Work vehicle system

Abstract

Provide information regarding troubles and maintenance of work vehicles that are suitable for the workers' operating conditions. [Solution] A work vehicle system according to one embodiment comprises an external communication means, a location information acquisition means, and a control device. The external communication means acquires weather information. The location information acquisition means acquires location information of the work vehicle. The control device has technical information including field map information of the field where the work vehicle works, route setting information of the work vehicle, crop information of the field, and the work vehicle's instruction manual, as well as acquiring performance information of the work vehicle. The control device, using a generation AI function, generates at least one of the following based on weather information, location information, field map information, route setting information, crop information, technical information, and performance information: maintenance status information of the work vehicle, field map including information of the work vehicle, abnormality diagnosis information diagnosing abnormalities in the work vehicle, and work schedule information of the work vehicle.

Description

Technical Field

[0001] The present invention relates to a work vehicle system.

Background Art

[0002] Conventionally, an agricultural work machine including a control device and a voice output device controlled by the control device has been known (see, for example, Patent Document 1). Such an agricultural work machine is controlled so that the voice output device outputs voice guidance including a solution method for an abnormal state (trouble) in agricultural work.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The above technology only has a simple search function for troubles of work vehicles in agricultural work. Therefore, the solution method is general and may not be suitable for the usage conditions of the operator (user). In addition, presentation of information regarding maintenance for preventing troubles of work vehicles is also desired. Therefore, there has been a demand for a work vehicle system that can present information regarding troubles and maintenance of work vehicles suitable for the usage conditions of the operator.

[0005] The present invention has been made in view of the above, and an object thereof is to provide a work vehicle system that can present information regarding troubles and maintenance of work vehicles suitable for the usage conditions of the operator.

Means for Solving the Problems

[0006] To solve the above-mentioned problems and achieve the objective, the system includes an external communication means (11) for acquiring weather information (A1), a location information acquisition means (12) for acquiring location information (A4) of the work vehicle (2), and a control device (3) that has field map information (A2) of the field (F) where the work vehicle (2) works, route setting information (A7) of the work vehicle (2), crop information (A8) of the field (F) and technical information (A9) including the instruction manual for the work vehicle (2), as well as acquiring performance information of the work vehicle (2). The control device (3) is equipped with a generation AI function that generates at least one of the following based on weather information (A1), location information (A4), field map information (A2), route setting information (A7), crop information (A8), technical information (A9), and performance information (A3): maintenance status information (B1) of the work vehicle (2), a field map (B2) including information on the work vehicle (2), abnormality diagnosis information (B3) diagnosing abnormalities in the work vehicle (2), and work schedule information (B4) of the work vehicle (2). [Effects of the Invention]

[0007] According to the work vehicle system of this embodiment, it is possible to provide information regarding work vehicle troubles and maintenance that are suitable for the worker's usage conditions. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a block diagram showing an example of the control system of a work vehicle system according to an embodiment. [Figure 2] Figure 2 is an explanatory diagram showing the configuration of input and output information for the work vehicle system. [Figure 3] Figure 3 is an explanatory diagram showing the output of the vehicle data log. [Figure 4] Figure 4 is an explanatory diagram showing the data output to the spreadsheet software. [Figure 5] Figure 5 is an explanatory diagram showing an example of maintenance status information. [Figure 6] Figure 6 is an explanatory diagram showing an example of a field map. [Figure 7] Figure 7 is an explanatory diagram showing an example of abnormality diagnosis information. [Figure 8] Figure 8 is an explanatory diagram showing an example of work schedule information. [Figure 9] Figure 9 is an explanatory diagram showing an example of work schedule information. [Figure 10] Figure 10 is an explanatory diagram showing an example of maintenance documentation. [Figure 11] Figure 11 is an explanatory diagram showing an example of abnormality diagnostic data. [Figure 12] Figure 12 is an explanatory diagram showing an example of an anomaly response work schedule. [Modes for carrying out the invention]

[0009] The following describes in detail, with reference to the drawings, a work vehicle system according to an embodiment of the present invention. Note that the components in the following embodiments include those that are substituted by those skilled in the art, or substantially identical, or equivalent. Furthermore, the present invention is not limited to the above embodiments and can be implemented with various modifications without departing from the core principles of the invention.

[0010] First, the overall configuration of the work vehicle system 1 will be described with reference to Figures 1 and 2. Figure 1 is a block diagram showing an example of the control system of the work vehicle system 1 according to this embodiment. Figure 2 is an explanatory diagram showing the configuration of input information and output information for the work vehicle system 1. In this application, a combine harvester 2 (see Figure 3) is used as an example work vehicle.

[0011] Combine harvester 2 travels through the field, cutting stalks of grains such as rice and wheat, and then threshing the grains from the cut stalks.

[0012] As shown in Figures 1 and 2, the work vehicle system 1 includes a control device 3. The control device 3 has technical information A9, which includes field map information A2 of the field where the combine harvester 2 will work, route setting information A7 of the combine harvester 2, crop information A8 of the field, and the instruction manual for the combine harvester 2, as well as acquiring performance information A3 of the combine harvester 2.

[0013] The control device 3 operates on a computer provided with a processing device such as a CPU (Central Processing Unit), a storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), and a HDD (Hard Disk Drive), and further an input / output device. For example, the control device 3 may be configured to be provided in the combine 2, may include a cloud server on the cloud, and may be configured to enable so-called cloud computing.

[0014] Various sensors such as an external communication means 11, a position information acquisition means 12, a main transmission sensor 13, a sub-transmission sensor 14, a vehicle speed sensor 15, an engine load sensor 16, a vehicle body tilt sensor 17, a cutting height sensor 18, a cutting HST rotation sensor 19, a grain and straw clogging sensor 20, an auger position sensor 21, a sheave position sensor 22, a layer thickness sensor 23, a fuel sensor 24, and an abnormality detection sensor 25 are connected to the control device 3.

[0015] The external communication means 11 acquires the weather information A1. The external communication means 11 is, for example, a communication device connected to the Internet. The external communication means 11 acquires the weather information A1 of the field where the combine 2 performs work.

[0016] The position information acquisition means 12 acquires the position information A4 of the combine 2. The position information acquisition means 12 is, for example, a GPS (Global Positioning System) device, and can measure its own position through communication with GPS satellites. The position information acquisition means 12 may include an inertial measurement unit (IMU), and the position information A4 may include inertial measurement unit information A5 measured by the inertial measurement unit.

[0017] Also, the main transmission sensor 13 detects the shift position of the main transmission lever (not shown). The sub-transmission sensor 14 detects the shift position of the sub-transmission lever (not shown). The vehicle speed sensor 15 detects the speed of the pair of left and right crawlers 4 (see Fig. 3), which are traveling devices. The engine load sensor 16 detects the engine load of the combine 2. The vehicle body tilt sensor 17 detects the tilt angle of the vehicle body of the combine 2. The cutting height sensor 18 detects the height (cutting height) from the field surface at the lower part of the tip of the cutting device 5 (see Fig. 3). The cutting HST rotation sensor 19 detects the rotation of the cutting HST (not shown). The grain straw clogging sensor 20 detects clogging in the conveyance of the cut grain straw. The auger position sensor 21 detects the position of the discharge auger 6 (see Fig. 3) that rotates upward or downward. The sieve position sensor 22 detects the opening degree (position) of the sieve (not shown) of the oscillating sorting shelf that receives the processed material after threshing and performs screening while oscillating and transferring. The layer thickness sensor 23 detects the amount of paddy (layer thickness) on the transfer shelf (not shown) of the oscillating sorting shelf. The fuel sensor 24 detects the remaining amount of fuel (fuel state). The abnormality detection sensor 25 detects abnormality information of the combine 2.

[0018] To the control device 3, weather information A1 from the external communication means 11, position information A4 from the position information acquisition means 12, the main transmission position from the main transmission sensor 13, the sub-transmission position from the sub-transmission sensor 14, the vehicle speed from the vehicle speed sensor 15, the engine load from the engine load sensor 16, the vehicle body tilt angle from the vehicle body tilt sensor 17, the cutting height from the cutting height sensor 18, the cutting HST rotation from the cutting HST rotation sensor 19, the grain straw clogging from the grain straw clogging sensor 20, the auger position from the auger position sensor 21, the sieve position from the sieve position sensor 22, the layer thickness from the layer thickness sensor 23, the fuel state from the fuel sensor 24, and the abnormality information from the abnormality detection sensor 25 are respectively input.

[0019] Furthermore, a display device 26 and an audio output device 27 are connected to the control device 3. The control device 3 can output signals to the display device 26 to display images, characters, etc. on the display device 26. The control device 3 can output signals to the audio output device 27 to output audio on the audio output device 27. The display device 26 and the audio output device 27 are, for example, portable tablet terminals.

[0020] The control device 3, using its generative AI function, generates at least one of the following based on weather information A1, location information A4, field map information A2, route setting information A7, crop information A8, technical information A9, and performance information A3: maintenance status information B1 for the combine harvester 2, field map B2 containing information on the combine harvester 2, abnormality diagnosis information B3 diagnosing abnormalities in the work vehicle, and work schedule information B4 for the combine harvester 2. Here, generative AI (Artificial Intelligence), also known as generative AI, is an AI capable of generating diverse content. By using the generative AI function, text, images, audio, etc., can be generated based on input information. Furthermore, it is also possible to link related data using the generative AI.

[0021] <Input Information> First, we will explain the input information for the work vehicle system 1 with reference to Figure 2.

[0022] As shown in Figure 2, the input information for the work vehicle system 1 includes weather information A1, field map information A2, performance information A3, location information A4, inertial measurement device information A5 acquired by the inertial measurement device, imaging device information A6 acquired by the imaging device (e.g., AI camera, LIDAR), route setting information A7, crop information A8, and technical information A9. Weather information A1 includes the outside temperature, outside humidity, and precipitation of the area. Field map information A2 includes, for example, worker field information A and worker field information B. Note that the field information is not limited to two fields, but may be one or three or more. Performance information A3 includes vehicle basic information, work equipment information, basic information, and vehicle current status. Basic information includes various basic information such as the main transmission position, sub-transmission position, vehicle speed, engine load, vehicle tilt, cutting height, harvesting HST rotation, grain stalk clogging, auger position, sieve position, and layer thickness. Vehicle current status includes the fuel status and abnormality information mentioned above. Imaging device information A6 includes data captured by the AI ​​camera and data measured by the LIDAR.

[0023] Route setting information A7 includes worker field information route A and worker field information route B. Note that the field information routes are not limited to two, and may be one or three or more depending on the number of field map information A2. Crop information A8 includes growth status, moisture data, image data (color), and user input information. Technical information A9 includes instruction manuals, past malfunctions, and abnormal display information. Instruction manuals include images, language, document characters, and symbols. For example, crop information A8 is obtained as the output result of inputting data of crops captured by an AI camera into a neural network.

[0024] <Maintenance Status Information> Next, the generation of maintenance status information for combine harvester 2 will be explained with reference to Figures 3 to 5. Figure 3 is an explanatory diagram showing the output of the vehicle data log. Figure 4 is an explanatory diagram showing the data output to the spreadsheet software 28. Figure 5 is an explanatory diagram showing an example of maintenance status information B1.

[0025] As shown in Figure 3, the control device 3 can output vehicle data logs to spreadsheet software 28. The vehicle data logs include "date and time and location," "weather and temperature," and "vehicle speed." As shown in Figure 4, all output data is linked to "date and time and location acquired by the GPS device" and "recent weather and temperature."

[0026] As shown in Figure 5, the control device 3 generates maintenance status information B1 using its generation AI function, based on the time of the output data and performance information A3 and technical information A9. The control device 3 displays the generated maintenance status information B1 on the display device 26. The maintenance status information B1 includes advice on the maintenance procedure for the combine harvester 2. The maintenance status information B1 displays links to videos, instruction manuals, and workshop manuals. For example, by selecting linked text, the operator can play a video or display the corresponding section of the instruction manual or workshop manual.

[0027] Traditionally, workers had little time to read instruction manuals and workshop manuals in their limited time, and there was a possibility that they might overlook inspections due to reasons such as "the lubrication points are difficult to see" or "the location of the belt to be tensioned is unclear." With the work vehicle system 1 of the present invention, workers can take immediate action to perform inspections after receiving advice from the work vehicle system 1.

[0028] <Field Map> Next, the generation of field map B2 of combine harvester 2 will be explained with reference to Figure 6. Figure 6 is an explanatory diagram showing an example of field map B2. Note that the vehicle data log records the position of the main transmission lever and the auxiliary transmission lever. In addition, the vehicle data log records the lifting gear position of the lifting gear lever (not shown) that switches the working speed of the lifting device 7 (see Figure 3).

[0029] As shown in Figure 6, the control device 3 uses its generation AI function to determine the recommended speed for driving in field F based on the annual main and sub-transmission lever positions recorded in the vehicle data log. Based on the recommended speed, field map information A2, and route setting information A7, the control device 3 uses its generation AI function to generate a recommended vehicle speed map, which is an example of field map B2. The control device 3 displays the generated recommended vehicle speed map on the display device 26. The recommended vehicle speed map displays the recommended vehicle speed for each driving route L. By driving the combine harvester 2 at the recommended speed, deviations in the work plan due to variations in working time for each worker in a single field F are suppressed, and a work plan that is less affected by variations in working time for each worker can be created. The control device 3 may also display cutting height, etc., in addition to the display of the recommended speed. Furthermore, the control device 3 may use its generation AI function to calculate the working time in field F based on the recommended vehicle speed map and feed this back into the work plan to revise the work schedule.

[0030] <Abnormal Diagnosis Information> Next, the generation of abnormality diagnosis information B3 for combine harvester 2 will be explained with reference to Figure 7. Figure 7 is an explanatory diagram showing an example of abnormality diagnosis information B3.

[0031] As shown in Figure 7, the control device 3 generates a composite screen that overlays abnormalities and inspection details onto an image of the combine harvester 2 using its generation AI function, based on the time in the vehicle data log and weather information A1, performance information A3, location information A4, crop information A8, and technical information A9. Alternatively, the control device 3 may display image data only of the parts related to the abnormalities and inspection details. Alternatively, the control device 3 may cut out the parts related to the abnormalities and inspection details from the instruction manual and display them in a scrolling format. By warning of troubles that may occur during operation in advance, the control device 3 can prevent troubles that would take a long time to deal with.

[0032] Alternatively, the control device 3 displays the abnormality details as a list of texts. In the list, items with higher relevance (priority) may be displayed at the top. For example, if an abnormality occurs where the engine load is high, and the humidity has been high recently, then information related to "whether or not the harvesting unit is clogged" due to the weather will be displayed, followed by information related to "vehicle speed." The control device 3 then scrolls through the list and displays the instruction manual corresponding to the section selected by the operator.

[0033] Furthermore, the control device 3 may use its generation AI function to link the vehicle data log with "the error code of the combine harvester 2 and the measurement data of each sensor 13-25". Specifically, the control device 3 searches for the conditions in which trouble occurred in the past (the nature of the trouble) and the above measurement data using the learning function of the generation AI, and links them with the error code.

[0034] Conventionally, while error codes and the criteria for outputting that error code are linked, other measurement data are not linked and stored. Therefore, when multiple anomalies are related and one error code is output, the CPU load is heavy, and searching on a mobile tablet device takes time, which can lead to delays in resolving the issue. With the work vehicle system 1 of the present invention, it is possible to know when, where, and what kind of trouble occurred, or when abnormal data was output, making it easier to resolve the problem.

[0035] Furthermore, the control device 3 may associate error codes with warning content. This allows for detailed information about the circumstances under which the error code and warning occurred, providing valuable information for troubleshooting.

[0036] <Work Schedule Information> Next, the generation of work schedule information B4 for combine harvester 2 will be explained with reference to Figures 8 and 9. Figure 8 is an explanatory diagram showing an example of work schedule information B41. Figure 9 is an explanatory diagram showing an example of work schedule information B42. Note that "vehicle height" below is the set value for the vehicle height of combine harvester 2, and is set in the control device 3.

[0037] As shown in Figures 8 and 9, based on the time in the vehicle data log, the AI ​​generation function generates work schedule information B41 and B42 based on weather information A1, field map information A2, performance information A3, location information A4, route setting information A7, and crop information A8.

[0038] For example, the control device 3 links the data, which is a combination of the error code of the combine harvester 2 and the measurement data of each sensor 13-25, with vehicle height, vehicle tilt, and wet field control (data used to determine whether the field is dry or wet, such as weather information A1, imaging device information A6, and engine load). The control device 3 also determines whether the field is dry or wet and measures the working time for each. Here, the control device 3 makes a correction so that the working time for wet fields is longer than that for dry fields, as wet fields consume more grease. The control device 3 sets a standard for working time, and if the working time for dry or wet fields exceeds the standard, it adds "greasing the undercarriage" to the work schedule information B41. As shown in Figure 8, greasing (lubrication) is added to the work plan in advance at the start of the day's work so that the deadline for greasing does not come in the middle of the day's work.

[0039] The undercarriage components, such as the idler rollers, road wheels, and the arm shafts that tilt the vehicle body, consume grease and oil differently in dry and wet fields. Neglecting lubrication will result in the parts being completely worn out before the entire assembly needs to be replaced. According to the present invention's work vehicle system 1, greasing is incorporated into the work plan based on the total time spent working in dry and wet fields, eliminating the need for workers to remember each step individually. Thus, the present invention's work vehicle system 1 allows for the creation of realistic work plans that include the time required for trouble prevention, enabling work plans tailored to the actual work vehicle, field F, and worker.

[0040] Furthermore, the control device 3 determines whether the field is dry or wet and measures the work time for each task in both dry and wet fields. The control device 3 provides feedback to the work schedule information B41 to revise the work plan. While workers themselves can roughly record work time and the condition of field F, this does not result in quantitative data. However, with the work vehicle system 1 of this invention, the work time for each task in dry and wet fields can be expressed numerically. In addition, the ambiguous condition of field F, which can only be known by the worker who actually performed the work, can be explained with objective data.

[0041] Furthermore, the control device 3 links the variable fertilizer map, main transmission, sub-transmission, harvesting unit height, lifting transmission position, and vehicle height to the vehicle data log. The control device 3 determines whether the field is dry or wet and measures the working time for each task in dry and wet fields. The control device 3 provides feedback to the work schedule information B4 and modifies the work plan. Conventionally, the degree of lodging in each field F is influenced by recent weather, temperature, and fertilizer application, and there is a tendency for lodging to occur in each field F. In the work vehicle system 1 of this invention, the standing crop and lodging of field F are expressed numerically. In addition, the ambiguous state of field F, which can only be known by actual workers, can be explained with objective data.

[0042] <Maintenance Documents> Next, the generation of maintenance document C1 will be explained with reference to Figure 10. Figure 10 is an explanatory diagram showing an example of maintenance document C1.

[0043] As shown in Figure 10, the control device 3 uses its generation AI function to synthesize maintenance status information B1 and field map B2 to create maintenance data C1 for the combine harvester 2. The control device 3 displays the maintenance data C1 on the display device 26. When the operator selects a position on the travel path L, the control device 3 displays more detailed settings for the combine harvester 10, such as the shift position of the main transmission lever, the shift position of the sub-transmission lever, and the position of the sheaves. This makes it easier for the operator to drive the combine harvester 2 appropriately at the recommended speed.

[0044] Furthermore, the control device 3 may use its generation AI function to display on the display device 26 the typical vehicle height at which work is performed in the target field F, according to the weather conditions, and provide advice to the worker. The condition of each field F is influenced by recent weather and temperature, and there is a general tendency for each field F to be either dry or wet on the harvest day. With the work vehicle system 1 of this invention, any worker can understand the specific drainage of the target field F. In addition, it is possible to prevent the crawler 4 from getting stuck in the mud, which would cause delays in response.

[0045] Furthermore, the control device 3 may use its AI generation function to display on the display device 26 the typical main gear position, sub-gear position, harvesting unit height, and lifting gear position used in the target field F each year, according to the weather, and provide advice to the operator. The main gear position, sub-gear position, harvesting unit height, and lifting gear position affect the conveying posture of the rice. If the rice heads lag behind, the threshing load increases, and if the rice heads advance too far, the amount of unthreshed rice increases. By checking the recommended values ​​(minimum values) generated by the AI ​​generation function and performing the work, the operator can reduce the threshing load on the combine harvester 2 and prevent the occurrence of unthreshed rice.

[0046] Furthermore, the control device 3 may use its generation AI function to display on the display device 26 the typical cutting height and vehicle height used in the target field F each year, according to the weather conditions, and provide advice to the operator. The cutting height and vehicle height affect the cut pattern, and if the stubble is long, subsequent processes such as plowing and decomposition of rice straw will be delayed. The control device 3 can standardize the cut patterns that vary among operators, reducing the differences in their impact on subsequent processes.

[0047] <Abnormality Diagnosis Data> Next, the generation of abnormality diagnostic data C2 will be explained with reference to Figure 11. Figure 11 is an explanatory diagram showing an example of abnormality diagnostic data C2.

[0048] As shown in Figure 11, the control device 3 uses its generation AI function to synthesize the field map B2 and the anomaly diagnosis information B3 to generate anomaly diagnosis data C2 for the combine harvester 2. The control device 3 displays the anomaly diagnosis data C2 on the display device 26. When an anomaly occurs, the control device 3 displays information about the current anomaly and information about past anomalies when the operator selects the location of the anomaly on the travel path L. This allows the operator to respond to the current anomaly while referring to past anomalies.

[0049] <Schedule for handling abnormal situations> Next, the generation of the abnormal response work schedule C3 will be explained with reference to Figure 12. Figure 12 is an explanatory diagram showing an example of the abnormal response work schedule C3.

[0050] As shown in Figure 12, the control device 3 uses its generation AI function to synthesize abnormality diagnosis information B3 and work schedule information B4 to generate an abnormality response work schedule C3 for the combine harvester 2. The control device 3 displays the abnormality response work schedule C3 on the display device 26. When an abnormality occurs, the control device 3 generates an abnormality response work schedule C3 with the work schedule corrected (for example, shifting the schedule later). This allows workers to perform their work according to the corrected work schedule even when an abnormality occurs.

[0051] Furthermore, the control device 3 outputs an approval for the anomaly diagnosis D1 and a change in the anomaly diagnosis schedule D2 based on the user's language-based approval of the generated maintenance document C1, anomaly diagnosis document C2, and anomaly response work schedule C3 (see Figure 2).

[0052] <Other features> Next, we will explain other features of the combine harvester 2 and their effects.

[0053] The control device 3 links the work schedule information B4 with the hour meter. The control device 3 calculates backward from the work schedule information B4 and displays the machine's consumables on the display device 26. In addition, the control device 3 displays the time required for maintenance along with the machine's consumables. Conventionally, although there are differences depending on the actual work conditions, the replacement of consumables such as belts and chains is performed based on the hour meter. According to the work vehicle system 1 of this invention, parts can be procured in advance by accurately calculating backward, preventing stockouts and shipping delays.

[0054] Furthermore, the control device 3 links the yield meter information, moisture meter information, auger position, hour meter, and discharge time to the vehicle data log. The control device 3 calculates the normal discharge time based on the yield meter information and the date, time, and location obtained from the GPS device. The standard discharge time is the design value. If maintenance is required based on the discharge time, the control device 3 provides feedback to the work schedule information B4 and modifies the work schedule. For example, the control device 3 compares the discharge times at 100 hours and 400 hours to determine whether maintenance is required.

[0055] Conventionally, the degree of wear of the spiral inside a zoom auger cannot be determined without disassembling it. While the length of discharge time is one indicator of wear, accurate comparison has been difficult. Since the amount of wear on the spiral varies depending on the rice variety, moisture content, and amount of foreign matter, the hour meter alone cannot determine the exact replacement time. According to the work vehicle system 1 of this application, the discharge time is used as a guideline to recommend parts replacement to the operator. In addition, consumables are inspected based on the discharge time.

[0056] Furthermore, the control device 3 links the work plan (work schedule information B41), the amount of crystal / soot deposits in the SCR / DPF, the fuel / urea water consumption, and the engine load. The control device 3 measures the trend of increasing deposit amounts using the above items and incorporates it into the work schedule information B41 using its generation AI function. Based on the work schedule information B41, the control device 3 enables forced regeneration regardless of the amount of deposits.

[0057] This process involves raising the exhaust temperature to automatically regenerate (refresh) the SCR catalyst, which accumulates unburned fuel, a film that degrades the catalyst, and white products derived from ammonia. There are two functions: automatic regeneration, which is performed automatically and periodically, and manual regeneration, which can be performed manually at any time.

[0058] Conventionally, automatic regeneration is fine for the operation, but manual regeneration is time-consuming (about 1 hour). It requires parking the combine harvester 2 and having the rice cart and dryer on standby, leaving the worker empty-handed. Also, frequent forced regeneration, which is not affected by the amount of crystal / soot accumulation, leads to increased filter wear. The work vehicle system 1 of this application prevents the need for manual regeneration during work and prevents deviations from the work plan. By incorporating the time for manual regeneration into the work plan itself, the discrepancy between reality and the plan can be reduced.

[0059] As described above, the work vehicle system 1 according to the embodiment includes an external communication means 11 for acquiring weather information A1, a location information acquisition means 12 for acquiring location information A4 of the combine harvester 2, and a control device 3 that has field map information A2 of the field F where the combine harvester 2 works, route setting information A7 of the combine harvester 2, crop information A8 of the field F, and technical information A9 including the instruction manual for the combine harvester 2, as well as acquiring performance information A3 of the combine harvester 2. The control device 3, using a generation AI function, generates at least one of the following based on weather information A1, location information A4, field map information A2, route setting information A7, crop information A8, technical information A9, and performance information A3: maintenance status information B1 of the combine harvester 2, field map B2 including information on the combine harvester 2, abnormality diagnosis information B3 diagnosing an abnormality in the combine harvester 2, and work schedule information B41 of the combine harvester 2.

[0060] According to the work vehicle system 1 configured as described above, the AI ​​generation function outputs unique output information based on input information tailored to the worker's usage. This makes it possible to present information regarding work vehicle troubles and maintenance that is suitable for the worker's usage conditions.

[0061] Furthermore, as described above, in the work vehicle system 1 according to the embodiment, the control device 3 synthesizes the maintenance status information B1 and the field map B2 to generate maintenance data C1 for the combine harvester 2.

[0062] With the work vehicle system 1 configured as described above, workers can perform their work appropriately in field F by referring to maintenance documents C1.

[0063] Furthermore, as described above, in the work vehicle system 1 according to the embodiment, the control device 3 synthesizes the field map B2 and the abnormality diagnosis information B3 to generate abnormality diagnosis data C2 for the combine harvester 2.

[0064] With the work vehicle system 1 configured as described above, workers can easily troubleshoot problems in field F by referring to the abnormality diagnosis data C2.

[0065] Furthermore, as described above, in the work vehicle system 1 according to the embodiment, the control device 3 synthesizes the abnormality diagnosis information B3 and the work schedule information B4 to generate the abnormality response work schedule C3 for the combine harvester 2.

[0066] According to the work vehicle system 1 configured as described above, the schedule can be automatically adjusted in the event of a problem.

[0067] Further effects and modifications can be readily derived by those skilled in the art. Therefore, broader aspects of the present invention are not limited to the specific details and representative embodiments expressed and described above. Accordingly, various modifications are possible without departing from the spirit or scope of the overall concept of the invention as defined by the appended claims and their equivalents. [Explanation of Symbols]

[0068] 1. Work Vehicle System 2. Combine harvester (work vehicle) 3. Control device 11 External communication means 12 Location information acquisition means A1 Weather Information A2 Field Map Information A3 performance information A4 Location information A7 Route setting information A8 Crop information A9 Technical information B1 Maintenance Status Information B2 Field Map B3 Abnormal Diagnosis Information B4 Work Schedule Information C1 Maintenance Document C2 Abnormality Diagnosis Data C3 Anomaly Response Work Schedule Field F

Claims

1. External communication means for obtaining weather information, A means for acquiring location information to acquire location information of work vehicles, The system includes a control device that has technical information including field map information of the field where the work vehicle operates, route setting information of the work vehicle, crop information of the field, and the operation manual of the work vehicle, and acquires performance information of the work vehicle. The control device is A work vehicle system that uses a generation AI function to generate at least one of the following based on weather information, location information, field map information, route setting information, crop information, technical information, and performance information: maintenance status information of the work vehicle, a field map including information on the work vehicle, abnormality diagnosis information diagnosing abnormalities in the work vehicle, and work schedule information of the work vehicle.

2. The control device is The work vehicle system according to claim 1, which generates maintenance data for the work vehicle by synthesizing the maintenance status information and the field map.

3. The control device is The work vehicle system according to claim 1, wherein the field map and the abnormality diagnosis information are combined to generate abnormality diagnosis data for the work vehicle.

4. The control device is The work vehicle system according to claim 1, which generates an abnormality response work schedule for the work vehicle by combining the abnormality diagnosis information and the work schedule information.

Images