Intelligent solar unmanned logistics distribution control method and system

Abstract

The invention discloses an intelligent solar unmanned logistics distribution control method and system, and relates to the technical field of renewable energy sources, and the method comprises the steps: obtaining real-time weather data and a current geographic position through a vehicle-mounted meteorological sensor and a GPS positioning system; the intelligent energy management system analyzes weather and geographic position data and calculates expected energy output of the solar panel; based on the distribution task list and the current geographic position, a distribution route is planned, the feasibility of the route is evaluated, and an optimal distribution route is generated; according to the optimal distribution route and the current position of the sun, the vehicle speed and the battery use strategy are dynamically adjusted; and after the distribution task is completed, summarizing the distributed energy consumption data and the solar panel efficiency report, and optimizing a dynamic path planning algorithm. Through a series of intelligent control methods, the energy utilization efficiency and the task execution reliability of the unmanned logistics distribution system are improved, and the distribution service quality and the customer satisfaction are further improved through continuously optimizing the path planning algorithm.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy technology, and in particular to an intelligent solar energy unmanned logistics distribution control method and system. Background Art

[0002] In recent years, with the rapid progress of the Internet of Things, big data analysis and renewable energy technology, intelligent logistics distribution systems have become the key driving force for the transformation and upgrading of the logistics industry. Traditional logistics distribution relies on fossil fuels, which is not only inefficient but also seriously pollutes the environment. However, the emergence of intelligent solar unmanned logistics distribution control methods marks an important step for the logistics industry to move towards green and intelligent directions.

[0003] Although intelligent logistics distribution control methods have brought significant progress, there are still some key challenges in existing technologies. First, most systems fail to fully consider the impact of weather changes on solar energy output, affecting the continuity and reliability of distribution tasks. Second, traditional path planning algorithms are often based on static data and lack a response mechanism to real-time environmental changes. In addition, existing systems adopt a fixed energy consumption pattern in battery usage strategy and fail to dynamically adjust vehicle speed according to the position of the sun and light intensity. Finally, although some systems attempt to collect energy consumption data during the distribution process, there is a lack of effective feedback mechanism to optimize the dynamic path planning algorithm, resulting in slow system improvement and difficulty in adapting to the ever-changing logistics environment. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent solar unmanned logistics distribution control method and system to solve the problems of low energy utilization efficiency and insufficient reliability of distribution task execution caused by the lack of effective energy management and dynamic path planning in the existing unmanned logistics distribution system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides an intelligent solar unmanned logistics distribution control method, which includes obtaining real-time weather data and current geographic location through a vehicle-mounted meteorological sensor and a GPS positioning system; The intelligent energy management system analyzes weather and geographic location data to calculate the expected energy output of solar panels, while evaluating the current battery power and the energy consumption requirements of the delivery task, and intelligently adjusts the charging strategy; Plan the delivery route based on the delivery task list and current geographic location, evaluate the feasibility of the route, and generate the optimal delivery route; Dynamically adjust vehicle speed and battery usage strategy based on the optimal delivery route and the current position of the sun; After the delivery task is completed, the energy consumption data and solar panel efficiency report of the delivery are summarized to optimize the dynamic path planning algorithm.

[0007] As a preferred solution of the intelligent solar unmanned logistics distribution control method of the present invention, the weather data includes light intensity and cloud coverage; the current geographical location includes longitude and latitude coordinates.

[0008] As a preferred solution of the intelligent solar unmanned logistics distribution control method of the present invention, the intelligent energy management system analyzes weather and geographic location data and calculates the expected energy output of solar panels, including the following steps: The system receives real-time light intensity and geographic location data from monitoring; Access the weather forecast API to obtain the predicted light intensity and cloud coverage for a specified geographic location in the next few hours; Use a light prediction model based on historical data and machine learning to calculate the predicted value of future light intensity; Based on the predicted light intensity and solar panel characteristics, the expected energy output of the solar panel is calculated as: ; in, represents the electrical energy generated by the solar panel at time t, is the conversion efficiency of the solar panel, A is the area of ​​the solar panel, is the predicted light intensity at time t, C(t) is the cloud cover at time t, d is the relative path length of light through the atmosphere, It is the relative path length of light through the atmosphere under standard atmospheric conditions.

[0009] As a preferred solution of the intelligent solar unmanned logistics distribution control method of the present invention, wherein: evaluating the current battery power and the energy consumption requirements of the distribution task, and intelligently adjusting the charging strategy includes the following steps: The system monitors the battery's current charge and maximum storage energy, and evaluates the battery status; Evaluate whether additional charging is needed based on the energy demand of the delivery task and the predicted solar energy output; The required charging time is calculated based on the difference between the energy demand and the predicted solar energy output, as well as the average charging efficiency of the solar panels. The expression is: ; in, is the required charging time, is the energy demand, is the current battery charge, is the average charging efficiency; Continuously monitor battery status and predicted solar energy output to dynamically adjust charging strategies.

[0010] As a preferred solution of the intelligent solar unmanned logistics distribution control method of the present invention, the following steps are included: based on the distribution task list and the current geographical location, planning the distribution route, evaluating the feasibility of the route, and generating the optimal distribution route: Combining the light intensity prediction and the volatility of energy output, a light and energy efficiency evaluation function is introduced to quantify the energy efficiency on different paths. The expression is: ; in, is the energy efficiency at a certain moment on the path, is the standard deviation of the light intensity prediction, reflecting the uncertainty of the light intensity, is the average light intensity during the forecast period, It is the exponential factor of the effect of light intensity on energy efficiency; Define a traffic and time window adaptability function to evaluate the impact of traffic conditions and delivery time windows on the delivery path. The expression is: ; in, is the adaptability of traffic and time window at a certain moment on the path, is the difference between the current time and the nearest delivery time window, and are the mean and standard deviation of the delivery time window distribution, is the real-time traffic congestion level on the route, is the maximum value of traffic congestion level, is the exponential factor of the impact of traffic conditions on adaptability; Combining the energy efficiency and traffic conditions on different paths and the impact of the delivery time window on the delivery path, a comprehensive evaluation function is constructed to perform dynamic path planning and optimization. The expression is: ; in, It is the comprehensive optimization score at a certain moment on the path; Continuously monitor all relevant real-time data and recalculate based on current data , and dynamically adjust the delivery route to maximize , so that the delivery vehicle always follows the most optimized route.

[0011] As a preferred solution of the intelligent solar unmanned logistics distribution control method of the present invention, the following steps are included in the dynamic adjustment of vehicle speed and battery usage strategy according to the optimal distribution route and the current position of the sun: Use the global positioning system (GPS) to determine the real-time location of the delivery vehicle and obtain the longitude and latitude coordinates; Based on the geographic location of the delivery vehicle and the current time, an astronomical algorithm is used to predict the change in the position of the sun during the entire delivery process; Adjust the initial angle of the solar panel according to the current sun position so that the panel faces the sun; Activate the tracking control system of the solar panel to adjust the angle of the solar panel in real time according to the changes in the sun's position; During the delivery process, the changes in the sun's position are continuously monitored, and the control system is used to automatically adjust the angle of the solar panels to ensure that the panels are always facing the sun; Combine delivery routes and estimated energy output to optimize solar panel tracking strategies to effectively track the sun as delivery vehicles move; Real-time monitoring of the energy output of solar panels and comparative analysis with the expected energy output; By fine-tuning the angle of the solar panels, the energy supply can be stabilized to cope with the fluctuation of light intensity; The delivery vehicle receives the optimal delivery route instruction and adjusts the solar panel to the initial best light receiving position according to the tracking angle of the solar panel; While the delivery vehicle is driving, the intelligent energy management system dynamically adjusts the vehicle speed and battery usage strategy based on real-time energy output and predicted energy consumption.

[0012] As a preferred solution of the intelligent solar unmanned logistics distribution control method of the present invention, after the distribution task is completed, the distribution energy consumption data and solar panel efficiency report are summarized, and the dynamic path planning algorithm is optimized, including the following steps: The delivery vehicle completes the delivery of goods to all delivery points and confirms the delivery status of each delivery point through QR code reading technology; The delivery vehicle uploads complete delivery progress, energy consumption data and solar panel performance reports to the central control system; The central control system analyzes the uploaded data and generates energy consumption and efficiency analysis reports; Based on energy consumption analysis, the intelligent energy management system automatically adjusts the energy management strategy; Review delivery routes and analyze energy efficiency and traffic adaptability along the routes; Use machine learning algorithms to train path planning models to improve path selection accuracy and energy efficiency for future delivery tasks; The system automatically checks the battery status. If the battery level is lower than the charging requirement, the system plans the best route back to the charging station to replenish energy. Apply the optimized energy management strategy and path planning algorithm to subsequent delivery tasks to form a closed loop of continuous improvement.

[0013] In a second aspect, the present invention provides an intelligent solar-powered unmanned logistics distribution control system, comprising: Environmental perception module: obtains real-time weather data and current geographic location through on-board meteorological sensors and GPS positioning system; Intelligent Energy Management Module: The intelligent energy management system analyzes weather and geographic location data, calculates the expected energy output of solar panels, evaluates the current battery power and the energy consumption requirements of the delivery task, and intelligently adjusts the charging strategy; Path planning and evaluation module: based on the delivery task list and current geographical location, plan the delivery route, evaluate the feasibility of the route, and generate the optimal delivery route; Dynamic strategy adjustment module: dynamically adjusts vehicle speed and battery usage strategy based on the optimal delivery route and the current position of the sun; System feedback and optimization module: After the delivery task is completed, the energy consumption data and solar panel efficiency report of the delivery are summarized to optimize the dynamic path planning algorithm.

[0014] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the intelligent solar unmanned logistics distribution control method as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the intelligent solar unmanned logistics distribution control method as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: the present invention utilizes on-board sensors to collect weather and geographic data, and the intelligent energy management system estimates solar energy output based on this data, optimizes battery charging strategies, and ensures a stable energy supply; based on the delivery task and geographic location, the feasibility of the route is planned and evaluated, and the energy-optimal path is generated to improve delivery efficiency and maximize the timeliness and cost-effectiveness of logistics delivery; based on the optimal route and the position of the sun, the vehicle speed and battery strategy are dynamically adjusted to minimize energy consumption; after delivery, the energy consumption data and solar panel efficiency are summarized, and the path algorithm is optimized using machine learning to improve future delivery efficiency and energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the intelligent solar unmanned logistics distribution control method in Example 1.

[0019] Figure 2 This is a diagram for dividing the light intensity threshold in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides an intelligent solar unmanned logistics distribution control method, comprising the following steps: S1. Obtain real-time weather data and current geographic location through vehicle-mounted meteorological sensors and GPS positioning system.

[0024] Weather data includes sunlight intensity and cloud cover; current geographic location includes longitude and latitude coordinates.

[0025] S2. The intelligent energy management system analyzes weather and geographic location data to calculate the expected energy output of the solar panels, including the following steps: The system receives real-time light intensity and geographic location data; accesses the weather forecast API to obtain light intensity forecast data and cloud coverage for a specified geographic location in the next few hours; uses a light forecast model based on historical data and machine learning to calculate future light intensity forecasts; and calculates the expected energy output of the solar panel based on the predicted light intensity and solar panel characteristics, expressed as: ; in, represents the electrical energy generated by the solar panel at time t, is the conversion efficiency of the solar panel, A is the area of ​​the solar panel, is the predicted light intensity at time t, C(t) is the cloud cover at time t, d is the relative path length of light through the atmosphere, It is the relative path length of light through the atmosphere under standard atmospheric conditions.

[0026] Furthermore, the illumination prediction model based on historical data and machine learning is used to calculate the expression for the future illumination intensity prediction: ; in, For light intensity prediction, is the coefficient of the influence of geographical location and time on light intensity, and are the rotation angles of the Earth corresponding to longitude and latitude, is the number of hours in a day, is the coefficient of cloud coverage on light intensity, is the coefficient of the effect of altitude on light intensity, H is the height of the ground relative to sea level, is the standard atmospheric height, used to calculate the effect of altitude on atmospheric transparency. is the atmospheric transparency that varies with time.

[0027] S3. Evaluate the current battery power and the energy consumption requirements of the delivery task, and intelligently adjust the charging strategy, including the following steps: The system monitors the battery's current charge and maximum storage energy, and evaluates the battery status; Evaluate whether additional charging is needed based on the energy demand of the delivery task and the predicted solar energy output; The required charging time is calculated based on the difference between the energy demand and the predicted solar energy output, as well as the average charging efficiency of the solar panels. The expression is: ; in, is the required charging time, is the energy demand, is the current battery charge, is the average charging efficiency; Continuously monitor battery status and predicted solar energy output to dynamically adjust charging strategies.

[0028] Furthermore, if , then plan the charging time; adopt intelligent charging strategy, give priority to charging when there is sufficient sunlight, that is, when (in is the light intensity threshold), the charging process is started.

[0029] Furthermore, the light intensity thresholds are divided as follows: Very low light threshold ( ): Set at 100 W / m 2 The following indicates that the light conditions are very weak and the solar panels can hardly generate effective electricity. It is determined that the output of the solar panels is insufficient to support the current energy demand. The system will trigger the backup energy plan, such as switching to battery power or starting an external charging plan; Low light threshold ( ): Set at 100 W / m 2 Up to 300 W / m 2 , which means that although the solar panels have a certain energy output, the efficiency is not high and may not be enough to meet the entire energy demand of the delivery vehicle. The system may adopt a conservative energy management strategy under this light intensity, such as limiting unnecessary energy consumption, optimizing the delivery route to reduce energy consumption, or starting a slow charging mode to avoid over-consumption of limited solar energy output; Medium light threshold ( ): Set at 300 W / m 2 Up to 600 W / m 2 The light intensity in this range provides good conditions for solar energy output. Solar panels can generate considerable electricity. They can use this light condition to charge and replenish the battery. At the same time, they can dynamically adjust the charging speed according to the current battery status and delivery task requirements to ensure sufficient energy reserves. High light threshold ( ): Set at 600 W / m 2 Up to 1000 W / m 2 ,Within this range, the output capacity of the solar panels reaches a high level, which can be regarded as an ideal solar energy utilization condition. The system will prioritize high light conditions for fast charging to ensure that the battery is fully charged. At the same time, the excess energy can be stored or used for future high-energy-consuming tasks, or fed back to the power grid through the smart grid; Extreme Light Threshold ( ): set at 1000 W / m 2 The above is equivalent to the condition of direct sunlight exposure. Under this light intensity, the output of the solar panel reaches its peak, and the system will charge at full capacity to ensure that the battery reaches a full charge in a short time, and activate the overcharge protection mechanism to prevent the battery from overheating or overcharging damage.

[0030] S4. Based on the delivery task list and the current geographical location, the delivery route is planned, the feasibility of the route is evaluated, and the generation of the optimal delivery route includes the following steps: Combining the light intensity prediction and the volatility of energy output, a light and energy efficiency evaluation function is introduced to quantify the energy efficiency on different paths. The expression is: ; in, is the energy efficiency at a certain moment on the path, is the standard deviation of the light intensity prediction, reflecting the uncertainty of the light intensity, is the average light intensity during the forecast period, It is the exponential factor of the effect of light intensity on energy efficiency; Define a traffic and time window adaptability function to evaluate the impact of traffic conditions and delivery time windows on the delivery path. The expression is: ; in, is the adaptability of traffic and time window at a certain moment on the path, is the difference between the current time and the nearest delivery time window, and are the mean and standard deviation of the delivery time window distribution, is the real-time traffic congestion level on the route, is the maximum value of traffic congestion level, is the exponential factor of the impact of traffic conditions on adaptability; Combining the energy efficiency and traffic conditions on different paths and the impact of the delivery time window on the delivery path, a comprehensive evaluation function is constructed to perform dynamic path planning and optimization. The expression is: ; in, It is the comprehensive optimization score at a certain moment on the path; Continuously monitor all relevant real-time data and recalculate based on current data , and dynamically adjust the delivery route to maximize , so that the delivery vehicle always follows the most optimized route.

[0031] Furthermore, when When it is larger, it means that the current path performs well in terms of energy efficiency, traffic adaptability, and distribution energy consumption requirements, and is the preferred path; on the contrary, a smaller The indicated route may have problems such as low energy efficiency, traffic congestion, or exceed the delivery time window, and the route needs to be adjusted.

[0032] S5. Dynamically adjusting the vehicle speed and battery usage strategy according to the optimal delivery route and the current position of the sun includes the following steps: Use the global positioning system (GPS) to determine the real-time location of the delivery vehicle and obtain its longitude and latitude coordinates; apply astronomical algorithms based on the delivery vehicle's geographic location and current time to predict changes in the sun's position throughout the delivery process; adjust the initial angle of the solar panel based on the current sun's position so that the panel faces the sun; activate the solar panel tracking control system to adjust the angle of the solar panel in real time based on changes in the sun's position; during the delivery process, continuously monitor changes in the sun's position and use the control system to automatically adjust the angle of the solar panel to ensure that the panel is always facing the sun; optimize the solar panel tracking strategy based on the delivery route and expected energy output to effectively track the sun when the delivery vehicle is moving; monitor the energy output of the solar panel in real time and compare and analyze it with the expected energy output; ensure stable energy supply and cope with fluctuations in light intensity by fine-tuning the angle of the solar panel; the delivery vehicle receives the optimal delivery route instruction and adjusts the solar panel to the initial best position to receive light based on the solar panel tracking angle; while the delivery vehicle is driving, the intelligent energy management system dynamically adjusts the vehicle speed and battery usage strategy based on real-time energy output and predicted energy consumption.

[0033] S6. After the delivery task is completed, the energy consumption data and solar panel performance report of the delivery are summarized, and the dynamic path planning algorithm is optimized, including the following steps: The delivery vehicle completes the delivery of goods to all delivery points and confirms the delivery status of each delivery point through QR code reading technology; the delivery vehicle uploads the complete delivery progress, energy consumption data and solar panel efficiency report to the central control system; the central control system analyzes the uploaded data, including the comparison of actual energy output and consumption, the evaluation of solar panel tracking effect, etc., and generates an energy consumption and efficiency analysis report, which contains energy consumption curves, solar panel efficiency curves and energy consumption distribution diagrams of the delivery routes; based on energy consumption analysis, the intelligent energy management system automatically adjusts energy management strategies, such as charging timing, charging speed and battery usage patterns; reviews the delivery route and analyzes the energy efficiency and traffic adaptability on the route; uses machine learning algorithms to train the path planning model to improve the path selection accuracy and energy efficiency of future delivery tasks; the system automatically checks the battery status, including the remaining power, health index and charging requirements. If the battery power is lower than the charging requirement, the system plans the optimal route back to the charging station to replenish energy; the optimized energy management strategy and path planning algorithm are applied to subsequent delivery tasks to form a closed loop of continuous improvement.

[0034] Furthermore, the charging demand is used to determine the amount of energy required for the delivery vehicle to return to the charging station to replenish energy. When the charging demand is greater than zero, it means that charging is needed; when it is less than or equal to zero, it means that the current battery power is sufficient to support the next delivery task and no immediate charging is required.

[0035] The present embodiment also provides an intelligent solar unmanned logistics distribution control system, including an environmental perception module: obtaining real-time weather data and current geographic location through on-board meteorological sensors and GPS positioning system; an intelligent energy management module: the intelligent energy management system analyzes weather and geographic location data, calculates the expected energy output of solar panels, and simultaneously evaluates the current battery power and energy consumption requirements of the distribution task, and intelligently adjusts the charging strategy; a path planning and evaluation module: based on the distribution task list and the current geographic location, plans the distribution route, evaluates the feasibility of the route, and generates the optimal distribution route; a dynamic strategy adjustment module: dynamically adjusts the vehicle speed and battery usage strategy according to the optimal distribution route and the current position of the sun; a system feedback and optimization module: after the distribution task is completed, summarizes the distribution energy consumption data and solar panel efficiency report, and optimizes the dynamic path planning algorithm.

[0036] This embodiment also provides a computer device, which is suitable for the case of an intelligent solar unmanned logistics distribution control method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent solar unmanned logistics distribution control method proposed in the above embodiment.

[0037] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0038] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent solar unmanned logistics distribution control method proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0039] In summary, the present invention uses on-board sensors to collect weather and geographic data, and the intelligent energy management system estimates solar energy output based on this, optimizes battery charging strategies, and ensures a stable energy supply; based on the delivery task and geographic location, the feasibility of the route is planned and evaluated, and the energy-optimal path is generated to improve delivery efficiency and maximize the timeliness and cost-effectiveness of logistics delivery; according to the optimal route and the position of the sun, the vehicle speed and battery strategy are dynamically adjusted to minimize energy consumption; after delivery, the energy consumption data and solar panel efficiency are summarized, and the path algorithm is optimized using machine learning to improve future delivery efficiency and energy utilization. Example 2

[0040] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the technical solution of the present invention, experimental simulation data of the intelligent solar unmanned logistics distribution control method are provided.

[0041] The experiment selected an electric delivery vehicle with integrated solar panels as the research object. The vehicle is equipped with on-board meteorological sensors, GPS positioning system, intelligent energy management system and path planning module. The experimental site is set up in a dense urban logistics distribution area, covering various lighting conditions and traffic conditions to comprehensively evaluate the performance of the system.

[0042] First, real-time weather data and geographic location information are collected through the vehicle-mounted meteorological sensors and GPS positioning system. Weather data includes light intensity and cloud coverage; geographic location data includes longitude and latitude coordinates. These data are transmitted to the intelligent energy management system in real time.

[0043] Next, the intelligent energy management system analyzes weather and geographic location data to calculate the expected energy output of the solar panels. It uses a light prediction model based on historical data and machine learning, combined with a weather forecast API, to calculate the predicted value of future light intensity and the expected energy output of the solar panels. At the same time, the system evaluates the current battery power and the energy consumption requirements of the delivery task, and intelligently adjusts the charging strategy to ensure that charging is prioritized when there is abundant sunlight to optimize energy utilization.

[0044] Then, based on the delivery task list and current geographic location, the system plans the delivery route, evaluates the feasibility of the route, and generates the optimal delivery route. The lighting and energy efficiency evaluation function and the traffic and time window adaptability function are introduced to construct a comprehensive evaluation function for dynamic route planning and optimization.

[0045] During the delivery process, the vehicle speed and battery usage strategy are dynamically adjusted according to the optimal delivery route and the current position of the sun. After the delivery task is completed, the energy consumption data and solar panel efficiency report of the delivery are summarized, and the dynamic path planning algorithm is optimized using machine learning algorithms to form a closed loop of continuous improvement, as shown in Table 1: Table 1 Experimental record table date longitude latitude Light intensity (W / m²) Energy output (kWh) Predicted energy consumption (kWh) Actual energy consumption (kWh) Optimized path saving time (min) 2024-08-01 116.39 39.91 850 4.5 4.0 3.8 15 2024-08-02 116.41 39.92 600 3.0 3.5 3.2 10 2024-08-03 116.42 39.93 450 2.5 3.0 2.7 8 2024-08-04 116.43 39.94 300 2.0 2.5 2.2 5 2024-08-05 116.44 39.95 150 1.5 2.0 1.8 3 2024-08-06 116.45 39.96 100 1.0 1.5 1.3 2 From the table data, it can be seen that the intelligent energy management system and path planning algorithm of the present invention have played a significant role in unmanned logistics delivery. Under different lighting conditions, there is a good match between the energy output of the solar panels and the actual energy consumption of the delivery task, which shows that the system can effectively predict and manage the balance between energy supply and demand.

[0046] For example, on August 1, 2024, although the predicted energy consumption was 4.0 kWh, the actual energy consumption was only 3.8 kWh through optimized path planning and energy management strategies, saving 15 minutes of delivery time, which shows that the system not only improves energy efficiency, but also significantly shortens the delivery cycle. On days with weaker sunlight, such as August 6, the system was also able to control the actual energy consumption at a low level through optimization strategies, and even when the light intensity was only 100 W / m², the delivery task could be successfully completed.

[0047] Compared with traditional delivery methods, the beneficial effects of the present invention in energy management and route planning are reflected in the following aspects: First, through real-time monitoring and prediction, accurate matching of energy output and demand is achieved, avoiding energy waste; second, by dynamically adjusting the charging strategy, it ensures that the delivery vehicle is charged first when the sunlight is sufficient, reducing dependence on the power grid; third, by introducing the sunlight and energy efficiency evaluation function and the traffic and time window adaptability function, the delivery route is optimized, saving delivery time and cost; fourth, through the continuous optimization of route planning through machine learning algorithms, a closed loop of continuous improvement is formed, which improves delivery efficiency and customer satisfaction.

[0048] In summary, the present invention has demonstrated its innovation and practicality in intelligent energy management and path planning, providing a new solution for the unmanned logistics and distribution industry, and is expected to lead the industry to develop in a more efficient and green direction.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent solar unmanned logistics distribution control method, characterized by: include, Obtain real-time weather data and current geographic location through on-board meteorological sensors and GPS positioning system; The intelligent energy management system analyzes weather and geographic location data to calculate the expected energy output of solar panels, while evaluating the current battery power and the energy consumption requirements of the delivery task, and intelligently adjusts the charging strategy; Plan the delivery route based on the delivery task list and current geographic location, evaluate the feasibility of the route, and generate the optimal delivery route; Dynamically adjust vehicle speed and battery usage strategy based on the optimal delivery route and the current position of the sun; After the delivery task is completed, the energy consumption data and solar panel efficiency report of the delivery are summarized to optimize the dynamic path planning algorithm.

2. The intelligent solar unmanned logistics distribution control method according to claim 1, characterized in that: The weather data includes light intensity and cloud coverage; The current geographic location includes longitude and latitude coordinates.

3. The intelligent solar unmanned logistics distribution control method according to claim 1, characterized in that: The smart energy management system analyzes weather and location data to calculate the expected energy output of solar panels. The following steps are involved: The system receives real-time light intensity and geographic location data from monitoring; Access the weather forecast API to obtain the predicted light intensity and cloud coverage for a specified geographic location in the next few hours; Use a light prediction model based on historical data and machine learning to calculate the predicted value of future light intensity; Based on the predicted light intensity and solar panel characteristics, the expected energy output of the solar panel is calculated as: ； in, represents the electrical energy generated by the solar panel at time t, is the conversion efficiency of the solar panel, A is the area of ​​the solar panel, is the predicted light intensity at time t, C(t) is the cloud cover at time t, d is the relative path length of light through the atmosphere, It is the relative path length of light through the atmosphere under standard atmospheric conditions.

4. The intelligent solar unmanned logistics distribution control method according to claim 3, characterized in that: Evaluate the current battery power and the energy consumption requirements of the delivery task, and intelligently adjust the charging strategy including the following steps: The system monitors the battery's current charge and maximum storage energy, and evaluates the battery status; Evaluate whether additional charging is needed based on the energy demand of the delivery task and the predicted solar energy output; The required charging time is calculated based on the difference between the energy demand and the predicted solar energy output, as well as the average charging efficiency of the solar panels. The expression is: ； in, is the required charging time, is the energy demand, is the current battery charge, is the average charging efficiency; Continuously monitor battery status and predicted solar energy output to dynamically adjust charging strategies.

5. The intelligent solar unmanned logistics distribution control method according to claim 4, characterized in that: Based on the delivery task list and current geographic location, planning the delivery route, evaluating the feasibility of the route, and generating the optimal delivery route include the following steps: Combining the light intensity prediction and the volatility of energy output, a light and energy efficiency evaluation function is introduced to quantify the energy efficiency on different paths. The expression is: ； in, is the energy efficiency at a certain moment on the path, is the standard deviation of the light intensity prediction, reflecting the uncertainty of the light intensity, is the average light intensity during the forecast period, It is the exponential factor of the effect of light intensity on energy efficiency; Define a traffic and time window adaptability function to evaluate the impact of traffic conditions and delivery time windows on the delivery path. The expression is: ； in, is the adaptability of traffic and time window at a certain moment on the path, is the difference between the current time and the nearest delivery time window, and are the mean and standard deviation of the delivery time window distribution, is the real-time traffic congestion level on the route, is the maximum value of traffic congestion level, is the exponential factor of the impact of traffic conditions on adaptability; Combining the energy efficiency and traffic conditions on different paths and the impact of the delivery time window on the delivery path, a comprehensive evaluation function is constructed to perform dynamic path planning and optimization. The expression is: ； in, is the comprehensive optimization score at a certain moment on the path; Continuously monitor all relevant real-time data and recalculate based on current data , and dynamically adjust the delivery route to maximize , so that the delivery vehicle always follows the most optimized route.

6. The intelligent solar unmanned logistics distribution control method according to claim 5, characterized in that: Dynamically adjusting vehicle speed and battery usage strategy based on the optimal delivery route and the current position of the sun includes the following steps: Use the global positioning system (GPS) to determine the real-time location of the delivery vehicle and obtain the longitude and latitude coordinates; Based on the geographic location of the delivery vehicle and the current time, an astronomical algorithm is used to predict the change in the position of the sun during the entire delivery process; Adjust the initial angle of the solar panel according to the current sun position so that the panel faces the sun; Activate the tracking control system of the solar panel to adjust the angle of the solar panel in real time according to the changes in the sun's position; During the delivery process, the changes in the sun's position are continuously monitored, and the control system is used to automatically adjust the angle of the solar panels to ensure that the panels are always facing the sun; Combine delivery routes and estimated energy output to optimize solar panel tracking strategies to effectively track the sun as delivery vehicles move; Real-time monitoring of the energy output of solar panels and comparative analysis with the expected energy output; By fine-tuning the angle of the solar panels, the energy supply can be stabilized to cope with the fluctuation of light intensity; The delivery vehicle receives the optimal delivery route instruction and adjusts the solar panel to the initial best light receiving position according to the tracking angle of the solar panel; While the delivery vehicle is driving, the intelligent energy management system dynamically adjusts the vehicle speed and battery usage strategy based on real-time energy output and predicted energy consumption.

7. The intelligent solar unmanned logistics distribution control method according to claim 6, characterized in that: After the delivery task is completed, the energy consumption data and solar panel performance report of the delivery are summarized, and the dynamic path planning algorithm is optimized, including the following steps: The delivery vehicle completes the delivery of goods to all delivery points and confirms the delivery status of each delivery point through QR code reading technology; The delivery vehicle uploads complete delivery progress, energy consumption data and solar panel performance reports to the central control system; The central control system analyzes the uploaded data and generates energy consumption and efficiency analysis reports; Based on energy consumption analysis, the intelligent energy management system automatically adjusts the energy management strategy; Review delivery routes and analyze energy efficiency and traffic adaptability along the routes; Use machine learning algorithms to train path planning models to improve path selection accuracy and energy efficiency for future delivery tasks; The system automatically checks the battery status. If the battery level is lower than the charging requirement, the system plans the best route back to the charging station to replenish energy. Apply the optimized energy management strategy and path planning algorithm to subsequent delivery tasks to form a closed loop of continuous improvement.

8. An intelligent solar unmanned logistics distribution control system, based on the intelligent solar unmanned logistics distribution control method according to any one of claims 1 to 7, characterized in that: include, Environmental perception module: obtains real-time weather data and current geographic location through on-board meteorological sensors and GPS positioning system; Intelligent Energy Management Module: The intelligent energy management system analyzes weather and geographic location data, calculates the expected energy output of solar panels, evaluates the current battery power and the energy consumption requirements of the delivery task, and intelligently adjusts the charging strategy; Path planning and evaluation module: based on the delivery task list and current geographical location, plan the delivery route, evaluate the feasibility of the route, and generate the optimal delivery route; Dynamic strategy adjustment module: dynamically adjusts vehicle speed and battery usage strategy based on the optimal delivery route and the current position of the sun; System feedback and optimization module: After the delivery task is completed, the energy consumption data and solar panel efficiency report of the delivery are summarized to optimize the dynamic path planning algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent solar unmanned logistics distribution control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent solar unmanned logistics distribution control method according to any one of claims 1 to 7 are implemented.

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