Seeder discharge anomaly monitoring module

Abstract

The application relates to the field of agricultural seeding technology, and particularly discloses a sowing machine discharging abnormality monitoring module, which comprises a data analysis unit. The data analysis unit monitors the advancing speed obtained by a speed sensor, compares the state scores before and after the advancing speed rises after the advancing speed rises, provides early warning and driving suggestions for the discharging process according to the comparison result, and obtains the state scores based on the preprocessing result. The application obtains temperature data, position data and vibration data after each start of the sowing machine to score the state, thereby adapting to the aging problem of the sowing machine under different use degrees, providing differentiated early warning services for sowing machines in different states, timely early warning before the driver speeds up, avoiding the sowing machine in a poor state from advancing at a high speed, and guaranteeing the service life of the sowing machine while maximizing the use efficiency.

Description

Technical Field

[0001] This invention relates to the field of agricultural sowing technology, specifically to a module for monitoring abnormal material feeding in a seeder. Background Technology

[0002] Seeders are an important component of automated agriculture. Based on their sowing methods, seeders can be categorized into row seeders, hill seeders, and broadcast seeders. Typically, during operation, seeders utilize built-in monitoring modules to track their speed and seed dispensing. Monitoring these parameters allows for timely alerts in case of malfunctions, enabling prompt detection and resolution of any issues.

[0003] For example, Chinese invention publication CN 102630410 B includes a seed box monitoring sensor for monitoring whether seeds are missing from the seed box, a seed metering monitoring sensor for monitoring whether seeds are falling through the seed metering pipe, and a furrow opener monitoring sensor for monitoring whether the furrow opener is blocked; all three sensors are capacitive proximity sensors. This allows for real-time monitoring of the seeder's current operating status and positioning, accurately determining the type of malfunction and the location of missed seeds, facilitating subsequent reseeding. While existing technologies can detect abnormal feeding in a timely manner using proximity sensors, and even accurately locate missed or incorrect seeds, they do not provide early warning services for these abnormalities or the resulting missed or incorrect seeds. The operating speed of a seeder is generally 10-12 km / h, and some models can reach 15-16 km / h. Within this speed range, to avoid missed seeds, the feeding process of the seeder can be adjusted within a certain range according to the actual travel speed of the seeder. However, the accuracy of the adjustment will affect the overall performance of the seeder. The higher the speed, the greater the load the seeder can bear, and the lower the accuracy after adjustment will be. Accumulated errors will lead to missed seeds. However, there is currently a lack of technology to provide warnings to the driver based on the seeder's status. Drivers often have to rely on experience to determine the travel speed.

[0004] In view of this, the present invention proposes a seeder feeding abnormality monitoring module, which is applicable to strip seeders and hill seeders. By monitoring the status of the seeder during the seeding process, it provides early warnings, making it easier for the driver to decide the travel speed. Summary of the Invention

[0005] The purpose of this invention is to provide a seeder feeding anomaly monitoring module to solve the following technical problems:

[0006] How to monitor the status of the seeder during the sowing process, provide early warnings, and help the driver decide on the travel speed?

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A seeder feeding anomaly monitoring module includes: a data acquisition unit, a data processing unit, a data analysis unit, a data storage unit, an interaction unit, and a missed seeding monitoring unit. The data acquisition unit is used to collect various parameters, including a speed sensor, a temperature sensor, a position sensor, and a vibration sensor. The speed sensor is used to collect the traveling speed of the seeder during operation. The temperature sensor is used to collect the temperature of the seeds in the seed guiding mechanism to obtain temperature data. The position sensor is used to detect the seed position to obtain seed position data. The vibration sensor is installed on the seeder to detect the vibration data of the seeder during its movement.

[0009] The data processing unit preprocesses the temperature data, location data, and vibration data to obtain the preprocessing results.

[0010] The data analysis unit monitors the traveling speed obtained by the speed sensor. After the traveling speed increases, it obtains and compares the status scores before and after the speed increase. Based on the comparison results, it provides early warnings and travel suggestions for the material unloading process. The status scores are obtained based on the preprocessing results.

[0011] The data storage unit is used to store the status score generated after the current seeding vehicle is started;

[0012] The interactive unit is used to display warning information and driving suggestions to the driver of the seeding vehicle.

[0013] The above technical solution provides a method for early warning of the material feeding status of a seeder based on multiple parameters. Specifically, the invention first preprocesses temperature data, position data, and vibration data to obtain preprocessing results, then obtains a status score based on the preprocessing results, and finally provides early warning for the material feeding process based on the status score. The invention uses temperature data, position data, and vibration data obtained after each start-up of the seeder to perform status scoring, thereby adapting to the aging problem of the seeder under different usage levels. It can provide differentiated early warning services for seeders in different states, thus providing timely warnings before the driver accelerates, preventing seeders in poor condition from operating at high speeds, ensuring the lifespan of the seeder while maximizing its efficiency.

[0014] As a further technical solution of the present invention: the process of obtaining the status score includes:

[0015] The data processing unit preprocesses the temperature, location, and vibration data, and then uses the following formula based on the preprocessing results:

[0016]

[0017] Get the travel speed as v i Status rating of the seeding vehicle under working conditions Where G is a preset rating conversion function, It is the temperature difference deviation coefficient. It is the material feeding interval deviation coefficient. These are the vibration deviation coefficients, and τ1, τ2, and τ3 are preset weighting coefficients.

[0018] As a further technical solution of the present invention: the pretreatment process includes:

[0019] For temperature data, the temperature difference between the seeds entering and leaving the seed guiding mechanism is obtained, and multiple temperature difference data that change over time are fitted on the corresponding coordinate system to obtain the first curve.

[0020] For the position data, the seed spacing between two adjacent feedings is obtained, and the multiple seed spacing data that change over time are fitted on the corresponding coordinate system to obtain the second curve;

[0021] For vibration data, the waveform of the vibration data is obtained as the third curve;

[0022] Then, the first curve, the second curve, and the third curve are compared with the corresponding standard curves to obtain the temperature difference deviation coefficient, the material dropping interval deviation coefficient, and the vibration deviation coefficient.

[0023] As a further technical solution of the present invention: the process of obtaining the temperature difference deviation coefficient, the material dropping interval deviation coefficient, and the vibration deviation coefficient includes: at a traveling speed of v i When the seeding vehicle is in operation, a preset detection period [t1, t2] is established.

[0024] Within the detection duration [t1, t2], the following formulas are used:

[0025]

[0026]

[0027]

[0028] Obtain the temperature difference deviation coefficient Feeding interval deviation coefficient and vibration deviation coefficient in, The current speed of the seeder is v. i The function corresponding to the first curve below, The current advance speed of the seeder is v i The function corresponding to the second curve below, The current speed of the seeder is v. i The function corresponding to the third curve below, T0(t), L0(t), and Q0(t), are respectively the functions of the seeder in its standard state at a travel speed of v. i The first, second, and third curves obtained below are R1, R2, and R3, which are preset transformation functions.

[0029] The above technical solution provides a process for evaluating the state of a seeder at a certain travel speed. Specifically, it uses temperature difference deviation coefficient, material drop interval deviation coefficient, and vibration deviation coefficient to represent the differences in the main parameters of the equipment before and after the speed change compared to the standard state. Then, a state score representing the overall state of the seeder is obtained by weighted summation. The temperature difference deviation coefficient, material drop interval deviation coefficient, and vibration deviation coefficient are obtained by the differences between the first curve, the second curve, and the third curve under the current state and the standard state. The larger the difference, the more unstable the seeder is, and the lower the score is. In addition, by using weighted summation, the impact of overall changes can be highlighted when individual parameters such as temperature data, seed position data, and vibration data are in a normal state, thus reflecting the state of the seeder more accurately.

[0030] As a further technical solution of the present invention: the process of providing early warning and travel suggestions for the material feeding process based on the comparison results includes:

[0031] S1. Divide the permissible travel speed range of the seeder into several identical and continuous speed intervals;

[0032] S2. Obtain the status scores at multiple different travel speeds after the current seeder starts;

[0033] S3. Based on the speed sensor monitoring, after the current seeder travel speed completes one increase, acquire the initial speed and the final speed during the speed increase process, as well as all the status scores corresponding to the travel speed falling into the increase process recorded in the data storage device after this start.

[0034] S4. Construct a travel speed-state score coordinate system and obtain the data points corresponding to the state score and travel speed;

[0035] S5. Perform linear regression analysis on all data points in the travel speed-state score coordinate system to obtain the regression line, and provide early warning and travel suggestions based on the regression line.

[0036] As a further technical solution of the present invention, the process of early warning based on regression lines includes:

[0037] Obtain the current allowable speed range of the seeder;

[0038] Using the regression line as the prediction line, predict the state score corresponding to the current maximum travel speed of the seeder;

[0039] The system compares the status score at maximum speed with a preset safety score. If the status score at maximum speed is lower than the safety score, a warning is issued and driving suggestions are provided. If the status score at maximum speed is greater than or equal to the safety score, no warning is issued, and the driver is allowed to accelerate within the permitted speed range.

[0040] As a further technical solution of the present invention: the process of making regression line-based travel suggestions includes:

[0041] Substitute the safety score into the prediction line to obtain the first speed;

[0042] Correct the initial speed to obtain a suggested speed;

[0043] The travel suggestion displayed by the interactive unit is a suggested speed.

[0044] The above technical solution provides a process for issuing early warnings and offering travel suggestions. Specifically, the present invention constructs a travel speed-state score coordinate system and obtains corresponding data points to construct a prediction line. Based on the prediction line, it predicts the state score and suggested speed corresponding to the current maximum travel speed of the seeder. This allows the present invention to provide early warnings and reminders to drivers who want to increase speed when the seeder is in poor condition, thus avoiding excessive burden on the seeder and the potential for missed seeding due to accumulated errors.

[0045] As a further technical solution of the present invention: the process of correcting the first speed includes:

[0046] Obtain the distance between all data points and the prediction line, and use the formula:

[0047]

[0048] Obtain the correction coefficient Re, where d j d1 is the distance between the j-th data point and the prediction line, d1 is the distance between the first data point and the prediction line, j∈n and j≥2, and n is the total number of data points;

[0049] Based on the correction coefficient Re, the correction amount is obtained from a preset table. The sum of the first speed and the correction amount is used as the suggested speed. The maximum value of the correction amount is 0.

[0050] The above technical solution provides a process for obtaining a suggested speed. This invention obtains a suggested speed by correcting the first speed with a correction amount. The correction amount is a downward correction of the first speed. The magnitude of the downward correction is based on the difference between the distance between all data points of the regression line and the regression line, and the distance between the first data point (i.e., the starting point of the upward process) and the diameter of the regression line. That is, the more discrete the data points of the prediction line are compared with the first data point, the larger the correction coefficient is, and the smaller the corresponding correction amount is. This corrects the predicted first speed and increases the safety of the given suggested speed.

[0051] As a further technical solution of the present invention: the monitoring module includes a missed seeding monitoring unit, which determines whether a missed seeding has occurred based on the seed location data.

[0052] The beneficial effects of this invention are:

[0053] (1) The present invention scores the condition based on the temperature data, position data and vibration data obtained after each start of the seeder, thereby adapting to the aging problem of the seeder under different usage conditions. It can provide differentiated early warning services for seeders in different conditions, so as to provide timely warnings before the driver accelerates, avoid seeders in poor condition from moving at high speeds, and maximize the efficiency of use while ensuring the service life of the seeder.

[0054] (2) The present invention uses temperature difference deviation coefficient, material drop interval deviation coefficient and vibration deviation coefficient to represent the difference in the main parameters of the equipment before and after the speed change compared with the standard state. Then, the state score representing the overall state of the seeder is obtained by weighted summation. The temperature difference deviation coefficient, material drop interval deviation coefficient and vibration deviation coefficient are obtained by the difference between the first curve, the second curve and the third curve under the current state and the standard state. The greater the difference, the more unstable the seeder is and the lower the score is. In addition, by weighted summation, the influence of the overall change can be highlighted when individual parameters such as temperature data, seed position data and vibration data are in a normal state, so as to more accurately reflect the state of the seeder.

[0055] (3) This invention constructs a travel speed-state score coordinate system and obtains corresponding data points to construct a prediction line. Based on the prediction line, it predicts the state score and suggested speed corresponding to the current maximum travel speed of the seeder. When the seeder is in poor condition, it can give early warning and reminder to the driver who wants to speed up, so as to avoid the seeder being overloaded and thus easily causing the problem of missed sowing due to the accumulation of errors.

[0056] (4) The present invention obtains a suggested speed by obtaining a correction amount to correct the first speed. The correction amount is to correct the first speed downward. The magnitude of the downward correction is based on the difference between the distance between all data points of the regression line and the diameter of the regression line and the distance between the first data point and the diameter of the regression line. That is, the more discrete the data points of the prediction line are compared with the first data point, the larger the correction coefficient is, and the smaller the corresponding correction amount is. The predicted first speed is corrected to increase the safety of the suggested speed. Attached Figure Description

[0057] The invention will now be further described with reference to the accompanying drawings.

[0058] Figure 1 This is a schematic diagram of the overall unit composition of the present invention;

[0059] Figure 2 This is a flowchart illustrating the process steps of the present invention in providing early warning and travel suggestions. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Please see Figures 1-2 As shown, in one embodiment, a seeder feeding anomaly monitoring module is provided, including: a data acquisition unit, a data processing unit, a data analysis unit, a data storage unit, an interaction unit, and a missed seeding monitoring unit. The data acquisition unit is used to collect various parameters, including a speed sensor, a temperature sensor, a position sensor, and a vibration sensor. The speed sensor is used to collect the traveling speed of the seeder during operation. The temperature sensor is used to collect the temperature of the seeds in the seed guiding mechanism to obtain temperature data. The position sensor is used to detect the seed position to obtain seed position data. The vibration sensor is installed on the seeder to detect the vibration data of the seeder during its movement.

[0062] The data processing unit preprocesses temperature data, location data, and vibration data to obtain preprocessing results.

[0063] The data analysis unit monitors the traveling speed acquired by the speed sensor. After the traveling speed increases, it acquires and compares the status scores before and after the speed increase. Based on the comparison results, it provides early warnings and travel suggestions for the material unloading process. The status scores are obtained based on the preprocessing results.

[0064] The data storage unit is used to store the status score generated after the current seeding vehicle is started. After the seeder is turned off, the data generated during this start can be saved or deleted. It is preferable to upload and then delete the data after the seeder is started again.

[0065] The interactive unit is used to display warning information and driving suggestions to the driver of the seeding vehicle.

[0066] This embodiment provides a technical solution for providing early warning of the material feeding status of the seeder using multiple parameters. Specifically, the invention first preprocesses temperature data, position data, and vibration data to obtain preprocessing results, then obtains a status score based on the preprocessing results, and finally provides early warning for the material feeding process based on the status score. The invention uses temperature data, position data, and vibration data obtained after each start-up of the seeder to perform status scoring, thereby adapting to the aging problem of the seeder under different usage levels. It can provide differentiated early warning services for seeders in different states, thus providing timely warnings before the driver accelerates, preventing seeders in poor condition from operating at high speeds, ensuring the lifespan of the seeder while maximizing its efficiency.

[0067] The process of obtaining a status score includes:

[0068] The data processing unit preprocesses the temperature, location, and vibration data, and then uses the following formula based on the preprocessing results:

[0069]

[0070] Get the travel speed as v i Status rating of the seeding vehicle under working conditions Where G is a preset rating conversion function, and the preferred rating system is a percentage system. It is the temperature difference deviation coefficient. It is the material feeding interval deviation coefficient. These are the vibration deviation coefficients, where τ1, τ2, and τ3 are preset weighting coefficients, set based on empirical data.

[0071] The preprocessing process includes:

[0072] For temperature data, the temperature difference between the seeds entering and leaving the seed guiding mechanism is obtained, and multiple temperature difference data that change over time are fitted on the corresponding coordinate system to obtain the first curve.

[0073] For the position data, the seed spacing between two adjacent feedings is obtained, and the multiple seed spacing data that change over time are fitted on the corresponding coordinate system to obtain the second curve;

[0074] For vibration data, the waveform of the vibration data is obtained as the third curve;

[0075] Then, the first curve, the second curve, and the third curve are compared with the corresponding standard curves to obtain the temperature difference deviation coefficient, the material dropping interval deviation coefficient, and the vibration deviation coefficient.

[0076] The process of obtaining the temperature difference deviation coefficient, the material dropping interval deviation coefficient, and the vibration deviation coefficient includes: at a travel speed of v i When the seeding vehicle is in operation, a preset detection time [t1, t2] is established.

[0077] Within the detection duration [t1, t2], the following formulas are used:

[0078]

[0079]

[0080]

[0081] Obtain the temperature difference deviation coefficient Feeding interval deviation coefficient and vibration deviation coefficient in, The current speed of the seeder is v. i The function corresponding to the first curve below, The current advance speed of the seeder is v i The function corresponding to the second curve below, The current speed of the seeder is v. i The function corresponding to the third curve, T0(t), L0(t), and Q0(t), are respectively the functions of the seeder in its standard state at a travel speed of v. i The first, second, and third curves were obtained in the same way. R1, R2, and R3 are preset transformation functions, which are monotonic functions, such as logarithmic functions, used to transform independent variables of different orders of magnitude into functions that describe them on the same scale.

[0082] This embodiment provides a process for evaluating the state of a seeder at a certain travel speed. Specifically, the temperature difference deviation coefficient, the material drop interval deviation coefficient, and the vibration deviation coefficient are used to represent the differences in the main parameters of the equipment before and after the speed change compared to the standard state. Then, a state score representing the overall state of the seeder is obtained by weighted summation. The temperature difference deviation coefficient, the material drop interval deviation coefficient, and the vibration deviation coefficient are obtained by the differences between the first curve, the second curve, and the third curve in the current state and the standard state. The larger the difference, the more unstable the seeder is, and the lower the score is. In addition, by using weighted summation, the impact of the overall change can be highlighted when individual parameters such as temperature data, seed position data, and vibration data are in a normal state, thereby more accurately reflecting the state of the seeder.

[0083] refer to Figure 2 The process of providing early warnings and travel suggestions for the material feeding process based on comparison results includes:

[0084] S1. Divide the permissible travel speed range of the seeder into several identical and continuous speed intervals;

[0085] S2. Obtain the status scores at multiple different travel speeds after the current seeder starts;

[0086] S3. Based on the speed sensor monitoring, after the current seeder travel speed completes one increase, acquire the initial speed and the final speed during the speed increase process, as well as all the status scores corresponding to the travel speed falling into the increase process recorded in the data storage device after this start.

[0087] S4. Construct a travel speed-state score coordinate system and obtain the data points corresponding to the state score and travel speed;

[0088] S5. Perform linear regression analysis on all data points in the travel speed-state score coordinate system to obtain the regression line, and provide early warning and travel suggestions based on the regression line.

[0089] Furthermore, the process of issuing early warnings based on regression lines includes:

[0090] Obtain the current allowable speed range of the seeder;

[0091] Using the regression line as the prediction line, predict the state score corresponding to the current maximum travel speed of the seeder;

[0092] The system compares the status score at maximum speed with a preset safety score. If the status score at maximum speed is lower than the safety score, a warning is issued and driving suggestions are provided. If the status score at maximum speed is greater than or equal to the safety score, no warning is issued, and the driver is allowed to accelerate within the permitted speed range.

[0093] The process of making recommendations based on regression lines includes:

[0094] Substitute the safety score into the prediction line to obtain the first speed;

[0095] Correct the initial speed to obtain a suggested speed;

[0096] The suggested travel speed is displayed in the interactive unit.

[0097] This embodiment provides a process for issuing early warnings and providing travel suggestions. Specifically, the present invention constructs a travel speed-state score coordinate system and obtains corresponding data points to construct a prediction line. Based on the prediction line, it predicts the state score and suggested speed corresponding to the current maximum travel speed of the seeder. When the seeder is in poor condition, it can provide early warnings and reminders to the driver who wants to speed up, avoiding excessive burden on the seeder, which can easily lead to missed seeding due to accumulated errors.

[0098] The process of correcting the first velocity includes:

[0099] Obtain the distance between all data points and the prediction line, and use the formula:

[0100]

[0101] Obtain the correction coefficient Re, where d j d1 is the distance between the j-th data point and the prediction line, d1 is the distance between the first data point and the prediction line, j∈n and j≥2, and n is the total number of data points;

[0102] Based on the correction coefficient Re, the correction amount is obtained from the preset table. The sum of the first speed and the correction amount is used as the suggested speed. The maximum value of the correction amount is 0.

[0103] This embodiment provides a process for obtaining a suggested speed. The present invention obtains a suggested speed by correcting the first speed with a correction amount. The correction amount is to correct the first speed downwards. The magnitude of the downward correction is based on the difference between the distance between all data points of the regression line and the regression line and the distance between the first data point (that is, the starting point of the rising process) and the diameter of the regression line. In other words, the more discrete the data points of the prediction line are compared with the first data point, the larger the correction coefficient is, and the smaller the corresponding correction amount is. This corrects the predicted first speed and increases the safety of the given suggested speed.

[0104] The monitoring module includes a missed seeding monitoring unit, which determines whether a missed seeding has occurred based on the seed location data. It can also locate the missed seeding system and record the location of the missed seeding when it is detected, thereby providing accurate guidance on missed seeding.

[0105] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A seeder feeding anomaly monitoring module, comprising a data acquisition unit, a data processing unit, a data analysis unit, a data storage unit, an interaction unit, and a missed seeding monitoring unit, characterized in that, The data acquisition unit is used to collect various parameters, including a speed sensor, a temperature sensor, a position sensor, and a vibration sensor. The speed sensor is used to collect the traveling speed of the seeder during its operation. The temperature sensor is used to collect the temperature of the seeds in the seed guiding mechanism to obtain temperature data. The position sensor is used to detect the seed position to obtain seed position data. The vibration sensor is installed on the seeder to detect the vibration data of the seeder during its movement. The data processing unit preprocesses the temperature data, location data, and vibration data to obtain the preprocessing results. The data analysis unit monitors the traveling speed acquired by the speed sensor. After the traveling speed increases, it acquires and compares the status scores before and after the speed increase. Based on the comparison results, it provides early warnings and travel suggestions for the material unloading process. The status scores are obtained based on the preprocessing results. The process of providing early warnings and guidance on the material handling process based on comparison results includes: S1. Divide the permissible travel speed range of the seeder into several identical and continuous speed intervals; S2. Obtain the status scores at multiple different travel speeds after the current seeder starts; S3. Based on the speed sensor monitoring, after the current seeder travel speed completes one increase, acquire the initial speed and the final speed during the speed increase process, as well as all the status scores corresponding to the travel speed falling into the increase process recorded in the data storage device after this start. S4. Construct a travel speed-state score coordinate system and obtain the data points corresponding to the state score and travel speed; S5. Perform linear regression analysis on all data points in the speed-state score coordinate system to obtain the regression line, and provide early warning and travel suggestions based on the regression line; The process of issuing early warnings based on regression lines includes: Obtain the current allowable speed range of the seeder; Using the regression line as the prediction line, predict the state score corresponding to the current maximum travel speed of the seeder; The system compares the status score at maximum speed with a preset safety score. If the status score at maximum speed is lower than the safety score, a warning is issued and driving suggestions are provided. If the status score at maximum speed is greater than or equal to the safety score, no warning is issued, and the driver is allowed to accelerate within the permitted speed range. The data storage unit is used to store the status score generated after the current seeding vehicle is started; The interactive unit is used to display warning information and driving suggestions to the driver of the seeding vehicle.

2. The seeder feeding anomaly monitoring module according to claim 1, characterized in that, The process of obtaining a status score includes: The data processing unit preprocesses the temperature, location, and vibration data, and then uses the following formula based on the preprocessing results: Get travel speed as Status rating of the seeding vehicle under working conditions ,in, It is a preset rating conversion function. It is the temperature difference deviation coefficient. It is the material feeding interval deviation coefficient. It is the vibration deviation coefficient. , and These are the preset weighting coefficients.

3. The seeder feeding anomaly monitoring module according to claim 1, characterized in that, The preprocessing process includes: For temperature data, the temperature difference between the seeds entering and leaving the seed guiding mechanism is obtained, and multiple temperature difference data that change over time are fitted on the corresponding coordinate system to obtain the first curve. For the position data, the seed spacing between two adjacent feedings is obtained, and the multiple seed spacing data that change over time are fitted on the corresponding coordinate system to obtain the second curve; For vibration data, the waveform of the vibration data is obtained as the third curve; Then, the first curve, the second curve, and the third curve are compared with the corresponding standard curves to obtain the temperature difference deviation coefficient, the material dropping interval deviation coefficient, and the vibration deviation coefficient.

4. The seeder feeding anomaly monitoring module according to claim 3, characterized in that, The process of obtaining the temperature difference deviation coefficient, the material drop interval deviation coefficient, and the vibration deviation coefficient includes: at a travel speed of When the seeding vehicle is in operation, a preset detection time is set. , During the detection time Inside, respectively through the formula: Obtain the temperature difference deviation coefficient material feeding interval deviation coefficient and vibration deviation coefficient ,in, The current speed of the seeder is The function corresponding to the first curve below, The current advance speed of the seeder is The function corresponding to the second curve below, The current speed of the seeder is The function corresponding to the third curve below, , and These are the standard state seeders at a travel speed of... The first, second, and third curves obtained below, , and These are the preset conversion functions.

5. The seeder feeding anomaly monitoring module according to claim 1, characterized in that, The process of making recommendations based on regression lines includes: Substitute the safety score into the prediction line to obtain the first speed; Correct the initial speed to obtain a suggested speed; The travel suggestion displayed by the interactive unit is a suggested speed.

6. The seeder feeding anomaly monitoring module according to claim 5, characterized in that, The process of correcting the first velocity includes: Obtain the distance between all data points and the prediction line, and use the formula: Obtain the correction coefficient ,in It is the first The distance between each data point and the prediction line It is the distance between the first data point and the prediction line. ,and , It is the total number of data points; Based on the correction factor The correction amount is obtained from a preset table, and the sum of the first speed and the correction amount is used as the suggested speed. The maximum value of the correction amount is 0.

7. The seeder feeding anomaly monitoring module according to claim 1, characterized in that, The monitoring module includes a missed seeding monitoring unit, which determines whether a missed seeding has occurred based on the seed location data.

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