A method and system for detecting abnormal operation of a manure scraper

Abstract

The present invention provides a method and system for detecting abnormal operation of a manure scraper, which relates to the field of measuring electrical variables. The system includes: a vibration detection module, including multiple vibration sensing components arranged on the scraper of the manure scraper; a motor detection module, used to obtain the current and voltage of the motor of the manure scraper; an abnormality judgment module, used to judge whether the operation of the manure scraper is abnormal based on the vibration data and current and voltage collected by the multiple vibration sensing components at multiple consecutive time points; the abnormality detection module is used to judge whether the scraper is stuck with a cow's hoof based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at multiple consecutive time points after it is determined that the operation of the manure scraper is abnormal; and an operation control module is used to control the motor to stop running and generate a warning message after it is determined that the scraper is stuck with the cow's hoof. The method has the advantage of realizing automatic real-time monitoring of abnormal operation of the manure scraper.

Description

Technical Field

[0001] The present invention relates to the field of measuring electrical variables, and in particular to a method and system for detecting abnormal operation of a manure scraper. Background Art

[0002] Manure scrapers are commonly used in cowsheds to clean manure. They work by using a motor, driven by a chain, to move a scraper through the manure chute, removing accumulated manure. If the chain pulls the scraper, it can accidentally get stuck on a cow's hoof, injuring it or causing it to trip. Due to the confined space in the cowshed and the cow's weight, a tripped cow can be difficult to get up, leading to serious injury or even death.

[0003] In the existing technology, it is necessary to manually observe whether the chain is stuck or pulling the cow's hoof during the process of pulling the scraper. It is easy for the scraper to get stuck or pull the cow's hoof and fail to adjust the operating state of the scraper in time, resulting in serious injury to the cow's hoof or death of the cow.

[0004] Therefore, it is necessary to provide a method and system for detecting abnormal operation of a manure scraper, so as to realize automatic real-time monitoring of abnormal operation of the manure scraper. Summary of the Invention

[0005] The present invention provides a manure scraper operation abnormality detection system, comprising: a vibration detection module, comprising a plurality of vibration sensing components arranged at a plurality of positions of the scraper of the manure scraper; a motor detection module, for obtaining the current and voltage of the motor of the manure scraper; an abnormality judgment module, for judging whether an abnormality occurs in the operation of the manure scraper based on the vibration data collected by the plurality of vibration sensing components at a plurality of consecutive time points and the current and voltage of the motor of the manure scraper at the plurality of consecutive time points; the abnormality detection module, for judging whether the scraper is stuck with a cow's hoof based on the vibration data collected by the plurality of vibration sensing components at a plurality of consecutive time points and the current and voltage of the motor of the manure scraper at the plurality of consecutive time points after the abnormality judgment module determines that the operation of the manure scraper is abnormal; an operation control module, for controlling the motor to stop running and generating a warning message after the abnormality detection module determines that the scraper is stuck with a cow's hoof.

[0006] Furthermore, the vibration detection module is also used to: determine multiple vibration test positions on the scraper, and set a vibration sensing component at each vibration test position; obtain simulated test data of the vibration sensing component set at each vibration test position under multiple simulated scraper jamming scenarios, wherein the simulated test data includes vibration data of multiple continuous time points; based on the simulated test data collected by the vibration sensing component set at each vibration test position under multiple simulated scraper jamming scenarios, the multiple vibration test positions are screened to determine multiple vibration monitoring positions; and based on the multiple vibration monitoring positions, the multiple vibration sensing components are set.

[0007] Furthermore, the vibration detection module screens the multiple vibration test positions according to the simulated test data collected by the vibration sensing component set at each vibration test position under the multiple simulated scraper stuck scenes, and determines multiple vibration monitoring positions, including: S11, for each vibration test position, according to the simulated test data collected by the vibration sensing component set at the vibration test position under the multiple simulated scraper stuck scenes, determining the vibration anomaly value of the vibration test position corresponding to each simulated scraper stuck scene; S12, for each vibration test position, according to the vibration anomaly value of the vibration test position corresponding to each simulated scraper stuck scene, judging whether the vibration test position is a target vibration test position, if so, executing S13, if not, screening out the vibration test position; S13, for any two target vibration test positions, according to the vibration anomaly value of the two target vibration test positions corresponding to each simulated scraper stuck scene, calculating the vibration anomaly similarity of the two target vibration test positions; S14, according to the vibration anomaly value of the two target vibration test positions corresponding to each simulated scraper stuck scene The plurality of target vibration test positions are grouped according to the vibration anomaly similarity of any two target vibration test positions included in the target vibration test position group to determine a plurality of target vibration test position groups; S15, for each target vibration test position group, deduplication is performed on the plurality of target vibration test positions included in the target vibration test position group according to the vibration anomaly similarity of any two target vibration test positions included in the target vibration test position group to generate a deduplication target vibration test position group; S16, for any two deduplication target vibration test position groups, according to the vibration anomaly value of each target vibration test position included in the two deduplication target vibration test position groups corresponding to each simulated scraper stuck scenario, the correlation coefficient of the two deduplication target vibration test position groups is calculated; S17, according to the correlation coefficient of any two deduplication target vibration test position groups, the plurality of deduplication target vibration test position groups are screened to determine the screened plurality of deduplication target vibration test position groups, and each target vibration test position included in the screened plurality of deduplication target vibration test position groups is used as a vibration monitoring position.

[0008] Furthermore, the abnormality judgment module judges whether the operation of the manure scraper has occurred abnormally based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points, including: calculating the real-time scraper abnormality value based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points; calculating the real-time motor operation abnormality value based on the current and voltage of the motor of the manure scraper at the multiple consecutive time points; judging to perform time difference verification based on the real-time scraper abnormality value and the real-time motor operation abnormality value; if it is determined to perform time difference verification, determining the vibration abnormality time starting point based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points, determining the motor operation abnormality time starting point based on the current and voltage of the motor of the manure scraper at the multiple consecutive time points, and calculating the time difference between the vibration abnormality time starting point and the motor operation abnormality time starting point; judging whether the time difference meets the preset conditions, and if so, judging that the operation of the manure scraper has occurred abnormally.

[0009] Furthermore, the abnormality judgment module calculates the real-time scraper abnormality value based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points, including: determining the real-time vibration abnormality value of each vibration monitoring position based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points; determining the comprehensive vibration abnormality value corresponding to each deduplicated target vibration test position group based on the real-time vibration abnormality value of each vibration monitoring position; calculating the real-time scraper abnormality value based on the correlation coefficient of any two deduplicated target vibration test position groups and the comprehensive vibration abnormality value corresponding to each deduplicated target vibration test position group.

[0010] Furthermore, the abnormality judgment module calculates the real-time motor operation abnormality value based on the current and voltage of the motor of the manure scraper at the multiple consecutive time points, including: generating a real-time current waveform based on the current of the motor of the manure scraper at the multiple consecutive time points; generating a real-time active power waveform based on the current and voltage of the motor of the manure scraper at the multiple consecutive time points; extracting real-time current waveform features from the real-time current waveform; extracting real-time active power waveform features from the real-time active power waveform; and calculating the real-time motor operation abnormality value based on the real-time current waveform features and the real-time active power waveform features.

[0011] Furthermore, the abnormality detection module determines whether the scraper is stuck to the cow's hoof based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points, including: determining the abnormal vibration monitoring position according to the real-time vibration abnormality value of each vibration monitoring position; determining the abnormal vibration characteristics of the abnormal vibration monitoring position according to the vibration data collected by the vibration sensing components at the abnormal vibration monitoring position at multiple consecutive time points; extracting the real-time current mutation characteristics from the real-time current waveform; extracting the real-time active power mutation characteristics from the real-time active power waveform; and judging whether the scraper is stuck to the cow's hoof based on the abnormal vibration characteristics of the abnormal vibration monitoring position, the real-time current mutation characteristics and the real-time active power mutation characteristics through a state judgment model.

[0012] Furthermore, the abnormality detection module determines the abnormal vibration characteristics of the abnormal vibration monitoring position based on the vibration data collected by the vibration sensing component at the abnormal vibration monitoring position at multiple consecutive time points, including: performing variational modal decomposition on the vibration data collected by the vibration sensing component at the abnormal vibration monitoring position at multiple consecutive time points to generate multiple natural modal components; and determining the abnormal vibration characteristics of the abnormal vibration monitoring position from the multiple natural modal components.

[0013] Furthermore, the real-time current mutation characteristics include at least real-time current mutation slope, real-time current mutation amplitude and real-time current mutation duration; the real-time active power mutation characteristics include at least real-time power mutation slope, real-time power mutation amplitude and real-time power mutation duration.

[0014] The present invention provides a method for detecting abnormal operation of a manure scraper, which is applied to the above-mentioned abnormal operation detection system of a manure scraper, including: judging whether the operation of the manure scraper is abnormal based on vibration data collected by multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points; after judging that the operation of the manure scraper is abnormal, judging whether the scraper is stuck with the cow's hoof based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points; after judging that the scraper is stuck with the cow's hoof, controlling the motor to stop running and generating a warning message.

[0015] Compared with the prior art, the method and system for detecting abnormal operation of a manure scraper provided by the present invention have at least the following beneficial effects:

[0016] 1. It can monitor the operating status of the manure scraper in real time, eliminating the need for continuous manual observation. This significantly reduces the burden of manual monitoring and improves the accuracy and timeliness of monitoring. Through the combination of a vibration detection module and a motor detection module, the system can quickly identify anomalies when the manure scraper is stuck on a cow's hoof or poses a danger to the cow. The operation control module promptly stops the motor, effectively avoiding the risk of hoof injury or tripping injuries. This improves the overall safety of the cowshed, reduces safety incidents caused by abnormal operation of the manure scraper, and provides a safer living environment for cattle. By promptly detecting and addressing abnormal operation of the manure scraper, the system helps reduce economic losses caused by injury or death of cattle, while also avoiding downtime and repair costs caused by equipment failure. It can automatically generate warning messages, allowing managers to quickly respond to and handle abnormal situations, improving the efficiency and responsiveness of cowshed management.

[0017] 2. By screening and grouping, the most representative vibration monitoring locations can be determined, thereby improving the accuracy of anomaly detection. By simulating test data and calculating vibration anomaly values, it is possible to identify which vibration test locations are most sensitive to the detection of abnormal operation of the manure scraper. This helps to optimize the layout of vibration sensing components and reduce unnecessary equipment investment while maintaining efficient monitoring capabilities. By calculating the vibration anomaly similarity and correlation coefficient, multiple target vibration test locations are grouped and deduplicated, which helps to identify and exclude redundant or highly correlated test locations, thereby improving the robustness and stability of the entire system. By accurately selecting vibration monitoring locations, the problem can be identified more quickly when the manure scraper is operating abnormally, thereby shortening the response time, reducing potential losses and risks, and being able to more accurately identify real abnormal events, reduce the occurrence of false alarms, and improve reliability and user experience.

[0018] 3. By calculating the real-time scraper abnormality value and the real-time motor operation abnormality value, and combining the time difference check, it is possible to more accurately determine whether the manure scraper is operating abnormally. The comprehensive analysis of vibration data and motor current and voltage data provides multi-dimensional monitoring information, which helps to reduce false alarms and missed alarms. By extracting the real-time current waveform and real-time active power waveform, and calculating the features, it is possible to quickly identify abnormalities in the motor operation state. Through the state judgment model, it is possible to quickly determine whether the scraper is stuck with the cow's hoof, so that timely measures can be taken to avoid accidents. The variational mode decomposition of vibration data and the extraction of abnormal vibration features help to more accurately identify abnormal situations such as the scraper getting stuck with the cow's hoof. The introduction of real-time current mutation features and real-time active power mutation features provides additional monitoring dimensions and enhances reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0020] Figure 1 It is a module diagram of a manure scraper operation abnormality detection system according to some embodiments of this specification;

[0021] Figure 2 is a schematic diagram of a process for determining multiple vibration monitoring locations according to some embodiments of this specification;

[0022] Figure 3 It is a flow chart of a method for detecting abnormal operation of a manure scraper according to some embodiments of this specification. DETAILED DESCRIPTION

[0023] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0024] Figure 1 This is a module diagram of a manure scraper operation abnormality detection system according to some embodiments of this specification, such as Figure 1 As shown, a manure scraper operation abnormality detection system may include a vibration detection module, a motor detection module, an abnormality judgment module, an abnormality detection module and an operation control module.

[0025] The vibration detection module may include a plurality of vibration sensing components arranged at a plurality of positions of the scraper of the manure scraper, wherein the vibration sensing components may include vibration sensors.

[0026] In some embodiments, the vibration detection module is further configured to:

[0027] Determine a plurality of vibration test positions on the scraper, and set a vibration sensing component at each vibration test position. For example, determine a plurality of uniform vibration test positions on the scraper at preset intervals;

[0028] Under multiple simulated scraper-stuck scenarios, simulated test data of the vibration sensing assembly set at each vibration test position is obtained, where the simulated test data includes vibration data (e.g., vibration displacement, vibration velocity, vibration acceleration, and amplitude, etc.) at multiple consecutive time points. Under different simulated scraper-stuck scenarios, the locations where the simulated cow's hoof is stuck on the scraper may be different and / or the cows corresponding to the simulated cow's hoof stuck on the scraper may have certain differences in body size.

[0029] Based on the simulated test data collected by the vibration sensing component provided at each vibration test position under multiple simulated scraper stuck scenarios, multiple vibration test positions are screened to determine multiple vibration monitoring positions;

[0030] Multiple vibration sensing components are set according to multiple vibration monitoring positions.

[0031] Figure 2 is a flow chart of determining multiple vibration monitoring positions according to some embodiments of this specification, such as Figure 2 As shown, in some embodiments, the vibration detection module screens multiple vibration test positions based on simulated test data collected by the vibration sensing component provided at each vibration test position in multiple simulated scraper stuck scenarios to determine multiple vibration monitoring positions, including:

[0032] S11. For each vibration test position, determine a vibration abnormality value corresponding to each simulated scraper stuck scenario at the vibration test position based on simulated test data collected by a vibration sensing component provided at the vibration test position under multiple simulated scraper stuck scenarios;

[0033] S12. For each vibration test position, determine whether the vibration test position is a target vibration test position based on the vibration abnormality value corresponding to each simulated scraper stuck scene at the vibration test position. If so, execute S13; if not, screen out the vibration test position;

[0034] S13. For any two target vibration test positions, calculate the vibration anomaly similarity of the two target vibration test positions based on the vibration anomaly values ​​corresponding to each simulated scraper stuck scenario at the two target vibration test positions;

[0035] S14. Grouping the multiple target vibration test positions according to the vibration anomaly similarity between any two target vibration test positions to determine multiple target vibration test position groups;

[0036] S15. For each target vibration test position group, deduplicating multiple target vibration test positions included in the target vibration test position group based on the vibration anomaly similarity between any two target vibration test positions included in the target vibration test position group to generate a deduplicated target vibration test position group.

[0037] S16. For any two deduplicated target vibration test position groups, calculate the correlation coefficient of the two deduplicated target vibration test position groups based on the vibration anomaly value of each target vibration test position included in the two deduplicated target vibration test position groups corresponding to each simulated scraper stuck scenario;

[0038] S17. Screen multiple deduplicated target vibration test position groups based on the correlation coefficient of any two deduplicated target vibration test position groups, determine multiple screened deduplicated target vibration test position groups, and use each target vibration test position included in the multiple screened deduplicated target vibration test position groups as a vibration monitoring position.

[0039] Specifically, standard test data collected by the vibration sensing component set at the vibration test position under the normal working scenario of the scraper can be obtained in advance. For each vibration test position, the vibration abnormality value of the vibration test position corresponding to each simulated scraper jam scenario is determined based on the simulated test data collected by the vibration sensing component set at the vibration test position under multiple simulated scraper jam scenarios and the standard test data collected by the vibration sensing component set at the vibration test position under the normal working scenario of the scraper.

[0040] As an example only, the simulated test data collected by the vibration sensing component set at the vibration test position under multiple simulated scraper stuck scenes and the standard test data collected by the vibration sensing component set at the vibration test position under the scraper normal working scene can be cleaned first to remove outliers and noise, and standardized to ensure that the data under the scraper normal working scene and the simulated scraper stuck scene have the same dimension and scale. The vibration displacement characteristics (for example, maximum vibration displacement, mean vibration displacement, standard deviation of vibration displacement, etc.), vibration velocity characteristics (for example, maximum vibration velocity, mean vibration velocity, standard deviation of vibration velocity, etc.), vibration acceleration characteristics (for example, maximum vibration acceleration, mean vibration acceleration, standard deviation of vibration acceleration, etc.), and amplitude characteristics (for example, maximum amplitude, mean amplitude, standard deviation of amplitude, etc.) corresponding to the simulated test data corresponding to the simulated scraper stuck scene after preprocessing can be extracted. From the standard test data corresponding to the scraper normal working scene after preprocessing, the vibration displacement characteristics, vibration velocity characteristics, vibration acceleration characteristics, and amplitude characteristics corresponding to the scraper normal working scene can be extracted. Then, based on the vibration displacement characteristics, vibration velocity characteristics, vibration acceleration characteristics and amplitude characteristics corresponding to the simulated scraper stuck scenario and the vibration displacement characteristics, vibration velocity characteristics, vibration acceleration characteristics and amplitude characteristics corresponding to the scraper normal working scenario, the vibration abnormal value of the vibration test position corresponding to the simulated scraper stuck scenario is calculated.

[0041] For example, the vibration anomaly value corresponding to the simulated scraper stuck scenario at the vibration test location can be calculated according to the following formula:

[0042]

[0043] S (i,j,n) =cos(F (i,j,n) ,F (i,n) ),n=1、2、3、4

[0044] Among them, O i is the vibration abnormal value of the jth simulated scraper stuck scenario corresponding to the i-th vibration test position, P1 is the preset parameter, P1 is greater than 0, a1, a2, a3 and a4 are the preset weights, a1, a2, a3 and a4 are greater than 0, a1+a2+a3+a4=1, S (i,j,1) is the similarity between the vibration displacement feature corresponding to the jth simulated scraper stuck scene at the i-th vibration test position and the vibration displacement feature corresponding to the normal working scraper scene at the i-th vibration test position, S (i,j,2) is the similarity between the vibration velocity feature corresponding to the jth simulated scraper stuck scene at the i-th vibration test position and the vibration velocity feature corresponding to the normal working scraper scene at the i-th vibration test position, S (i,j,3) is the similarity between the vibration acceleration feature of the i-th vibration test position in the j-th simulated scraper stuck scene and the vibration acceleration feature of the i-th vibration test position in the scraper normal working scene, S (i,j,4) is the amplitude feature similarity between the amplitude feature corresponding to the jth simulated scraper stuck scene at the i-th vibration test position and the amplitude feature corresponding to the normal working scraper scene at the i-th vibration test position, S (i,j,n) is the vibration displacement feature similarity, vibration velocity feature similarity, vibration acceleration feature similarity or amplitude feature similarity, F (i,j,n) is the vibration displacement characteristic, vibration velocity characteristic, vibration acceleration characteristic or amplitude characteristic corresponding to the jth simulated scraper stuck scene at the i-th vibration test position, F (i,n) is the vibration displacement characteristic, vibration velocity characteristic, vibration acceleration characteristic or amplitude characteristic corresponding to the i-th vibration test position in the normal working scene of the scraper, cos(F (i,j,n) ,F (i,n) ) is F (i,j,n) and F (i,n) The cosine similarity of .

[0045] The vibration anomaly values ​​corresponding to each simulated scraper stuck scenario at the vibration test position can be averaged to obtain the average vibration anomaly value corresponding to the vibration test position. When the average vibration anomaly value corresponding to the vibration test position is greater than the vibration anomaly value average threshold, the vibration test position can be used as the target vibration test position.

[0046] For each target vibration test position, the abnormality flag of the target vibration test position corresponding to each simulated scraper jam scenario is determined according to the vibration abnormality value of the target vibration test position corresponding to each simulated scraper jam scenario. When the vibration abnormality value of the target vibration test position corresponding to a certain simulated scraper jam scenario is greater than the vibration abnormality value threshold, the abnormality flag of the target vibration test position corresponding to the simulated scraper jam scenario is 1, otherwise it is 0.

[0047] The vibration anomaly similarity between two target vibration test locations can be calculated according to the following formula:

[0048]

[0049] Among them, S (e,f) is the vibration anomaly similarity between the e-th target vibration test position and the f-th target vibration test position, P2 is the preset parameter, P2 is greater than 0, sign (e,n) is the abnormal sign of the nth simulated scraper stuck scene corresponding to the eth target vibration test position, sign (f,n) is the abnormal identification of the nth simulated scraper stuck scenario corresponding to the fth target vibration test position, and N is the total number of simulated scraper stuck scenarios.

[0050] The K-means algorithm can be used to group multiple target vibration test positions according to the vibration anomaly similarity between any two target vibration test positions to determine multiple target vibration test position groups.

[0051] The target vibration test positions included in the target vibration test position group may be deduplicated according to the following process:

[0052] S21, two target vibration test positions whose vibration anomaly similarity is greater than a vibration anomaly similarity threshold are used as target vibration test positions to be deduplicated;

[0053] S22, averaging the vibration anomaly similarities of the target vibration test position to be deduplicated and the other target vibration test positions included in the target vibration test position group, calculating the mean vibration anomaly similarities corresponding to the target vibration test position to be deduplicated, and filtering out the one with the larger mean vibration anomaly similarity between the two target vibration test positions to be deduplicated;

[0054] S23: Determine whether the vibration anomaly similarities of any two target vibration test positions included in the target vibration test position group are both less than or equal to the vibration anomaly similarity threshold. If so, perform deduplication. If not, execute S21.

[0055] For each deduplicated target vibration test position group, the mean of the vibration anomaly values ​​corresponding to the simulated scraper stuck scenario for each target vibration test position included in the deduplicated target vibration test position group can be used as the vibration anomaly value corresponding to the simulated scraper stuck scenario for the deduplicated target vibration test position group. The correlation coefficient of any two deduplicated target vibration test position groups can be calculated based on the vibration anomaly values ​​corresponding to each simulated scraper stuck scenario for any two deduplicated target vibration test position groups.

[0056] For example, the correlation coefficient of two target vibration test position groups after deduplication can be calculated according to the following formula:

[0057]

[0058] Among them, r (e,f) For the e-th target vibration test position and the f-th target vibration test position, O (e,n) is the vibration anomaly value of the nth simulated scraper stuck scenario corresponding to the eth target vibration test position group after deduplication, O (f,n) It is the vibration anomaly value of the n-th simulated scraper stuck scenario corresponding to the f-th target vibration test position group after deduplication.

[0059] Multiple target vibration test location groups after deduplication can be screened according to the following process:

[0060] S31. For each deduplicated target vibration test position group, the correlation coefficients of the deduplicated target vibration test position group and each other deduplicated target vibration test position group may be averaged to obtain the correlation coefficient mean of the deduplicated target vibration test position groups.

[0061] S32, determining whether there is at least one target vibration test position group whose correlation coefficient mean after deduplication is less than the correlation coefficient mean threshold; if so, executing S33; if not, completing the screening;

[0062] S33: Filter out the target vibration test position group after deduplication with the smallest mean correlation coefficient, and execute S31.

[0063] The motor detection module can be used to obtain the current and voltage of the motor of the manure scraper.

[0064] Specifically, the motor detection module may include a current sensor and a voltage sensor for obtaining the current and voltage of the motor of the manure scraper.

[0065] The abnormality judgment module can be used to judge whether the operation of the manure scraper is abnormal based on the vibration data collected by multiple vibration sensing components at multiple consecutive time points and the current and voltage of the manure scraper motor at multiple consecutive time points.

[0066] Specifically include:

[0067] Calculate real-time scraper anomaly values ​​based on vibration data collected by multiple vibration sensing components at multiple consecutive time points;

[0068] Calculate the real-time motor operation abnormality value based on the current and voltage of the manure scraper motor at multiple consecutive time points;

[0069] According to the real-time abnormal value of scraper and the real-time abnormal value of motor operation, the time difference calibration is performed;

[0070] If it is determined that a time difference check is to be performed, the starting point of the vibration abnormality is determined based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points, the starting point of the motor operation abnormality is determined based on the current and voltage of the manure scraper motor at multiple consecutive time points, and the time difference between the starting point of the vibration abnormality and the starting point of the motor operation abnormality is calculated;

[0071] Determine whether the time difference meets the preset conditions. If so, determine that the manure scraper is operating abnormally.

[0072] In some embodiments, the abnormality determination module calculates a real-time scraper abnormality value based on vibration data collected by multiple vibration sensing components at multiple consecutive time points, including:

[0073] Determine a real-time vibration anomaly value at each vibration monitoring location based on vibration data collected by multiple vibration sensing components at multiple consecutive time points;

[0074] Determine the comprehensive vibration anomaly value corresponding to each target vibration test position group after deduplication based on the real-time vibration anomaly value of each vibration monitoring position;

[0075] The real-time scraper anomaly value is calculated based on the correlation coefficient of any two target vibration test position groups after deduplication and the comprehensive vibration anomaly value corresponding to each target vibration test position group after deduplication.

[0076] Specifically, for each vibration monitoring position, the real-time vibration displacement characteristics, vibration velocity characteristics, vibration acceleration characteristics and amplitude characteristics of the scraper are extracted from the vibration data collected by the vibration sensing component set at the vibration monitoring position at multiple consecutive time points. Then, based on the real-time vibration displacement characteristics, vibration velocity characteristics, vibration acceleration characteristics and amplitude characteristics of the scraper and the vibration displacement characteristics, vibration velocity characteristics, vibration acceleration characteristics and amplitude characteristics corresponding to the normal working scenario of the scraper, the real-time vibration anomaly value of the vibration monitoring position is calculated. The method of calculating the real-time vibration anomaly value of the vibration monitoring position is similar to the method of calculating the vibration anomaly value of the vibration test position corresponding to the simulated scraper stuck scenario, and will not be repeated here.

[0077] The real-time vibration anomaly value of each vibration monitoring position included in the target vibration test position group after deduplication may be averaged to serve as the comprehensive vibration anomaly value corresponding to the target vibration test position group after deduplication.

[0078] A multiple regression model may be established, wherein the independent variables of the multiple regression model include the comprehensive vibration anomaly value corresponding to each deduplicated target vibration test position group, the dependent variables of the multiple regression model include the real-time scraper anomaly value, and the parameters of the multiple regression model include the correlation coefficient between any two deduplicated target vibration test position groups. The real-time scraper anomaly value is calculated using the multiple regression model based on the correlation coefficient between any two deduplicated target vibration test position groups and the comprehensive vibration anomaly value corresponding to each deduplicated target vibration test position group.

[0079] In some embodiments, the abnormality determination module calculates a real-time motor operation abnormality value based on the current and voltage of the manure scraper motor at multiple consecutive time points, including:

[0080] Generate a real-time current waveform according to the current of the manure scraper motor at multiple consecutive time points;

[0081] Generate a real-time active power waveform based on the current and voltage of the manure scraper motor at multiple consecutive time points;

[0082] Extracting real-time current waveform features from the real-time current waveform, wherein the real-time current waveform features may include at least a real-time current peak value, a real-time current average value, and a real-time current fluctuation rate;

[0083] Extracting real-time active power waveform features from the real-time active power waveform, wherein the real-time active power waveform features may include at least a real-time active power peak value, a real-time active power average value, and a real-time active power fluctuation rate;

[0084] The real-time motor operation abnormality value is calculated based on the real-time current waveform characteristics and the real-time active power waveform characteristics.

[0085] Specifically, the current and voltage of the manure scraper motor at multiple consecutive time points in the normal working scenario of the scraper can be obtained, and the standard current waveform characteristics and standard active power waveform characteristics corresponding to the normal working scenario of the scraper can be extracted from the current and voltage of the manure scraper motor at multiple consecutive time points in the normal working scenario of the scraper. The real-time motor operation abnormality value can be calculated based on the standard current waveform characteristics and standard active power waveform characteristics corresponding to the normal working scenario of the scraper as well as the real-time current waveform characteristics and real-time active power waveform characteristics.

[0086] For example, the real-time motor operation abnormality value can be calculated according to the following formula:

[0087]

[0088] Among them, O electrical P2 is the preset parameter, P2 is greater than 0, b1 and b2 are the preset weights, b1 and b2 are greater than 0, b1+b2=1, F (current,1) is the standard current waveform characteristic, (F (current,2) is the real-time current waveform characteristic, cos(F (current,1) ,F (current,2) ) is the cosine similarity between the standard current waveform feature and the real-time current waveform feature, F (voltage,1) is the standard active power waveform characteristic, F (voltage,2) is the real-time active power waveform characteristic, cos(F (voltage,1) ,F (voltage,2) ) is the cosine similarity between the standard active power waveform characteristics and the real-time active power waveform characteristics.

[0089] The abnormality detection module can be used to determine whether the scraper is stuck with the cow's hoof based on the vibration data collected by multiple vibration sensing components at multiple consecutive time points and the current and voltage of the scraper's motor at multiple consecutive time points after the abnormality judgment module determines that the scraper is operating abnormally.

[0090] Specifically include:

[0091] Determine an abnormal vibration monitoring position according to the real-time vibration abnormality value of each vibration monitoring position, for example, a vibration monitoring position whose real-time vibration abnormality value is greater than a real-time vibration abnormality value threshold is used as an abnormal vibration monitoring position;

[0092] determining abnormal vibration characteristics of the abnormal vibration monitoring location based on vibration data collected by the vibration sensing component at a plurality of consecutive time points;

[0093] Extracting a real-time current mutation feature from the real-time current waveform, wherein the real-time current mutation feature at least includes a real-time current mutation slope, a real-time current mutation amplitude, and a real-time current mutation duration;

[0094] Extracting a real-time active power mutation feature from the real-time active power waveform, wherein the real-time active power mutation feature at least includes a real-time power mutation slope, a real-time power mutation amplitude, and a real-time power mutation duration;

[0095] The state judgment model is used to judge whether the scraper is stuck with the cow's hoof based on the abnormal vibration characteristics, real-time current mutation characteristics and real-time active power mutation characteristics of the abnormal vibration monitoring position. The state judgment model can be a convolutional neural network model.

[0096] Specifically, the real-time current mutation feature can be extracted from the real-time current waveform and the real-time active power mutation feature can be extracted from the real-time active power waveform through a mutation feature extraction model, wherein the mutation feature extraction model can be a convolutional neural network model.

[0097] In some embodiments, the abnormality detection module determines the abnormal vibration characteristics of the abnormal vibration monitoring location based on vibration data collected by the vibration sensing component at multiple consecutive time points, including:

[0098] Performing variational modal decomposition on vibration data collected by the vibration sensing component at the abnormal vibration monitoring location at multiple consecutive time points to generate multiple natural modal components;

[0099] The abnormal vibration characteristics of the abnormal vibration monitoring location are determined from multiple natural mode components, wherein the abnormal vibration characteristics of the abnormal vibration monitoring location may include at least the vibration frequency (e.g., center frequency, frequency bandwidth, etc.), time domain characteristics (e.g., maximum amplitude, average amplitude, amplitude fluctuation, etc.), energy characteristics (e.g., energy value, energy proportion, etc.), phase characteristics (e.g., phase information, phase stability, etc.) of each natural mode component. The center frequency of each natural mode component is the main vibration frequency represented by the natural mode component. The frequency range or bandwidth of the natural mode component reflects the degree of dispersion of the frequency component. The maximum amplitude value of the natural mode component in the time domain reflects the vibration intensity of the frequency component. The average amplitude value of the natural mode component in the time domain is used to evaluate the overall vibration level of the frequency component. The degree of change of the amplitude of the natural mode component over time can reflect the stability or periodicity of the vibration. The energy or power spectral density of the natural mode component represents the energy contribution of the frequency component in the vibration signal. The energy proportion represents the proportion of the energy of the natural mode component to the total energy of the entire signal, which is used to evaluate the importance of the frequency component. Phase information can characterize the change of the phase of the natural modal component over time, and can reflect the phase delay or phase difference of the vibration signal. Phase stability can characterize the stability or rate of change of the phase of the natural modal component, and is used to evaluate the phase consistency of vibration.

[0100] The operation control module can be used to control the motor to stop running and generate a warning message after the abnormality detection module determines that the scraper is stuck with the cow's hoof.

[0101] Figure 3 This is a flow chart of a method for detecting abnormal operation of a manure scraper according to some embodiments of this specification, such as Figure 3 As shown, a method for detecting abnormal operation of a manure scraper may include the following steps:

[0102] Step 310, determining whether the manure scraper is operating abnormally based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the manure scraper motor at multiple consecutive time points;

[0103] Step 320 , after determining that the manure scraper is operating abnormally, determine whether the scraper is stuck with the cow's hoof based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the manure scraper motor at multiple consecutive time points;

[0104] Step 330: After determining that the scraper is stuck on the cow's hoof, the motor is controlled to stop running and a warning message is generated.

[0105] A method for detecting abnormal operation of a manure scraper can be applied to a system for detecting abnormal operation of a manure scraper. For more description of the method for detecting abnormal operation of a manure scraper, please refer to the relevant description of the system for detecting abnormal operation of a manure scraper, which will not be repeated here.

[0106] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A manure scraper operation abnormality detection system, characterized in that: include: A vibration detection module includes a plurality of vibration sensing components arranged at multiple positions of the scraper of the manure scraper; A motor detection module is used to obtain the current and voltage of the motor of the manure scraper; an abnormality judgment module, configured to judge whether an abnormality occurs in the operation of the manure scraper based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points; an abnormality detection module, configured to determine, after the abnormality determination module determines that an abnormality has occurred in the operation of the manure scraper, whether the scraper is stuck with a cow's hoof based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points; An operation control module, configured to control the motor to stop running and generate a warning message after the abnormality detection module determines that the scraper is stuck with the cow's hoof; The vibration detection module is also used for: Determine multiple vibration test positions on the scraper, and set a vibration sensing component at each vibration test position; Acquire simulated test data of the vibration sensing assembly provided at each vibration test position under multiple simulated scraper stuck scenarios, wherein the simulated test data includes vibration data at multiple consecutive time points; According to the simulated test data collected by the vibration sensing assembly provided at each vibration test position under the multiple simulated scraper stuck scenarios, the multiple vibration test positions are screened to determine multiple vibration monitoring positions; The plurality of vibration sensing components are set according to the plurality of vibration monitoring positions.

2. A manure scraper operation abnormality detection system according to claim 1, characterized in that: The vibration detection module screens the multiple vibration test positions based on the simulated test data collected by the vibration sensing component provided at each vibration test position under multiple simulated scraper stuck scenarios to determine multiple vibration monitoring positions, including: S11. For each vibration test position, determine, based on simulated test data collected by a vibration sensing assembly provided at the vibration test position under multiple simulated scraper stuck scenarios, a vibration abnormality value corresponding to each simulated scraper stuck scenario at the vibration test position; S12, for each vibration test position, judging whether the vibration test position is a target vibration test position based on the vibration abnormality value corresponding to each simulated scraper stuck scenario at the vibration test position; if so, executing S13; if not, screening out the vibration test position; S13. For any two target vibration test positions, calculate the vibration anomaly similarity of the two target vibration test positions based on the vibration anomaly values ​​corresponding to each simulated scraper stuck scenario at the two target vibration test positions; S14. Grouping the multiple target vibration test positions according to the vibration anomaly similarity between any two target vibration test positions to determine multiple target vibration test position groups; S15. For each target vibration test position group, deduplicating multiple target vibration test positions included in the target vibration test position group according to the vibration anomaly similarity between any two target vibration test positions included in the target vibration test position group to generate a deduplicated target vibration test position group; S16. For any two deduplicated target vibration test position groups, calculate the correlation coefficient of the two deduplicated target vibration test position groups based on the vibration anomaly value of each target vibration test position included in the two deduplicated target vibration test position groups corresponding to each simulated scraper stuck scenario; S17. Screen multiple deduplicated target vibration test position groups based on the correlation coefficient of any two deduplicated target vibration test position groups, determine multiple screened deduplicated target vibration test position groups, and use each target vibration test position included in the multiple screened deduplicated target vibration test position groups as a vibration monitoring position.

3. The manure scraper operation abnormality detection system according to claim 2, characterized in that: The abnormality judgment module judges whether an abnormality occurs in the operation of the manure scraper based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points, including: Calculating a real-time scraper abnormality value based on the vibration data collected by the plurality of vibration sensing components at a plurality of consecutive time points; Calculating a real-time motor operation abnormality value based on the current and voltage of the motor of the manure scraper at the plurality of consecutive time points; According to the real-time scraper abnormal value and the real-time motor operation abnormal value, it is determined to perform time difference calibration; If it is determined that a time difference check is to be performed, the starting point of the vibration abnormality is determined based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points, the starting point of the motor operation abnormality is determined based on the current and voltage of the manure scraper motor at the multiple consecutive time points, and the time difference between the starting point of the vibration abnormality and the starting point of the motor operation abnormality is calculated; Determine whether the time difference meets a preset condition, and if so, determine that the manure scraper is operating abnormally.

4. A manure scraper operation abnormality detection system according to claim 3, characterized in that: The abnormality judgment module calculates a real-time scraper abnormality value based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points, including: determining a real-time vibration abnormality value at each vibration monitoring location based on vibration data collected by the plurality of vibration sensing components at a plurality of consecutive time points; Determine the comprehensive vibration anomaly value corresponding to each target vibration test position group after deduplication based on the real-time vibration anomaly value of each vibration monitoring position; The real-time scraper anomaly value is calculated based on the correlation coefficient of any two target vibration test position groups after deduplication and the comprehensive vibration anomaly value corresponding to each target vibration test position group after deduplication.

5. The manure scraper operation abnormality detection system according to claim 4, characterized in that: The abnormality judgment module calculates a real-time motor operation abnormality value based on the current and voltage of the motor of the manure scraper at multiple consecutive time points, including: generating a real-time current waveform according to the current of the motor of the manure scraper at the plurality of consecutive time points; generating a real-time active power waveform based on the current and voltage of the motor of the manure scraper at the plurality of consecutive time points; extracting real-time current waveform features from the real-time current waveform; Extracting real-time active power waveform features from the real-time active power waveform; A real-time motor operation abnormality value is calculated according to the real-time current waveform characteristics and the real-time active power waveform characteristics.

6. The manure scraper operation abnormality detection system according to claim 5, characterized in that: The abnormality detection module determines whether the scraper is stuck with the cow's hoof based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points, including: Determine the abnormal vibration monitoring location based on the real-time abnormal vibration value of each vibration monitoring location; determining abnormal vibration characteristics of the abnormal vibration monitoring location based on vibration data collected by the vibration sensing component at a plurality of consecutive time points; extracting a real-time current mutation feature from the real-time current waveform; Extracting a real-time active power mutation feature from the real-time active power waveform; Whether the scraper is stuck with the cow's hoof is determined by the state judgment model based on the abnormal vibration characteristics of the abnormal vibration monitoring position, the real-time current mutation characteristics and the real-time active power mutation characteristics.

7. The manure scraper operation abnormality detection system according to claim 6, characterized in that: The abnormality detection module determines the abnormal vibration characteristics of the abnormal vibration monitoring position based on vibration data collected by the vibration sensing component at multiple consecutive time points, including: Performing variational modal decomposition on vibration data collected by the vibration sensing component at the abnormal vibration monitoring location at multiple consecutive time points to generate multiple natural modal components; An abnormal vibration feature of an abnormal vibration monitoring position is determined from the plurality of natural mode components.

8. The manure scraper operation abnormality detection system according to claim 6, characterized in that: The real-time current mutation characteristics include at least the real-time current mutation slope, the real-time current mutation amplitude and the real-time current mutation duration; The real-time active power mutation characteristics include at least a real-time power mutation slope, a real-time power mutation amplitude, and a real-time power mutation duration.

9. A method for detecting abnormal operation of a manure scraper, characterized in that: A manure scraper operation abnormality detection system applied to any one of claims 1 to 8, comprising: Determining whether the operation of the manure scraper is abnormal based on vibration data collected by the multiple vibration sensing components at multiple consecutive time points and current and voltage of the motor of the manure scraper at the multiple consecutive time points; After determining that the manure scraper is operating abnormally, determining whether the scraper is stuck with the cow's hoof based on the vibration data collected by the multiple vibration sensing components at multiple consecutive time points and the current and voltage of the motor of the manure scraper at the multiple consecutive time points; After determining that the scraper is stuck on the cow's hoof, the motor is controlled to stop running and a warning message is generated.

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