Unmanned equipment autonomous task optimization method and system based on cross-modal data alignment

Abstract

The invention relates to the technical field of task optimization, in particular to an unmanned equipment autonomous task optimization method and system based on cross-modal data alignment, and the method comprises the steps: collecting logistics demand data of different unmanned aerial vehicle distribution regions and resource data of different unmanned aerial vehicles, and collecting environment data at the same time; constructing an order quantity prediction model for different unmanned aerial vehicle delivery areas, and outputting a predicted order quantity of each delivery area in future 2 hours; comprehensive priority indexes of different unmanned aerial vehicle distribution areas are calculated, resource comprehensive gap indexes of different unmanned aerial vehicle distribution areas are calculated, and a priority sequence is generated; an unmanned aerial vehicle dispatching objective function is constructed, constraint conditions are set, the constraint conditions comprise electric quantity constraint, load constraint and airspace constraint, and an optimal dispatching scheme is generated.

Description

Background Art

[0002] In recent years, with the rapid development of e-commerce and the surge in consumer demand for instant delivery, drone logistics has become an important technical direction for solving delivery problems due to its strong flexibility, fast delivery speed, and good adaptability to complex terrain. However, with the complexity and scale of application scenarios, drone logistics faces severe challenges in dynamic task allocation, multimodal data fusion, and resource optimization and scheduling.

[0003] Disadvantages of existing technologies: Traditional systems often only preset fixed delivery routes and assign tasks according to time windows. They are unable to respond to sudden orders or environmental changes and ignore the power decay of drones (such as insufficient remaining power to return after a long flight). Most existing methods do not integrate multi-dimensional environmental data such as weather, traffic, and airspace, resulting in incomplete decision-making information and large prediction errors. Existing systems often allocate priorities based on manual experience, which is highly subjective and makes it difficult to quantify the weight relationship between urgency and distance. Urgent orders and ordinary tasks are not prioritized, which may lead to delays in high-value orders. Summary of the invention

[0004] The main purpose of the present invention is to provide an unmanned equipment autonomous task optimization method based on cross-modal data alignment, and further to provide an unmanned equipment autonomous task optimization system based on cross-modal data alignment that can run and implement the above method, so as to effectively solve the above problems mentioned in the background technology.

[0005] The technical solution of the present invention is as follows: First, an unmanned equipment autonomous task optimization method based on cross-modal data alignment is proposed, which includes the following steps: S1, collect logistics demand data of different drone delivery areas and resource data of different drones, and collect environmental data at the same time; S2, build an order quantity prediction model for different drone delivery areas, and output the predicted order volume for each delivery area in the next 2 hours; S3, calculating the comprehensive priority index of different drone delivery areas, and calculating the comprehensive resource gap index of different drone delivery areas to generate a priority sequence; S4. Construct the UAV scheduling objective function and set constraints, including power constraints, load constraints and airspace constraints, to generate the optimal scheduling plan.

[0006] A further improvement of the present invention is that S1 comprises the following specific steps: S11. Collect logistics demand data of different drone delivery areas, where the logistics demand data includes order quantity , Latitude and longitude of delivery address , Total weight of cargo and urgency labels , where i represents the i-th delivery area. When the urgency is normal, , when the urgency is justified, 1; S12. Collect resource data of different drones, where the resource data includes drone availability status tags , Remaining battery power of the drone 、Maximum payload of drone And the latitude and longitude of the drone’s current location , where j represents the jth UAV. When the jth UAV is available, , when the jth UAV is unavailable, ; S13, collecting environmental data, the environmental data includes weather tags, airspace restriction tags of the delivery area and date type label day; where weather is sunny, weather=0, rainy, weather=1, and windy, weather=2; when area i is open, , when area i is restricted, ; When the date type is a weekday, day=0; when the date type is a holiday, day=1.

[0007] A further improvement of the present invention is that S2 comprises the following specific steps: S21. Construct an order quantity prediction model for different drone delivery areas, wherein the order quantity prediction model uses a feature vector formed by a combination of the number of orders in the history of a single delivery area for 24 hours, a weather label, and a date type label as input, uses the predicted value of the order quantity corresponding to the feature vector of a single delivery area as output, uses the actual value of the order quantity corresponding to the feature vector of a single delivery area as a prediction target, and uses minimizing the mean square error between the actual value of the order quantity and the predicted value of the order quantity as a training target, until the mean square error between the actual value of the order quantity and the predicted value of the order quantity reaches convergence, and stops training; outputs the order quantity predicted by the order quantity prediction model for different drone delivery areas in the next 2 hours; the order quantity prediction model is an LSTM neural network model; S22. The training objective formula of the order quantity prediction model is: ; in, represents the actual value of the order quantity in the i-th delivery area, represents the predicted value of the order quantity of the i-th delivery area, and n represents the total number of delivery areas.

[0008] A further improvement of the present invention is that the calculation of the comprehensive priority index of different drone delivery areas in S3 includes the following specific steps: S31. Calculate the mission urgency weights of different drone delivery areas. The calculation formula for the mission urgency weights is: ; in, represents the task urgency weight of the i-th delivery area, represents the urgency label of the i-th delivery area, is the emergency order coefficient, which takes a value of 0.7; S32. Calculate the distance weights of different drone delivery areas. The distance weight calculation formula is: ; in, Indicates the longitude and latitude of the delivery address in the i-th delivery area Latitude and longitude of the distribution center The spherical distance between represents the distance weight of the i-th delivery area; S33. Calculate the comprehensive priority index of different drone delivery areas. The calculation formula of the comprehensive priority index is: ; in, represents the comprehensive priority index of the i-th distribution area, It is the coefficient of task urgency weight, and its value is 0.6.

[0009] A further improvement of the present invention is that the calculation of the comprehensive resource gap index of different drone delivery areas in S3 includes the following specific steps: S34. Calculate the number of drones required for different drone delivery areas. The calculation formula is: ; in, represents the number of drones required for the i-th delivery area, Indicates rounding up. represents the maximum load of the jth UAV; S35. Calculate the power shortage index of different UAVs. The calculation formula of the power shortage index is: ; in, represents the power shortage index of the jth UAV, Indicates the minimum return power of the drone. represents the remaining battery power of the jth drone; S36. Calculate the comprehensive resource gap index of different drone delivery areas. The calculation formula of the comprehensive resource gap index is: ; in, represents the comprehensive resource gap index of the i-th distribution area, Indicates the resource gap weight, with a value of 0.8. Represents the available state label of the j-th drone.

[0010] A further improvement of the present invention is that S3 further includes: arranging the comprehensive resource gap indexes of different drone delivery areas in descending order to generate a priority sequence.

[0011] A further improvement of the present invention is that the drone scheduling objective function in S4 is: ; in, is the fuel cost per unit distance of the drone, is the path length from drone j to delivery area i, is the penalty cost per unit time of order delay, is the estimated time for drone j to arrive at delivery area i, The latest delivery time for orders in delivery area i.

[0012] A further improvement of the present invention is that the constraint conditions in S4 include power constraint, load constraint and airspace constraint; the calculation formula of the power constraint is: ; in, is the flight speed of the jth UAV, is the energy consumption rate per unit distance of the UAV; the calculation formula of the load constraint is: ; in, represents the set of all delivery areas assigned to drone j; the calculation formula of the airspace constraint is: .

[0013] Secondly, an unmanned equipment autonomous task optimization system based on cross-modal data alignment is proposed, which includes: data collection module, order volume prediction module, priority generation module, and scheduling module; The data collection module is used to collect logistics demand data of different drone delivery areas and resource data of different drones, and collect environmental data at the same time; The order quantity prediction module is used to build an order quantity prediction model for different drone delivery areas and output the predicted order quantity for each delivery area in the next 2 hours; The priority generation module is used to calculate the comprehensive priority index of different drone delivery areas, and calculate the comprehensive resource gap index of different drone delivery areas to generate a priority sequence; The scheduling module is used to construct a UAV scheduling objective function and set constraints, which include power constraints, load constraints, and airspace constraints, to generate an optimal scheduling solution.

[0014] The technical effects of the present invention are as follows: A method for optimizing autonomous tasks of unmanned equipment based on cross-modal data alignment was constructed. This method overcomes the data fragmentation problem of traditional systems and ensures that the scheduling plan fully considers order attributes, drone capabilities and environmental restrictions. The long short-term memory network (LSTM) is used to model the spatiotemporal dependencies of historical orders, and multimodal features such as weather and date type are input to accurately predict the order distribution in the next 2 hours to avoid resource waste or shortages caused by prediction bias. By combining the urgency weight and distance weight, a dynamic priority sequence is generated to ensure that high-urgency and short-distance tasks are executed first, thereby improving the quality of delivery services. At the same time, this method dynamically adjusts the path to minimize operating costs under multiple constraints such as power, load, and airspace. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 This is a flow chart of an unmanned equipment autonomous task optimization method based on cross-modal data alignment according to Embodiment 1 of the present invention; Figure 2 This is a structural diagram of an unmanned equipment autonomous task optimization system based on cross-modal data alignment according to Example 2 of the present invention. DETAILED DESCRIPTION

[0016] Example 1 This embodiment constructs an unmanned equipment autonomous task optimization method based on cross-modal data alignment. This method overcomes the data fragmentation problem of traditional systems and ensures that the scheduling plan fully considers order attributes, drone capabilities and environmental restrictions; it uses a long short-term memory network (LSTM) to model the spatiotemporal dependencies of historical orders, and inputs multimodal features such as weather and date type to accurately predict the order distribution in the next 2 hours to avoid resource waste or shortages caused by prediction bias; by combining urgency weights and distance weights, a dynamic priority sequence is generated to ensure that high-urgency, short-distance tasks are executed first and improve the quality of delivery services. At the same time, this method dynamically adjusts the path to minimize operating costs under multiple constraints such as power, load, and airspace.

[0017] Unmanned equipment autonomous task optimization method based on cross-modal data alignment, such as Figure 1 As shown, the following specific steps are included: S1, collect logistics demand data of different drone delivery areas and resource data of different drones, and collect environmental data at the same time; S2, build an order quantity prediction model for different drone delivery areas, and output the predicted order volume for each delivery area in the next 2 hours; S3, calculating the comprehensive priority index of different drone delivery areas, and calculating the comprehensive resource gap index of different drone delivery areas to generate a priority sequence; S4. Construct the UAV scheduling objective function and set constraints, including power constraints, load constraints and airspace constraints, to generate the optimal scheduling plan.

[0018] In this embodiment, S1 includes the following specific steps: S11. Collect logistics demand data of different drone delivery areas, where the logistics demand data includes order quantity , Latitude and longitude of delivery address , Total weight of cargo and urgency labels , where i represents the i-th delivery area. When the urgency is normal, , when the urgency is justified, 1; S12. Collect resource data of different drones, where the resource data includes drone availability status tags , Remaining battery power of the drone 、Maximum payload of drone And the latitude and longitude of the drone’s current location , where j represents the jth UAV. When the jth UAV is available, , when the jth UAV is unavailable, ; S13, collecting environmental data, the environmental data includes weather tags, airspace restriction tags of the delivery area and date type label day; where weather is sunny, weather=0, rainy, weather=1, and windy, weather=2; when area i is open, , when area i is restricted, ; When the date type is a weekday, day=0; when the date type is a holiday, day=1.

[0019] In this embodiment, S2 includes the following specific steps: S21. Construct an order quantity prediction model for different drone delivery areas, wherein the order quantity prediction model uses a feature vector formed by a combination of the number of orders in the history of a single delivery area for 24 hours, a weather label, and a date type label as input, uses the predicted value of the order quantity corresponding to the feature vector of a single delivery area as output, uses the actual value of the order quantity corresponding to the feature vector of a single delivery area as a prediction target, and uses minimizing the mean square error between the actual value of the order quantity and the predicted value of the order quantity as a training target, until the mean square error between the actual value of the order quantity and the predicted value of the order quantity reaches convergence, and stops training; outputs the order quantity predicted by the order quantity prediction model for different drone delivery areas in the next 2 hours; the order quantity prediction model is an LSTM neural network model; S22. The training objective formula of the order quantity prediction model is: ; in, represents the actual value of the order quantity in the i-th delivery area, represents the predicted value of the order quantity of the i-th delivery area, and n represents the total number of delivery areas.

[0020] In this embodiment, the calculation of the comprehensive priority index of different drone delivery areas in S3 includes the following specific steps: S31. Calculate the mission urgency weights of different drone delivery areas. The calculation formula for the mission urgency weights is: ; in, represents the task urgency weight of the i-th delivery area, represents the urgency label of the i-th delivery area, is the emergency order coefficient, which takes a value of 0.7; S32. Calculate the distance weights of different drone delivery areas. The distance weight calculation formula is: ; in, Indicates the longitude and latitude of the delivery address in the i-th delivery area Latitude and longitude of the distribution center The spherical distance between represents the distance weight of the i-th delivery area; S33. Calculate the comprehensive priority index of different drone delivery areas. The calculation formula of the comprehensive priority index is: ; in, represents the comprehensive priority index of the i-th distribution area, It is the coefficient of task urgency weight, and its value is 0.6.

[0021] In this embodiment, the calculation of the comprehensive resource gap index of different drone delivery areas in S3 includes the following specific steps: S34. Calculate the number of drones required for different drone delivery areas. The calculation formula is: ; in, represents the number of drones required for the i-th delivery area, Indicates rounding up. represents the maximum load of the jth UAV; S35. Calculate the power shortage index of different UAVs. The calculation formula of the power shortage index is: ; in, represents the power shortage index of the jth UAV, Indicates the minimum return power of the drone. represents the remaining battery power of the jth drone; S36. Calculate the comprehensive resource gap index of different drone delivery areas. The calculation formula of the comprehensive resource gap index is: ; in, represents the comprehensive resource gap index of the i-th distribution area, Indicates the resource gap weight, with a value of 0.8. Represents the available state label of the j-th drone.

[0022] In this embodiment, S3 further includes: arranging the comprehensive resource gap indexes of different drone delivery areas in descending order to generate a priority sequence.

[0023] In this embodiment, the drone scheduling objective function in S4 is: ; in, is the fuel cost per unit distance of the drone, is the path length from drone j to delivery area i, is the penalty cost per unit time of order delay, is the estimated time for drone j to arrive at delivery area i, The latest delivery time for orders in delivery area i.

[0024] In this embodiment, the constraint conditions in S4 include power constraint, load constraint and airspace constraint; the calculation formula of the power constraint is: ; in, is the flight speed of the jth UAV, is the energy consumption rate per unit distance of the UAV; the calculation formula of the load constraint is: ; in, represents the set of all delivery areas assigned to drone j; the calculation formula of the airspace constraint is: .

[0025] Example 2 This embodiment proposes an unmanned equipment autonomous task optimization system based on cross-modal data alignment, such as Figure 2 As shown, it includes: data collection module, order quantity prediction module, priority generation module, and scheduling module; The data collection module is used to collect logistics demand data of different drone delivery areas and resource data of different drones, and collect environmental data at the same time; The order quantity prediction module is used to build an order quantity prediction model for different drone delivery areas and output the predicted order quantity for each delivery area in the next 2 hours; The priority generation module is used to calculate the comprehensive priority index of different drone delivery areas, and calculate the comprehensive resource gap index of different drone delivery areas to generate a priority sequence; The scheduling module is used to construct a UAV scheduling objective function and set constraints, which include power constraints, load constraints, and airspace constraints, to generate an optimal scheduling solution.

[0026] In this embodiment, the implementation of the data collection module includes the following specific steps: first, collect the logistics demand data of different drone delivery areas, and the logistics demand data includes the order quantity , Latitude and longitude of delivery address , Total weight of cargo and urgency labels , where i represents the i-th delivery area. When the urgency is normal, , when the urgency is justified, 1; further collect resource data of different drones, the resource data includes drone available status tags , Remaining battery power of the drone 、Maximum payload of drone And the latitude and longitude of the drone’s current location , where j represents the jth UAV. When the jth UAV is available, , when the jth UAV is unavailable, ; Finally, collect environmental data, which includes weather tags, airspace restriction tags for delivery areas and date type label day; where weather is sunny, weather=0, rainy, weather=1, and windy, weather=2; when area i is open, , when area i is restricted, ; When the date type is a weekday, day=0; when the date type is a holiday, day=1.

[0027] In this embodiment, the implementation of the order quantity prediction module includes the following specific steps: first, construct an order quantity prediction model for different drone delivery areas, wherein the order quantity prediction model uses a feature vector formed by combining the historical 24-hour order quantity, weather label, and date type label of a single delivery area as input, and uses the predicted order quantity value corresponding to the feature vector of a single delivery area as output, and uses the actual value of the order quantity corresponding to the feature vector of a single delivery area as the prediction target, and uses minimizing the mean square error between the actual value of the order quantity and the predicted value of the order quantity as the training target, until the mean square error between the actual value of the order quantity and the predicted value of the order quantity reaches convergence, and stops training; outputs the order quantity prediction model predicted by the order quantity prediction model for different drone delivery areas in the next 2 hours; the order quantity prediction model is an LSTM neural network model; the training target formula of the order quantity prediction model is: ; in, represents the actual value of the order quantity in the i-th delivery area, represents the predicted value of the order quantity of the i-th delivery area, and n represents the total number of delivery areas.

[0028] In this embodiment, calculating the comprehensive priority index of different drone delivery areas includes the following specific steps: first, calculating the task urgency weights of different drone delivery areas, and the calculation formula of the task urgency weights is: ; in, represents the task urgency weight of the i-th delivery area, represents the urgency label of the i-th delivery area, is the emergency order coefficient, which is 0.7. The distance weights of different drone delivery areas are further calculated. The calculation formula of the distance weight is: ; in, Indicates the longitude and latitude of the delivery address in the i-th delivery area Latitude and longitude of the distribution center The spherical distance between represents the distance weight of the i-th delivery area; finally, the comprehensive priority index of different drone delivery areas is calculated, and the calculation formula of the comprehensive priority index is: ; in, represents the comprehensive priority index of the i-th distribution area, It is the coefficient of task urgency weight, and its value is 0.6.

[0029] In this embodiment, calculating the comprehensive resource gap index of different drone delivery areas includes the following specific steps: first, calculating the number of drones required for different drone delivery areas, and the calculation formula is: ; in, represents the number of drones required for the i-th delivery area, Indicates rounding up. represents the maximum load of the jth UAV; further calculating the power gap index of different UAVs, the calculation formula of the power gap index is: ; in, represents the power shortage index of the jth UAV, Indicates the minimum return power of the drone. represents the remaining battery power of the jth drone; finally, the comprehensive resource gap index of different drone delivery areas is calculated, and the calculation formula of the comprehensive resource gap index is: ; in, represents the comprehensive resource gap index of the i-th distribution area, Indicates the resource gap weight, with a value of 0.8. Represents the available status label of the jth drone. Arrange the comprehensive resource gap indexes of different drone delivery areas in descending order to generate a priority sequence.

[0030] In this embodiment, the drone scheduling objective function is: ; in, is the fuel cost per unit distance of the drone, is the path length from drone j to delivery area i, is the penalty cost per unit time of order delay, is the estimated time for drone j to arrive at delivery area i, The latest delivery time for orders in delivery area i.

[0031] In this embodiment, the constraint conditions include power constraint, load constraint and airspace constraint; the calculation formula of the power constraint is: ; in, is the flight speed of the jth UAV, is the energy consumption rate per unit distance of the UAV; the calculation formula of the load constraint is: ; in, represents the set of all delivery areas assigned to drone j; the calculation formula of the airspace constraint is: .

[0032] The above-mentioned parameters and steps for each unit module to implement corresponding functions in the unmanned equipment autonomous task optimization system based on cross-modal data alignment of the present invention can refer to the parameters and steps in the embodiment of the unmanned equipment autonomous task optimization method based on cross-modal data alignment in Example 1 above.

[0033] Example 3 This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-mentioned unmanned equipment autonomous task optimization method based on cross-modal data alignment by calling the computer program stored in the memory.

[0034] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the unmanned equipment autonomous task optimization method based on cross-modal data alignment provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0035] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as a "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0036] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0037] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowchart and block diagram, as well as the combination of processes and blocks in the flowchart or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 Process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0038] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 Process or multiple processes and boxes Figure 1 The steps for the functions specified in one or more boxes.

[0039] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. An unmanned equipment autonomous task optimization method based on cross-modal data alignment, characterized by: The specific steps include: S1, collect logistics demand data of different drone delivery areas and resource data of different drones, and collect environmental data at the same time; S2, build an order quantity prediction model for different drone delivery areas, and output the predicted order quantity for each delivery area in the next 2 hours; S3, calculating the comprehensive priority index of different drone delivery areas, and calculating the comprehensive resource gap index of different drone delivery areas to generate a priority sequence; S4. Construct the UAV scheduling objective function and set constraints, including power constraints, load constraints and airspace constraints, to generate the optimal scheduling plan.

2. The method for optimizing autonomous tasks of unmanned equipment based on cross-modal data alignment according to claim 1, characterized in that: The S1 comprises the following specific steps: S11. Collect logistics demand data of different drone delivery areas, where the logistics demand data includes order quantity , Latitude and longitude of delivery address , Total weight of cargo and urgency labels , where i represents the i-th delivery area. When the urgency is normal, , when the urgency is justified, 1; S12. Collect resource data of different drones, where the resource data includes drone availability status tags , Remaining battery power of the drone 、Maximum payload of drone And the latitude and longitude of the drone’s current location , where j represents the jth UAV. When the jth UAV is available, , when the jth UAV is unavailable, ; S13, collecting environmental data, the environmental data includes weather tags, airspace restriction tags of the delivery area and the date type label day; where weather is sunny, weather=0, rainy, weather=1, and windy, weather=2; when area i is open, , when area i is restricted, ; When the date type is a weekday, day=0; when the date type is a holiday, day=1.

3. The unmanned equipment autonomous task optimization method based on cross-modal data alignment according to claim 2, characterized in that: The S2 comprises the following specific steps: S21. Construct an order quantity prediction model for different drone delivery areas, wherein the order quantity prediction model uses a feature vector formed by a combination of the number of orders in the history of a single delivery area for 24 hours, a weather label, and a date type label as input, uses the predicted value of the order quantity corresponding to the feature vector of a single delivery area as output, uses the actual value of the order quantity corresponding to the feature vector of a single delivery area as a prediction target, and uses minimizing the mean square error between the actual value of the order quantity and the predicted value of the order quantity as a training target, until the mean square error between the actual value of the order quantity and the predicted value of the order quantity reaches convergence, and stops training; outputs the order quantity predicted by the order quantity prediction model for different drone delivery areas in the next 2 hours; the order quantity prediction model is an LSTM neural network model; S22. The training objective formula of the order quantity prediction model is: ； in, represents the actual value of the order quantity in the i-th delivery area, represents the predicted value of the order quantity of the i-th delivery area, and n represents the total number of delivery areas.

4. The method for optimizing autonomous tasks of unmanned equipment based on cross-modal data alignment according to claim 3 is characterized in that: The calculation of the comprehensive priority index of different drone delivery areas in S3 includes the following specific steps: S31. Calculate the mission urgency weights of different drone delivery areas. The calculation formula for the mission urgency weights is: ； in, represents the task urgency weight of the i-th delivery area, represents the urgency label of the i-th delivery area, is the emergency order coefficient, which takes a value of 0.7; S32. Calculate the distance weights of different drone delivery areas. The distance weight calculation formula is: ； in, Indicates the longitude and latitude of the delivery address in the i-th delivery area Latitude and longitude of the distribution center The spherical distance between represents the distance weight of the i-th delivery area; S33. Calculate the comprehensive priority index of different drone delivery areas. The calculation formula of the comprehensive priority index is: ； in, represents the comprehensive priority index of the i-th distribution area, It is the coefficient of task urgency weight, and its value is 0.

6.

5. The unmanned equipment autonomous task optimization method based on cross-modal data alignment according to claim 4 is characterized in that: The calculation of the comprehensive resource gap index of different drone delivery areas in S3 includes the following specific steps: S34. Calculate the number of drones required for different drone delivery areas. The calculation formula is: ； in, represents the number of drones required for the i-th delivery area, Indicates rounding up. represents the maximum load of the jth UAV; S35. Calculate the power shortage index of different UAVs. The calculation formula of the power shortage index is: ； in, represents the power shortage index of the jth UAV, Indicates the minimum return power of the drone. represents the remaining battery power of the jth drone; S36. Calculate the comprehensive resource gap index of different drone delivery areas. The calculation formula of the comprehensive resource gap index is: ； in, represents the comprehensive resource gap index of the i-th distribution area, Indicates the resource gap weight, with a value of 0.

8. Represents the available status label of the j-th drone.

6. The unmanned equipment autonomous task optimization method based on cross-modal data alignment according to claim 5 is characterized in that: The S3 also includes: arranging the comprehensive resource gap indexes of different drone delivery areas in descending order to generate a priority sequence.

7. The method for optimizing autonomous tasks of unmanned equipment based on cross-modal data alignment according to claim 6, characterized in that: The objective function of the UAV scheduling in S4 is: ； in, is the fuel cost per unit distance of the drone, is the path length from drone j to delivery area i, is the penalty cost per unit time of order delay, is the estimated time for drone j to arrive at delivery area i, The latest delivery time for orders in delivery area i.

8. The method for optimizing unmanned equipment autonomous tasks based on cross-modal data alignment according to claim 7, characterized in that: The constraints in S4 include power constraints, load constraints and airspace constraints; the calculation formula of the power constraints is: ； in, is the flight speed of the jth UAV, is the energy consumption rate per unit distance of the UAV; the calculation formula of the load constraint is: ； in, represents the set of all delivery areas assigned to drone j; the calculation formula of the airspace constraint is: 。 9. An unmanned equipment autonomous task optimization system based on cross-modal data alignment, which is implemented based on the unmanned equipment autonomous task optimization method based on cross-modal data alignment according to any one of claims 1 to 8, characterized in that: The system includes: a data collection module, an order quantity prediction module, a priority generation module, and a scheduling module; The data collection module is used to collect logistics demand data of different drone delivery areas and resource data of different drones, and collect environmental data at the same time; The order quantity prediction module is used to build an order quantity prediction model for different drone delivery areas and output the predicted order quantity for each delivery area in the next 2 hours; The priority generation module is used to calculate the comprehensive priority index of different drone delivery areas, and calculate the comprehensive resource gap index of different drone delivery areas to generate a priority sequence; The scheduling module is used to construct a UAV scheduling objective function and set constraints, which include power constraints, load constraints, and airspace constraints, to generate an optimal scheduling solution.

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