Intelligent operation and maintenance method and system for agricultural machinery navigation equipment, medium and terminal

Abstract

The invention provides an intelligent operation and maintenance method and system for agricultural machinery navigation equipment, a medium and a terminal.The intelligent operation and maintenance method comprises the steps that intention recognition and scene matching are conducted on received user problem messages based on a dynamic weight scheduling knowledge base so as to lock a problem scene, and a diagnosis function sequence is obtained according to the locked problem scene and a corresponding diagnosis function; and based on each diagnosis function in the diagnosis function sequence, performing fault analysis on equipment related total information obtained by fusing the collected heterogeneous source data, and sending an equipment diagnosis result obtained according to an analysis result to a user. The technical problem that the operation and maintenance work of agricultural machinery navigation equipment in the prior art depends on manual work is solved; in the agricultural environment, the automatic operation and maintenance capability of agricultural machinery navigation equipment is improved, and the working efficiency of agricultural machinery is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance, and in particular to an intelligent operation and maintenance method, system, medium and terminal for agricultural machinery navigation equipment. Background Technology

[0002] With the deep integration of IoT technology and agricultural production, equipment equipped with intelligent navigation systems, such as agricultural drones, autonomous tractors, and facility agriculture robots, has become the core carrier of smart agriculture. These navigation devices integrate key technologies such as positioning, multi-sensor fusion, and path planning algorithms. Their operational stability directly determines the accuracy and production efficiency of agricultural machinery operations, such as sowing row spacing error and plant protection coverage rate, playing an irreplaceable role in ensuring timely planting and increasing agricultural yields. However, the operation and maintenance of agricultural machinery navigation equipment is facing multiple challenges arising from the interplay of technological complexity and the unique characteristics of application scenarios, making the existing operation and maintenance system insufficient to meet the industry's development needs.

[0003] On the one hand, the current operation and maintenance of agricultural machinery navigation equipment relies on manual labor. When users discover abnormalities in agricultural machinery navigation, they often manually describe the fault symptoms through social media tools, and the descriptions are often vague. Technical support personnel reconstruct the fault scenario through text communication, and then log into multiple independent backends such as the satellite positioning service platform, equipment management system, and operation data terminal one by one to manually query discrete data such as satellite signal strength, differential source connection status, controller version number, and real-time sensor parameters, and finally rely on personal experience to determine the root cause of the fault.

[0004] On the other hand, the operating environment of agricultural machinery navigation equipment is unique. In facility agriculture, the obstruction of greenhouse frames and crop branches and leaves can lead to signal instability. Dynamic obstacles in livestock farming and temperature and salinity changes in underwater operations can all interfere with the accuracy of sensor data. In field operations, during the busy farming season, agricultural machinery needs to operate continuously at high intensity, resulting in frequent navigation system failures. The timeliness of troubleshooting directly affects crop yields, and delays can cause losses of over a thousand yuan per mu. In addition, agricultural machinery operating across regions is often scattered in remote areas, requiring technicians to spend several hours to several days traveling to and from the site for on-site troubleshooting, further increasing the difficulty of operation and maintenance.

[0005] In summary, the current operation and maintenance of agricultural machinery navigation equipment relies on manual labor, and the varying skill levels of maintenance personnel make standardization difficult. This often leads to low efficiency during peak operating seasons. Furthermore, the real-time data generated by the equipment, historical operation and maintenance knowledge, and handling solutions are fragmented, making it difficult to form a data-driven approach. Summary of the Invention

[0006] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide an intelligent operation and maintenance method, system, medium and terminal for agricultural machinery navigation equipment, so as to solve the problem that the operation and maintenance of agricultural machinery navigation equipment in the prior art relies on manual operation.

[0007] To achieve the above and other related objectives, a first aspect of the present invention provides an intelligent operation and maintenance method for agricultural machinery navigation equipment, comprising: based on a dynamic weighted scheduling knowledge base, performing intent recognition and scenario matching on received user problem messages to lock down the problem scenario, and obtaining a sequence of diagnostic functions according to the locked problem scenario and the corresponding diagnostic functions; based on each diagnostic function in the sequence of diagnostic functions, performing fault analysis on the full amount of equipment-related information obtained by fusing collected heterogeneous source data, and sending the equipment diagnostic results obtained based on the analysis results to the user.

[0008] In some embodiments of the first aspect of the present invention, a dynamic weight scheduling knowledge base is constructed based on the mapping relationship between all problem scenarios and their corresponding keywords, the mapping relationship between all problem scenarios and their corresponding diagnostic functions, and the mapping relationship between each diagnostic function and its corresponding weight; wherein, the weight of each diagnostic function for each problem scenario is determined according to the fault cases of the corresponding problem scenario.

[0009] In some embodiments of the first aspect of the present invention, the intention recognition and scenario matching of the received user question message includes: performing intention recognition on the received user question message based on regular expressions to extract corresponding keywords; and matching the corresponding question scenario based on the mapping relationship between the question scenario and its corresponding keywords in the dynamic weight scheduling knowledge base.

[0010] In some embodiments of the first aspect of the present invention, obtaining a diagnostic function sequence based on the locked problem scenario and the corresponding diagnostic function includes: obtaining the corresponding diagnostic function based on the mapping relationship between the problem scenario and its corresponding diagnostic function in the dynamic weight scheduling knowledge base, and constructing a diagnostic function list; sorting each diagnostic function in the diagnostic function list from largest to smallest according to its corresponding weight to obtain a diagnostic function sequence.

[0011] In some embodiments of the first aspect of the present invention, fault analysis is performed on the full amount of device-related information obtained by fusing the collected heterogeneous source data based on each diagnostic function in the diagnostic function sequence: diagnostic functions are extracted sequentially according to the order of each diagnostic function in the diagnostic function sequence, and the diagnostic functions sequentially perform fault analysis on the corresponding full amount of device-related information obtained by fusing the collected heterogeneous source data.

[0012] In some embodiments of the first aspect of the present invention, the fault analysis method includes: using the currently extracted diagnostic function to analyze the corresponding full information related to the device; if the cause of the fault is determined, the corresponding diagnostic result is sent to the user to automatically trigger the fault handling action of the corresponding device; if the cause of the fault is not determined or the fault handling of the device fails, the next diagnostic function is extracted to analyze the corresponding full information related to the device until the cause of the fault is determined and the fault handling is successful.

[0013] In some embodiments of the first aspect of the present invention, the total device-related information obtained by fusing the collected heterogeneous source data includes: real-time telemetry data of the device, static attribute data of the device, real-time status snapshot of the device, and external environment data.

[0014] To achieve the above and other related objectives, a second aspect of the present invention provides an intelligent operation and maintenance system for agricultural machinery navigation equipment, comprising: a dynamic weighted scheduling knowledge base module, used to perform intent recognition and scenario matching on received user problem messages based on the dynamic weighted scheduling knowledge base to lock the problem scenario, and obtain a sequence of diagnostic functions according to the locked problem scenario and the corresponding diagnostic functions; and a fault diagnosis module, connected to the dynamic weighted scheduling knowledge base module, used to perform fault analysis based on each diagnostic function in the sequence of diagnostic functions, using the full amount of equipment-related information obtained by fusing collected heterogeneous source data, and sending the equipment diagnosis results obtained based on the analysis results to the user.

[0015] To achieve the above and other related objectives, a third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the intelligent operation and maintenance method for the agricultural machinery navigation device.

[0016] To achieve the above and other related objectives, a fourth aspect of the present invention provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the intelligent operation and maintenance method for the agricultural machinery navigation device.

[0017] As described above, the intelligent operation and maintenance method, system, medium, and terminal for agricultural machinery navigation equipment of the present invention have the following beneficial effects: Based on a dynamic weighted scheduling knowledge base, the present invention performs intent recognition and scenario matching on received user problem messages to pinpoint the problem scenario, and obtains a sequence of diagnostic functions based on the pinpointed problem scenario and corresponding diagnostic functions; based on each diagnostic function in the sequence, it performs fault analysis on the full range of equipment-related information obtained from the fusion of collected heterogeneous source data, and sends the equipment diagnostic results obtained based on the analysis results to the user. The present invention solves the technical problem of reliance on manual operation in the operation and maintenance of agricultural machinery navigation equipment in the prior art; it improves the automatic operation and maintenance capabilities of agricultural machinery navigation equipment in agricultural environments, thereby increasing the efficiency of agricultural machinery operations. Attached Figure Description

[0018] Figure 1 The diagram shows a flowchart of an embodiment of the intelligent operation and maintenance method for agricultural machinery navigation equipment of the present invention.

[0019] Figure 2 A schematic diagram of an embodiment of the intelligent operation and maintenance system for agricultural machinery navigation equipment of the present invention is shown.

[0020] Figure 3 The diagram shows a structural schematic of an embodiment of the intelligent operation and maintenance terminal for agricultural machinery navigation equipment of the present invention. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0022] In embodiments of the present invention, terms such as "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, "first XX" and "second XX" are merely used to distinguish different XXs and do not limit their order. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0023] It should be noted that in the embodiments of the present invention, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0024] In this embodiment of the invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0025] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0026] <1> Regular expressions are tools used for string matching, extraction, and validation, describing string characteristics through specific syntax rules. In this invention, they are used to accurately extract key information (such as device ID and problem description keywords) from user natural language messages. Specifically, the `ClassifyDeviceIds` method is a dedicated method for device ID classification and extraction; the `HandleInfo` method is a method for extracting and classifying keywords from user problem descriptions.

[0027] <2> YAML: YAML is a lightweight, easy-to-read and easy-to-edit data serialization format. In this invention, it is used as the storage carrier for the dynamic weight scheduling knowledge base, and is specifically used for the structured storage of operation and maintenance knowledge of "scenario-keyword-function-weight".

[0028] The first aspect of this invention provides an intelligent operation and maintenance method for agricultural machinery navigation equipment. Figure 1 This document illustrates a flowchart of an intelligent operation and maintenance method for agricultural machinery navigation equipment according to an embodiment of the present invention. The intelligent operation and maintenance method for agricultural machinery navigation equipment in this embodiment mainly includes the following steps:

[0029] Step S11: Based on the dynamic weighted scheduling knowledge base, perform intent recognition and scenario matching on the received user question messages to lock down the question scenario, and obtain a sequence of diagnostic functions based on the locked question scenario and the corresponding diagnostic function.

[0030] In one embodiment of the present invention, the dynamic weighted scheduling knowledge base stores operation and maintenance knowledge and fault cases in configurable text files such as YAML format. The dynamic weighted scheduling knowledge base uses a four-level structured mapping of problem scenarios, keywords, diagnostic functions, and weights. It constructs the dynamic weighted scheduling knowledge base based on the mapping relationships between all problem scenarios and their corresponding keywords, the mapping relationships between all problem scenarios and their corresponding diagnostic functions, and the mapping relationships between each diagnostic function and its corresponding weight. This dynamic weighted scheduling knowledge base transforms scattered operation and maintenance experience into configurable and dynamically adjustable textual knowledge, realizing an automated process of user problem input, automatic matching of diagnostic logic, and output of diagnostic sequences according to weight priority. The system comprises several layers: a scenario layer defines major categories of operational issues, such as inaccurate positioning scenarios for agricultural drones and tractors, corresponding to positioning drift and path deviation; a keyword layer covers user-generated colloquial expressions like "positioning drift," "deviated," and "always" as scenario trigger conditions; a diagnostic function layer associates specific diagnostic tools, such as GNSS satellite signal strength detection and differential source connection status diagnosis, with executable diagnostic actions; and a weight layer assigns weight values ​​to each function based on fault case analysis of the corresponding problem scenario, considering factors like fault occurrence probability, impact, and diagnostic efficiency. Higher weight values ​​result in higher execution priority. These weights are dynamic and adjustable, adaptable to agricultural seasons, historical data, and new fault types. For example, the weight for downtime fault diagnosis can be increased during busy farming seasons, based on function accuracy data from historical data, or when new scenarios are added. The YAML configuration file clearly defines the scenario ID, name, description, trigger keywords, and associated diagnostic function ID, name, weight, and description, forming a complete structured knowledge storage and scheduling system. Weight adjustments require no code modification, only editing of the YAML file, significantly reducing maintenance costs and improving adaptability and diagnostic efficiency.

[0031] For example, in a YAML file stored in the dynamic weight scheduling knowledge base, the problem scenario is set as "floating," belonging to the "positioning anomaly" scenario of agricultural machinery navigation. The keywords mapped to the problem scenario are "always," "frequently," and "floating." That is, when the user's question contains these expressions, the diagnostic logic of the scenario is triggered. The keywords serve as the triggering condition group for the problem scenario "floating," triggering the mapping relationship between the problem scenario "floating" and the diagnostic function sequence. The diagnostic functions "AAGetParameter," "QxStatusCheck," "IonosphereAnalysis," and "CheckIsInCMCC" are extracted. Based on the "floating" problem scenario and the corresponding fault cases, the mapping relationship between each diagnostic function and its corresponding weight is further determined, forming the diagnostic function sequence "AAGetParameter: 1.9," "QxStatusCheck: 1.5," "IonosphereAnalysis: 1.5," and "CheckIsInCMCC: 0.77."

[0032] In one embodiment of this invention, the invention is deployed in the form of a voice robot on collaborative platforms such as DingTalk, serving as the core user-side entry point and a two-way interactive bridge connecting users and the system backend. For natural language question messages submitted by users on collaborative platforms like DingTalk, this invention uses regular expressions and the `ClassifyDeviceIds` and `HandleInfo` methods to transform unstructured information into system-recognizable structured information for intent recognition. This allows for accurate extraction of device IDs (e.g., 5100ABCD) and question descriptions such as "floating location" from the user's natural language messages. Furthermore, based on dynamic weight scheduling, the mapping relationship between question scenarios and their corresponding keywords in the knowledge base is used to match the extracted keywords to the corresponding question scenarios. In addition, for secure communication, the HMAC-SHA256 algorithm and the `CheckSign` method are used to verify request signatures, preventing data tampering or theft and ensuring interaction security. After obtaining the diagnostic results, the voice robot converts the technical processing results of the decision engine into user-understandable text, directly providing feedback in DingTalk, including the device ID, diagnostic conclusion, and operational suggestions. In actual use, users only need to send a natural language message to the robot via @ in collaboration platforms such as DingTalk to initiate maintenance requests. This invention automatically completes keyword extraction, security verification, result conversion, and feedback.

[0033] In one embodiment of the present invention, the sorting of diagnostic functions by weight is a key step in achieving automatic diagnosis. Its core is to arrange the diagnostic functions corresponding to the locked problem scenario in order of priority based on preset rules in the dynamic weight scheduling knowledge base, providing a clear execution order for subsequent automatic diagnosis. The step of obtaining the diagnostic function sequence based on the locked problem scenario and its corresponding diagnostic function includes: based on the mapping relationship between the problem scenario and its corresponding diagnostic function in the dynamic weight scheduling knowledge base, obtaining the corresponding diagnostic function for the locked problem scenario, and constructing a diagnostic function list; and sorting each diagnostic function in the diagnostic function list according to its corresponding weight in descending order to obtain the diagnostic function sequence.

[0034] For example, after identifying a locked problem scenario such as "abnormal positioning of agricultural drones," the system first retrieves all diagnostic functions corresponding to that scenario from the dynamic weighted scheduling knowledge base, constructing a list of diagnostic functions, such as GNSS signal detection, differential source status diagnosis, and sensor calibration verification. Then, the system reads the weight values ​​labeled for each diagnostic function in the knowledge base. These weight values ​​quantify the importance of the function in solving the current scenario problem; for example, GNSS signal detection has a weight of 2.0 in the abnormal positioning scenario, differential source diagnosis has a weight of 1.5, and sensor calibration verification has a weight of 1.3. Finally, the system sorts the diagnostic functions in the list from largest to smallest weight value, forming a diagnostic function sequence, such as GNSS signal detection 2.0, differential source status diagnosis 1.5, and sensor calibration verification 1.3.

[0035] Step S12: Based on each diagnostic function in the diagnostic function sequence, perform fault analysis on the full amount of equipment-related information obtained by fusing the collected heterogeneous source data, and send the equipment diagnostic results obtained based on the analysis results to the user.

[0036] In one embodiment of the present invention, fault analysis based on a diagnostic function sequence is the execution step for realizing the automatic operation and maintenance of agricultural machinery navigation equipment. Its core logic is to replace the inefficient mode of manual judgment based on experience and cross-system data lookup in the prior art by orderly calling diagnostic functions and using full data support. Diagnostic functions are extracted sequentially according to the order of their arrangement in the diagnostic function sequence. Each diagnostic function will accurately call the required equipment data sequentially to perform fault analysis based on its own functional requirements and the full information related to the corresponding equipment obtained by fusing the collected heterogeneous source data.

[0037] Specifically, this invention first generates a sequence of diagnostic functions sorted by weight from high to low using a dynamic weighted scheduling knowledge base, such as GNSS signal detection 2.0, differential source status diagnosis 1.5, and sensor calibration verification 1.3. Then, based on the full information of the device obtained from the fusion of collected heterogeneous source data, including real-time telemetry data such as satellite count and network signal, static attribute data such as device model, real-time status snapshots such as differential source connection status, and external environmental data such as geographical location and ionospheric intensity, this invention extracts diagnostic functions sequentially. Each function accurately calls the required data from the full information for targeted analysis according to its own functional requirements. For example, GNSS signal detection, as the diagnostic function with the highest weight, executes the diagnostic process first, extracting data such as satellite count, ionospheric intensity, and network signal strength to determine whether the fault is caused by weak signal or environmental interference. If this function fails to locate the fault, the next differential source status diagnosis function is executed sequentially to extract data such as differential source connection status and SIM card information to investigate whether the problem is caused by differential source interruption, and so on, until the cause of the fault is located.

[0038] In one embodiment of the present invention, the collected heterogeneous source data is integrated and fused to solve the technical problems of manual querying of multiple systems and data fragmentation that prevent linkage in the prior art. The integrated heterogeneous source data includes, but is not limited to:

[0039] Real-time telemetry data of the device: High-frequency time-series data such as IMU gyroscope data, number of satellites, network signal strength, lateral control error, motor fault codes, and event records (such as controller restart and drag events) are obtained through IoT platform interfaces (such as oriTrace2).

[0040] Static device attribute data: Obtain static information such as device model, hardware version, sales region, and activation time from the device management database (such as UAV / Basic / Info).

[0041] Real-time device status snapshot: Obtain the device's most critical status information through the latest trajectory interface (such as last-trace), including differential source connection status, IMU / gyroscope online status, and currently used SIM card.

[0042] External environment data: Integrate third-party APIs, such as map services (for parsing device geographic location) and ionospheric monitoring services (for assessing positioning signal quality).

[0043] In one embodiment of the present invention, the fault analysis method includes: First, the present invention calls the diagnostic function with the highest weight from a sequence of diagnostic functions ordered by weight as the first diagnostic function. This function extracts and analyzes the full information of the device provided by the collected heterogeneous source data. For example, for the "positioning fluctuation" scenario, the first function "GNSS signal detection" will analyze the number of satellites, signal strength, ionospheric data, etc., to determine whether the fault is caused by weak signal. If the function determines the cause of the fault, such as positioning fluctuation due to weak signal, a diagnostic result containing the cause of the fault and handling suggestions is immediately generated and sent to the user through a voice robot, and the corresponding handling action is automatically triggered. If the fault is resolved after the handling action is executed, the fault analysis ends. If the cause of the fault is not determined or the device fault handling fails, the next diagnostic function is extracted to analyze the corresponding full information of the device until the cause of the fault is determined and the fault handling is successful.

[0044] If the current diagnostic function fails to determine the cause of the fault (e.g., after analysis, signal problems are ruled out), or if the cause is determined but the processing action fails, the system will automatically extract the next function from the diagnostic function sequence according to the second highest priority and repeat the above analysis process. For example, the second diagnostic function, "Differential Source Status," will be called to analyze the differential source connection status, SIM card information, etc., to check whether the fault is caused by a differential source interruption, until the exact cause of the fault is found and the corresponding processing action successfully resolves the fault.

[0045] A second aspect of the present invention provides an intelligent operation and maintenance system for agricultural machinery navigation equipment. Figure 2 This is a schematic diagram of the intelligent operation and maintenance system for agricultural machinery navigation equipment provided in an embodiment of the present invention. Figure 2 As shown, the device includes a dynamic weighted scheduling knowledge base module 201 and a fault diagnosis module 202.

[0046] The dynamic weighted scheduling knowledge base module 201 is used to perform intent recognition and scenario matching on the received user question messages based on the dynamic weighted scheduling knowledge base 201 to lock the question scenario, and obtain a sequence of diagnostic functions based on the locked question scenario and the corresponding diagnostic function.

[0047] The fault diagnosis module 202 is connected to the dynamic weight scheduling knowledge base module 201. It is used to perform fault analysis based on the full amount of equipment-related information obtained by fusing the collected heterogeneous source data according to each diagnostic function in the diagnostic function sequence, and send the equipment diagnosis results obtained based on the analysis results to the user.

[0048] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0049] It should also be understood that the module division in the embodiments of the present invention is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of the present invention can be integrated into a single processor, exist as separate physical entities, or two or more modules can be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0050] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0051] A fourth aspect of the present invention provides an electronic terminal, Figure 3 This is a schematic diagram of the structure of the electronic terminal provided in an embodiment of the present invention. Figure 3 As shown, the electronic terminal includes at least one processor 301, a memory 302, at least one network interface 303, and a user interface 305. The various components in the device are coupled together via a bus system 304. It is understood that the bus system 304 is used to implement communication between these components. In addition to a data bus, the bus system 304 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 3 The general will label all buses as bus systems.

[0052] The user interface 305 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0053] It is understood that memory 302 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0054] In this embodiment of the invention, the memory 302 is used to store various types of data to support the operation of the electronic terminal 300. Examples of this data include: any executable program for operation on the electronic terminal 300, such as the operating system 3021 and application programs 3022; the operating system 3021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 3022 may contain various applications, such as media players, browsers, etc., for implementing various application services. The intelligent operation and maintenance method for agricultural machinery navigation equipment provided in this embodiment of the invention can be included in the application program 3022.

[0055] The methods disclosed in the above embodiments of the present invention can be applied to processor 301, or implemented by processor 301. Processor 301 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 301 or by instructions in the form of software. The processor 301 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 301 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 301 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0056] In an exemplary embodiment, the electronic terminal 300 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0057] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0058] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0059] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0060] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0061] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0062] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0063] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).

[0064] If a function is implemented as a software functional unit 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0066] In summary, this invention provides an intelligent operation and maintenance method, system, medium, and terminal for agricultural machinery navigation equipment. Based on a dynamic weighted scheduling knowledge base, this invention performs intent recognition and scenario matching on received user problem messages to pinpoint the problem scenario. Based on the pinpointed problem scenario and its corresponding diagnostic function, a sequence of diagnostic functions is obtained. Based on each diagnostic function in the sequence, fault analysis is performed using comprehensive equipment-related information obtained from the fusion of collected heterogeneous source data. The equipment diagnostic results obtained from the analysis are then sent to the user. This invention solves the technical problem of existing agricultural machinery navigation equipment operation and maintenance relying on manual labor; it improves the automatic operation and maintenance capabilities of agricultural machinery navigation equipment in agricultural environments, thereby increasing the efficiency of agricultural machinery operations.

[0067] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An intelligent operation and maintenance method for an agricultural machine navigation device, characterized by, Comprise: Based on the dynamic weight scheduling knowledge base, the received user problem message is subjected to intention recognition and scene matching to lock the problem scene, and according to the locked problem scene and the corresponding diagnostic function, a diagnostic function sequence is obtained; Based on each diagnostic function in the diagnostic function sequence, the device-related full information obtained by fusion of the collected heterogeneous source data is subjected to fault analysis, and the device diagnostic result obtained according to the analysis result is sent to the user.

2. The agricultural machine navigation device intelligent operation and maintenance method of claim 1, wherein, Based on the mapping relationship between all problem scenes and their corresponding keywords, the mapping relationship between all problem scenes and their corresponding diagnostic functions, and the mapping relationship between each diagnostic function and its corresponding weight, a dynamic weight scheduling knowledge base is constructed; wherein the weight of each diagnostic function of each problem scene is determined according to the fault case of the corresponding problem scene.

3. The agricultural machine navigation device intelligent operation and maintenance method of claim 2, wherein, The intention recognition and scene matching of the received user problem message includes: based on regular expression, the corresponding keywords are extracted from the received user problem message; according to the extracted keywords, the corresponding problem scene is matched according to the mapping relationship between the problem scene and its corresponding keywords in the dynamic weight scheduling knowledge base.

4. The intelligent operation and maintenance method for the agricultural machine navigation device according to claim 2, characterized in that, The diagnostic function sequence is obtained according to the locked problem scene and the corresponding diagnostic function, which comprises: According to the mapping relationship between the problem scene and its corresponding diagnostic function in the dynamic weight scheduling knowledge base, the diagnostic function corresponding to the locked problem scene is obtained, and a diagnostic function list is constructed; Each diagnostic function in the diagnostic function list is sorted according to its corresponding weight from large to small to obtain a diagnostic function sequence.

5. The agricultural navigation device intelligent operation and maintenance method of claim 1, wherein, Based on each diagnostic function in the diagnostic function sequence, the device-related full information obtained by fusion of the collected heterogeneous source data is subjected to fault analysis: according to the arrangement order of each diagnostic function in the diagnostic function sequence, the diagnostic functions are extracted in turn, and the diagnostic functions are subjected to fault analysis on the corresponding device-related full information obtained by fusion of the collected heterogeneous source data in turn.

6. The intelligent operation and maintenance method for the agricultural machine navigation device according to claim 5, characterized in that, The fault analysis method comprises: The current extracted diagnostic function is used to analyze the corresponding device-related full information; If the fault cause is determined, the corresponding diagnostic result is sent to the user to automatically trigger the fault handling action of the corresponding device; If the fault cause is not determined or the fault handling of the device fails, the next diagnostic function is extracted to analyze the corresponding device-related full information until the fault cause is determined and the fault handling is successful.

7. The agricultural navigation device intelligent operation and maintenance method of claim 1, wherein, The device-related full information obtained by fusion of the collected heterogeneous source data comprises: device real-time telemetry data, device static attribute data, device real-time state snapshot and external environment data.

8. An agricultural machine navigation device intelligent operation and maintenance system, characterized in that, Comprise: A dynamic weight scheduling knowledge base module is used to lock the problem scene by intention recognition and scene matching of the received user problem message based on the dynamic weight scheduling knowledge base, and a diagnostic function sequence is obtained according to the locked problem scene and the corresponding diagnostic function; The fault diagnosis module is connected with the dynamic weight scheduling knowledge base module, is used for carrying out fault analysis based on each diagnosis function in the diagnosis function sequence and the device related full information obtained by fusing the collected heterogeneous source data, and sends the device diagnosis result obtained according to the analysis result to the user.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the method of any one of claims 1 to 7.

10. An electronic terminal comprising a memory, a processor and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the intelligent operation and maintenance method of the agricultural machinery navigation device of any one of claims 1 to 7.

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