A harvester grain yield data management system and method

Abstract

The application discloses a kind of harvester grain yield data management system and method, it is related to grain yield data management technical field.The system includes: feed stage management module, discharging stage management module, measurement accuracy management module and yield data upload storage module.The application provides basic data support for subsequent discharging control through feed stability detection, improves the continuity and reliability of yield measurement from the source, avoids the abnormal or measurement error of discharging amount caused by feed fluctuation.Further establish dynamic association between feed and discharge, realize adaptive discharging adjustment, effectively improve the stability and response accuracy of measurement process.On this basis, through the linkage of data upload and precision determination, it is helpful to ensure the authenticity and timeliness of yield data, to further improve the reliability of yield data management, realize the precision control and management from feed to data upload whole process, significantly improve the credibility of harvester grain yield data.

Description

Technical Field

[0001] This invention relates to the field of grain yield data management technology, and in particular to a harvester grain yield data management system and method. Background Technology

[0002] As a crucial component of agricultural machinery, the effective management of grain yield data from harvesters is vital for optimizing agricultural production. Current technologies typically involve data acquisition, processing, storage, and analysis / display. Specifically, during operation, photoelectric sensors and flow sensors on the harvester collect raw data such as grain quantity and flow rate in real time. The collected raw data then undergoes preprocessing operations such as filtering, noise reduction, and outlier removal. The processed data is stored in the harvester's control terminal or mobile storage device according to factors such as time, location, and yield. After the operation is completed, the data is uploaded to the backend via wireless network or data interface for storage and visual management of the grain yield data.

[0003] For example, Chinese patent application CN114331289A discloses a big data-based warehouse management system and method, which includes: a big data platform, a warehouse unit, a consumption unit, and a production unit. The consumption unit is used to collect data on grain consumption in the market and to predict consumption in the next cycle. The production unit is used to collect data on the yield and quality of various grains from farmers. The warehouse unit is used to adjust the inventory ratio of different types of grains.

[0004] For example, Chinese invention patent CN115222305B discloses a method for early warning management based on wheat yield data, which includes: collecting environmental data of grid areas through remote sensing satellites, dividing the wheat field into several grid areas, analyzing the environmental data of the grid areas collected by remote sensing satellites; marking each grid area as a wheat field grid, issuing early warnings for the yield of the wheat field grids, marking low-yield wheat field grids as low-yield wheat field grids, and marking over-yield wheat field grids as over-yield wheat field grids; and displaying the location of the grid with the highest priority analysis value on the staff's mobile terminal.

[0005] The above-mentioned technology has at least the following technical problems:

[0006] Due to the inconsistent size of grain particles, some larger grains may not be able to be fed smoothly, while smaller grains may overlap and flow in, causing inaccurate identification by the photoelectric sensor and affecting the accuracy of yield data measurement. At the same time, when too much grain accumulates, the control mechanism that blocks the feed by the piston plate has a response delay, making it impossible to control the grain flow rate in time. This can easily lead to excessive accumulation inside the feed pipe, affecting the real-time performance of the measurement and the feeding efficiency, and further causing the measurement error to continue to increase.

[0007] In addition, it is necessary to consider that photoelectric sensors are used to detect the number of grains during the feeding process and provide feedback. However, under high flow conditions, there may be problems such as response time delay and insufficient signal processing capabilities, resulting in untimely output calculation and difficulty in achieving high-precision and intelligent management of harvester output data. Summary of the Invention

[0008] To address the technical problem of low accuracy in yield data measurement in existing technologies, which makes it difficult to achieve high-precision and intelligent management of harvester yield data, this invention provides a harvester grain yield data management system and method, the technical solution of which is as follows:

[0009] On one hand, a harvester grain yield data management system is provided, comprising: a feeding stage management module for detecting the feeding stability of the harvester during the feeding stage to obtain feeding management detection results, and feeding them back to the unloading stage management module; an unloading stage management module for determining whether to trigger unloading management strategy adjustment to improve the accuracy of harvester grain yield measurement based on the received feeding management detection results; if unloading management strategy adjustment is not triggered, the operation of the measurement accuracy management module is triggered; if unloading management strategy adjustment is triggered, the operation of the measurement accuracy management module is determined after the unloading management strategy adjustment is performed; a measurement accuracy management module for judging the measurement accuracy of the harvester yield data during the unloading stage to obtain yield measurement accuracy judgment results; and a yield data upload and storage module for judging whether to upload and store yield data based on the yield measurement accuracy judgment results.

[0010] On the other hand, a method for managing grain yield data of a harvester is provided. This method includes: detecting the feeding stability of the harvester during the feeding stage to obtain feeding management detection results and providing feedback; determining whether to trigger the adjustment of the feeding management strategy to improve the accuracy of grain yield measurement of the harvester based on the feeding management detection results; if not triggered, determining the measurement accuracy of the yield data of the harvester during the feeding stage to obtain a yield measurement accuracy determination result; if triggered, determining whether to obtain the yield measurement accuracy determination result after adjusting the feeding management strategy; and determining whether to upload and store the yield data based on the yield measurement accuracy determination result.

[0011] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0012] 1. This invention includes a feeding stage management module, a discharging stage management module, a measurement accuracy management module, and a production data upload and storage module. Compared with existing technologies, through the synergistic effect of these modules, a closed-loop data management structure is constructed that spans the entire process from feeding and discharging to data storage, achieving overall precision in production measurement and intelligent data management. Specifically, the feeding stage management module is used to detect the feeding stability of the harvester during the feeding stage. By acquiring parameters such as the standard deviation of the feeding amount and the average feeding amount, the feeding fluctuation rate is quantified and calculated to obtain the feeding management detection result reflecting the uniformity of the feeding. This detection result is then fed back to the discharging stage management module to achieve feedforward control of the operating status during the discharging stage. Through the setting of this module, uneven feeding, blockage, or instantaneous fluctuations can be identified in real time at the beginning of the operation, improving the reliability of the feeding data from the source and providing a foundation for subsequent discharging adjustment and measurement accuracy improvement. The feeding stage management module determines whether to trigger feeding management strategy adjustments based on the received feeding management detection results. If feeding stability is good, the original feeding parameters are maintained and the measurement accuracy management module is directly triggered for detection. If feeding stability is poor, adaptive adjustments to feeding speed, flow rate, or pipeline pressure are performed to improve the stability of the feeding process. After adjustment, the module again determines whether to trigger the measurement accuracy management module. This module enables dynamic correlation control between the feeding and feeding processes, keeping the feeding flow rate stable and reducing measurement signal fluctuations caused by uneven grain flow, thus improving the accuracy and consistency of overall yield measurement. The measurement accuracy management module is used to determine the measurement accuracy of yield data during the feeding stage. By analyzing parameters such as the feeding signal phase lag delay, sensor response consistency, and data deviation rate, the module obtains the yield measurement accuracy determination result. This module enables real-time monitoring of the measurement system performance, promptly detecting anomalies caused by sensor drift, data delay, or material blockage, ensuring the accuracy and reliability of yield data. The yield data upload and storage module comprehensively judges the upload and storage of yield data based on the yield measurement accuracy assessment results. Upload and storage operations are only performed when the measurement accuracy meets a set threshold, ensuring the authenticity and validity of the data ultimately stored in the cloud or database. This module enables the system to automatically filter high-quality data, avoiding interference from erroneous data on statistical and analytical results, and improving the reliability of overall data management and decision support. Overall, through the coordinated linkage of feed detection, feed adjustment, accuracy assessment, and data storage, a closed-loop control mechanism for harvester grain yield data management is constructed. This achieves stable and accurate control of the data acquisition process, not only improving the accuracy and real-time performance of yield measurement during the harvesting operation but also effectively enhancing the accuracy control and management efficiency throughout the entire process from feed to data upload.

[0013] 2. During harvester operation, the standard deviation and average feed rate are calculated based on the feed rate data collected within the feeding time interval. The inverse proportionality of the feed rate fluctuation rate is used as the feed stability characterization value. Simultaneously, to eliminate the influence of operating speed variations on the detection results, the header operating speed is further acquired, and an operating speed factor is introduced to correct the feed stability characterization value, thereby improving the accuracy and robustness of the detection. Compared with existing technologies that rely solely on instantaneous or time-averaged feed rates for judgment, this invention introduces a feed rate fluctuation index combined with a header speed deviation correction mechanism, achieving a comprehensive assessment of the dynamic stability of the feeding process. This effectively identifies abnormal feeding states caused by uneven feeding, blockages, or changes in operating speed, providing accurate and effective input data for subsequent unloading control, improving the overall coordination and accuracy of the operation. Furthermore, it significantly enhances the dynamic response capability and anti-interference performance of feed detection, overcoming the limitations of traditional methods that cannot distinguish between speed changes and feed fluctuations, ensuring more stable and reliable measurement results. Based on this, a graded response-based feeding control mechanism is implemented through intelligent judgment of the feeding status. This enables the system to execute differentiated adjustment strategies for different degrees of feeding fluctuations, thereby avoiding over-adjustment or response lag and improving the overall system's adaptability and stability. In existing technologies, feeding adjustment typically relies on fixed parameters or periodic corrections, failing to dynamically respond to real-time feeding status. This invention, based on graded triggering logic of feeding detection results, achieves multi-level adaptive linkage of strategies, significantly improving the targeting and intelligence level of feeding adjustment. Furthermore, the introduction of a baseline adjustment mechanism for feeding management strategies effectively reduces subsequent measurement deviations caused by excessive fluctuations in feeding volume, ensuring the continuity and uniformity of grain inflow, thus providing stable input conditions for precise control in subsequent feeding stages. Compared to existing technologies that rely on fixed thresholds or single monitoring parameters for feeding control, this invention achieves adaptive adjustment based on real-time data, significantly improving the dynamic response performance and operational adaptability of the feeding stage. Meanwhile, existing technologies typically only make single-point adjustments when significant blockages occur, lacking continuous feedback. The optimized adjustment mechanism for the feeding management strategy further introduces real-time monitoring of key parameters such as the pressure and flow rate of the feeding pipeline, enabling precise adjustment of the flow state during the feeding stage. This prevents blockages, accumulation, or empty flow, effectively improving the consistency of grain flow and the stability of measurement signals. It overcomes the problems of traditional delayed response and misadjustment, significantly improving the dynamic control accuracy during the feeding stage, and realizing an intelligent, precise, and highly robust production data management process.

[0014] 3. During the material feeding stage, the phase delay characteristics of the feeding quantity signal are extracted to calculate the feeding phase lag delay value, which is then compared with the preset maximum feeding lag limit to determine whether there is a phase response lag problem in the current measurement process. If the lag value exceeds the limit, it is considered an abnormal measurement accuracy; if it is within the normal range, the high-frequency gain of the signal is further detected to determine the sensor's response sensitivity to changes in high-frequency signals. Through the dual-parameter judgment mechanism of phase delay and high-frequency gain, a comprehensive accuracy assessment of the production measurement signal in both time response and amplitude response dimensions can be achieved. This more comprehensively reflects the sensor status and signal sampling quality than a single threshold judgment method, thus effectively avoiding misjudgments caused by single-point noise, delay drift, and other factors. Based on this, the production data upload and storage judgment mechanism realizes the linkage control of data upload and accuracy judgment, preventing abnormal measurement data from being directly uploaded and causing subsequent analysis distortion, ensuring the validity of uploaded data. Compared with the traditional real-time upload method without a judgment mechanism, this improves the system's data quality control capability and self-correction capability. When abnormal measurement accuracy is detected, the introduction of yield measurement acquisition strategy optimization enables adaptive adjustment of the sampling frequency. This allows the data acquisition strategy to be dynamically optimized based on real-time measurement errors, avoiding the accumulation of sampling distortion and delay errors under a fixed sampling frequency. Compared with existing technologies, by establishing a collaborative closed loop of measurement accuracy judgment, data upload judgment, and acquisition strategy optimization during the material feeding stage, dynamic monitoring and adaptive correction of yield measurement data throughout the entire process are achieved. This significantly improves the measurement accuracy, data reliability, and system adaptability during the harvester feeding stage, ensuring high reliability and stability of the yield data acquisition process. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the structure of a harvester grain yield data management system provided in an embodiment of the present invention;

[0017] Figure 2 A flowchart of the feed stability detection process for a harvester grain yield data management system provided in this embodiment of the invention;

[0018] Figure 3 A logic diagram for determining the measurement accuracy of a harvester grain yield data management system provided in this embodiment of the invention;

[0019] Figure 4A flowchart illustrating a harvester grain yield data management method provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] In agricultural production, combine harvesters, as important agricultural machinery, are responsible for harvesting grain crops. In order to improve agricultural production efficiency, optimize resource allocation, and achieve refined management, the management of grain yield data from combine harvesters has become particularly important.

[0023] like Figure 1 The diagram shows a structural schematic of a harvester grain yield data management system provided in an embodiment of the present invention. The system includes: a feeding stage management module, a discharging stage management module, a measurement accuracy management module, and a yield data upload and storage module. Through the coordinated operation of these modules, a closed-loop management system is achieved, encompassing the entire process from feeding monitoring and discharging adjustment to data accuracy control and storage determination. The modules are logically linked through detection, feedback, and determination, unifying the data flow and control flow. Furthermore, data interaction between modules forms a real-time feedback mechanism. Feeding detection results directly drive discharging strategy adjustments, and discharging adjustment results provide a more stable environment for measurement accuracy determination. Ultimately, the accuracy results control data upload and storage. This hierarchical linkage between modules not only improves the system's response speed but also forms a closed-loop, self-optimizing intelligent control system, achieving full-process, end-to-end quality control of harvester grain yield data.

[0024] Specifically, the feeding stage management module is used to detect the feeding stability of the harvester during the feeding stage of the operation to obtain the feeding management detection results, and then feed the feeding management detection results back to the unloading stage management module. By detecting the feeding stability of the harvester during the feeding stage of the operation, the fluctuation of the feeding process can be quantified in real time, and the source of production measurement error caused by uneven feeding can be identified in a timely manner. Then, the feeding stability detection results are fed back to the unloading stage management module to provide a basis for the adaptive adjustment of the subsequent unloading strategy, thereby improving the stability and reliability of the entire production measurement process from the source.

[0025] refer to Figure 2 ,like Figure 2The diagram shows a flowchart of the feed stability detection process for a harvester grain yield data management system according to an embodiment of the present invention. The corresponding logic is as follows: Based on the obtained standard deviation and average feed rate, a feed stability characterization value is obtained. If the feed stability characterization value is greater than the feed stability reference limit, the feed management detection result is recorded as feed detection compliant, and the feeding management strategy adjustment is not triggered. The feed stability characterization value continues to be monitored. If the feed stability characterization value is not greater than the feed stability set limit, the feed stability characterization value is compared with the feed control benchmark interval. If the feed stability characterization value is within the feed control benchmark interval, the feed management detection result is recorded as feed detection qualified, triggering the feed management strategy benchmark adjustment. Conversely, the feed management detection result is recorded as feed detection unqualified, triggering the feeding management strategy optimization adjustment. The specific steps for detecting the feed stability of the harvester during the feed stage are as follows:

[0026] S1. Based on the feed rate data obtained by the flow sensor built into the feed channel of the harvester within the feed time interval, the standard deviation of the feed rate and the average feed rate are obtained. The feed stability correlation quantification is performed on the two to obtain the feed volatility. The feed stability correlation quantification is a ratio calculation. The result of the inverse proportional processing of the feed volatility is used as the feed stability characterization value to characterize the feed stability.

[0027] S2, determine the numerical relationship between the feed stability characterization value and the stored feed stability reference limit; the feed stability reference limit is stored in the grain production management database. The grain production management database is a database created first when designing a harvester grain production data management system to store core configuration information. This database stores various limits and mapping sets necessary for the operation of the system, such as the feed stability reference limit. The initial setting of these limits is not arbitrarily specified. Technicians can manually set, adjust or fine-tune them at any time according to the specific performance of the system in actual testing.

[0028] S3. If the feed stability characterization value is greater than the feed stability reference limit, the feed management test result is recorded as the feed test meets the standard, and the obtained feed stability characterization value continues to be monitored.

[0029] S4. If the feed stability characterization value is not greater than the feed stability set limit, then the feed stability characterization value is compared with the stored feed control benchmark interval. Specifically, if the feed stability characterization value is within the feed control benchmark interval, then the feed management test result is recorded as feed test qualified; otherwise, the feed management test result is recorded as feed test unqualified. The feed control benchmark interval is the closed interval corresponding to the feed stability benchmark limit to the feed stability set limit.

[0030] The standard deviation and average feed rate calculated based on accurate feed rate data can more realistically characterize the fluctuation characteristics of the feeding stage. This makes the subsequently calculated feed volatility and feed stability values ​​more representative and reliable, providing an accurate basis for the dynamic adjustment of the feeding stage management module. This enables quantitative detection and real-time feedback of feed stability, effectively improving the front-end data quality of overall machine output measurement and the intelligence level of system response.

[0031] As a further measure, during harvester operation, feed stability is not only affected by changes in grain flow rate but also closely related to the header speed. When the header speed changes, the amount of feed entering the threshing mechanism changes synchronously or with a lag, resulting in a shift in the feed volatility. If the influence of header speed is not corrected, the feed stability characterization value may be misjudged, leading to false or missed triggering of adjustment strategies during the feeding phase. Therefore, it is necessary to correct the feed stability characterization value to eliminate the interference of operating speed changes on the feed stability detection results. Specifically, detecting the feed stability of the harvester during the feeding phase also includes:

[0032] Q1. The harvester’s header working speed is obtained during the feeding time interval by the built-in travel speed sensor. The deviation between the header working speed and the stored working reference speed is quantified to obtain the header working speed deviation value, which is the absolute value of the difference between the two.

[0033] Q2. Determine the numerical relationship between the deviation value of the header working speed and the stored header speed deviation range. The header speed deviation range is the closed interval corresponding to the minimum deviation of the set speed to the maximum deviation of the set speed, which is obtained from the grain production management database.

[0034] Q3. If the deviation value of the cutting table working speed is within the deviation range of the cutting table speed, no processing is required for the feed stability characterization value. Otherwise, the feed stability characterization value is corrected. Specifically, the feed stability characterization value is corrected based on the introduced operating speed factor. The correction process involves multiplying the operating speed factor and the feed stability characterization value. The feed stability characterization value is updated based on the result of the correction process. The operating speed factor is used to correct the influence of the cutting table working speed on the feed stability detection and is set by professionals according to industry standards.

[0035] By introducing header speed analysis into the feed stability detection process, dynamic correction of feed fluctuation rate can be achieved, effectively compensating for feed fluctuation errors caused by changes in harvester travel speed. Simultaneously, judging the header speed deviation value and speed deviation range allows for the selection of data ranges belonging to normal operating conditions, avoiding misjudgments of feed stability characterization values ​​under unstable operating conditions. Furthermore, the introduction of the operating speed factor enables the feed stability characterization value to more accurately reflect the stability of grain flow itself, rather than being affected by changes in operating speed, further improving the accuracy of feed detection results.

[0036] The feeding stage management module determines whether to trigger feeding management strategy adjustments to improve the accuracy of grain yield measurement in harvesters based on the received feeding management detection results. If the feeding management strategy adjustment is not triggered, the operation of the measurement accuracy management module is triggered. If the feeding management strategy adjustment is triggered, the module determines whether to trigger the operation of the measurement accuracy management module after the adjustment is performed. This module is set to intelligently determine whether to trigger feeding management strategy adjustments based on the feeding management detection results fed back during the feeding stage. This allows for automatic optimization and adjustment of corresponding parameters during the feeding process when the feeding is unstable, reducing flow fluctuations during the feeding stage and ensuring the matching of grain flow rate with sensor detection. When the feeding is stable, ineffective adjustments are avoided, improving the overall operating efficiency and measurement accuracy of the harvester.

[0037] As a further solution, the receiving feed management detection results are used to determine whether to trigger the discharge management strategy adjustment. The discharge management strategy adjustment includes a baseline adjustment for the feed management strategy to control the feeding speed and feed flow rate to dynamically optimize the stability of the feeding stage, and an optimization adjustment for the discharge management strategy to control the discharge speed and discharge flow rate to dynamically optimize the stability of the discharge stage. The specific process is as follows:

[0038] M1 determines whether the feed management detection result is that the feed detection meets the standard: if so, the feed management strategy adjustment will not be triggered.

[0039] M2, conversely, determines whether the material feeding management inspection result is qualified. Specifically: if the material feeding management inspection result is qualified, the material feeding management strategy baseline adjustment is triggered; otherwise, the material unloading management strategy optimization adjustment is triggered.

[0040] It should be noted that the specific process for adjusting the baseline of the material feeding management strategy is as follows:

[0041] First, the feed stability characterization value is input into the feed rate mapping table to output the header feed adjustment speed. Based on the output header feed adjustment speed, a prompt is sent to adjust the header feed speed. The feed rate mapping table is pre-set using historical feed stability characterization values ​​and header feed adjustment speeds set by professionals based on experience rules. The header feed speed is adaptively adjusted according to the real-time fluctuations during the feeding stage, making the grain flow into the threshing and feeding mechanism more uniform and stable. This achieves feedforward control of the dynamic flow rate during the feeding stage, which helps to eliminate unstable factors in the early stages of operation and ensures the smoothness and accuracy of subsequent feeding and measurement processes.

[0042] Secondly, after adjusting the feed speed of the cutting table, the feed management detection results for the next feeding time interval are obtained and judged. The specific judgment method is as follows:

[0043] If the feeding management detection result in the next feeding time interval is that the feeding detection meets the standard, the operation of the measurement accuracy management module will be triggered.

[0044] If the feeding management detection result in the next feeding time interval is qualified, the corresponding feeding stability characterization values ​​in the two feeding time intervals are arithmetically coupled and then input into the feeding flow mapping table to output the feeding adjustment flow rate. The initial feeding flow rate is set according to the output feeding adjustment flow rate, that is, the feeding adjustment flow rate is set as the initial feeding flow rate. The feeding flow mapping table is pre-set using historical feeding stability characterization values ​​and feeding adjustment flow rates set by professionals based on experience rules. By comprehensively considering the feeding stability trend in the continuous operation interval, the flow adjustment result has smoothness and trend guidance, avoiding frequent adjustments or repeated oscillations caused by fluctuations in a single detection. In addition, by dynamically correcting the initial feeding flow rate, the density and flow state of the grain entering the discharge channel can be further stabilized, reducing discharge pressure fluctuations and signal noise interference caused by instantaneous flow changes, thereby improving the repeatability and reliability of the measurement signal in the discharge stage. At the same time, this adjustment strategy can achieve a balanced control between feeding flow rate and feeding stability while maintaining operational efficiency, so that the feeding to discharge link forms a dynamically optimized closed loop during the operation.

[0045] If the feeding management detection result is abnormal in the next feeding time interval, a prompt of abnormal adjustment of the cutting table feeding speed will be sent, the cutting table feeding speed will be returned to the previous speed, and the material feeding management strategy optimization adjustment will be triggered.

[0046] It should also be noted that the specific process for optimizing and adjusting the material feeding management strategy is as follows:

[0047] First, if the feed management strategy baseline adjustment is triggered, the feed stability characterization value is updated; otherwise, the updated feed stability characterization value is directly used. This updated value is then input into the discharge speed mapping table to output the discharge adjustment speed. The discharge speed is adjusted based on this output speed, essentially setting the discharge adjustment speed as the actual discharge speed. The discharge speed mapping table is pre-set using historical feed stability characterization values ​​and discharge adjustment speeds set by professionals based on experience rules. Real-time adjustment of the discharge speed is achieved based on fluctuations during the feeding phase, enabling dynamic matching of the feed and discharge rates. This adaptive adjustment significantly improves the uniformity and stability of grain flow within the discharge pipe, providing a reliable flow basis for pressure control and subsequent measurement accuracy. Furthermore, the discharge speed adjustment process effectively reduces the risk of pipe blockage and sensor data delay, thereby enhancing the dynamic responsiveness and reliability of the entire discharge phase.

[0048] Secondly, the average pressure of the discharge pipe after the discharge speed is adjusted is obtained by the pressure sensor built into the discharge pipe of the harvester. If the average pressure of the discharge pipe is within the set pressure control range, the monitoring of the average pressure of the discharge pipe continues. Otherwise, it is determined whether the average pressure of the discharge pipe is greater than the maximum value corresponding to the set pressure control range.

[0049] If the average pressure in the discharge pipeline is greater than the maximum value corresponding to the set pressure control range, it indicates that the discharge resistance is increasing and there is a tendency to accumulate. Therefore, the discharge flow rate should be adjusted downwards. The specific method is as follows: the updated feed stability characterization value and the average pressure in the discharge pipeline are weighted and input into the discharge flow rate adjustment mapping set to output the discharge flow rate reduction value used to adjust the discharge flow rate. The weighting process involves multiplying the feed stability characterization value and the feed stability weight, and then adding the result of multiplying the average pressure in the discharge pipeline and the discharge pipeline pressure weight. The discharge flow rate reduction mapping set is pre-set using the results of weighting the historical feed stability characterization value and the average pressure in the discharge pipeline, as well as the discharge flow rate reduction value set by professionals based on experience rules. This set describes the mapping relationship between the weighted results of the feed stability characterization value and the average pressure in the discharge pipeline and the discharge flow rate reduction value.

[0050] Conversely, if the feed rate is insufficient, the feed flow rate should be adjusted upwards. Specifically, the updated feed stability characterization value and the average pressure of the feed pipeline are weighted and input into the feed flow rate adjustment mapping set. This outputs the adjusted feed flow rate value used to adjust the feed flow rate. The feed flow rate adjustment mapping set is pre-set using the weighted results of historical feed stability characterization values ​​and the average pressure of the feed pipeline, along with feed flow rate adjustment values ​​set by professionals based on empirical rules. It describes the mapping relationship between the weighted results of the feed stability characterization values ​​and the average pressure of the feed pipeline and the adjusted feed flow rate value. This feed flow rate adjustment strategy effectively balances the relationship between feed speed and pressure stability, significantly improving the fluid dynamic stability and data measurement continuity during the feed stage. Real-time monitoring and judgment of the average pressure of the feed pipeline enable adaptive optimization of the feed flow rate. When the average pipeline pressure is detected to be higher than the upper limit of the set control range, the feed flow rate is adjusted downward to effectively alleviate pipeline accumulation and flow blockage. Conversely, when the pressure is lower than the lower limit of the set range, the feed flow rate is increased based on the obtained feed flow rate adjustment value to ensure feed continuity and the integrity of the measurement signal.

[0051] During the optimization and adjustment of the material feeding management strategy, dynamic judgment and adaptive optimization control of the feeding stage are achieved by linking the updated feed stability characterization value with the average pressure of the feeding pipeline. Through a two-layer adjustment mechanism, coordinated linkage and precise control of speed and flow rate can be achieved during the feeding stage. This not only effectively improves the flow stability and anti-clogging performance of the feeding process, but also significantly enhances the accuracy and data continuity of the production measurement link, realizing intelligent closed-loop optimization control of the entire process from feeding to feeding to measurement.

[0052] The measurement accuracy management module is used to determine the measurement accuracy of the harvester's yield data during the feeding stage of the harvester, obtaining the yield measurement accuracy judgment result. This module can dynamically determine the measurement accuracy of the yield data during the feeding stage by analyzing characteristics such as the phase delay, signal fluctuation rate, and noise interference of the feeding signal to evaluate the reliability of the yield data in real time. This can effectively identify measurement errors caused by mechanical vibration, sensor delay, or changes in grain flow rate, ensuring that the final uploaded yield data has higher accuracy and consistency.

[0053] refer to Figure 3 ,like Figure 3The diagram shown illustrates the measurement accuracy determination logic of a harvester grain yield data management system according to an embodiment of the present invention. The logic is as follows: First, determine whether the acquired feeding phase lag delay value is greater than the maximum feeding lag limit. If so, the yield measurement accuracy determination result is determined to be abnormal. Then, optimize the yield measurement acquisition strategy to improve yield measurement accuracy before determining the yield data upload and storage. Otherwise, the yield measurement accuracy determination result is determined to be normal, and the high-frequency gain of the feeding quantity signal is acquired. If the high-frequency gain of the feeding quantity signal is less than the set minimum signal gain, the yield measurement accuracy determination result is updated to abnormal. Otherwise, continue monitoring the feeding phase lag delay value and the high-frequency gain of the feeding quantity signal. Specifically, the steps for determining the measurement accuracy of the harvester's yield data during the feeding stage are as follows:

[0054] N1 uses timing analysis tools (such as LabVIEW) to obtain the feeding phase lag delay value, which reflects the phase delay of the feeding quantity signal, and determines the numerical relationship between the feeding phase lag delay value and the stored maximum feeding delay lag limit.

[0055] N2, if the material feeding phase lag delay value is greater than the maximum limit of material feeding lag, then the production measurement accuracy judgment result will be determined as an abnormal measurement accuracy judgment.

[0056] N3, otherwise, the production measurement accuracy judgment result is determined as normal, and the high-frequency gain of the feeding quantity signal is obtained by the spectrum analyzer and judged. Specifically, if the high-frequency gain of the feeding quantity signal is less than the set minimum signal gain, the production measurement accuracy judgment result is updated to abnormal; otherwise, the feeding phase lag delay value and the high-frequency gain of the feeding quantity signal continue to be monitored.

[0057] Detecting the feeding phase lag delay value can accurately reflect the degree of timing deviation of the feeding signal, and promptly detect signal lag caused by fluctuations in grain flow rate, blockage of the feeding pipe, or mechanical vibration. Monitoring the high-frequency gain of the feeding quantity signal can reveal the response amplitude and stability of the measurement signal, ensuring that the sensor can effectively capture instantaneous changes even under high-speed flow variation conditions. By combining the above two signal characteristics to determine the accuracy of yield measurement, measurement anomalies can be detected at an early stage, preventing low-precision or abnormal data from being included in the system database, thereby improving the reliability and usability of yield data.

[0058] The production data upload and storage module is used to determine the upload and storage of production data based on the production measurement accuracy judgment results. The specific steps are as follows:

[0059] If the production measurement accuracy determination result is normal, then the production data will be uploaded and stored to transmit the production data to the server.

[0060] If the production measurement accuracy determination result is abnormal, then the production measurement acquisition strategy to improve the production measurement accuracy will be optimized first, and then the production data upload and storage determination will be made.

[0061] It should be noted that the optimization of the production measurement and data acquisition strategy follows the following process:

[0062] First, the optimized coupling result is obtained and input into the production data acquisition frequency mapping table to obtain the optimized production acquisition frequency. The optimized coupling result is the arithmetic mean of the deviation of the feeding phase lag delay value and the deviation of the high-frequency gain of the feeding quantity signal. Production data is acquired based on the optimized production acquisition frequency to improve the response sensitivity to the feeding signal. Among them, the deviation of the feeding phase lag delay value is the result of the ratio of the absolute value of the difference between the maximum and minimum feeding phase lag delay values ​​to the maximum feeding lag delay value. The deviation of the high-frequency gain of the feeding quantity signal is the result of the ratio of the difference between the high-frequency gain and minimum gain of the feeding quantity signal to the minimum gain. The production data acquisition frequency mapping table is obtained by using historical optimized coupling results and production optimized acquisition frequencies set by professionals based on experience rules. It is used to describe the mapping relationship between the optimized coupling result and the optimized production acquisition frequency. By dynamically adjusting the optimized production acquisition frequency based on the optimized coupling result, the sampling density can be automatically increased when the signal fluctuation intensifies or the measurement delay increases, thereby reducing the probability of data loss and ensuring the integrity and continuity of the production measurement signal during the feeding stage.

[0063] Secondly, the production measurement accuracy judgment result is reacquired. If the reacquired production measurement accuracy judgment result is normal, a qualified prompt for measurement strategy optimization is sent. Otherwise, the difference value of the optimized coupling result before and after production measurement acquisition strategy optimization is input into the acquisition frequency optimization mapping table to output the acquisition frequency optimization multiple. The difference value of the optimized coupling result is the difference between the optimized coupling result before and after production measurement acquisition strategy optimization. The acquisition frequency optimization mapping table is pre-set using historical optimized coupling result difference values ​​and acquisition frequency optimization multiples set by professionals based on experience rules. By calculating the difference value of the optimized coupling result before and after production measurement acquisition strategy optimization and using it as input to adjust the acquisition frequency multiple, adaptive optimization closed-loop adjustment of the acquisition frequency is achieved. When an abnormal measurement accuracy is detected, the sampling strategy can be automatically adjusted until the set accuracy conditions are met or an abnormality prompt is triggered, ensuring that the system maintains a stable and reliable acquisition state under different operating conditions.

[0064] Determine if the production optimization acquisition frequency is greater than the set maximum acquisition frequency limit. If so, send a production data acquisition frequency adjustment error message. Otherwise, adjust the production optimization acquisition frequency by the acquisition frequency optimization multiple until a production data acquisition frequency adjustment error message is sent or the production data is uploaded and stored.

[0065] By introducing optimized yield measurement and acquisition strategies, the yield data acquisition frequency can be adaptively optimized and adjusted according to the characteristics of the feeding signal. This significantly improves the signal response capability and measurement accuracy of the harvester during the yield data measurement process, reduces the risk of data distortion, and ensures the authenticity and reliability of the yield data acquisition.

[0066] like Figure 4 The diagram shows a flowchart of a harvester grain yield data management method provided in an embodiment of the present invention. The method includes: feeding stage management, unloading stage management, and yield data management. Specifically, feeding stage management involves detecting the feeding stability of the harvester during the feeding stage to obtain feeding management detection results and providing feedback; unloading stage management involves determining whether to trigger unloading management strategy adjustment to improve the accuracy of harvester grain yield measurement based on the feeding management detection results. If not triggered, the measurement accuracy of the harvester's yield data during the unloading stage is determined to obtain a yield measurement accuracy determination result. If triggered, after adjusting the unloading management strategy, it is determined whether to determine the measurement accuracy of the harvester's yield data during the unloading stage to obtain a yield measurement accuracy determination result; and yield data management involves determining whether to upload and store yield data based on the yield measurement accuracy determination result.

[0067] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0071] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0072] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0073] The above are merely specific embodiments 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.

Claims

1. A harvester grain yield data management system characterized by, The system comprises: The feeding stage management module is used for detecting the feeding stability of the harvester in the feeding stage during the operation to obtain a feeding management detection result and feeding back to the discharging stage management module; The detection of the feeding stability of the harvester in the feeding stage during the operation comprises the following steps: obtaining the standard deviation and the average feeding amount of the feeding amount data in the feeding time interval, quantifying the feeding stability correlation of the two to obtain the feeding fluctuation rate, and taking the result of inverse proportion processing of the feeding fluctuation rate as a feeding stability representation value for representing the feeding stability; judging the numerical relationship between the feeding stability representation value and the stored feeding stability reference limit value; if the feeding stability representation value is greater than the feeding stability reference limit value, the feeding management detection result is recorded as feeding detection compliance, and the obtained feeding stability representation value is continuously monitored; if the feeding stability representation value is not greater than the feeding stability set limit value, the feeding stability representation value is compared with the stored feeding control reference interval, specifically: if the feeding stability representation value is within the feeding control reference interval, the feeding management detection result is recorded as feeding detection qualified, otherwise, the feeding management detection result is recorded as feeding detection unqualified; the feeding control reference interval is a closed interval corresponding to the feeding stability reference limit value to the feeding stability set limit value; The discharging stage management module is used for judging whether to trigger the discharging management strategy adjustment for improving the grain yield measurement accuracy of the harvester according to the received feeding management detection result; if the discharging management strategy adjustment is not triggered, the operation of the measurement precision management module is triggered; if the discharging management strategy adjustment is triggered, it is judged whether to trigger the operation of the measurement precision management module after the discharging management strategy adjustment, and if the result of the judgment is yes, the operation of the measurement precision management module is triggered; The measurement precision management module is used for judging the measurement precision of the yield data of the harvester in the discharging stage during the operation to obtain a yield measurement precision judgment result; The yield data uploading and storage module is used for judging the uploading and storage of the yield data in combination with the yield measurement precision judgment result.

2. A harvester grain yield data management system according to claim 1 wherein, The detection of the feeding stability of the harvester in the feeding stage during the operation further comprises: Obtaining the header working speed of the harvester during the operation in the feeding time interval, and quantifying the deviation of the header working speed from the stored operation reference speed to obtain a header working speed deviation value; Judging the numerical relationship between the header working speed deviation value and the stored header speed deviation interval, the header speed deviation interval being a closed interval corresponding to the set speed minimum deviation to the set speed maximum deviation; If the header working speed deviation value is within the header speed deviation interval, the feeding stability representation value is not processed, otherwise, the feeding stability representation value is corrected and processed, specifically: the feeding stability representation value is corrected and processed based on the introduced operation speed factor, the feeding stability representation value is updated according to the result of the correction and processing, and the operation speed factor is used to correct the influence of the header working speed on the feeding stability detection.

3. A harvester grain yield data management system according to claim 1 wherein, The method comprises the following steps of: judging whether to trigger a feeding management strategy adjustment for improving the measurement accuracy of the grain yield of the harvester according to the received feeding management detection result, wherein the specific process is as follows: judging whether the feeding management detection result is feeding detection up to standard: if yes, the feeding management strategy adjustment is not triggered; otherwise, if the feeding management detection result is feeding detection qualified, a feeding management strategy benchmark adjustment is triggered; if no, a feeding management strategy optimization adjustment is triggered. The feeding management strategy adjustment comprises the feeding management strategy benchmark adjustment for dynamically optimizing the stability of the feeding stage by controlling the feeding speed and the feeding flow, and the feeding management strategy optimization adjustment for dynamically optimizing the stability of the feeding stage by controlling the feeding speed and the feeding flow.

4. A harvester grain yield data management system according to claim 3, wherein, The feeding management strategy benchmark adjustment comprises the following steps of: inputting the feeding stability representation value into a feeding speed mapping table to output a header feeding adjustment speed, and sending a prompt for adjusting the header feeding speed according to the output header feeding adjustment speed; after adjusting the header feeding speed, the feeding management detection result in the next feeding time interval is obtained, and the specific judgment method is as follows: if the feeding management detection result in the next feeding time interval is feeding detection up to standard, the operation of the measurement accuracy management module is triggered; if the feeding management detection result in the next feeding time interval is feeding detection qualified, the feeding stability representation values corresponding to the two feeding time intervals are subjected to arithmetic coupling processing and then input into a feeding flow mapping table to output a feeding adjustment flow, and the initial feeding flow is set according to the output feeding adjustment flow; if the feeding management detection result in the next feeding time interval is feeding detection abnormal, a header feeding speed adjustment abnormal prompt is sent, the header feeding speed before adjustment is returned, and the feeding management strategy optimization adjustment is triggered.

5. A harvester grain yield data management system according to claim 3 wherein, The feeding management strategy optimization adjustment comprises the following steps of: if the feeding management strategy benchmark adjustment is triggered, the feeding stability representation value is updated; otherwise, the feeding stability representation value is directly used as the updated feeding stability representation value, the updated feeding stability representation value is input into a feeding speed mapping table to output a feeding adjustment speed, and the feeding speed is adjusted according to the output feeding adjustment speed; after the feeding speed adjustment, the average value of the feeding pipe pressure is obtained, if the average value of the feeding pipe pressure is within the set pressure control interval, the monitoring of the average value of the feeding pipe pressure is continued; otherwise, it is judged whether the average value of the feeding pipe pressure is greater than the maximum value corresponding to the set pressure control interval; if the average value of the feeding pipe pressure is greater than the maximum value corresponding to the set pressure control interval, it indicates that the feeding pipe pressure is in the accumulation trend, and the feeding flow is set down, and the specific setting method is as follows: the result of the weighting processing of the updated feeding stability representation value and the average value of the feeding pipe pressure is input into a feeding flow setting down mapping set to output a feeding flow setting down value for setting down the feeding flow. Conversely, it indicates that the blanking is insufficient, and the blanking flow is set to be increased. The specific setting method is that the updated blanking stability value and the result of the weighting processing of the blanking pipeline pressure average are input into the blanking flow increase mapping set to output a blanking flow increase value for setting the blanking flow to be increased.

6. A harvester grain yield data management system according to claim 3 wherein, The measurement accuracy of the yield data of the harvester in the blanking stage in the working process is determined, and the specific steps are as follows: An underfeeding phase lag delay value reflecting the phase delay of the underfeeding amount signal is obtained, and the numerical relationship between the underfeeding phase lag delay value and the stored underfeeding delay lag maximum limit value is determined. If the underfeeding phase lag delay value is greater than the underfeeding delay lag maximum limit value, the yield measurement accuracy determination result is determined to be measurement accuracy determination abnormal; Otherwise, the yield measurement accuracy determination result is determined to be measurement accuracy determination normal, and the underfeeding amount signal high-frequency gain is obtained and determined. Specifically, if the underfeeding amount signal high-frequency gain is less than the set signal minimum gain, the yield measurement accuracy determination result is updated to be measurement accuracy determination abnormal, otherwise, the underfeeding phase lag delay value and the underfeeding amount signal high-frequency gain are continuously monitored.

7. A harvester grain yield data management system according to claim 6 wherein, The determination of yield data uploading and storage combined with the yield measurement accuracy determination result is as follows: If the yield measurement accuracy determination result is measurement accuracy determination normal, the yield data is uploaded and stored to transmit the yield data to the server; If the yield measurement accuracy determination result is measurement accuracy determination abnormal, the yield measurement acquisition strategy optimization for improving the yield measurement accuracy is performed before the determination of yield data uploading and storage.

8. A harvester grain yield data management system according to claim 7, wherein, The yield measurement acquisition strategy optimization has the following specific process: An optimized coupling result is obtained and input into a yield data acquisition frequency mapping table to obtain an optimized yield acquisition frequency by mapping. The optimized coupling result is the result of the arithmetic average of the underfeeding phase lag delay value deviation and the underfeeding amount signal high-frequency gain deviation. The yield data is collected based on the optimized yield acquisition frequency to improve the response sensitivity to the underfeeding signal. If the newly obtained yield measurement accuracy determination result is measurement accuracy determination normal, a measurement strategy optimization qualified prompt is sent, otherwise, the difference value between the optimized coupling results before and after the yield measurement acquisition strategy optimization is input into the acquisition frequency optimization mapping table to output the acquisition frequency optimization multiple. It is determined whether the optimized yield acquisition frequency is greater than the set acquisition frequency maximum limit value. If yes, a yield data acquisition frequency adjustment abnormal prompt is sent, otherwise, the optimized yield acquisition frequency is adjusted by the acquisition frequency optimization multiple until the yield data acquisition frequency adjustment abnormal prompt is sent or the yield data uploading and storage is performed.

9. A method for managing grain yield data of a harvester, applied to the system for managing grain yield data of a harvester according to any one of claims 1 to 8, characterized by, The method comprises the following steps: The feed stability of the harvester in the feeding stage in the working process is detected to obtain a feed management detection result, and feedback is performed. According to the feeding management detection result, it is judged whether to trigger the discharge management strategy adjustment for improving the grain yield measurement accuracy of the harvester. If not, the measurement accuracy of the yield data of the harvester in the discharge stage during the operation process is judged to obtain the yield measurement accuracy judgment result. If yes, after the discharge management strategy adjustment is performed, it is judged whether to judge the measurement accuracy of the yield data of the harvester in the discharge stage during the operation process to obtain the yield measurement accuracy judgment result. The judgment of yield data uploading and storage is performed in combination with the yield measurement accuracy judgment result.

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