An intelligent solar unmanned logistics distribution control method and system

Abstract

The present invention discloses an intelligent solar unmanned logistics distribution control method and system, which relates to the technical field of renewable energy. It includes obtaining real-time weather data and the current geographical location through on-vehicle meteorological sensors and GPS positioning systems; the intelligent energy management system analyzes the weather and geographical location data to calculate the expected energy output of the solar panels; based on the distribution task list and the current geographical location, plan the distribution route, evaluate the feasibility of the path, and generate the optimal distribution route; according to the optimal distribution route and the current position of the sun, dynamically adjust the vehicle speed and battery usage strategy; after the distribution task is completed, summarize the energy consumption data and the solar panel performance report of the distribution, and optimize the dynamic path planning algorithm. Through a series of intelligent control methods, the present invention improves the energy utilization efficiency of the unmanned logistics distribution system and the reliability of task execution, and further improves the quality of distribution services and customer satisfaction by continuously optimizing the path planning algorithm.

Description

Technical Field

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

[0002] In recent years, with the rapid progress of Internet of Things, big data analysis, and renewable energy technologies, intelligent logistics distribution systems have become a 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 causes serious environmental pollution. However, the emergence of intelligent solar unmanned logistics distribution control methods marks an important step for the logistics industry towards green and intelligent development.

[0003] Despite the significant progress brought by intelligent logistics distribution control methods, there are still some key challenges in the existing technologies. Firstly, most systems fail to fully consider the impact of weather changes on solar energy output, affecting the continuity and reliability of distribution tasks. Secondly, traditional path planning algorithms often rely on static data and lack a response mechanism to real-time environmental changes. In addition, existing systems adopt a fixed energy consumption mode in battery usage strategies and fail to dynamically adjust vehicle speed according to the sun position and light intensity. Finally, although some systems attempt to collect energy consumption data during the distribution process, they lack an effective feedback mechanism to optimize the dynamic path planning algorithm, resulting in slow system improvement and difficulty in adapting to the 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 in executing distribution tasks in the existing unmanned logistics distribution system due to the lack of effective energy management and dynamic path planning.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the 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 the current geographical location through an in-vehicle weather sensor and a GPS positioning system;

[0008] An intelligent energy management system analyzes the weather and geographical location data, calculates the expected energy output of the solar panel, and at the same time evaluates the current battery power and the energy consumption requirements of the distribution task, and intelligently adjusts the charging strategy;

[0009] Based on the distribution task list and the current geographical location, plan the distribution route, evaluate the feasibility of the route, and generate the optimal distribution route;

[0010] Dynamically adjust the vehicle speed and battery usage strategy according to the optimal delivery route and the current position of the sun;

[0011] After the delivery task is completed, summarize the energy consumption data of the delivery and the efficiency report of the solar panels, and optimize the dynamic path planning algorithm.

[0012] As a preferred solution of the intelligent solar unmanned logistics delivery control method described in the present invention, wherein: the weather data includes light intensity and cloud cover rate; the current geographical location includes longitude and latitude coordinates.

[0013] As a preferred solution of the intelligent solar unmanned logistics delivery control method described in the present invention, wherein: the intelligent energy management system analyzes weather and geographical location data, and calculating the expected energy output of the solar panels includes the following steps:

[0014] The system receives the monitored real-time light intensity and geographical location data;

[0015] Access the weather forecast API to obtain the predicted light intensity data and cloud cover rate of the specified geographical location within the next few hours;

[0016] Adopt a light prediction model based on historical data and machine learning to calculate the predicted future light intensity value;

[0017] According to the predicted light intensity and the characteristics of the solar panels, calculate the expected energy output of the solar panels, and the expression is:

[0018] ;

[0019] Wherein, represents the electric 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 rate at time t, d is the relative path length of light passing through the atmosphere, is the relative path length of light passing through the atmosphere under standard atmospheric conditions.

[0020] As a preferred solution of the intelligent solar unmanned logistics delivery control method described in the present invention, wherein: evaluating the current battery power and the energy consumption demand of the delivery task, and the intelligent adjustment of the charging strategy includes the following steps:

[0021] The system monitors the current battery power and the maximum storage energy, and evaluates the battery status;

[0022] According to the energy consumption demand of the delivery task and the predicted solar energy output, evaluate whether additional charging is required;

[0023] Calculate the required charging time based on the difference between the energy consumption demand and the predicted solar energy output, as well as the average charging efficiency of the solar panel. The expression is:

[0024] ;

[0025] where, is the required charging time, is the energy consumption demand, is the current battery power, is the average charging efficiency;

[0026] Continuously monitor the battery status and the predicted solar energy output, and dynamically adjust the charging strategy.

[0027] As a preferred solution of the intelligent solar unmanned logistics distribution control method described in the present invention, wherein: based on the distribution task list and the current geographical location, plan the distribution route, evaluate the feasibility of the path, and generate the optimal distribution route, including the following steps:

[0028] Combined with the prediction of light intensity and the volatility of energy output, introduce a light and energy efficiency evaluation function to quantify the energy efficiency on different paths. The expression is:

[0029] ;

[0030] where, is the energy efficiency at a certain moment on the path, is the standard deviation of the predicted light intensity, reflecting the uncertainty of the light intensity, is the average light intensity within the predicted time period, is the exponential factor of the influence of light intensity on energy efficiency;

[0031] Define a traffic and time window adaptability function to evaluate the influence of traffic conditions and distribution time windows on the distribution path. The expression is:

[0032] ;

[0033] where, is the traffic and time window adaptability at a certain moment on the path, is the difference between the current time and the nearest distribution time window, and are the mean and standard deviation of the distribution of the distribution time window respectively, is the real-time traffic congestion degree on the path, is the maximum value of the traffic congestion degree, is the exponential factor of the influence of traffic conditions on adaptability;

[0034] Combining the energy efficiency, traffic conditions, and the impact of delivery time windows on different paths, a comprehensive evaluation function is constructed for dynamic route planning and optimization. The expression is as follows:

[0035] ;

[0036] Among them, is the comprehensive optimization score at a certain moment on the path;

[0037] Continuously monitor all relevant real-time data, and recalculate according to the current data, and dynamically adjust the delivery route to pursue the maximization of , so that the delivery vehicle always follows the optimized route.

[0038] As a preferred solution of the intelligent solar unmanned logistics delivery control method described in the present invention, among them: 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:

[0039] Use the Global Positioning System (GPS) to determine the real-time position of the delivery vehicle and obtain the longitude and latitude coordinates;

[0040] According to the geographical location and current time of the delivery vehicle, apply astronomical algorithms to predict the position change of the sun during the entire delivery process;

[0041] Adjust the initial angle of the solar panel according to the current sun position so that the panel faces the sun direction;

[0042] Activate the tracking control system of the solar panel and adjust the angle of the solar panel in real time according to the change of the sun position;

[0043] During the delivery process, continuously monitor the change of the sun position, and use the control system to automatically adjust the angle of the solar panel to ensure that the panel always faces the sun;

[0044] Combine the delivery route and the predicted energy output to optimize the solar panel tracking strategy to effectively track the sun when the delivery vehicle is moving;

[0045] Real-time monitor the energy output of the solar panel and conduct comparative analysis with the predicted energy output;

[0046] Ensure stable energy supply by fine-tuning the angle of the solar panel to cope with the volatility of light intensity;

[0047] When the delivery vehicle receives the optimal delivery route instruction, adjust the solar panel to the initial best light-receiving position according to the tracking angle of the solar panel;

[0048] During the driving process of the delivery vehicle, the intelligent energy management system dynamically adjusts the vehicle speed and battery usage strategy according to the real-time energy output and predicted energy consumption.

[0049] As a preferred solution of the intelligent solar unmanned logistics distribution control method described in the present invention, where: after the distribution task is completed, summarize the energy consumption data and solar panel efficiency reports of the distribution, and the steps for optimizing the dynamic path planning algorithm include the following:

[0050] The delivery vehicle completes the delivery of goods at all delivery points, and confirms the delivery status of each delivery point through QR code reading technology;

[0051] The delivery vehicle uploads the complete delivery progress, energy consumption data and solar panel efficiency report to the central control system;

[0052] The central control system analyzes the uploaded data and generates an energy consumption and efficiency analysis report;

[0053] Based on the energy consumption analysis, the intelligent energy management system automatically adjusts the energy management strategy;

[0054] Review the delivery route and analyze the energy efficiency and traffic condition adaptability on the route;

[0055] Use machine learning algorithms to train the path planning model to improve the path selection accuracy and energy efficiency of future delivery tasks;

[0056] The system automatically checks the battery status. If the battery power is lower than the charging requirement, the system plans the optimal route to return to the charging station to replenish energy;

[0057] Apply the optimized energy management strategy and path planning algorithm to subsequent delivery tasks to form a continuously improving closed loop.

[0058] In a second aspect, the present invention provides an intelligent solar unmanned logistics distribution control system, including,

[0059] An environment perception module: obtains real-time weather data and the current geographical location through an on-vehicle meteorological sensor and a GPS positioning system;

[0060] An intelligent energy management module: the intelligent energy management system analyzes the weather and geographical location data, calculates the expected energy output of the solar panel, and at the same time evaluates the current battery power and the energy consumption demand of the delivery task, and intelligently adjusts the charging strategy;

[0061] A path planning and evaluation module: plans a delivery route based on the delivery task list and the current geographical location, evaluates the feasibility of the route, and generates an optimal delivery route;

[0062] A dynamic strategy adjustment module: dynamically adjusts the vehicle speed and battery usage strategy according to the optimal delivery route and the current position of the sun;

[0063] System feedback and optimization module: After the delivery task is completed, summarize the energy consumption data of the delivery and the solar panel efficiency report, and optimize the dynamic path planning algorithm.

[0064] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the intelligent solar unmanned logistics delivery control method described in the first aspect of the present invention is implemented.

[0065] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the intelligent solar unmanned logistics delivery control method described in the first aspect of the present invention is implemented.

[0066] The beneficial effects of the present invention are as follows: The present invention uses on-vehicle sensors to collect weather and geographical data, and the intelligent energy management system estimates solar power generation based on this, optimizes the battery charging strategy to ensure stable energy supply; based on the delivery task and geographical location, plans and evaluates the feasibility of the route, generates the optimal energy consumption path, improves the delivery efficiency, and maximizes the timeliness and cost-effectiveness of logistics delivery; according to the optimal route and the position of the sun, dynamically adjusts the vehicle speed and battery strategy to minimize energy consumption; after delivery, summarizes the energy consumption data and the efficiency of the solar panel, and uses machine learning to optimize the path algorithm to improve future delivery efficiency and energy utilization. Description of the Drawings

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0068] Figure 1 It is a flowchart of the intelligent solar unmanned logistics delivery control method in Embodiment 1.

[0069] Figure 2 It is a diagram for dividing the light intensity threshold in Embodiment 1. Detailed Embodiments

[0070] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed embodiments of the present invention in conjunction with the drawings of the specification.

[0071] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0072] Secondly, as used herein, "an embodiment" or "embodiments" refer to specific features, structures, or characteristics that may be included in at least one implementation of the present invention. The phrase "in an embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments.

[0073] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides an intelligent solar unmanned logistics distribution control method, including the following steps:

[0074] S1. Obtain real-time weather data and the current geographical location through in-vehicle weather sensors and a GPS positioning system.

[0075] The weather data includes light intensity and cloud cover; the current geographical location includes longitude and latitude coordinates.

[0076] S2. The intelligent energy management system analyzes the weather and geographical location data and calculates the expected energy output of the solar panels, including the following steps:

[0077] The system receives the monitored real-time light intensity and geographical location data; accesses the weather forecast API to obtain the predicted light intensity data and cloud cover for a specified geographical location within the next few hours; uses a light prediction model based on historical data and machine learning to calculate the predicted future light intensity value; calculates the expected energy output of the solar panels according to the predicted light intensity and the characteristics of the solar panels. The expression is:

[0078] ;

[0079] Wherein, represents the electric energy generated by the solar panels at time t, is the conversion efficiency of the solar panels, A is the area of the solar panels, 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 passing through the atmosphere, is the relative path length of light passing through the atmosphere under standard atmospheric conditions.

[0080] Furthermore, the expression for calculating the predicted future light intensity using a light prediction model based on historical data and machine learning is:

[0081] ;

[0082] Wherein, is the prediction of light intensity, is the coefficient of the influence of geographical location and time on light intensity, and are the angles of the Earth's rotation corresponding to longitude and latitude respectively, is the number of hours in a day, is the coefficient of the influence of cloud cover on light intensity, is the coefficient of the influence 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 influence of altitude on atmospheric transparency, is the atmospheric transparency varying with time.

[0083] S3. Evaluate the current battery power and the energy consumption requirements of the delivery task, and the intelligent charging strategy adjustment includes the following steps:

[0084] The system monitors the current battery power and the maximum storage energy, and evaluates the battery state;

[0085] According to the energy consumption requirements of the delivery task and the predicted solar energy output, evaluate whether additional charging is required;

[0086] According to the difference between the energy consumption requirements and the predicted solar energy output, and the average charging efficiency of the solar panel, calculate the required charging time, and the expression is:

[0087] ;

[0088] Wherein, is the required charging time, is the energy consumption requirement, is the current battery power, is the average charging efficiency;

[0089] Continuously monitor the battery state and the predicted solar energy output, and dynamically adjust the charging strategy.

[0090] Furthermore, if , then plan the charging time; adopt an intelligent charging strategy, and preferentially charge when the light is sufficient, that is, when (where is the light intensity threshold) is reached, start the charging process.

[0091] Furthermore, the division of the light intensity threshold is as follows:

[0092] Very low light threshold ( ): Set at 100 W / m2 The following indicates that the lighting condition is extremely weak and the solar panel can hardly generate effective electricity. It is determined that the output of the solar panel is insufficient to support the current energy demand, and the system will trigger an alternative energy plan, such as switching to battery power supply or starting an external charging plan;

[0093] Low light threshold ( ): Set between 100 W / m 2 and 300 W / m 2 , which means that although the solar panel has a certain amount of energy output, its efficiency is not high and may not be sufficient to meet all the energy demands of the delivery vehicle. The system may adopt a conservative energy management strategy at this light intensity, such as restricting non-essential energy consumption, optimizing the delivery route to reduce energy consumption, or starting a slow charging mode to avoid excessive consumption of the limited solar output;

[0094] Medium light threshold ( ): Set between 300 W / m 2 and 600 W / m 2 , and the light intensity in this range provides good conditions for solar energy output. The solar panel can generate relatively considerable electricity. Use this light condition for charging to supplement the battery power. At the same time, the charging speed may be dynamically adjusted according to the current battery status and delivery task requirements to ensure sufficient energy reserves;

[0095] High light threshold ( ): Set between 600 W / m 2 and 1000 W / m 2 , within this range, the output capacity of the solar panel reaches a relatively high level, which can be regarded as an ideal condition for solar energy utilization. The system will give priority to using the high light condition 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;

[0096] Extremely strong light threshold ( ): Set above 1000 W / m 2 , which is equivalent to the condition of direct sunlight. At this light intensity, the output of the solar panel reaches the peak. The system will fully charge to ensure that the battery is fully charged in a short time and activate the overcharge protection mechanism to prevent the battery from overheating or being damaged by overcharging.

[0097] S4. Based on the delivery task list and the current geographical location, plan the delivery route, evaluate the feasibility of the path, and generate the optimal delivery route, including the following steps:

[0098] Combined with the volatility of light intensity prediction and energy output, a light and energy efficiency evaluation function is introduced to quantify the energy efficiency on different paths. The expression is as follows:

[0099] ;

[0100] where, 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 prediction period, is the exponential factor of the impact of light intensity on energy efficiency;

[0101] 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 as follows:

[0102] ;

[0103] where, is the traffic and time window adaptability 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 distribution of the delivery time window respectively, is the real-time traffic congestion degree on the path, is the maximum value of the traffic congestion degree, is the exponential factor of the impact of traffic conditions on adaptability;

[0104] Combined with the energy efficiency on different paths and the impact of traffic conditions and delivery time windows on the delivery path, a comprehensive evaluation function is constructed for dynamic path planning and optimization. The expression is as follows:

[0105] ;

[0106] where, is the comprehensive optimization score at a certain moment on the path;

[0107] Continuously monitor all relevant real-time data, recalculate according to the current data, and dynamically adjust the delivery path to pursue the maximization of so that the delivery vehicle always follows the optimized path.

[0108] Furthermore, when is relatively large, it indicates that the current path performs well in terms of energy efficiency, traffic adaptability, and delivery energy consumption requirements, and is an optimal path; on the contrary, a smaller The indicated route may have problems such as low energy efficiency, traffic congestion, or exceeding the delivery time window, and route adjustment is required.

[0109] S5. According to the optimal delivery route and the current position of the sun, dynamically adjusting the vehicle speed and battery usage strategy includes the following steps:

[0110] Use the Global Positioning System (GPS) to determine the real-time position of the delivery vehicle and obtain longitude and latitude coordinates; according to the geographical location and current time of the delivery vehicle, apply astronomical algorithms to predict the position change 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 direction; activate the tracking control system of the solar panel and adjust the angle of the solar panel in real time according to the change of the sun position; during the delivery process, continuously monitor the change of the sun position and use the control system to automatically adjust the angle of the solar panel to ensure that the panel always faces the sun; combine the delivery route and the expected energy output to optimize the solar panel tracking strategy and 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 by fine-tuning the angle of the solar panel to cope with the volatility of light intensity; when the delivery vehicle receives the optimal delivery route instruction, adjust the solar panel to the initial best light-receiving position according to the tracking angle of the solar panel; during the driving process of the delivery vehicle, the intelligent energy management system dynamically adjusts the vehicle speed and battery usage strategy according to the real-time energy output and predicted energy consumption.

[0111] S6. After the delivery task is completed, summarizing the energy consumption data and solar panel performance report of the delivery and optimizing the dynamic route planning algorithm includes the following steps:

[0112] The delivery vehicle completes the delivery of goods at 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 performance 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 performance analysis report, which includes an energy consumption curve, a solar panel performance curve, and an energy consumption distribution map of the delivery route; based on the energy consumption analysis, the intelligent energy management system automatically adjusts the energy management strategy, such as charging timing, charging speed, and battery usage mode; review the delivery route and analyze the energy efficiency and traffic condition adaptability on the route; use machine learning algorithms to train the route planning model to improve the route selection accuracy and energy efficiency of future delivery tasks; the system automatically checks the battery status, including remaining battery 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; apply the optimized energy management strategy and route planning algorithm to subsequent delivery tasks to form a continuously improving closed loop.

[0113] Furthermore, the charging requirement is used to determine the amount of energy needed for the delivery vehicle to return to the charging station for replenishment. When the charging requirement is greater than zero, it indicates 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 immediate charging is not required.

[0114] This embodiment also provides an intelligent solar unmanned logistics distribution control system, including: an environmental perception module: obtaining real-time weather data and the current geographical location through in-vehicle meteorological sensors and a GPS positioning system; an intelligent energy management module: the intelligent energy management system analyzes weather and geographical location data, calculates the expected energy output of the solar panels, and at the same time evaluates the current battery power and the energy consumption requirements of the delivery task, and intelligently adjusts the charging strategy; a path planning and evaluation module: based on the delivery task list and the current geographical location, plans the delivery route, evaluates the feasibility of the path, and generates the optimal delivery route; a dynamic strategy adjustment module: dynamically adjusts the vehicle speed and battery usage strategy according to the optimal delivery route and the current position of the sun; a system feedback and optimization module: after the delivery task is completed, summarizes the energy consumption data of the delivery and the solar panel performance report, and optimizes the dynamic path planning algorithm.

[0115] This embodiment also provides a computer device applicable to the 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 the computer-executable instructions to implement the intelligent solar unmanned logistics distribution control method as proposed in the above embodiment.

[0116] This computer device can be a terminal, and this 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 this computer device is used to provide computing and control capabilities. The memory of this 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 this 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, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0117] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the intelligent solar unmanned logistics distribution control method proposed in the above embodiment; 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, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0118] In summary, the present invention uses vehicle-mounted sensors to collect weather and geographical data. Based on this, the intelligent energy management system estimates solar power output, optimizes the battery charging strategy, and ensures a stable energy supply; based on the distribution task and geographical location, it plans and evaluates the feasibility of the route, generates the path with the optimal energy consumption, improves the distribution efficiency, and maximizes the timeliness and cost-effectiveness of logistics distribution; according to the optimal route and the position of the sun, it dynamically adjusts the vehicle speed and battery strategy to minimize energy consumption; after distribution, it summarizes the energy consumption data and the efficiency of the solar panels, and uses machine learning to optimize the path algorithm to improve the future distribution efficiency and energy utilization. Embodiment 2

[0119] Referring to Table 1, this is the second embodiment of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the intelligent solar unmanned logistics distribution control method are given.

[0120] An electric delivery vehicle integrated with solar panels was selected as the research object in the experiment. The vehicle is equipped with vehicle-mounted meteorological sensors, a GPS positioning system, an intelligent energy management system, and a path planning module. The experimental site is set in a dense urban logistics distribution area, covering various lighting conditions and traffic conditions to comprehensively evaluate the performance of the system.

[0121] First, real-time weather data and geographical location information were collected through vehicle-mounted meteorological sensors and the GPS positioning system. The weather data includes light intensity and cloud cover; the geographical location data includes longitude and latitude coordinates. These data are transmitted to the intelligent energy management system in real time.

[0122] Next, the intelligent energy management system analyzes weather and geographical location data and calculates the expected energy output of the solar panels. Using a light prediction model based on historical data and machine learning, combined with a weather forecast API, it calculates the predicted future light intensity and the expected energy output of the solar panels. At the same time, the system evaluates the current battery charge and the energy consumption requirements of the delivery tasks, and intelligently adjusts the charging strategy to ensure priority charging during abundant light to optimize energy utilization.

[0123] Then, based on the delivery task list and the current geographical location, the system plans the delivery route, evaluates the feasibility of the route, and generates the optimal delivery route. By introducing a light and energy efficiency evaluation function and a traffic and time window adaptability function, a comprehensive evaluation function is constructed for dynamic route planning and optimization.

[0124] During the delivery process, according to the optimal delivery route and the current position of the sun, the vehicle speed and battery usage strategy are dynamically adjusted. After the delivery task is completed, the energy consumption data of the delivery and the solar panel performance report are summarized, and machine learning algorithms are used to optimize the dynamic route planning algorithm to form a continuously improving closed loop, as shown in Table 1:

[0125] Table 1 Experimental Record Table

[0126] Date Longitude Latitude Illumination intensity (W / m²) Energy output (kWh) Predicted energy consumption (kWh) Actual energy consumption (kWh) Time saved by the optimized path (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

[0127] It can be seen from the tabular data that the intelligent energy management system and route planning algorithm of the present invention have played significant advantages in unmanned logistics delivery. Under different lighting conditions, there is a good matching relationship between the energy output of the solar panels and the actual energy consumption of the delivery tasks, indicating that the system can effectively predict and manage the energy supply-demand balance.

[0128] For example, on August 1, 2024, although the predicted energy consumption was 4.0 kWh, through optimized route planning and energy management strategies, the actual energy consumption was only 3.8 kWh, saving 15 minutes of delivery time, indicating that the system not only improves energy utilization efficiency but also significantly shortens the delivery cycle. On days with weak light, such as August 6, the system can also control the actual energy consumption at a low level through optimized strategies, and ensure the smooth completion of the delivery task even when the light intensity is only 100 W / m².

[0129] Compared with traditional distribution methods, the beneficial effects of the present invention in energy management and path planning are reflected in the following aspects: First, through real-time monitoring and prediction, the precise matching of energy output and demand is achieved, avoiding energy waste; Second, by dynamically adjusting the charging strategy, it ensures that the delivery vehicle charges preferentially when there is sufficient sunlight, reducing the dependence on the power grid; Third, by introducing the sunlight and energy efficiency evaluation function and the traffic and time window adaptability function, the distribution path is optimized, saving distribution time and costs; Fourth, through machine learning algorithms, the path planning is continuously optimized, forming a closed loop of continuous improvement, improving the distribution efficiency and customer satisfaction.

[0130] In summary, the present invention demonstrates its innovation and practicality in intelligent energy management and path planning, provides a new solution for the unmanned logistics distribution industry, and is expected to lead the industry to develop in a more efficient and green direction.

[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within 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; 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: Among them, E eff (t) is the energy efficiency at a certain moment on the path, σ I is the standard deviation of the light intensity prediction, reflecting the uncertainty of the light intensity, is the average light intensity during the prediction period, γ 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: Among them, T adapt (t) is the adaptability of traffic and time window at a certain moment on the path, Δt is the difference between the current time and the nearest delivery time window, μ t and σ t are the mean and standard deviation of the delivery time window distribution, d tr is the real-time traffic congestion level on the path, D max is the maximum value of traffic congestion, δ 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: P opt (t)=E eff (t)×T adapt (t)×exp(-E d / E solar (t)); Among them, P opt (t) is the comprehensive optimization score at a certain moment on the path; Continuously monitor all relevant real-time data and recalculate P based on current data opt (t), and dynamically adjust the delivery path to maximize P opt (t) so that the delivery vehicle always follows the most optimized 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: Among them, E solar (t) represents the electrical energy generated by the solar panel at time t, η solar is the conversion efficiency of the solar panel, A is the area of ​​the solar panel, I pred (t) 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, and d0 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: Among them, t c is the required charging time, E d is the energy demand, E bat is the current battery charge, η c 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: 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.

6. The intelligent solar unmanned logistics distribution control method according to claim 5, 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.

7. 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 6, characterized in that: include, The environmental perception module is responsible for obtaining real-time weather data and current geographic location through the vehicle-mounted meteorological sensors and GPS positioning system; Intelligent energy management module, responsible for the intelligent energy management system to analyze weather and geographic location data, calculate the expected energy output of solar panels, evaluate the current battery power and the energy consumption requirements of the delivery task, and intelligently adjust the charging strategy; The route planning and evaluation module is responsible for planning the delivery route, evaluating the feasibility of the route, and generating the optimal delivery route based on the delivery task list and current geographic location; Dynamic strategy adjustment module, responsible for dynamically adjusting vehicle speed and battery usage strategy based on the optimal delivery route and the current position of the sun; The system feedback and optimization module is responsible for summarizing the energy consumption data and solar panel efficiency reports after the delivery task is completed, and optimizing the dynamic path planning algorithm.

8. 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 according to any one of claims 1 to 6 are implemented.

9. 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 6 are implemented.

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