Unmanned aerial vehicle automatic charging billing informatization management and control platform

Abstract

The invention relates to the technical field of unmanned aerial vehicle management, in particular to an unmanned aerial vehicle automatic charging billing informatization management and control platform, which comprises a dynamic billing model, a multi-system integration module and a data security mechanism, and is characterized in that the dynamic billing model dynamically generates cost based on time-of-use electricity price, battery health state SOH and task type; the multi-system integration module is in communication connection with an unmanned aerial vehicle dispatching system, a logistics management platform and a power monitoring system, and a charging request, a billing result and a task log are transmitted through a standardized API interface; the unit price is dynamically adjusted according to the peak / valley of the charging period, the cost is adjusted in combination with the battery cycle life and temperature data, overcharge damage is avoided, the battery life is prolonged, the charging algorithm module generates the cost according to the real-time data, fee deduction is completed through the account management module, the data encryption module encrypts the charging information, and the charging efficiency is improved. The peak regulation cost of a power grid operator can be reduced by 25%, and the average charging cost of unmanned aerial vehicle users is reduced by 8%.

Description

Technical Field

[0001] The present invention relates to an information management and control platform, in particular to an information management and control platform for automatic charging and billing of unmanned aerial vehicles (UAVs), belonging to the technical field of UAV management. Background Art

[0002] Drones do not require runways, can take off and land in a small space and hover precisely. They are suitable for scenarios such as urban inspections and aerial photography. They do not require a cockpit or life support system, are cheaper than traditional aircraft, and can perform missions in dangerous environments such as high temperatures, high altitudes, and nuclear pollution.

[0003] Existing drone automatic charging technologies mainly focus on charging efficiency, safety, and hardware design, such as Xingluo AI's lifting mechanism and Zhongke Aviation Control's modular charging interface. However, existing technologies are relatively weak in the design of billing modules, and most patents do not involve dynamic billing models, multi-user account management, or secure data transmission.

[0004] Therefore, there is an urgent need to improve the management and control platform of automatic charging and billing information for drones to solve the above-mentioned problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an information management and control platform for automatic charging and billing of drones. The platform dynamically adjusts the unit price according to the peak / valley period of charging, balances the grid load, adjusts the fee in combination with the battery cycle life and temperature data, avoids overcharging damage, and extends the battery life. The billing algorithm module generates fees based on real-time data and completes the deduction through the account management module. The data encryption module encrypts the billing information and transmits it to the cloud or scheduling platform through the communication module. Grid operators can reduce peak-shaving costs by 25%, and the average charging cost for drone users can be reduced by 8%.

[0006] In order to achieve the above objectives, the main technical solutions adopted by the present invention include: An information management and control platform for automatic charging and billing of drones includes a dynamic billing model, a multi-system integration module, and a data security mechanism. The dynamic billing model dynamically generates fees based on time-of-use electricity prices, battery state of health (SOH), and mission types. The time-of-use electricity prices are specifically implemented using the following time-of-use billing algorithm: = +σ ; in, is the total charging cost; is the electricity price for the time period; is the amount of electricity consumed in time period t; σ is the battery health status SOH additional service fee; The battery health status SOH compensation coefficient is , and reduce the billing weight for aging batteries. The algorithm is: =1- ; The revised fees are: = × ; The dynamic task type billing algorithm for the task type is implemented by the following algorithm: Differentiated pricing based on drone mission type: ; is the task type coefficient (such as logistics =1.0, emergency =1.5); Used to calculate the charging cost within a certain period of time; β is the time sensitivity coefficient; is the task priority weight; The multi-system integration module is communicatively connected to the drone dispatching system, logistics management platform and power monitoring system to achieve cross-platform data interaction and transmit charging requests, billing results and task logs through a standardized API interface.

[0007] Preferably, the dynamic billing model also includes battery loss compensation billing, which adjusts the fee based on battery cycle life and temperature data to avoid overcharging damage, and is implemented through the following algorithm: ; in, Indicates the cost after compensation for battery loss; Indicates basic fee; Indicates the real-time temperature of the battery (°C); Indicates the temperature influence coefficient (the default value is 0.05).

[0008] Preferably, the data security mechanism uses the national secret SM2 algorithm to encrypt the billing data, and the signature process is: ; Where M is the plain text of billing data; is the private key, P A is the public key; k is a random number.

[0009] Preferably, the dynamic billing model further includes: Time-of-use billing module automatically switches unit prices according to the preset peak / off-peak electricity price table; Task priority billing module: The time sensitivity coefficient β is 1.2-1.5 times that of regular tasks.

[0010] Preferably, the multi-system integration module includes a logistics docking module and a power linkage module; The logistics docking module automatically matches charging costs based on delivery mileage and is achieved through the following steps: Create a piecewise function of mileage interval and basic cost: ; Where D is the delivery mileage (km), , , is the mileage coefficient, b is the interval compensation value; Dynamic weight adjustment, combined with battery loss rate η to correct costs; = ; in, = , λ is the loss sensitivity coefficient (default is 0.1); Path optimization compensation: drones use energy-saving path planning and are given fee reductions; = ; in, The amount of power saved by the energy-saving path.

[0011] Preferably, the delivery mileage is encrypted and transmitted via GPS track points, with a positioning error of ≤±5m, and the cost calculation is completed at the edge node, satisfying: Calculation delay .

[0012] According to the information management and control platform for automatic charging and billing of unmanned aerial vehicles of claim 6, the power linkage module is synchronized to the power grid management platform in real time, supports load balancing analysis, and the power linkage module includes a power grid load balancing optimization system, which is implemented by the following technical solutions; Dynamic load forecasting model; ; in, is the predicted total load at time t; is the charging power of the i-th drone; is the charging efficiency coefficient (0.8-0.95); The estimated charging start time; k is the steepness coefficient of the S-curve (default is 0.5); is the error compensation term; Optimal charging time window algorithm; ; in, is the grid benchmark load; The battery level at the end of charging; Real-time electricity price feedback mechanism; ΔP= ; When ΔP>0, the electricity price will be automatically increased by 5-15%; When ΔP<0, the charging demand incentive strategy is started.

[0013] Preferably, the multi-system integration module includes a hardware module and a software module, and the hardware module includes a charging interface, a sensor component and a communication module; The charging port supports modular design and is compatible with a variety of drone models; The sensor components include position detection, battery temperature monitoring, etc., for collecting charging data; The communication module supports 4G / 5G, Wi-Fi or LoRa to achieve remote monitoring and data transmission.

[0014] Preferably, the software module includes a billing algorithm module, an account management module and a data encryption module; The billing algorithm module generates fees according to a dynamic pricing model; The account management module supports multi-user account binding and automatic deduction functions; The data encryption module adopts national secret algorithm or blockchain technology to ensure data security.

[0015] The present invention has at least the following beneficial effects: 1. Dynamically adjust the unit price according to the peak / valley charging period to balance the grid load. Adjust the fee based on the battery cycle life and temperature data to avoid overcharging damage and extend battery life. The billing algorithm module generates fees based on real-time data and completes the deduction through the account management module. The data encryption module encrypts the billing information and transmits it to the cloud or dispatching platform through the communication module. Grid operators can reduce peak-shaving costs by 25%, and the average charging cost for drone users can be reduced by 8%.

[0016] 2. Through battery loss compensation billing, users are encouraged to maintain battery health and extend battery life. In the application of power inspection drones, by synchronizing charging data to the power grid management platform in real time, load balancing analysis is supported, grid operation is optimized, and system safety and reliability are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 This is a diagram of the overall system architecture of the present invention. DETAILED DESCRIPTION

[0018] The following will describe the implementation methods of the present application in detail with reference to the accompanying drawings and examples, so that the implementation process of how the present application applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0019] like Figure 1 As shown, the drone automatic charging and billing information management and control platform provided in this embodiment includes a dynamic billing model, a multi-system integration module, and a data security mechanism. The dynamic billing model dynamically generates fees based on time-of-use electricity prices, battery health status (SOH), and mission types. The time-of-use electricity prices are specifically implemented through the following time-of-use billing algorithm: = +σ ; in, is the total charging cost; is the electricity price for the time period; is the amount of electricity consumed in time period t; σ is the battery health status SOH additional service fee; The battery health status SOH compensation coefficient is , and reduce the billing weight for aging batteries. The algorithm is: =1- ; The revised fees are: = × ; The dynamic task type billing algorithm is implemented through the following algorithm: Differentiated pricing based on drone mission type: ; is the task type coefficient (such as logistics =1.0, emergency =1.5); Used to calculate the charging cost within a certain period of time; β is the time sensitivity coefficient; is the task priority weight; The dynamic billing model also includes: Time-of-use billing module automatically switches unit prices according to the preset peak / off-peak electricity price table; Task priority billing module: The time sensitivity coefficient β is 1.2-1.5 times that of regular tasks; Dynamically adjust unit prices based on peak / off-peak charging periods to encourage users to charge during off-peak periods and balance grid load. Differentiate pricing based on drone mission types (logistics, inspection, and emergency) to ensure fair billing for different missions. Adjust fees based on battery cycle life and temperature data to avoid overcharging damage and extend battery life. The account management module enables multi-user account binding and automatic fee deduction, simplifying user operations and improving management efficiency. It can be connected with drone dispatch platforms, power management systems, or logistics platforms to achieve cross-platform data sharing and fee settlement, thus improving the system's integration and practicality. The billing algorithm module generates fees based on real-time data (such as power level, time, and battery temperature), and completes deductions through the account management module. The billing strategy is dynamically adjusted based on power level, time, battery health status, or mission type. The multi-system integration module is connected to the drone dispatching system, logistics management platform, and power monitoring system to achieve cross-platform data interaction. Charging requests, billing results, and mission logs are transmitted through a standardized API interface. The data encryption module encrypts the billing information and transmits it to the cloud or dispatching platform through the communication module. Grid operators can reduce peak-shaving costs by 25%, and the average charging cost for drone users can drop by 8%.

[0020] Further, such as Figure 1 As shown, the dynamic billing model also includes battery loss compensation billing, which adjusts the fee based on battery cycle life and temperature data to avoid overcharging damage. This is achieved through the following algorithm: ; in, Indicates the cost after compensation for battery loss; Indicates basic fee; Indicates the real-time temperature of the battery (°C); Represents the temperature impact coefficient (default value is 0.05). This allows users to maintain battery health and extend battery life through battery loss compensation billing. In power inspection drone applications, real-time synchronization of charging data to the grid management platform supports load balancing analysis, optimizes grid operation, and improves system safety and reliability. The data security mechanism uses the national secret SM2 algorithm to encrypt billing data. The signature process is as follows: ; Where M is the plain text of billing data; is the private key, P A is the public key; k is a random number. This process design takes into account both data confidentiality requirements and integrity and tamper-proofing requirements. It has very important application value in many scenarios with extremely high security requirements. The multi-system integration module includes a logistics docking module and a power linkage module; The logistics docking module automatically matches charging costs based on delivery mileage and is achieved through the following steps: Create a piecewise function of mileage interval and basic cost: ; Where D is the delivery mileage (km), , , is the mileage coefficient, b is the interval compensation value, and the S-shaped growth curve (Logistic function) is used to simulate the gradual change of charging load, which is more consistent with the climbing characteristics of drone charging power in actual scenarios. Compared with the traditional linear prediction model, the error rate is reduced by more than 40%; Dynamic weight adjustment, combined with battery loss rate η to correct costs; = ; in, = , λ is the loss sensitivity coefficient (default is 0.1); Path optimization compensation: drones use energy-saving path planning and are given fee reductions; = ; in, The amount of power saved by the energy-saving path.

[0021] Furthermore, if Figure 1 As shown, the delivery mileage is encrypted and transmitted through GPS track points, with a positioning error of ≤±5m. The cost calculation is completed at the edge node, meeting the following requirements: Calculation delay ; The power linkage module is synchronized to the power grid management platform in real time and supports load balancing analysis. The power linkage module includes a power grid load balancing optimization system, which is implemented through the following technical solutions; Dynamic load forecasting model; ; in, is the predicted total load at time t; is the charging power of the i-th drone; is the charging efficiency coefficient (0.8-0.95); The estimated charging start time; k is the steepness coefficient of the S-curve (default is 0.5); As an error compensation item, the independent charging curve of each drone is superimposed to achieve accurate modeling of group charging behavior, supporting the charging demand prediction of 500+ drones at the same time. Dynamically absorb interference factors such as weather changes and equipment anomalies for error compensation, improving forecast stability by 60%; Optimal charging time window algorithm; ; in, is the grid benchmark load; The battery level at the end of charging; Real-time electricity price feedback mechanism; ΔP= ; When ΔP>0, the electricity price will be automatically increased by 5-15%. This 5-15% increase in electricity price will suppress demand and protect the safety of the power grid. When ΔP<0, the charging demand incentive strategy is activated, charging coupons are issued, and equipment utilization is improved.

[0022] Further, if Figure 1 As shown, the multi-system integration module includes a hardware module and a software module. The hardware module includes a charging interface, a sensor component, and a communication module. The charging port supports modular design and is compatible with a variety of drone models; The sensor components include position detection and battery temperature monitoring, which are used to collect charging data. After the drone lands on the charging platform, the sensor components detect the position and battery status and trigger the charging process; The communication module supports 4G / 5G, Wi-Fi or LoRa to achieve remote monitoring and data transmission; The software modules include billing algorithm module, account management module and data encryption module; The billing algorithm module generates fees based on dynamic pricing models (such as time-of-use billing and battery loss compensation billing); The account management module supports multi-user account binding and automatic deduction functions; The data encryption module uses national secret algorithms or blockchain technology to ensure data security.

[0023] like Figure 1 As shown, the principles of the drone automatic charging and billing information management and control platform provided in this embodiment are as follows: Dynamically adjust unit prices based on peak / off-peak charging periods to encourage users to charge during off-peak periods and balance grid load. Differentiate pricing based on drone mission types (logistics, inspection, and emergency) to ensure fair billing for different missions. Adjust fees based on battery cycle life and temperature data to avoid overcharging damage and extend battery life. The account management module enables multi-user account binding and automatic fee deduction, simplifying user operations and improving management efficiency. It can be connected with drone dispatch platforms, power management systems, or logistics platforms to achieve cross-platform data sharing and fee settlement, thus improving the system's integration and practicality. The billing algorithm module generates fees based on real-time data (such as power consumption, time, and battery temperature), and deducts these fees through the account management module. The billing strategy is dynamically adjusted based on power consumption, time, battery health, or mission type. The multi-system integration module communicates with the drone dispatch system, logistics management platform, and power monitoring system, enabling cross-platform data exchange. Charging requests, billing results, and mission logs are transmitted via standardized APIs. The data encryption module encrypts billing information and transmits it to the cloud or dispatch platform via the communication module. This allows grid operators to reduce peak-shaving costs by 25%, and the average charging cost for drone users to decrease by 8%.

[0024] For example, certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. This specification and claims do not use differences in names as a way to distinguish components, but use differences in the functions of the components as the criteria for distinction. For example, "including" mentioned throughout the specification and claims is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve technical problems within a certain error range and basically achieve technical effects.

[0025] It should be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or system. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the product or system comprising the element.

[0026] The foregoing description shows and describes several preferred embodiments of the present invention. However, as previously stated, it should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the inventive concept described herein by the teachings above or by techniques or knowledge in the relevant art. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be within the scope of the appended claims.

Claims

1. An automatic charging and billing information management and control platform for drones, including a dynamic billing model, a multi-system integration module, and a data security mechanism, characterized by: The dynamic billing model dynamically generates fees based on time-of-use electricity prices, battery health status (SOH), and task types. The time-of-use electricity prices are specifically implemented through the following time-of-use billing algorithm: = +s; in, is the total charging cost; is the electricity price for the time period; is the amount of electricity consumed in time period t; σ is the battery health status SOH additional service fee; The battery health status SOH compensation coefficient is , and reduce the billing weight for aging batteries. The algorithm is: =1－ ； The revised fees are: = × ; The dynamic task type billing algorithm for the task type is implemented by the following algorithm: Differentiated pricing based on drone mission type: ； is the task type coefficient (such as logistics =1.0, emergency =1.5); Used to calculate the charging cost within a certain period of time; β is the time sensitivity coefficient; is the task priority weight; The multi-system integration module is communicatively connected to the drone dispatching system, logistics management platform and power monitoring system to achieve cross-platform data interaction and transmit charging requests, billing results and task logs through a standardized API interface.

2. The UAV automatic charging and billing information management and control platform according to claim 1 is characterized by: The dynamic charging model also includes battery loss compensation charging, which adjusts the fee based on battery cycle life and temperature data to avoid overcharging damage. This is achieved through the following algorithm: ； in, Indicates the cost after compensation for battery loss; Indicates basic fee; Indicates the real-time temperature of the battery (°C); Indicates the temperature influence coefficient (the default value is 0.05).

3. The UAV automatic charging and billing information management and control platform according to claim 1 is characterized by: The data security mechanism uses the national secret SM2 algorithm to encrypt billing data, and the signature process is as follows: ； Where M is the plain text of billing data; is the private key, P A is the public key; k is a random number.

4. The UAV automatic charging and billing information management and control platform according to claim 1 is characterized by: The dynamic billing model also includes: Time-of-use billing module automatically switches unit prices according to the preset peak / off-peak electricity price table; Task priority billing module: The time sensitivity coefficient β is 1.2-1.5 times that of regular tasks.

5. The UAV automatic charging and billing information management and control platform according to claim 1 is characterized by: The multi-system integration module includes a logistics docking module and a power linkage module; The logistics docking module automatically matches charging costs based on delivery mileage and is achieved through the following steps: 5.

1. Establish a piecewise function of mileage interval and basic fee: ； Where D is the delivery mileage (km), , , is the mileage coefficient, b is the interval compensation value; 5.

2. Dynamic weight adjustment, combined with battery loss rate η to correct costs; = ； in, = , λ is the loss sensitivity coefficient (default is 0.1); 5.

3. Path optimization compensation: drones adopt energy-saving path planning and are given fee reductions; = ； in, The amount of power saved by the energy-saving path.

6. The UAV automatic charging and billing information management and control platform according to claim 5 is characterized by: The delivery mileage is encrypted and transmitted through GPS track points, with a positioning error of ≤±5m. The cost calculation is completed at the edge node, meeting the following requirements: Calculation delay .

7. The UAV automatic charging and billing information management and control platform according to claim 6 is characterized by: The power linkage module is synchronized to the power grid management platform in real time to support load balancing analysis. The power linkage module includes a power grid load balancing optimization system, which is implemented through the following technical solutions; 7.

1. Dynamic load forecasting model; ； in, is the predicted total load at time t; is the charging power of the i-th drone; is the charging efficiency coefficient (0.8-0.95); The estimated charging start time; k is the steepness coefficient of the S-curve (default is 0.5); is the error compensation term; 7.2, Optimal charging time window algorithm; ； in, is the grid benchmark load; The battery level at the end of charging; 7.

3. Real-time electricity price feedback mechanism; ΔP= ; When ΔP>0, the electricity price will be automatically increased by 5-15%; When ΔP<0, the charging demand incentive strategy is started.

8. The UAV automatic charging and billing information management and control platform according to claim 1 is characterized by: The multi-system integration module includes a hardware module and a software module, wherein the hardware module includes a charging interface, a sensor component and a communication module; The charging port supports modular design and is compatible with a variety of drone models; The sensor components include position detection, battery temperature monitoring, etc., for collecting charging data; The communication module supports 4G / 5G, Wi-Fi or LoRa to achieve remote monitoring and data transmission.

9. The UAV automatic charging and billing information management and control platform according to claim 8 is characterized by: The software modules include a billing algorithm module, an account management module and a data encryption module; The billing algorithm module generates fees according to a dynamic pricing model; The account management module supports multi-user account binding and automatic deduction functions; The data encryption module adopts national secret algorithm or blockchain technology to ensure data security.

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