Interregional agricultural machine path mining method, electronic equipment and storage medium
Through the collection and processing of crop data and agricultural machinery operating data in various cities, the cross -regional agricultural machinery path is determined, the problem of large computing resources in the existing technology has been solved, and the more efficient agricultural machine path mining is realized.
Patent Information
- Application Number
- CN202411386815.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-30
- Publication Date
- 2025-01-21
AI Technical Summary
The prior art consumes huge computing resources when processing and fitting a large number of agricultural machinery trajectories, resulting in increased costs, reduced processing speed and efficiency.
By collecting crop data and agricultural machinery operation data from various cities, preprocessing is performed to determine cross-region agricultural machinery operation data, and at least one cross-region agricultural machinery path is determined based on these data. Use efficient algorithms and indexing systems for data analysis and path matching.
It improves the efficiency of the intercourse of agricultural machinery cross -regional paths, reduces the consumption of computing resources, reduces costs, and improves the speed and efficiency.
Smart Images
Figure CN119336814A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a method for mining cross-regional agricultural machinery paths, an electronic device and a storage medium. Background Art
[0002] Cross-regional operation of agricultural machinery is an important part of my country's agricultural modernization and an effective way to improve agricultural production efficiency and increase farmers' income. In the process of cross-regional path mining, the existing technology consumes huge computing resources for the processing and fitting of a large number of agricultural machinery trajectories, which not only increases costs but also limits processing speed and efficiency. Summary of the invention
[0003] The purpose of the embodiments of the present application is to provide a method, electronic device and storage medium for excavating cross-regional agricultural machinery paths, so as to improve the efficiency of excavating cross-regional agricultural machinery paths.
[0004] In a first aspect, the present invention provides a method for mining a cross-region agricultural machinery path, the method comprising:
[0005] Collect crop data and agricultural machinery operation data from various cities within a preset range; pre-process the agricultural machinery operation data to determine cross-regional agricultural machinery operation data; and determine at least one cross-regional agricultural machinery route based on the crop data and cross-regional agricultural machinery operation data from various cities.
[0006] In an optional embodiment, the method further includes responding to a received path screening instruction and determining a target cross-region agricultural machinery path that matches the agricultural machinery demand information indicated by the path screening instruction.
[0007] In an optional implementation manner, the cross-region agricultural machinery operation data is determined in the following manner:
[0008] Based on the location data of each agricultural machinery, determine whether the agricultural machinery is a cross-regional agricultural machinery; based on the historical operation paths of all cross-regional agricultural machinery, determine the cross-regional agricultural machinery migration road network map data; and based on the agricultural machinery operation data of all cross-regional agricultural machinery, build a key information index system.
[0009] In an optional embodiment, for each agricultural machinery, the cross-regional agricultural machinery is determined in the following manner: based on the agricultural machinery location data of the agricultural machinery, the starting position and the ending position of the historical operating path of the agricultural machinery and the total mileage of the historical operating path of the agricultural machinery are determined; it is determined whether the total mileage of the historical operating path of the agricultural machinery is greater than a preset mileage; if so, it is determined whether the starting position and the ending position of the agricultural machinery are located in different regions; if so, the agricultural machinery is determined to be a cross-regional agricultural machinery.
[0010] In an optional implementation, the step of determining at least one cross-regional agricultural machinery route based on the crop data of the prefecture and city and the cross-regional agricultural machinery operation data specifically includes:
[0011] For each cross-regional agricultural machinery, determine the starting and ending positions of the historical operation path of the agricultural machinery; based on the agricultural machinery migration road network map data, determine all feasible paths between the starting and ending positions; for each feasible path, based on the crop data of each city corresponding to the feasible path, determine whether the arrangement order of each node in the feasible path is correct. If correct, determine that the feasible path is a cross-regional agricultural machinery path.
[0012] In an optional implementation, the agricultural machinery demand information includes one or more of the agricultural machinery type, the crop type, the crop harvest time, the starting position, the ending position, and the minimum number of cross-region vehicles. The step of determining the target cross-region agricultural machinery path that matches the agricultural machinery demand information indicated by the path screening instruction specifically includes:
[0013] Through the key information indexing system, the agricultural machinery demand information is used as the key information, and the target cross-regional agricultural machinery path matching the key information is searched in the cross-regional agricultural machinery path. In an optional implementation, the step of constructing the key information indexing system based on the agricultural machinery operation data of all cross-regional agricultural machinery specifically includes:
[0014] Based on the crop data of various cities and the historical operation paths of each agricultural machinery, a path node index is constructed; based on the starting position, ending position and intermediate position of the historical operation paths of each agricultural machinery, a cross-regional agricultural machinery migration index is constructed; based on the starting position, ending position, intermediate position and corresponding time of the historical operation paths of each agricultural machinery, a cross-regional agricultural machinery vehicle flow index is constructed.
[0015] In an optional implementation, the target cross-region agricultural machinery path is matched in the following manner:
[0016] In the traffic flow index, the total number of inter-regional vehicles on each inter-regional agricultural machinery path is determined, and whether the total number of inter-regional vehicles is less than the minimum number of inter-regional vehicles in the agricultural machinery demand information is determined to determine the first alternative inter-regional agricultural machinery path; the first alternative inter-regional agricultural machinery path is similarity classified by a pre-trained classification model; for each type of first alternative inter-regional agricultural machinery path, the similarity between the first alternative inter-regional agricultural machinery path and the standard inter-regional agricultural machinery path is calculated to determine a second alternative inter-regional agricultural machinery path whose similarity meets the conditions, wherein the standard inter-regional agricultural machinery path is a complete path between the starting position and the ending position of the first alternative inter-regional agricultural machinery path; based on the traffic flow on each second alternative inter-regional agricultural machinery path, the paths are sorted from small to large to determine a preset number of second alternative inter-regional agricultural machinery paths as target inter-regional agricultural machinery paths.
[0017] In a second aspect, the present invention provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for excavating cross-regional agricultural machinery paths as described in any of the aforementioned embodiments.
[0018] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for mining cross-regional agricultural machinery paths as described in any of the aforementioned embodiments are executed.
[0019] The present application provides a method, electronic device and storage medium for mining cross-regional agricultural machinery paths, wherein the method includes collecting crop data and agricultural machinery operation data from various cities within a preset range; pre-processing the agricultural machinery operation data to determine cross-regional agricultural machinery operation data; and determining at least one cross-regional agricultural machinery path based on the crop data from various cities and cross-regional agricultural machinery operation data. By using a basic high-efficiency, low-computation algorithm, the pre-processed data is deeply analyzed and summarized, and the efficiency of obtaining path data is higher. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0021] Figure 1 A flow chart of a method for mining cross-regional agricultural machinery paths provided in an embodiment of the present application;
[0022] Figure 2 A flowchart of a data preprocessing step provided in an embodiment of the present application;
[0023] Figure 3 A flowchart of steps for determining a target cross-region agricultural machinery path provided in an embodiment of the present application;
[0024] Figure 4 A flowchart of a feasible path determination step provided in an embodiment of the present application;
[0025] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0026] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.
[0027] Embodiment 1
[0028] Figure 1 A flow chart of a method for mining cross-regional agricultural machinery paths provided in an embodiment of the present application. A method for mining cross-regional agricultural machinery paths provided in an embodiment of the present application specifically includes:
[0029] S1. Collect crop data and agricultural machinery operation data from various cities within the preset range.
[0030] In one embodiment of the present application, mainly for the busy farming seasons such as spring ploughing, summer and autumn, the cross-regional operation routes of tractors, wheat machines, rice machines, and corn machines in various regions across the country are mined, and the main cross-regional routes are identified and analyzed. Through cross-regional routes, problems including optimizing the allocation of agricultural machinery resources, improving agricultural production efficiency, and reducing transportation costs and time can be solved. Specifically, by mining the routes of cross-regional operations of agricultural machinery, the movement paths of agricultural machinery can be more accurately predicted and planned to ensure that agricultural machinery can arrive at various farmlands in time for operations during the busy farming season. This can not only improve the utilization rate of agricultural machinery and reduce idle time, but also help farmers better arrange farming activities and improve agricultural production efficiency.
[0031] In step S1, through powerful data collection and processing capabilities, a complete agricultural machinery cross-regional operation data platform can be established to achieve data sharing and interoperability, conduct technical research and innovation, introduce advanced algorithms and models, increase the intelligence and automation level of route mining, and improve the effect and accuracy of agricultural machinery cross-regional route mining.
[0032] like Figure 2 As shown in the figure, in the data preprocessing stage, the actual situation of various agricultural machinery in various provinces and cities can be considered, and invalid and redundant data can be eliminated through fine screening and filtering to ensure the input data quality of subsequent mining algorithms. At the same time, a series of standardized processing methods are used to convert agricultural machinery data into a unified format, improve the comparability and analyzability of the data, and lay a solid foundation for subsequent mining work.
[0033] The crop data for each city here includes the different types of crops planted in different cities and the expected harvest time. Then, the start and end time of agricultural machinery operation in each region can be determined, and based on these time nodes, the transfer and operation sequence of agricultural machinery between different regions can be inferred to ensure the accuracy of the operation route.
[0034] The agricultural machinery operation data here include the daily positioning data of each agricultural machinery vehicle and agricultural machinery vehicle information, etc. It can be used to further analyze and generate a vehicle cross-region zipper table for calculation. This method can accurately track the movement trajectory of vehicles between different regions, and make detailed statistics on the number of agricultural machinery vehicles moving in and out of each city.
[0035] S2. Preprocess the agricultural machinery operation data to determine cross-regional agricultural machinery operation data.
[0036] In step S2, the cross-region agricultural machinery operation data can be determined in the following manner:
[0037] Based on the location data of each agricultural machine, determine whether the agricultural machine is a cross-region agricultural machine. Specifically, for each agricultural machine, determine whether the cross-region agricultural machine is:
[0038] Based on the agricultural machine location data of the agricultural machine, the starting position and the ending position of the historical operation path of the agricultural machine and the total mileage of the historical operation path of the agricultural machine are determined. It is determined whether the total mileage of the historical operation path of the agricultural machine is greater than a preset mileage. If so, it is determined whether the starting position and the ending position of the agricultural machine are located in different regions. If so, it is determined that the agricultural machine is a cross-regional agricultural machine.
[0039] In order to process agricultural machinery information more effectively and reduce the complexity of subsequent calculations, the agricultural machinery data is carefully screened. The agricultural machinery information that reaches the cross-region threshold and has actual operation mileage is screened out. Such screening criteria can ensure that the algorithm focuses on those agricultural machinery that have actually crossed regions and have actual operations. At the same time, agricultural machinery data that has no cross-region operation records or only cross-region operations within a small range is removed to reduce data redundancy and calculation burden. Through such screening and deduplication, the cross-region operation of agricultural machinery can be more accurately grasped, providing strong support for subsequent analysis and route decisions.
[0040] Based on the historical operation paths of all cross-regional agricultural machinery, the cross-regional agricultural machinery migration road network map data is determined. And based on the agricultural machinery operation data of all cross-regional agricultural machinery, a key information index system is constructed. Among them, the steps of constructing a key information index system based on the agricultural machinery operation data of all cross-regional agricultural machinery specifically include:
[0041] Based on the crop data of various cities and the historical operation paths of each agricultural machinery, a path node index is constructed. Based on the starting position, ending position and intermediate position of the historical operation paths of each agricultural machinery, a cross-regional agricultural machinery migration index is constructed. Based on the starting position, ending position, intermediate position and corresponding time of the historical operation paths of each agricultural machinery, a cross-regional agricultural machinery traffic index is constructed.
[0042] In order to have a more comprehensive understanding of the flow of major cross-regional vehicles, it is necessary to count the vehicle transfers between various cities. This is calculated by analyzing the daily vehicle positioning data and generating a cross-regional zipper table. This method can accurately track the movement of vehicles between different regions.
[0043] In the statistical process, it is necessary to distinguish the cross-regional routes of various types of agricultural machinery, and clarify the operation date, current operation city, previous operation city, next operation city and other information of each agricultural machinery.
[0044] In order to have a more comprehensive understanding of the flow of agricultural machinery, it is necessary to conduct detailed statistics on the number of agricultural machinery vehicles moving in and out of each city. These data will serve as an important basis for subsequent algorithm mining of cross-regional routes, helping to more accurately identify the flow trends and hot spots of agricultural machinery vehicles. At the same time, the data must be cleaned and pre-processed to remove duplicate and erroneous data to improve the accuracy and efficiency of subsequent algorithm mining.
[0045] In the data preprocessing stage, detailed data on operation locations, vehicle operation routes, and vehicle flow data have been obtained from multiple data sources. These data have been carefully cleaned and organized using a variety of data cleaning and organization methods, including removing duplicate data, correcting erroneous data, and filling in missing data, to ensure their accuracy and consistency.
[0046] Next, we created efficient indexing systems for each of these data. For the data on the cities where operations are performed, we built indexes based on key information such as the city name and geographic location, which can quickly locate the relevant information of any city. For the data on vehicle operation routes, we built indexes based on key information such as the starting point and the end point, which facilitates querying and tracking vehicle migration information. For the data on vehicle traffic, we built indexes based on key information such as time and location, which can quickly obtain the traffic conditions at any time and location.
[0047] Then, based on the vehicle operation route index, we further construct the vehicle migration road network data between cities. It includes the city nodes of cross-regional vehicle operations, the vehicle flow lines between cities, and the number of vehicle flows. These data provide a basis for subsequent calculations and analysis, and help understand the migration patterns and characteristics of vehicles between cities.
[0048] S3. Based on the crop data of cities and counties and the cross-regional agricultural machinery operation data, determine at least one cross-regional agricultural machinery route.
[0049] Step S3 may specifically include:
[0050] For each cross-regional agricultural machinery, determine the starting and ending positions of the historical operation path of the agricultural machinery. Based on the agricultural machinery migration road network data, determine all feasible paths between the starting and ending positions. For each feasible path, based on the crop data of each city corresponding to the feasible path, determine whether the order of each node in the feasible path is correct. If correct, determine that the feasible path is a cross-regional agricultural machinery path.
[0051] In the process of building a cross-regional path, first obtain the various parameters in the configuration file to clarify the key information required to build the target path. Based on these parameters, use the work city index to quickly retrieve and determine the starting node and ending node of the target path. These two nodes are the basis for building the path. After the starting node and the ending node are determined, based on the previously constructed vehicle migration road network diagram, all feasible paths from the starting node to the ending node are found according to the migration flow between various cities.
[0052] Then, according to the start and end time of crop harvest in each city, these time factors can be used as an important reference for the order of vehicle migration. After all feasible paths are determined, the order of each path is corrected and adjusted according to the crop harvest schedule to ensure that the order of vehicle migration is in line with the actual situation.
[0053] This application provides a method for mining cross-regional agricultural machinery paths. In the data preprocessing stage, the actual conditions of various agricultural machinery in various provinces, cities and regions are fully considered. Through fine screening and filtering, invalid and redundant data are eliminated to ensure the input data quality of subsequent mining algorithms. At the same time, a series of standardized processing methods are used to convert agricultural machinery data into a unified format, improve the comparability and analyzability of the data, and lay a solid foundation for subsequent mining work.
[0054] Embodiment 2
[0055] In one embodiment of the present application, the method further includes determining, in response to a received path screening instruction, a target cross-region agricultural machinery path that matches the agricultural machinery demand information indicated by the path screening instruction.
[0056] The agricultural machinery demand information includes one or more of the agricultural machinery type, crop type, crop harvest time, starting position, ending position, and minimum number of cross-region vehicles. The step of determining a target cross-region agricultural machinery path that matches the agricultural machinery demand information indicated by the path screening instruction specifically includes:
[0057] Through the key information indexing system, the agricultural machinery demand information is used as the key information, and the target cross-regional agricultural machinery paths that match the key information are searched among the cross-regional agricultural machinery paths.
[0058] Figure 3 A flowchart of the steps for determining a target cross-region agricultural machinery path provided in an embodiment of the present application. Figure 3 Specifically, the target cross-region agricultural machinery path can be matched in the following ways:
[0059] S31. In the vehicle flow index, determine the total number of inter-regional vehicles on each inter-regional agricultural machinery path, and determine whether the total number of inter-regional vehicles is less than the minimum number of inter-regional vehicles in the agricultural machinery demand information, so as to determine the first alternative inter-regional agricultural machinery path;
[0060] S32, performing similarity classification on the first candidate inter-regional agricultural machinery path through a pre-trained classification model; for each type of first candidate inter-regional agricultural machinery path, calculating the similarity between the first candidate inter-regional agricultural machinery path and the standard inter-regional agricultural machinery path to determine a second candidate inter-regional agricultural machinery path whose similarity meets the conditions, wherein the standard inter-regional agricultural machinery path is a complete path between the starting position and the ending position of the first candidate inter-regional agricultural machinery path;
[0061] S33. Sort the traffic volume on each second candidate inter-regional agricultural machinery path from small to large to determine a preset number of second candidate inter-regional agricultural machinery paths as target inter-regional agricultural machinery paths.
[0062] like Figure 4 As shown, it can be understood that in this embodiment, several critical conditions can be determined based on the configuration parameters of the target path. These conditions include the total number of alternative routes, the minimum number of cross-region vehicles required, and the similarity ratio between the actual cross-region path and the complete cross-region path. These parameters will serve as a benchmark for evaluating the route.
[0063] First, according to the requirement of the minimum number of cross-region vehicles, the total number of cross-region vehicles on each feasible path is quickly calculated using the traffic flow index. Routes that fail to reach the required number of cross-region vehicles are excluded.
[0064] After excluding routes that do not meet the basic conditions, the kmeans classification algorithm can be used to group the remaining routes. Routes with high similarity are grouped together to facilitate more efficient subsequent analysis. Within each group, the similarity between feasible paths is further calculated, and parameters are configured based on the similarity ratio between the actual cross-region path and the complete cross-region path. Route groups with a similarity parameter less than this are excluded to ensure that the final selected route meets a certain standard in terms of similarity.
[0065] When the similarity requirement is met, the routes with the greatest difference from other groups are selected from each group, and these routes are used as the focus of subsequent analysis. The routes are sorted according to the number of vehicles on each route, and the routes with the largest number of vehicles are given priority to ensure the universality of the cross-region routes.
[0066] Through this series of screening and evaluation processes, a path that meets all requirements can be selected to provide strong support for subsequent work.
[0067] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5As shown in , the electronic device 500 includes a processor 510 , a memory 520 and a bus 530 .
[0068] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 communicates with the memory 520 via the bus 530. When the machine-readable instructions are executed by the processor 510, the steps of a method for mining cross-regional agricultural machinery paths in the above-mentioned method embodiment can be executed. The specific implementation method can be found in the method embodiment, which will not be repeated here.
[0069] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for mining cross-regional agricultural machinery paths in the above method embodiment can be executed. The specific implementation method can be found in the method embodiment, which will not be repeated here.
[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0071] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.
[0072] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0073] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
[0074] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM) random access memory (RAM), disk or optical disk, and other media that can store program codes.
[0075] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.
[0076] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims
1. A method for mining cross-regional agricultural machinery paths, characterized in that: The method comprises: Collect crop data and agricultural machinery operation data from various cities within the preset range; Preprocessing the agricultural machinery operation data to determine cross-region agricultural machinery operation data; Based on the crop data of the cities and the cross-regional agricultural machinery operation data, at least one cross-regional agricultural machinery route is determined.
2. The method according to claim 1, characterized in that Also includes: In response to the received path screening instruction, a target cross-region agricultural machinery path that matches the agricultural machinery demand information indicated by the path screening instruction is determined.
3. The method according to claim 1, characterized in that The cross-regional agricultural machinery operation data is determined by the following methods: Based on the agricultural machine location data of each agricultural machine, determining whether the agricultural machine is a cross-region agricultural machine; Based on the historical operation paths of all cross-regional agricultural machinery, the cross-regional agricultural machinery migration road network map data is determined, and based on the agricultural machinery operation data of all cross-regional agricultural machinery, a key information index system is constructed.
4. The method according to claim 3, characterized in that For each agricultural machine, the cross-region agricultural machine is determined by the following method: Based on the agricultural machine position data of the agricultural machine, determine the starting position and the ending position of the historical operation path of the agricultural machine, and the total mileage of the historical operation path of the agricultural machine; Determine whether the total mileage of the historical operation path of the agricultural machinery is greater than the preset mileage; If so, determining whether the starting position and the ending position of the agricultural machinery are located in different regions; If so, it is determined that the agricultural machinery is a cross-regional agricultural machinery.
5. The method according to claim 3, characterized in that: The step of determining at least one cross-regional agricultural machinery route based on the crop data of the prefecture and city and the cross-regional agricultural machinery operation data specifically includes: For each cross-region agricultural machinery, determine the starting position and ending position of the historical operation path of the agricultural machinery; Based on the agricultural machinery migration road network map data, determining all feasible paths between the starting position and the ending position; For each feasible path, based on the crop data of various cities corresponding to the feasible path, determine whether the arrangement order of each node in the feasible path is correct. If correct, determine that the feasible path is a cross-regional agricultural machinery path.
6. The method according to claim 3, characterized in that The agricultural machinery demand information includes one or more of agricultural machinery type, crop type, crop harvest time, starting position, ending position, and minimum number of cross-region vehicles. The step of determining a target cross-region agricultural machinery path that matches the agricultural machinery demand information indicated by the path screening instruction specifically includes: Through the key information indexing system, the agricultural machinery demand information is used as key information, and a target inter-regional agricultural machinery path matching the key information is searched in the inter-regional agricultural machinery path.
7. The method according to claim 3, characterized in that The step of constructing a key information index system based on the agricultural machinery operation data of all cross-regional agricultural machinery specifically includes: Based on the crop data of various cities and the historical operation paths of various agricultural machinery, a path node index is constructed; Based on the starting position, ending position and intermediate position of each agricultural machinery's historical operation path, a cross-region agricultural machinery migration index is constructed; Based on the starting position, ending position, intermediate position and corresponding time of the historical operation path of each agricultural machinery, a cross-regional agricultural machinery traffic index is constructed.
8. The method according to claim 6, characterized in that The target cross-region agricultural machinery path is matched in the following ways: In the vehicle flow index, the total number of inter-regional vehicles on each inter-regional agricultural machinery path is determined, and whether the total number of inter-regional vehicles is less than the minimum number of inter-regional vehicles in the agricultural machinery demand information is determined to determine the first alternative inter-regional agricultural machinery path; The first candidate cross-region agricultural machinery route is classified by similarity using a pre-trained classification model; For the first candidate inter-regional agricultural machinery path under each category, the similarity between the first candidate inter-regional agricultural machinery path and the standard inter-regional agricultural machinery path is calculated to determine a second candidate inter-regional agricultural machinery path whose similarity meets the conditions, wherein the standard inter-regional agricultural machinery path is a complete path between the starting position and the ending position of the first candidate inter-regional agricultural machinery path; Based on the traffic flow on each second candidate inter-regional agricultural machinery path, the paths are sorted from small to large to determine a preset number of second candidate inter-regional agricultural machinery paths as target inter-regional agricultural machinery paths.
9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for excavating cross-regional agricultural machinery paths as described in any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for mining cross-regional agricultural machinery paths as claimed in any one of claims 1 to 8 are executed.