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CN-121998256-A - Intelligent monitoring management system and method applied to manure scraping machine

CN121998256ACN 121998256 ACN121998256 ACN 121998256ACN-121998256-A

Abstract

The invention discloses an intelligent monitoring management system and method applied to a manure scraper, and relates to the technical field of artificial intelligence; the method comprises the steps of calculating to obtain an operation score through an operation score model, screening an execution path of the manure scraper, collecting operation parameters of the manure scraper on the execution path, calculating to obtain an actual operation score, calculating to obtain an absolute difference value with the operation score, generating an analysis adjustment model based on the absolute difference value and an analysis time period, collecting real-time work records and analyzing to determine update time of a manure scraper management platform, and effectively prolonging service life of equipment, reducing failure occurrence rate and ensuring long-term efficient operation of the manure scraper through accurate real-time feedback and adjustment.

Inventors

  • WANG DAXIANG
  • WANG YUHUA
  • Cai Shishi

Assignees

  • 江苏乾宝牧业有限公司

Dates

Publication Date
20260508
Application Date
20260128

Claims (10)

  1. 1. The intelligent monitoring and management method applied to the manure scraper is characterized by comprising the following steps of: step 100, in a manure scraper management platform, acquiring a plane space model of a feeding area in a work record, extracting a working space model of the manure scraper according to a functional area of the manure scraper operation, dividing the working space model into a plurality of subspaces, and generating time sequence collection data corresponding to each subspace; Step 200, constructing a fecal quality prediction model and a path generation model according to the historical work record; Step 300, setting a training period, acquiring a working record in the training period, calculating to obtain the stool prediction quality of each feature space through a stool quality prediction model, generating a plurality of candidate paths based on a path generation model, collecting operation parameters of the candidate paths executed by a stool scraper, calculating to obtain an operation score, and combining the operation score with the feature parameters of the candidate paths to generate an operation scoring model; Step 400, calculating to obtain an operation score through an operation score model, screening an execution path of the manure scraper, collecting operation parameters of the manure scraper on the execution path, calculating to obtain an actual operation score, calculating to obtain an absolute difference value with the operation score, and generating an analysis and adjustment model based on the absolute difference value and an analysis time period; And S500, collecting and analyzing real-time work records to determine the updating time of the manure scraper management platform.
  2. 2. The intelligent monitoring and management method for a manure scraper according to claim 1, wherein the step S100 includes the following steps: Step S101, acquiring field information of a feeding area where the manure scraper works, constructing a plane space model of the feeding area according to the field information of the feeding area, collecting the feed throwing amount and the corresponding feed consumption amount in the feeding area, collecting operation parameters of the manure scraper in the operation process in the feeding area, combining the feed throwing amount, the feed consumption parameters and the operation parameters to generate a working record of the manure scraper, and uploading the working record to a manure scraper management platform; Step S102, dividing each functional area in each feeding area in the plane space model, obtaining a functional area for the operation of the manure scraper, extracting an operation space model of the manure scraper, dividing the operation space model into a plurality of subspaces and marking the subspaces; Step 103, acquiring a corresponding operation time stamp when the manure scraper operates in each subspace, acquiring the mass of manure acquired by the manure scraper in each subspace, combining the operation time stamp with the mass of the manure, and generating time sequence collection data corresponding to each subspace.
  3. 3. The intelligent monitoring and management method for a manure scraper according to claim 2, wherein the step S200 includes the following steps: Step S201, in a historical work record set, based on time sequence collection data of each subspace in the historical work record, constructing a fecal quality prediction model; step S202, collecting operation parameters of the manure scraper in a historical work record, and constructing a manure scraper path generation model based on a manure quality prediction model.
  4. 4. The intelligent monitoring and management method for a manure scraper according to claim 3, wherein the constructing the manure quality prediction model in step S201 includes the following steps: S201-1, extracting the stool quality collected by a stool scraper in each subspace in a historical work record, collecting the quantity of livestock in a feeding area in the historical work record, respectively carrying out normalization calculation on the stool quality and the quantity of livestock, carrying out weighted summation on the stool quality and the quantity of livestock after normalization calculation according to a preset weight, calculating to obtain a stool characteristic value of each subspace, summarizing the stool characteristic value corresponding to the historical work record of each subspace, calculating to obtain a stool characteristic average value of each subspace, presetting a stool characteristic average threshold, and marking subspaces exceeding the stool characteristic average threshold as characteristic spaces; Step S201-2, in a historical work record, collecting a time stamp of last feed throwing, calculating with the time stamp of the historical work record to obtain a thrown time length of the feed, obtaining weather parameters of the thrown time length, carrying out normalization calculation on the weather parameters, carrying out weighted summation on the normalized weather parameters according to a preset weight, calculating to obtain weather scores, extracting the time stamp of the historical work record, presetting a day time as a plurality of time periods, marking, determining a time period corresponding to the historical work record, obtaining the feed consumption amount in the thrown time length, dividing by the thrown time length, calculating to obtain feed consumption frequency, summarizing the feed consumption frequency of feed corresponding to the historical work record in each time period, calculating to obtain a feed consumption frequency average value of each time period, sequencing the time periods from low to high according to the consumption frequency average value, collecting a bit sequence corresponding to each time period, and assigning a value to the time period according to the bit sequence; Step S201-3, summarizing historical working records of a certain feature space, respectively carrying out normalization calculation on the thrown duration, the time period value and the weather score, taking the normalized thrown duration, the normalized time period value and the normalized weather score as input, taking the stool quality of the feature space as output, training through a random forest regression model, generating a plurality of decision trees through random sampling in the training process, respectively learning mapping relations between input features and stool quality, and integrating prediction results of the decision trees to generate a stool quality prediction model.
  5. 5. The intelligent monitoring and management method for a manure scraper according to claim 3, wherein the constructing a path generating model in the step S202 includes the following steps: Step S202-1, extracting operation parameters of the manure scraper in a historical work record, wherein the operation parameters comprise operation speed, single operation duration and motor load parameters of the manure scraper, multiplying the operation speed and the single operation duration, calculating to obtain a theoretical operation distance of the single operation, multiplying the theoretical operation distance and a cleaning width of the manure scraper, calculating to obtain a theoretical cleaning area of the single operation, calculating to obtain a load correction coefficient based on the motor load parameters, multiplying the theoretical cleaning area and the load correction coefficient, calculating to obtain an effective cleaning area of the single operation, summarizing the effective cleaning areas corresponding to the single operation in the historical work record, calculating to obtain an average value of the effective cleaning areas, and taking the average effective cleaning area as a capacity threshold value of the manure scraper in a unit operation period; Step S202-2, based on the fecal predicted quality of each feature space output by the fecal quality prediction model, acquiring a spatial position relation among the feature spaces, presetting a fecal quality threshold, setting the feature space with the fecal predicted quality higher than the fecal quality threshold as a preferential cleaning space, and combining the preferential cleaning spaces under the condition of meeting the fecal scraper capacity threshold to construct a running path model of the fecal scraper.
  6. 6. The intelligent monitoring and management method for a manure scraper according to claim 1, wherein the step S300 includes the following steps: Step 301, selecting a plurality of continuous days as a training period, acquiring a working record in the training period, collecting weather parameters in the working record, a time stamp of last feed feeding and a current time stamp, calculating to obtain weather scores and fed duration, determining the numerical value of a time period corresponding to the working record, carrying out normalization calculation on the fed duration, the numerical value of the time period and the weather scores, inputting the normalized calculation into a fecal quality prediction model of each feature space, and calculating to obtain fecal prediction quality of each feature space; Step S302, inputting the feces prediction quality of each feature space into a running path model, generating a plurality of candidate paths, acquiring candidate paths selected by the current working path, and collecting operation parameters of the feces scraping machine, wherein the operation parameters comprise actual cleaning area, cleaning amount per unit time, motor load and electric energy consumption, carrying out normalization calculation on the operation parameters, carrying out weighted summation on the normalized operation parameters according to preset weights, and calculating to obtain an operation score; And S303, extracting characteristic parameters of the candidate paths, wherein the characteristic parameters comprise path length, the number of covered characteristic spaces and the predicted fecal quality sum, taking the characteristic parameters of the candidate paths as input, taking the operation scores as output, training through a deep learning model, and generating an operation scoring model by iteratively updating the model parameters by using an optimizer in the training process, wherein the error between the operation scores output by the model and the actual operation scores is minimized.
  7. 7. The intelligent monitoring and management method for a manure scraper according to claim 6, wherein the step S400 includes the following steps: Step S401, inputting characteristic parameters of candidate paths of each work record into a work scoring model, calculating to obtain a work score, and screening candidate paths corresponding to the highest value of the work score as execution paths of the manure scraper; Step S402, collecting operation parameters of the manure scraper on an execution path, calculating to obtain an actual operation score, calculating an absolute difference value with the operation score, presetting an absolute difference value threshold value, and marking the operation record as an abnormal analysis record if the operation parameter exceeds the absolute difference value threshold value; Step S403, setting a time period between two adjacent abnormal analysis records as an analysis time period, obtaining absolute differences of operation scores corresponding to each work record in the analysis time period, constructing an absolute difference time sequence set, taking the absolute difference time sequence set as input, taking the analysis time period as output, training through a long-short-period memory network model, wherein the training process is to calculate errors between the model output result and the actual analysis result by adopting a loss function, and updating model parameters through a back propagation algorithm to generate an analysis adjustment model.
  8. 8. The intelligent monitoring and management method for a manure scraper according to claim 7, wherein the step S500 includes the following steps: Step S501, inputting the normalized real-time released duration, the real-time period value and the real-time weather score into a fecal quality prediction model, and calculating to obtain the real-time fecal prediction quality of each feature space; Step S502, inputting the real-time fecal prediction quality of each characteristic space into a running path model to generate a plurality of real-time candidate paths, inputting the characteristic parameters of each real-time candidate path into a job scoring model, calculating to obtain a real-time job score, and selecting the highest value of the real-time job score as a real-time execution path of the fecal scraper; Step S503, after the manure scraper performs work, calculating to obtain a real-time actual operation score of the manure scraper, calculating a real-time absolute difference value of the real-time operation score, if the real-time absolute difference value exceeds a threshold value of the absolute difference value, immediately updating the manure scraper management platform, if the real-time absolute difference value does not exceed the threshold value of the absolute difference value, constructing a real-time absolute difference time sequence set, inputting the real-time absolute difference time sequence set into an analysis and adjustment model, calculating to obtain an updating time duration, and updating the manure scraper management platform according to the updating time duration.
  9. 9. An intelligent monitoring and management system applied to a manure scraper, which is used for realizing the intelligent monitoring and management method applied to the manure scraper according to any one of claims 1-8, and is characterized in that the system comprises a time sequence collection data module, a manure quality prediction model and path generation model module, a job scoring model module, an analysis and adjustment model module and an update time module; the time sequence collection data module is used for acquiring a plane space model of a feeding area in a working record in a manure scraper management platform, extracting an operation space model of the manure scraper according to a functional area of operation of the manure scraper, dividing the operation space model into a plurality of subspaces, and generating time sequence collection data corresponding to each subspace; The fecal quality prediction model and path generation model module is used for constructing a fecal quality prediction model and a path generation model according to the historical work record; Setting a training period, acquiring a working record in the training period, calculating to obtain the stool prediction quality of each feature space through a stool quality prediction model, generating a plurality of candidate paths based on a path generation model, collecting operation parameters of the execution candidate paths of the stool scraper, calculating to obtain an operation score, and combining the operation score with the feature parameters of the candidate paths to generate an operation scoring model; The analysis and adjustment model module is used for obtaining an operation score through calculation of an operation score model, screening an execution path of the manure scraper, collecting operation parameters of the manure scraper on the execution path, obtaining an actual operation score through calculation, obtaining an absolute difference value through calculation of the actual operation score and the operation score, and generating an analysis and adjustment model based on the absolute difference value and an analysis time period; And the updating time module is used for collecting and analyzing the real-time work record and determining the updating time of the manure scraper management platform.
  10. 10. The intelligent monitoring and management system for a manure scraper according to claim 9, wherein the update time module comprises a real-time execution path unit determination and an update time unit determination: The real-time execution path determining unit is used for inputting the normalized real-time released duration, the normalized real-time period value and the normalized real-time weather score into the fecal quality prediction model, calculating to obtain the real-time fecal prediction quality of each feature space, inputting the real-time fecal prediction quality of each feature space into the operation path model, generating a plurality of real-time candidate paths, inputting the feature parameters of each real-time candidate path into the operation scoring model, calculating to obtain a real-time operation score, and selecting the highest value of the real-time operation score as the real-time execution path of the fecal scraper; the updating time determining unit is used for calculating to obtain a real-time actual operation score of the manure scraper after the manure scraper is operated, calculating a real-time absolute difference value of the real-time operation score, updating the manure scraper management platform immediately if the real-time absolute difference value exceeds a threshold value of the absolute difference value, constructing a real-time absolute difference time sequence set if the real-time absolute difference value does not exceed the threshold value of the absolute difference value, inputting the real-time absolute difference time sequence set into an analysis and adjustment model, calculating to obtain an updating time duration, and updating the manure scraper management platform according to the updating time duration.

Description

Intelligent monitoring management system and method applied to manure scraping machine Technical Field The invention relates to the technical field of artificial intelligence, in particular to an intelligent monitoring and management system and method applied to a manure scraper. Background In a large-scale lake sheep farm, the manure scraping equipment is used as a key facility for cleaning manure in a feeding area, the operation mode of the manure scraping equipment is gradually developed from a traditional fixed manure scraping device to an unmanned manure scraping locomotive with autonomous walking capability, and compared with the traditional manure scraping locomotive which runs along a fixed track or a steel wire traction, the unmanned manure scraping locomotive is generally provided with a driving unit, a power supply unit and a basic sensing unit, can autonomously move in the feeding area according to a preset route to complete manure cleaning operation, and provides a hardware foundation for flexible activation of manure cleaning; In practical application, the existing unmanned manure scraping locomotives only support route cruising and timing operation based on manual setting or simple rules, lack deep analysis of manure load differences in different areas, often adopt uniform configuration of manure scraping paths, cleaning frequencies and running power, and are difficult to dynamically adjust according to manure distribution changes in a cultivation area; While the part of the unmanned manure scraping locomotives are introduced into environments or load sensors to realize state monitoring, the control strategies are mostly remained on a passive response level, and the follow-up operation strategies cannot be predicted and planned by combining with manure production trends; Therefore, how to improve the sensing and predicting capability of the manure scraping machine on the variation of manure and realize intelligent cooperative control of the running path, the power configuration and the rhythm adjustment becomes a technical problem which needs to be solved in the field of the current manure scraping equipment management. Disclosure of Invention The invention aims to provide an intelligent monitoring management system and method applied to a manure scraper, which are used for solving the problems in the prior art. In order to solve the technical problems, the invention provides the following technical scheme that the intelligent monitoring and management method applied to the manure scraper comprises the following steps: step 100, in a manure scraper management platform, acquiring a plane space model of a feeding area in a work record, extracting a working space model of the manure scraper according to a functional area of the manure scraper operation, dividing the working space model into a plurality of subspaces, and generating time sequence collection data corresponding to each subspace; Step 200, constructing a fecal quality prediction model and a path generation model according to the historical work record; Step 300, setting a training period, acquiring a working record in the training period, calculating to obtain the stool prediction quality of each feature space through a stool quality prediction model, generating a plurality of candidate paths based on a path generation model, collecting operation parameters of the candidate paths executed by a stool scraper, calculating to obtain an operation score, and combining the operation score with the feature parameters of the candidate paths to generate an operation scoring model; Step 400, calculating to obtain an operation score through an operation score model, screening an execution path of the manure scraper, collecting operation parameters of the manure scraper on the execution path, calculating to obtain an actual operation score, calculating to obtain an absolute difference value with the operation score, and generating an analysis and adjustment model based on the absolute difference value and an analysis time period; And S500, collecting and analyzing real-time work records to determine the updating time of the manure scraper management platform. Further, step S100 includes: Step S101, acquiring field information of a feeding area where the manure scraper works, constructing a plane space model of the feeding area according to the field information of the feeding area, collecting the feed throwing amount and the corresponding feed consumption amount in the feeding area, collecting operation parameters of the manure scraper in the operation process in the feeding area, combining the feed throwing amount, the feed consumption parameters and the operation parameters to generate a working record of the manure scraper, and uploading the working record to a manure scraper management platform; Step S102, dividing each functional area in each feeding area in the plane space model, obtaining a functional area for the operation of the manure