanded to two or more sets, the communication units of each system can realize data interaction between each other through network technology, complete running state sharing and collaborative control instruction transmission between multiple systems. 8.The intelligent wind-solar complementary charging control system according to claim 1, characterized in that: It also comprises an energy coordination unit connected with the energy conversion module, when the system is expanded to two or more sets, the central control unit can control the energy conversion module through the energy coordination unit to convert and allocate the electric energy of different systems, realize energy scheduling and sharing between multiple systems. 9.The intelligent wind-solar complementary charging control system according to claim 1, characterized in that: The central control unit can dynamically allocate electric energy according to the real-time load conditions of the wired charging unit and the wireless charging unit in the charging module, and preferentially guarantee power supply of the charging unit with high priority or high load.

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