Livestock machinery full life cycle supervision method

Abstract

The invention discloses a full life cycle supervision method for stockbreeding machinery, relates to the related technical field of control systems, and generates a regional discharge target sequence by determining a plurality of regional discharge targets according to a transportation path of an automatic discharge vehicle. And selecting a first discharging target from the regional discharging target sequence, and carrying out discharging speed optimization analysis by taking the minimum deviation from the first discharging target as an expectation to obtain a first optimized discharging speed. Discharging angle optimization analysis is carried out, a first optimized discharging angle is output, and a first optimized discharging parameter is generated in combination with the discharging speed. And continuously carrying out discharging parameter optimization analysis on other discharging targets to generate an optimized discharging parameter sequence, and carrying out discharging error correction on the optimized discharging parameter sequence to obtain an optimal discharging scheme for carrying out automatic discharging control on the cow farm. The technical problems that in the prior art, livestock machinery lacks a dynamic full-life-cycle supervision mechanism, the discharging deviation in the automatic discharging process is large, and the feeding effect is affected are solved.

Description

Technical Field

[0001] This application relates to the technical field of control systems, and specifically relates to a full-life-cycle supervision method for livestock machinery. Background Art

[0002] With the continuous expansion of dairy farming scale, the traditional manual feeding method is increasingly showing deficiencies in terms of accuracy and efficiency. On the one hand, it is difficult to precisely control the weight and distribution of feed by manual operation, which easily leads to feed waste or nutritional imbalance; on the other hand, factors such as feed ratio, dairy breed, and its growth stage change frequently, and relying solely on experience or extensive management often fails to adjust and optimize in a timely manner. To address the above problems, more and more farms have begun to introduce automatic feeding trucks to improve the feeding efficiency through mechanization and intelligent means. However, in the existing technology during the automatic discharging process, most are single control of speed or angle, lacking a dynamic full-life-cycle supervision mechanism. Especially after the equipment usage time increases, the wear and performance decay of mechanical components often lead to continuous accumulation of discharging deviation, which cannot be automatically compensated and corrected, ultimately affecting the feeding accuracy, feed utilization rate, and the health level of dairy cows.

[0003] Therefore, in the prior art, there is a technical problem that livestock machinery lacks a dynamic full-life-cycle supervision mechanism, resulting in a large discharging deviation during the automatic discharging process and affecting the feeding effect. Summary of the Invention

[0004] This application provides a full-life-cycle supervision method for livestock machinery, solving the technical problem in the prior art that livestock machinery lacks a dynamic full-life-cycle supervision mechanism, resulting in a large discharging deviation during the automatic discharging process and affecting the feeding effect. It realizes accurate and efficient feed delivery in different usage periods and breeding environments, significantly improves the feed utilization rate, and ensures the health of dairy cows.

[0005] The present application provides a method for the whole life cycle supervision of livestock machinery. The method includes: obtaining several dairy cattle breeding characteristics of several areas in a dairy cattle farm, performing feeding analysis based on the current feed ratio and several dairy cattle breeding characteristics, determining the discharging targets of several areas, and mapping and sorting the discharging targets of several areas according to the transportation path of an automatic discharging vehicle to generate a regional discharging target sequence; selecting a first discharging target from the regional discharging target sequence, taking the minimum deviation from the first discharging target as the expectation, combining the vehicle discharging duration and the current feed ratio to perform discharging speed optimization analysis, and obtaining a first optimized discharging speed; based on the current feed ratio, vehicle discharging duration, and first optimized discharging speed, taking the feed falling evenly into a predetermined area as the expectation, performing discharging angle optimization analysis, outputting a first optimized discharging angle, and generating a first optimized discharging parameter in combination with the first optimized discharging speed; continuing to perform discharging parameter optimization analysis on other discharging targets in the regional discharging target sequence, generating an optimized discharging parameter sequence, and performing discharging error correction on the optimized discharging parameter sequence to obtain an optimal discharging plan for the automatic discharging control of the dairy cattle farm.

[0006] In an implementation manner, obtaining several dairy cattle breeding characteristics of several areas in a dairy cattle farm and performing feeding analysis based on the current feed ratio and several dairy cattle breeding characteristics to determine the discharging targets of several areas includes: obtaining several dairy cattle breeding characteristics of several areas in a dairy cattle farm, where the dairy cattle breeding characteristics at least include dairy cattle breed, quantity, and growth stage, and the growth stage at least includes a growth period, prime period, lactation period, pregnancy period, and old age; obtaining the current feed ratio of the automatic discharging vehicle, where the current feed ratio includes the material type and the proportion occupied; based on a dairy cattle breeding specification table, performing feeding standard retrieval respectively according to the current feed ratio and several dairy cattle breeding characteristics, and obtaining the discharging weights of several areas, which are set as the discharging targets of several areas.

[0007] In an implementation manner, selecting a first discharging target from the regional discharging target sequence, taking the minimum deviation from the first discharging target as the expectation, combining the vehicle discharging duration and the current feed ratio to perform discharging speed optimization analysis, and obtaining a first optimized discharging speed includes: selecting the first regional discharging target in the regional discharging target sequence as the first discharging target; performing simulation modeling on the automatic discharging vehicle in a three-dimensional simulation platform to generate a discharging simulation model; obtaining the discharging speed adjustment threshold of the automatic discharging vehicle, and based on the discharging speed adjustment threshold and the discharging simulation model, taking the minimum deviation from the first discharging target as the expectation, combining the vehicle discharging duration and the current feed ratio to perform discharging speed optimization analysis, and outputting the first optimized discharging speed.

[0008] In the implementation manner, based on the discharging speed adjustment threshold and the discharging simulation model, with the expectation of minimizing the deviation from the first discharging target, combining the vehicle discharging duration and the current feed ratio, an optimization analysis of the discharging speed is carried out, and the first optimized discharging speed is output, including: rendering the vehicle discharging duration and the current feed ratio to the discharging simulation model to generate a real-time discharging simulation space; randomly selecting a first discharging speed from the discharging speed adjustment threshold, and using the real-time discharging simulation space to simulate and obtain a first simulated discharging weight according to the first discharging speed; calculating the deviation between the first simulated discharging weight and the first discharging target to obtain a first weight difference; continuing to randomly select a discharging speed from the discharging speed adjustment threshold for simulation until the predetermined speed optimization times are reached, outputting a plurality of discharging speeds and a plurality of weight differences, and selecting the discharging speed corresponding to the minimum weight difference as the first optimized discharging speed.

[0009] In the implementation manner, based on the current feed ratio, the vehicle discharging duration and the first optimized discharging speed, with the expectation that the feed evenly falls into a predetermined area, an optimization analysis of the discharging angle is carried out, and the first optimized discharging angle is output, including: collecting a sample feed ratio set, a sample discharging duration set, a sample discharging speed set and a sample discharging angle set according to the breeding discharging logs in the historical time zone, and marking the feed distribution under different sample feed ratios, sample discharging durations, sample discharging speeds and sample discharging angles to obtain a sample feed distribution set, where the sample feed distribution includes a sample distribution uniformity and a ratio of the sample overflow area, and the ratio of the overflow area is the ratio of the area where the feed exceeds the predetermined area to the area of the predetermined area; using the sample feed ratio set, the sample discharging duration set, the sample discharging speed set, the sample discharging angle set and the sample feed distribution set as training data to perform supervised learning on a random forest to obtain a feed discharge prediction plug-in; using the feed discharge prediction plug-in, based on the current feed ratio, the vehicle discharging duration and the first optimized discharging speed, with the expectation that the feed evenly falls into the predetermined area, an optimization analysis of the discharging angle is carried out, and the first optimized discharging angle is output.

[0010] In the implementation manner, the sample feed ratio set, the sample discharging duration set, the sample discharging speed set, the sample discharging angle set, and the sample feed distribution set are used as training data to perform supervised learning on the random forest to obtain a feed discharge prediction plug-in, including: using the sample feed ratio set, the sample discharging duration set, the sample discharging speed set, the sample discharging angle set, and the sample feed distribution set as training data, and equally dividing the training data into P parts, and selecting P times with replacement to obtain a first training set, and continuing to iterate and select P times to obtain P training sets, where P is an integer greater than 10; using the sample feed ratio, the sample discharging duration, the sample discharging speed, and the sample discharging angle as inputs, and using the sample distribution uniformity and the proportion of the sample overflow area as supervision, and performing supervised learning on the random forest using the P training sets until convergence to obtain P feed discharge prediction units; integrally constructing the feed discharge prediction plug-in based on the P feed discharge prediction units, where the output of the feed discharge prediction plug-in is the mean value of the outputs of the P feed discharge prediction units.

[0011] In the implementation manner, using the feed discharge prediction plug-in, according to the current feed ratio, the vehicle discharging duration, and the first optimized discharging speed, with the expectation that the feed evenly falls into a predetermined area, perform discharging angle optimization analysis and output the first optimized discharging angle, including: obtaining the discharging angle adjustment threshold of the automatic discharging vehicle, and randomly selecting a first discharging angle from the discharging angle adjustment threshold; inputting the current feed ratio, the vehicle discharging duration, the first optimized discharging speed, and the first discharging angle into the feed discharge prediction plug-in to output the first distribution uniformity and the first proportion of the overflow area; obtaining a first discharge fitness according to the evaluation of the first distribution uniformity and the first proportion of the overflow area, where the discharge fitness is positively correlated with the distribution uniformity and negatively correlated with the proportion of the overflow area; continuing to perform discharging angle optimization analysis according to the discharging angle adjustment threshold until a predetermined number of angle optimization times is reached, obtaining multiple discharging angles and multiple discharge fitnesses, and selecting the discharging angle with the maximum discharge fitness as the first optimized discharging angle.

[0012] In the implementation manner, perform discharging error correction on the optimized discharging parameter sequence to obtain an optimal discharging plan, including: obtaining the cumulative working duration of the automatic discharging vehicle, performing discharging error analysis according to the cumulative working duration, and determining the discharging speed compensation coefficient and the discharging angle compensation coefficient, where an error analysis channel is constructed based on a BP neural network to perform discharging error analysis, and a sample working duration set, a sample discharging speed compensation coefficient set, and a sample discharging angle compensation coefficient set are collected to train the error analysis channel until convergence; according to the discharging speed compensation coefficient and the discharging angle compensation coefficient, perform discharging error traversal correction on the optimized discharging parameter sequence to obtain an optimal discharging parameter sequence, and combine the transportation path to generate the optimal discharging plan.

[0013] A full - life - cycle supervision method for livestock machinery proposed in this application obtains several dairy - farming characteristics in several areas within a dairy farm, conducts feeding analysis based on the current feed ratio and several dairy - farming characteristics, determines the discharging targets for several areas, maps and sorts the discharging targets for several areas according to the transportation path of the automatic discharging vehicle to generate a regional discharging - target sequence; selects the first discharging target in the regional discharging - target sequence, takes the minimum deviation from the first discharging target as the expectation, combines the vehicle discharging duration and the current feed ratio to conduct discharging - speed optimization analysis, and obtains the first optimized discharging speed; based on the current feed ratio, vehicle discharging duration and the first optimized discharging speed, takes the uniform falling of feed into a predetermined area as the expectation, conducts discharging - angle optimization analysis, outputs the first optimized discharging angle, and generates the first optimized discharging parameter by combining the first optimized discharging speed; continues to conduct discharging - parameter optimization analysis on other discharging targets in the regional discharging - target sequence, generates an optimized discharging - parameter sequence, and corrects the discharging error of the optimized discharging - parameter sequence to obtain the optimal discharging plan for the automatic discharging control of the dairy farm. This solves the technical problem in the prior art that livestock machinery lacks a dynamic full - life - cycle supervision mechanism, resulting in large discharging deviation during the automatic discharging process and affecting the feeding effect. It realizes accurate and efficient feed delivery at different usage times and breeding environments, significantly improves feed utilization rate and ensures the health of dairy cows. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the operations before or below do not necessarily need to be executed precisely in sequence. On the contrary, according to needs, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0015] Figure 1 It is a schematic flowchart of a full - life - cycle supervision method for livestock machinery provided by an embodiment of the present application; Figure 2 It is a schematic flowchart of a full - life - cycle supervision method for livestock machinery provided by an embodiment of the present application to obtain discharging targets for several areas. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically illustrates the specific embodiments of this application.

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following will further describe this application in detail with reference to the accompanying drawings. The described embodiments should not be construed as limitations on this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0018] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0019] An embodiment of this application provides a method for the full-life cycle supervision of livestock machinery, as Figure 1 shown, the method includes: Obtain the livestock breeding characteristics of several areas in a dairy farm, perform feeding analysis based on the current feed ratio and the livestock breeding characteristics of several areas, determine the discharge targets of several areas, and map and sort the discharge targets of several areas according to the transportation path of the automatic discharging vehicle to generate a regional discharge target sequence; Select the first discharging target from the regional discharging target sequence, aiming to minimize the deviation from the first discharging target. Combine the vehicle discharging duration and the current feed ratio to conduct an optimization analysis of the discharging speed, and obtain the first optimized discharging speed. Obtain several dairy cow breeding characteristics of several regions in the dairy farm. The dairy cow breeding characteristics include dairy cow breed, quantity, and growth stage, and the growth stage at least includes the growth period, prime period, lactation period, pregnancy period, and old age period. Conduct a feeding analysis based on the current feed ratio and several dairy cow breeding characteristics to determine the discharging targets for several regions, where the area of each region is the same. According to the transportation path of the automatic discharging vehicle, map and sort the discharging targets for several regions, that is, map according to the feeding order and the corresponding regional discharging targets to generate a regional discharging target sequence. Further, select the first discharging target from the regional discharging target sequence, and the first discharging target is the discharging target ranked first in the regional discharging target sequence. Aim to minimize the deviation from the first discharging target, combine the vehicle discharging duration and the current feed ratio to conduct an optimization analysis of the discharging speed, and obtain the first optimized discharging speed.

[0020] As Figure 2 As shown, the method provided in the embodiment of the present application further includes: obtaining several dairy cow breeding characteristics of several regions in the dairy farm, where the dairy cow breeding characteristics at least include dairy cow breed, quantity, and growth stage, and the growth stage at least includes the growth period, prime period, lactation period, pregnancy period, and old age period; obtaining the current feed ratio of the automatic discharging vehicle, where the current feed ratio includes the material type and the proportion occupied; based on the dairy cow breeding specification table, respectively conduct a feeding standard retrieval according to the current feed ratio and several dairy cow breeding characteristics to obtain the discharging weights for several regions, which are set as the discharging targets for several regions.

[0021] Obtain several dairy cow breeding characteristics in several areas within a dairy farm, conduct feeding analysis based on the current feed ratio and several dairy cow breeding characteristics, and determine the discharge targets for several areas, including: The dairy cow breeding characteristics at least include dairy cow breed, quantity, and growth stage, and the growth stage at least includes the growth period, prime period, lactation period, pregnancy period, and old age period. Subsequently, obtain the current feed ratio of the automatic discharging vehicle, where the current feed ratio includes the material type and the proportion occupied, and the material type includes feeding materials such as forage, potatoes, corn, etc., and the proportion occupied is the actual ratio of each material. Obtain the dairy cow breeding specification table, which contains the nutritional requirements for different dairy cow breeds and different growth stages and the recommended daily nutritional intake. Based on the dairy cow breeding specification table, conduct feeding standard retrieval respectively according to the current feed ratio and several dairy cow breeding characteristics, obtain the quality of the current feed ratio that meets the daily nutritional standard requirements of a single dairy cow, obtain the single-head standard requirements of dairy cows in each area, and obtain the discharging weights for several areas according to the quantity of each area and the single-head standard requirements, and set them as the discharging targets for several areas.

[0022] The method provided by the embodiment of the present application further includes: Select the first discharge target in the discharge target sequence of the area as the first discharge target; In the three-dimensional simulation platform, conduct simulation modeling on the automatic discharging vehicle to generate a discharge simulation model; Obtain the discharge speed adjustment threshold of the automatic discharging vehicle, and based on the discharge speed adjustment threshold and the discharge simulation model, with the minimum deviation from the first discharge target as the expectation, combine the vehicle discharging duration and the current feed ratio to conduct discharge speed optimization analysis, and output the first optimized discharge speed.

[0023] Select the first discharging target from the target sequence of area discharging, aiming to minimize the deviation from the first discharging target. Combine the vehicle discharging duration and the current feed ratio to conduct an optimization analysis of the discharging speed, and obtain the first optimized discharging speed, including: Select the first area discharging target at the head of the target sequence of area discharging as the first discharging target. In the 3D simulation platform, simulate and visualize the process of the automatic discharging vehicle dropping materials, which is constructed by professional software to generate a discharging simulation model. When constructing, it includes: Construct a vehicle structure model and input the basic parameters of the automatic discharging vehicle, such as: body size, load capacity; discharging port size and shape; speed range and adjustment method of the feed conveyor belt. The discharging model simulates the rate, throwing angle, particle distribution, etc. when the feed drops from the silo opening or the conveyor belt, and adjusts the characteristics such as feed density, viscosity, moisture content, etc. according to the feed ratio. Environment modeling: A simple scene model can be made according to the information such as the road terrain, temperature and humidity, and cowshed structure on site, but the most important thing in this step is the virtual simulation of the discharging process. Assemble and integrate the above models in the 3D simulation platform to form a runnable simulation model, which is used to simulate the output weight, time and feed distribution under different "discharging speed" inputs. Obtain the discharging speed adjustment threshold of the automatic discharging vehicle, that is, the adjustable range of the discharging speed of the automatic discharging vehicle. Based on the discharging speed adjustment threshold and the discharging simulation model, aiming to minimize the deviation from the first discharging target, combine the vehicle discharging duration and the current feed ratio to conduct an optimization analysis of the discharging speed, and output the first optimized discharging speed.

[0024] The method provided by the embodiment of this application further includes: Render the vehicle discharging duration and the current feed ratio to the discharging simulation model to generate a real-time discharging simulation space; Randomly select a first discharging speed from the discharging speed adjustment threshold, and use the real-time discharging simulation space to simulate and obtain a first simulated discharging weight according to the first discharging speed; Calculate the deviation between the first simulated discharging weight and the first discharging target to obtain a first weight difference; Continue to randomly select a discharging speed from the discharging speed adjustment threshold for simulation until the predetermined number of speed optimization times is reached, output a plurality of discharging speeds and a plurality of weight differences, and select the discharging speed corresponding to the minimum weight difference as the first optimized discharging speed.

[0025] Based on the discharging speed adjustment threshold and the discharging simulation model, with the minimum deviation from the first discharging target as the expectation, combined with the vehicle discharging duration and the current feed ratio, perform discharging speed optimization analysis and output the first optimized discharging speed, including: rendering the vehicle discharging duration and the current feed ratio to the discharging simulation model to generate a real-time discharging simulation space. The vehicle discharging duration is a preset discharging duration, and since the area of each region is the same, the set vehicle discharging duration is also the same. Input the "vehicle discharging duration" and the "current feed ratio" into the discharging simulation model in the 3D simulation platform to simulate the material flow and distribution in the actual discharging process, so as to obtain a real-time discharging simulation space for evaluating the feed feeding effect at different discharging speeds. Further, randomly select a first discharging speed from the discharging speed adjustment threshold, and use the real-time discharging simulation space to simulate and obtain a first simulated discharging weight according to the first discharging speed. Calculate the deviation between the first simulated discharging weight and the first discharging target to obtain a first weight difference. Further, continue to randomly select discharging speeds from the discharging speed adjustment threshold for simulation until the predetermined speed optimization times are reached, output multiple discharging speeds and multiple weight differences, and select the discharging speed corresponding to the minimum weight difference as the first optimized discharging speed. Further, use the same method to obtain the optimized discharging speeds of the remaining regions.

[0026] Based on the current feed ratio, vehicle discharging duration and the first optimized discharging speed, with the expectation that the feed evenly falls into the predetermined region, perform discharging angle optimization analysis, output the first optimized discharging angle, and generate the first optimized discharging parameters in combination with the first optimized discharging speed; continue to perform discharging parameter optimization analysis on other discharging targets in the regional discharging target sequence, generate an optimized discharging parameter sequence, and perform discharging error correction on the optimized discharging parameter sequence to obtain the optimal discharging plan for the automatic discharging control of the dairy farm.

[0027] Based on the current feed ratio, vehicle discharging duration, and the first optimized discharging speed, randomly select a discharging angle within the discharging angle adjustment threshold for discharging angle optimization analysis. Taking the uniform falling of feed into the predetermined area as the expectation, obtain the discharging angle with the highest discharge fitness according to the feed discharge prediction plug-in, output the first optimized discharging angle, and generate the first optimized discharging parameter in combination with the first optimized discharging speed. Continue to use the same analysis method to optimize the discharging parameters of other discharging targets in the regional discharging target sequence, generate an optimized discharging parameter sequence, and correct the discharging error of the optimized discharging parameter sequence to avoid the discharging error caused by the increase in the running duration of the automatic discharging vehicle, so as to obtain the optimal discharging plan for the automatic discharging control of the dairy farm. This solves the technical problem in the prior art that the livestock machinery lacks a dynamic full-life cycle supervision mechanism, resulting in a large discharging deviation during the automatic discharging process and affecting the feeding effect. It realizes the accurate and efficient completion of feed delivery in different usage periods and breeding environments, significantly improves the feed utilization rate, and ensures the health of dairy cows.

[0028] The method provided by the embodiment of the present application further includes: collecting a sample feed ratio set, a sample discharging duration set, a sample discharging speed set, and a sample discharging angle set according to the breeding discharging logs in the historical time zone, and marking the feed distribution under different sample feed ratios, sample discharging durations, sample discharging speeds, and sample discharging angles to obtain a sample feed distribution set. Among them, the sample feed distribution includes the sample distribution uniformity and the proportion of the sample overflow area. The proportion of the overflow area is the ratio of the area where the feed exceeds the predetermined area to the area of the predetermined area; using the sample feed ratio set, the sample discharging duration set, the sample discharging speed set, the sample discharging angle set, and the sample feed distribution set as training data, perform supervised learning on the random forest to obtain a feed discharge prediction plug-in; using the feed discharge prediction plug-in, based on the current feed ratio, vehicle discharging duration, and the first optimized discharging speed, taking the uniform falling of feed into the predetermined area as the expectation, perform discharging angle optimization analysis and output the first optimized discharging angle.

[0029] Through the above steps, the optimal discharge speed that can make the discharged feed weight as close as possible to the target value under given feed ratios and discharge durations is obtained. However, only ensuring the accuracy of the discharged weight cannot guarantee the uniform distribution of the feed within a predetermined area (such as a feeding trough or a specific area in a cowshed). If the discharge angle is too large or too small, it often causes uneven distribution of the feed or the feed to overflow the predetermined area. Therefore, in order to ensure the uniform distribution of the feed within the predetermined area, it is necessary to optimize the discharge angle. By obtaining the historical data records formed during the daily feed discharge on the farm within the historical time interval, including "feed ratio", "discharge duration", "discharge speed", "discharge angle" during each discharge, and the actual distribution of the feed in the cowshed or feeding trough after discharge, etc., the breeding discharge log within the historical time zone is obtained. According to the breeding discharge log within the historical time zone, a sample feed ratio set, a sample discharge duration set, a sample discharge speed set, and a sample discharge angle set are collected, and the feed distribution under different sample feed ratios, sample discharge durations, sample discharge speeds, and sample discharge angles is labeled, and the corresponding sample feed distribution conditions are labeled to obtain a sample feed distribution set. Among them, the sample feed distribution includes the sample distribution uniformity and the ratio of the overflow area, and the ratio of the overflow area is the ratio of the area where the feed exceeds the predetermined area to the area of the predetermined area, and the uniformity is an evaluation index reflecting the feed distribution situation, with 1 being completely uniform, and the lower the value, the more uneven the distribution. Subsequently, using the sample feed ratio set, the sample discharge duration set, the sample discharge speed set, the sample discharge angle set, and the sample feed distribution set as training data, supervised learning is performed on the random forest to construct a feed discharge prediction plug-in. Using the feed discharge prediction plug-in, based on the current feed ratio, the vehicle discharge duration, and the first optimized discharge speed, with the expectation that the feed evenly falls into the predetermined area, discharge angle optimization analysis is carried out, and the first optimized discharge angle is output. The first optimized discharge angle corresponds to the first optimized discharge speed.

[0030] The method provided in the embodiment of the present application further includes: using the sample feed ratio set, the sample discharge duration set, the sample discharge speed set, the sample discharge angle set, and the sample feed distribution set as training data, and equally dividing the training data into P parts, and selecting P times with replacement to obtain a first training set, and continuing to iterate and select P times to obtain P training sets, where P is an integer greater than 10; using the sample feed ratio, the sample discharge duration, the sample discharge speed, and the sample discharge angle as inputs, and using the sample distribution uniformity and the ratio of the overflow area as supervision, using the P training sets to perform supervised learning on the random forest until convergence, and harvesting P feed discharge prediction units; based on the P feed discharge prediction units, the feed discharge prediction plug-in is integrally constructed, where the output of the feed discharge prediction plug-in is the mean value of the outputs of the P feed discharge prediction units.

[0031] Using the sample feed ratio set, sample discharging duration set, sample discharging speed set, sample discharging angle set, and sample feed distribution set as training data, perform supervised learning on a random forest to obtain a feed discharge prediction plug-in, including: Using the sample feed ratio set, sample discharging duration set, sample discharging speed set, sample discharging angle set, and sample feed distribution set as training data, and equally dividing the training data into P parts, selecting P times with replacement to obtain a first training set, and iteratively selecting P times in the same way to obtain P training sets, where P is an integer greater than 10. Further, using the sample feed ratio, sample discharging duration, sample discharging speed, and sample discharging angle as input data, and the sample distribution uniformity and the proportion of the sample overflow area as supervised data, perform supervised learning on P random forests respectively through the P training sets until convergence, and obtain P feed discharge prediction units. Based on the P feed discharge prediction units, integrally construct the feed discharge prediction plug-in, where the output of the feed discharge prediction plug-in is the mean of the outputs of the P feed discharge prediction units.

[0032] The method provided in the embodiment of this application further includes: obtaining a discharging angle adjustment threshold of the automatic discharging vehicle, and randomly selecting a first discharging angle from the discharging angle adjustment threshold; inputting the current feed ratio, vehicle discharging duration, first optimized discharging speed, and first discharging angle into the feed discharge prediction plug-in to output a first distribution uniformity and a first proportion of the overflow area; evaluating to obtain a first discharge fitness according to the first distribution uniformity and the first proportion of the overflow area, where the discharge fitness is positively correlated with the distribution uniformity and negatively correlated with the proportion of the overflow area; continuing to perform discharging angle optimization analysis according to the discharging angle adjustment threshold until a predetermined number of angle optimization times is reached, obtaining multiple discharging angles and multiple discharge fitnesses, and selecting the discharging angle with the maximum discharge fitness as the first optimized discharging angle.

[0033] Output the first optimized discharging angle, including: obtaining the discharging angle adjustment threshold of the automatic discharging vehicle, and randomly selecting a first discharging angle from the discharging angle adjustment threshold. Further, input the current feed ratio, vehicle discharging duration, first optimized discharging speed, and first discharging angle into the feed discharge prediction plug-in to output the corresponding first distribution uniformity and first overflow area ratio. Perform weight allocation on the first distribution uniformity and first overflow area according to actual requirements. For example, if better discharging uniformity is desired, the weight parameter configuration ratio of the corresponding distribution uniformity is higher. Exemplarily, if the first distribution uniformity result is A, the first overflow area result is B, and the weight parameter assigned to the first distribution uniformity is 0.7, then the weight parameter assigned to the first overflow area is 1 - 0.7 = 0.3. Evaluate the first discharge fitness based on the first distribution uniformity and first overflow area ratio. The first discharge fitness is calculated according to the fitness formula 0.7*A - 0.3*B. Among them, the discharge fitness is positively correlated with the distribution uniformity and negatively correlated with the overflow area ratio. Continue to randomly select within the discharging angle adjustment threshold, and perform discharging angle optimization analysis according to the selection result until the predetermined angle optimization times are reached, obtaining multiple discharging angles and multiple discharge fitnesses, and selecting the discharging angle with the maximum discharge fitness as the first optimized discharging angle.

[0034] The method provided by the embodiment of the present application further includes: obtaining the cumulative working duration of the automatic discharging vehicle, performing discharging error analysis according to the cumulative working duration, and determining the discharging speed compensation coefficient and discharging angle compensation coefficient. Among them, an error analysis channel is constructed based on the BP neural network for discharging error analysis, and a sample working duration set, a sample discharging speed compensation coefficient set, and a sample discharging angle compensation coefficient set are collected to train the error analysis channel until convergence; according to the discharging speed compensation coefficient and discharging angle compensation coefficient, perform discharging error traversal correction on the optimized discharging parameter sequence to obtain the optimal discharging parameter sequence, and generate the optimal discharging plan in combination with the transportation path.

[0035] Performing nesting error correction on the optimized nesting parameter sequence to obtain an optimal nesting plan, including: obtaining the cumulative working duration of the automatic nesting vehicle, performing nesting error analysis based on the cumulative working duration to obtain a nesting speed compensation coefficient and a nesting angle compensation coefficient. When performing nesting error analysis, an error analysis channel is constructed based on a BP neural network for nesting error analysis. The error analysis channel is constructed based on a BP neural network model. By obtaining and collecting the historical working record data of the automatic nesting vehicle, a sample working duration set, a sample nesting speed compensation coefficient set, and a sample nesting angle compensation coefficient set are collected. The sample working duration, the sample nesting speed compensation coefficient, and the sample nesting angle compensation coefficient correspond to each other. The sample nesting speed compensation coefficient and the sample nesting angle compensation coefficient are compensation ratios. Using the sample working duration set as training data and the sample nesting speed compensation coefficient set and the sample nesting angle compensation coefficient set as supervision data to perform supervised training on the BP neural network until the accuracy of the model output meets the requirements to obtain the error analysis channel.

[0036] Inputting the cumulative working duration of the automatic nesting vehicle into the error analysis channel to obtain the output nesting speed compensation coefficient and nesting angle compensation coefficient. According to the nesting speed compensation coefficient and the nesting angle compensation coefficient, perform nesting error traversal correction on the optimized nesting parameter sequence. Taking the nesting speed compensation as an example, the optimized nesting speed is the sum of 1 and the nesting speed compensation coefficient multiplied by the original nesting speed. After compensating and correcting all the nesting parameter sequences, an optimal nesting parameter sequence is obtained, and the optimal nesting plan is generated in combination with the transportation path.

[0037] For the technical solution provided by the embodiments of the present invention, the method includes: obtaining several dairy cow breeding characteristics of several areas in a dairy farm, performing feeding analysis according to the current feed ratio and several dairy cow breeding characteristics, determining the discharging targets of several areas, and mapping and sorting the discharging targets of several areas according to the transportation path of the automatic discharging vehicle to generate a sequence of area discharging targets; selecting a first discharging target from the sequence of area discharging targets, taking the minimum deviation from the first discharging target as the expectation, combining the vehicle discharging duration and the current feed ratio to perform discharging speed optimization analysis, and obtaining a first optimized discharging speed; based on the current feed ratio, vehicle discharging duration and the first optimized discharging speed, taking the uniform falling of feed into a predetermined area as the expectation, performing discharging angle optimization analysis, outputting a first optimized discharging angle, and generating a first optimized discharging parameter in combination with the first optimized discharging speed; continuing to perform discharging parameter optimization analysis on other discharging targets in the sequence of area discharging targets, generating a sequence of optimized discharging parameters, and performing discharging error correction on the sequence of optimized discharging parameters to obtain an optimal discharging plan for automatic discharging control of the dairy farm. This solves the technical problem in the prior art that livestock machinery lacks a dynamic full-life cycle supervision mechanism, resulting in large discharging deviation during the automatic discharging process and affecting the feeding effect. It realizes accurate and efficient feed delivery at different usage times and breeding environments, significantly improves feed utilization rate and ensures the health of dairy cows.

[0038] The above are only the preferred embodiments of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, it can also include more other equivalent embodiments.

Claims

1. A method for supervising the entire life cycle of animal husbandry machinery, characterized in that: include: Acquire several dairy cow breeding characteristics of several areas in the dairy farm, perform feeding analysis according to the current feed ratio and several dairy cow breeding characteristics, determine several regional discharge targets, and map and sort the several regional discharge targets according to the transportation path of the automatic discharge vehicle to generate a regional discharge target sequence; Selecting a first discharging target in the regional discharging target sequence, with the minimum deviation from the first discharging target as the expectation, performing a discharging speed optimization analysis in combination with the vehicle discharging time and the current feed ratio, and obtaining a first optimized discharging speed; Based on the current feed ratio, vehicle discharge time and the first optimized discharge speed, with the expectation that the feed falls evenly into the predetermined area, a discharge angle optimization analysis is performed, a first optimized discharge angle is output, and a first optimized discharge parameter is generated in combination with the first optimized discharge speed; Continue to perform discharging parameter optimization analysis on other discharging targets in the regional discharging target sequence, generate an optimized discharging parameter sequence, and perform discharging error correction on the optimized discharging parameter sequence to obtain the optimal discharging plan for automatic discharging control of the dairy farm.

2. A method for monitoring the entire life cycle of animal husbandry machinery according to claim 1, characterized in that: Obtain several dairy cow breeding characteristics in several areas of the dairy farm, conduct feeding analysis based on the current feed ratio and several dairy cow breeding characteristics, and determine several regional feeding targets, including: Acquire a plurality of dairy cow breeding characteristics of a plurality of areas in a dairy cow breeding farm, wherein the dairy cow breeding characteristics at least include dairy cow breed, quantity and growth stage, and the growth stage at least includes growth period, middle-aged period, lactation period, pregnancy period and old age period; Obtaining the current feed ratio of the automatic feeding vehicle, wherein the current feed ratio includes the material type and the proportion; Based on the dairy cattle breeding standard table, feeding standard retrieval is performed according to the current feed ratio and several dairy cattle breeding characteristics, and several regional discharge weights are obtained and set as the several regional discharge targets.

3. The method for monitoring the entire life cycle of animal husbandry machinery according to claim 2, characterized in that: Selecting a first discharging target in the regional discharging target sequence, with the minimum deviation from the first discharging target as the expectation, performing discharging speed optimization analysis in combination with the vehicle discharging time and the current feed ratio, and obtaining a first optimized discharging speed, including: Selecting the first regional material placement target in the regional material placement target sequence and setting it as the first material placement target; In the 3D simulation platform, the automatic nesting vehicle is simulated and modeled to generate a nesting simulation model; The discharge speed adjustment threshold of the automatic discharge vehicle is obtained, and based on the discharge speed adjustment threshold and the discharge simulation model, the discharge speed optimization analysis is performed with the minimum deviation from the first discharge target as the expectation, combined with the discharge time of the vehicle and the current feed ratio, and the first optimized discharge speed is output.

4. A method for monitoring the entire life cycle of animal husbandry machinery according to claim 3, characterized in that: Based on the discharge speed adjustment threshold and the discharge simulation model, with the minimum deviation from the first discharge target as the expectation, the discharge speed optimization analysis is performed in combination with the vehicle discharge time and the current feed ratio, and the first optimized discharge speed is output, including: Rendering the vehicle discharging time and the current feed ratio to the discharging simulation model to generate a real-time discharging simulation space; Randomly selecting a first discharge speed from the discharge speed adjustment threshold, using the real-time discharge simulation space, and simulating and obtaining a first simulated discharge weight according to the first discharge speed; Calculating the deviation between the first simulated discharge weight and the first discharge target to obtain a first weight difference; Continue to randomly select discharge speeds within the discharge speed adjustment threshold for simulation until a predetermined number of speed optimizations is reached, output multiple discharge speeds and multiple weight differences, and select the discharge speed corresponding to the minimum weight difference as the first optimized discharge speed.

5. The method for monitoring the entire life cycle of animal husbandry machinery according to claim 1, characterized in that: Based on the current feed ratio, vehicle discharge time and the first optimized discharge speed, with the expectation that the feed falls evenly into the predetermined area, a discharge angle optimization analysis is performed to output the first optimized discharge angle, including: According to the breeding feeding log in the historical time zone, collect the sample feed ratio set, sample feeding time set, sample feeding speed set and sample feeding angle set, and mark the feed distribution under different sample feed ratios, sample feeding time, sample feeding speed and sample feeding angle to obtain the sample feed distribution set, where the sample feed distribution includes the sample distribution uniformity and the sample overflow area ratio, and the overflow area ratio is the ratio of the area of ​​the feed exceeding the predetermined area to the area of ​​the predetermined area; The sample feed ratio set, the sample discharge time set, the sample discharge speed set, the sample discharge angle set and the sample feed distribution set are used as training data to perform supervised learning on the random forest to obtain a feed discharge prediction plug-in; By using the feed discharge prediction plug-in, according to the current feed ratio, vehicle discharge time and the first optimized discharge speed, the feed is expected to fall evenly into the predetermined area, and the discharge angle optimization analysis is performed to output the first optimized discharge angle.

6. A method for monitoring the entire life cycle of animal husbandry machinery according to claim 5, characterized in that: The sample feed ratio set, sample discharge time set, sample discharge speed set, sample discharge angle set and sample feed distribution set are used as training data, and supervised learning is performed on the random forest to obtain a feed discharge prediction plug-in, including: The sample feed ratio set, the sample discharge time set, the sample discharge speed set, the sample discharge angle set and the sample feed distribution set are used as training data, and the training data is equally divided into P parts, and the first training set is obtained by selecting P parts with replacement, and the selection is continued iteratively for P times to obtain P training sets, wherein P is an integer greater than 10; Taking the sample feed ratio, sample discharge time, sample discharge speed and sample discharge angle as input, and taking the sample distribution uniformity and sample overflow area ratio as supervision, the random forest is supervised learning is performed using the P training sets until convergence, and P feed discharge prediction units are obtained; The feed emission prediction plug-in is integrated and constructed based on the P feed emission prediction units, wherein the output of the feed emission prediction plug-in is the average of the outputs of the P feed emission prediction units.

7. The method for monitoring the entire life cycle of animal husbandry machinery according to claim 5, characterized in that: By using the feed discharge prediction plug-in, according to the current feed ratio, vehicle discharge time and the first optimized discharge speed, the feed is expected to fall evenly into the predetermined area, and the discharge angle optimization analysis is performed to output the first optimized discharge angle, including: Obtaining a discharge angle adjustment threshold of an automatic discharge vehicle, and randomly selecting a first discharge angle from the discharge angle adjustment threshold; Input the current feed ratio, vehicle discharge time, first optimized discharge speed and first discharge angle into the feed discharge prediction plug-in, and output a first distribution uniformity and a first overflow area ratio; A first discharge fitness is obtained by evaluating the first distribution uniformity and the first overflow area ratio, wherein the discharge fitness is positively correlated with the distribution uniformity and negatively correlated with the overflow area ratio; Continue to perform optimization analysis on the discharge angle according to the discharge angle adjustment threshold until a predetermined number of angle optimizations is reached, to obtain multiple discharge angles and multiple discharge fitnesses, and select the discharge angle with the largest discharge fitness as the first optimized discharge angle.

8. The method for monitoring the entire life cycle of animal husbandry machinery according to claim 1, characterized in that: Performing nesting error correction on the optimized nesting parameter sequence to obtain an optimal nesting solution, including: Obtain the cumulative working time of the automatic discharging vehicle, perform discharging error analysis according to the cumulative working time, determine the discharging speed compensation coefficient and the discharging angle compensation coefficient, wherein an error analysis channel is constructed based on a BP neural network to perform discharging error analysis, and collect a sample working time set, a sample discharging speed compensation coefficient set, and a sample discharging angle compensation coefficient set to train the error analysis channel until convergence; According to the discharge speed compensation coefficient and the discharge angle compensation coefficient, the discharge error traversal correction is performed on the optimized discharge parameter sequence to obtain the optimal discharge parameter sequence, and the optimal discharge plan is generated in combination with the transportation path.

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