A breeding equipment monitoring data processing system

Abstract

The present invention relates to a breeding equipment monitoring data processing system, and relates to the field of data prediction and processing, including: a user end receives breeding control information, including a feed feeding plan, an environmental control plan, a target animal type and an initial state; a growth state prediction module performs prediction to obtain a state change trend curve; a feed feeding plan optimization module optimizes the plan when the trend curve is inconsistent to obtain a recommended plan; a breeding equipment monitoring module collects feed feeding and environmental control monitoring information; a monitoring data processing module performs abnormal analysis on the recommended plan based on the monitoring information to obtain an abnormal coefficient; a monitoring and early warning module generates an abnormal warning signal for breeding equipment and alarms at the user end when the abnormal coefficient exceeds a threshold. By predicting the growth state of the target animal, optimizing the feed feeding plan and the environmental control plan, and performing abnormal analysis and early warning on the monitoring data, the breeding efficiency and success rate are improved.

Description

Technical Field

[0001] The present invention relates to the field of data prediction and processing, and in particular to a breeding equipment monitoring data processing system. Background Art

[0002] With the development of modern aquaculture, in order to achieve effective monitoring and management of the aquaculture process, a variety of aquaculture equipment monitoring data processing systems have been proposed in the prior art. However, these systems can often only achieve simple monitoring and data collection of aquaculture equipment, and lack the ability to predict, optimize and warn the aquaculture process, resulting in difficulty in further improving aquaculture efficiency and success rate. Specifically, the existing aquaculture equipment monitoring data processing system usually adopts a data collection and processing mode, which is unable to dynamically predict and optimize the aquaculture process according to the characteristics and needs of the aquaculture object, resulting in the inability to adjust the feed feeding plan and environmental control plan in time, making it difficult to meet the optimal growth needs of the aquaculture object. At the same time, due to the lack of an effective abnormal analysis and early warning mechanism, once equipment failure or environmental abnormality occurs, it is often difficult to detect and handle it in time, which in turn affects the aquaculture efficiency and success rate. Summary of the invention

[0003] The present invention aims to solve the technical problem that the existing breeding equipment monitoring data processing system lacks prediction, optimization and early warning of the breeding process, resulting in low breeding efficiency and success rate, and provides a breeding equipment monitoring data processing system to solve the problem.

[0004] The technical solution of the present invention to solve the above technical problems is as follows:

[0005] In a first aspect, the present invention provides a breeding equipment monitoring data processing system, including: a user end, which is used to communicate with the breeding equipment to receive breeding control information before the start of breeding, wherein the breeding control information includes a feed feeding plan, an environmental control plan, a target animal type and an initial state of the target animal; a growth state prediction module, which is used to receive the feed feeding plan, the environmental control plan, and the initial state of the target animal to predict the growth state of the target animal type and obtain a target animal state change trend curve; a feed feeding plan optimization module, which is used to optimize the feed feeding plan and the environmental control plan when the target animal state change trend curve is inconsistent with the expected animal state change trend curve. Optimization, obtain recommended feed feeding plan and recommended environmental control plan; a breeding equipment monitoring module, used to collect feed feeding monitoring information and environmental control monitoring information of breeding equipment after breeding starts; a monitoring data processing module, used to perform abnormal analysis on the recommended feed feeding plan and recommended environmental control plan based on the feed feeding monitoring information and environmental control monitoring information, and obtain the feed feeding plan abnormality coefficient and the environmental control abnormality coefficient; a monitoring and early warning module, used to generate a breeding equipment abnormality early warning signal and alarm on the user side when the feed feeding plan abnormality coefficient is greater than or equal to the first abnormality coefficient threshold, or / and the environmental control abnormality coefficient is greater than or equal to the second abnormality coefficient threshold.

[0006] In a second aspect, the present application provides an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute a breeding equipment monitoring data processing system provided by the present application.

[0007] In a third aspect, the present application provides a computer-readable storage medium storing a computer program, and the computer program is used to execute a breeding equipment monitoring data processing system provided by the present application.

[0008] The beneficial effects of the present invention are:

[0009] Before the start of breeding, the user terminal receives breeding control information, including feed feeding plan, environmental control plan, target animal type and target animal initial state, which lays the foundation for subsequent prediction, optimization and early warning; the growth state prediction module is used to combine the received feed feeding plan, environmental control plan and target animal initial state to predict the growth state of a specific type of target animal, and obtain the target animal state change trend curve, which realizes the dynamic prediction of the breeding process and provides a basis for optimizing the breeding plan; when the predicted target animal state change trend curve is inconsistent with the expected curve, the feed feeding plan and environmental control plan are optimized through the feed feeding plan optimization module to obtain the recommended feed feeding plan and environmental control plan, which realizes the dynamic optimization of the breeding plan and ensures that the breeding process is always in the best state; after entering the breeding stage, the feed feeding monitoring information and environmental control monitoring information of the breeding equipment are collected through the breeding equipment monitoring module to realize Real-time monitoring and data collection of the breeding process are carried out, providing data support for subsequent abnormal analysis and early warning; after obtaining the monitoring data, the monitoring data processing module is used to perform abnormal analysis on the recommended feed feeding plan and environmental control plan, and the corresponding abnormal coefficient is obtained, realizing abnormal detection of the breeding process and providing a basis for early warning; when the abnormal coefficient of the feed feeding plan or the abnormal coefficient of environmental control exceeds the set threshold, the monitoring and early warning module generates an abnormal warning signal for the breeding equipment, and an alarm is issued through the user end, realizing timely early warning of abnormal conditions in the breeding process, ensuring the safety and stability of the breeding process, and solving the technical problem that the existing breeding equipment monitoring data processing system lacks prediction, optimization and early warning of the breeding process, resulting in low breeding efficiency and success rate, and achieving the technical effect of improving breeding efficiency and success rate by predicting the growth status of target animals, optimizing feed feeding plans and environmental control plans, and performing abnormal analysis and early warning on monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A structural schematic diagram of a breeding equipment monitoring data processing system provided by the present invention;

[0011] Figure 2 A schematic diagram of the structure of an electronic device provided by the present invention;

[0012] Figure 3 A schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0013] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0014] User end 11, growth status prediction module 12, feed feeding plan optimization module 13, breeding equipment monitoring module 14, monitoring data processing module 15, monitoring early warning module 16, electronic device 500, memory 510, processor 520, computer program 511, computer readable storage medium 600, computer program 611. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0016] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0017] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0018] Embodiment 1:

[0019] like Figure 1 As shown, an embodiment of the present invention provides a farming equipment monitoring data processing system, including:

[0020] The user terminal 11 is used to communicate with the farming equipment to receive farming control information before the start of farming, wherein the farming control information includes a feeding plan, an environmental control plan, a target animal type and an initial state of the target animal.

[0021] Specifically, the user terminal 11 is used to communicate with the breeding equipment and receive breeding control information before the start of breeding. The above-mentioned breeding control information includes but is not limited to: feed feeding plan, environmental control plan, target animal type and target animal initial state. The feed feeding plan is the daily diet composition and feeding amount formulated according to the growth needs of the animal to be farmed; the environmental control plan is the control range or threshold of indicators such as temperature, humidity, and light set to ensure the healthy growth of the animal; the target animal type is the animal species to be farmed, such as pigs, chickens, fish, etc.; the target animal initial state is the age, weight, number and other parameters of the target animal when the breeding begins.

[0022] The aquaculture control information is received by the user terminal 11, so as to identify the initial parameters of the aquaculture process and provide a basis for subsequent monitoring data processing.

[0023] The growth state prediction module 12 is used to receive the feed feeding plan, the environmental control plan, and the initial state of the target animal to predict the growth state of the target animal type and obtain a state change trend curve of the target animal.

[0024] Specifically, the growth state prediction module 12 uses mathematical modeling, machine learning and other technical means to comprehensively consider multiple factors such as feed formula, environmental parameters, animal breeds, initial status, etc., to simulate the growth and development process of animals throughout the entire breeding cycle. Its output result is a target animal state change trend curve, which can intuitively display the various growth indicators of animals at different time points, such as weight, body length, feed conversion rate, etc. Through the growth state prediction module 12, the growth of animals is estimated and judged before the start of breeding, providing a reference for the optimization and control of the breeding process. For example, according to the prediction curve, the feeding strategy can be adjusted in time to avoid problems such as malnutrition or obesity in animals; health management measures such as animal immunization and drug treatment can also be arranged according to the prediction curve, thereby improving breeding efficiency and economic benefits.

[0025] The feed feeding scheme optimization module 13 is used to optimize the feed feeding scheme and the environmental control scheme when the target animal state change trend curve is inconsistent with the expected animal state change trend curve to obtain a recommended feed feeding scheme and a recommended environmental control scheme.

[0026] Specifically, the expected animal state change trend curve is an ideal growth curve summarized based on the knowledge of breeding experts and historical breeding experience, reflecting the growth level that animals should achieve under optimal breeding management conditions. If the target animal state change trend curve deviates greatly from the expected animal state change trend curve, it means that the current feed feeding plan and environmental control plan may be unreasonable and need to be optimized and improved. Therefore, the feed feeding plan optimization module 13 uses mathematical programming, heuristic search and other algorithms to find the optimal solution in the feasible space of feed formula and feeding amount. The optimization goal is to minimize the difference measure between the target animal state change trend curve and the expected animal state change trend curve, such as mean square error, Manhattan distance, etc.; at the same time, considering the constraints such as feed cost and nutritional balance, the optimized plan is also optimal in terms of economy and operability. The optimization of the environmental control plan is similar to the feed feeding plan. It is to find the best setting combination of environmental parameters such as temperature and humidity while ensuring the growth performance of the animal, taking into account factors such as energy consumption and cost.

[0027] The output results of the feed feeding program optimization module 13 are recommended feed feeding programs and recommended environmental control programs. The recommended feed feeding programs and recommended environmental control programs improve and upgrade the original programs on the basis of meeting the growth needs of animals, and can better guide the animals to grow and develop in the direction of the desired animal state change trend curve, thereby realizing dynamic optimization management of the breeding process and improving breeding efficiency and benefits.

[0028] The breeding equipment monitoring module 14 is used to collect feed feeding monitoring information and environmental control monitoring information of the breeding equipment after the breeding starts.

[0029] Specifically, the feed feeding monitoring information includes parameters such as the amount of remaining feed in the feed bin, the feed feeding speed, the feed weighing data, the number of feeding times and time, etc., which reflects the working status and execution of the feed feeding equipment. By analyzing the feed feeding monitoring information, the actual feed intake of the animal can be mastered, the feeding effect can be evaluated, and feeding abnormalities can be discovered and solved in time. The environmental control monitoring information includes parameters such as temperature, humidity, carbon dioxide concentration, ammonia concentration, wind speed, and illumination, which reflect the real-time status of the breeding environment. By collecting and recording the environmental control monitoring information, the changes in environmental conditions during the breeding process can be completely restored, providing a basis for the optimization of environmental control strategies and the diagnosis of environmental abnormalities. The breeding equipment monitoring module 14 is connected to various sensors and data acquisition devices, such as feed weighing sensors, environmental parameter sensors, cameras, etc., and receives feed feeding monitoring information and environmental control monitoring information in real time by wired or wireless means. After necessary preprocessing (such as filtering, calibration, synchronization, etc.), it is transmitted to the monitoring data processing module 15 at a certain frequency and format for subsequent analysis and calculation.

[0030] Through the breeding equipment monitoring module 14, equipment operation data is collected comprehensively, accurately and in real time, providing important data support for the optimization and control of the breeding process.

[0031] The monitoring data processing module 15 is used to perform abnormal analysis on the recommended feed feeding plan and the recommended environmental control plan based on the feed feeding monitoring information and the environmental control monitoring information to obtain a feed feeding plan abnormality coefficient and an environmental control abnormality coefficient.

[0032] Specifically, the monitoring data processing module 15 first compares the feed feeding monitoring information with the recommended feed feeding scheme, calculates the difference between the actual feed amount, feeding time and other parameters and the recommended values, and generates a series of deviation indicators. Then, taking into account the importance and influence of each deviation indicator, the normalized feed feeding scheme abnormal coefficient is calculated to quantitatively characterize the degree to which the current feed feeding execution status deviates from the optimal scheme. Similarly, the monitoring data processing module 15 compares the environmental control monitoring information with the recommended environmental control scheme, calculates the difference between the actual environmental parameters and the recommended values, generates an environmental deviation index, and obtains a normalized environmental control abnormal coefficient, which reflects the degree to which the current environmental control execution status deviates from the optimal scheme. Among them, the size of the abnormal coefficient intuitively reflects the execution quality and risk level of the breeding process. The larger the abnormal coefficient, the greater the deviation between the actual breeding scheme and the recommended scheme, the higher the breeding risk, and the more likely it is to have a negative impact on animal growth performance and product quality. On the contrary, the smaller the abnormal coefficient, the more the breeding management tends to the optimal state, and the more stable and controllable the breeding process.

[0033] By monitoring the data processing module 15, data comparison and abnormal diagnosis are realized, and the execution effect and deviation risk of the breeding process are accurately evaluated, which provides strong data guarantee for breeding decision-making. In a data-driven way, human errors are minimized and breeding efficiency and quality are improved.

[0034] The monitoring and early warning module 16 is used to generate an abnormal early warning signal for breeding equipment and alarm at the user end when the abnormal coefficient of the feed feeding plan is greater than or equal to the first abnormal coefficient threshold, or / and the abnormal coefficient of the environmental control is greater than or equal to the second abnormal coefficient threshold.

[0035] Specifically, the monitoring and early warning module 16 includes two submodules: a feed feeding early warning submodule and an environmental control early warning submodule. The feed feeding early warning submodule is responsible for monitoring the abnormal coefficient of the feed feeding scheme. When the abnormal coefficient is greater than or equal to the first abnormal coefficient threshold, it is determined to be feed feeding abnormal. Similarly, the environmental control early warning submodule is responsible for monitoring the environmental control abnormal coefficient. When the abnormal coefficient is greater than or equal to the second abnormal coefficient threshold, it is determined to be environmental control abnormal. When any of the situations of feed feeding abnormality or environmental control abnormality occurs, an abnormal early warning is triggered to generate an abnormal early warning signal for breeding equipment. Among them, the first abnormal coefficient threshold and the second abnormal coefficient threshold are set according to factors such as breeding experience and historical data, and are dynamically adjusted according to the actual application effect. When the monitoring and early warning module 16 generates an abnormal early warning signal for breeding equipment, it is immediately sent to the user terminal 11 through the communication network to prompt the user to pay attention in the form of pop-up windows, voice, text messages, etc. Among them, the abnormal early warning signal for breeding equipment contains detailed information such as the time, location, and severity of the abnormality, which is convenient for users to quickly locate the problem and take timely action.

[0036] Through the monitoring and early warning module 16, aquaculture risks are warned in time to ensure the safety and stability of the aquaculture process, so that users can perceive the aquaculture status at the first time, improve the foresight and initiative of aquaculture management, and thus improve aquaculture efficiency and success rate.

[0037] Furthermore, the embodiment of the present application also includes:

[0038] According to the feed feeding scheme, the environmental control scheme, and the initial state of the target animal, a neighboring growth sample search is performed on the target animal type to obtain a first sample animal state change trend curve, a second sample animal state change trend curve, and finally an Nth sample animal state change trend curve;

[0039] Performing pairwise similarity analysis on the first sample animal state change trend curve, the second sample animal state change trend curve, and the Nth sample animal state change trend curve to obtain similarity coefficients of several sample animal state change trend curves;

[0040] According to the similarity coefficients of the several sample animal state change trend curves, curve fitting analysis is performed on the first sample animal state change trend curve, the second sample animal state change trend curve, and up to the Nth sample animal state change trend curve to obtain the target animal state change trend curve.

[0041] In a feasible implementation, first, the current feeding scheme, environmental control scheme and initial state of the target animal received by the user terminal 11 are used as search conditions to search for growth samples similar to the current breeding scene in the historical breeding database. Through the similarity matching algorithm, the most relevant sample data is quickly and accurately screened from the massive historical data to form a set of sample animal state change trend curves, and the sample animal state change trend curves in the sample animal state change trend curve set are marked as the first sample animal state change trend curve, the second sample animal state change trend curve, and the Nth sample animal state change trend curve. These curves reflect the changes in various indicators of the target animal at different growth stages under similar breeding conditions, providing a reference benchmark for subsequent curve comparison and trend prediction.

[0042] After obtaining the set of sample animal state change trend curves, these curves are compared in pairs to quantitatively evaluate the similarity between them. Among them, similarity analysis can use a variety of mathematical methods, such as Euclidean distance, Manhattan distance, dynamic time warping, etc., to calculate the differences between the two curves in shape, trend, fluctuation, etc. The result of the analysis is a similarity coefficient of several sample animal state change trend curves, with a value between 0 and 1. The closer to 1, the more similar the two curves are. It is used to identify the typical pattern in the set of sample animal state change trend curves and fit the target animal state change trend curve.

[0043] Next, based on the similarity coefficients of several sample animal state change trend curves obtained by similarity analysis, the first sample animal state change trend curve, the second sample animal state change trend curve, and the Nth sample animal state change trend curve are integrated to generate a comprehensive target animal state change trend curve. Among them, the fitting analysis can use a variety of curve fitting algorithms, such as least squares method, spline interpolation, Gaussian process regression, etc., to connect discrete sample curve points into a smooth and continuous curve. During the fitting process, sample curves with higher similarity coefficients will be given greater weights, which will have a greater impact on the final fitting results. Through curve fitting analysis, while retaining the overall characteristics of the sample curve, the noise and fluctuations therein are eliminated to obtain a more stable and reliable target animal growth trend prediction curve.

[0044] The growth status prediction module 12 converts scattered historical breeding data into targeted and coherent growth trend prediction results, namely, the target animal status change trend curve, through three steps of neighboring growth sample retrieval, similarity analysis, and curve fitting analysis. It makes full use of the growth rules and similarity information contained in the historical data, and makes forward-looking predictions on new breeding processes in the absence of complete monitoring data, thereby providing a reference for optimizing breeding decisions and improving breeding plans.

[0045] Furthermore, the embodiment of the present application also includes:

[0046] Construct a proximity evaluation function:

[0047] ,

[0048] in, Characterize proximity;

[0049] In the proximity analysis of the feeding regimen, The weight of the i-th feed ingredient representing the sample to be evaluated, The weight of the i-th feed ingredient characterizing the feeding regimen of the feed, represents the deviation threshold of the weight portion of the i-th feed component, Q represents the feed component dimension of the feed feeding plan, represents the preset ratio, a represents the preset feed component weight deviation threshold;

[0050] In the environmental control scheme proximity analysis, Characterizes the characteristic value of the i-th environmental control index of the sample to be evaluated, Characterize the characteristic value of the i-th environmental control index of the environmental control scheme, represents the deviation threshold of the characteristic value of the i-th environmental control indicator, Q represents the dimension of the environmental indicator of the environmental control scheme, represents the preset ratio, a represents the deviation threshold of the characteristic value of the preset environmental control index;

[0051] In the initial state proximity analysis of the target animal, Characterize the characteristic value of the i-th animal state indicator of the sample to be evaluated, The characteristic value of the i-th animal state index representing the initial state of the target animal, represents the characteristic value deviation threshold of the i-th animal state indicator, Q represents the dimension of the animal state indicator of the initial state of the target animal, represents a preset ratio, a represents a preset animal status indicator characteristic value deviation threshold;

[0052] When the proximity analysis results of the feeding plan, the environmental control plan and the target animal's initial state are all equal to 1, the selected samples are added according to the samples to be evaluated to construct the first sample animal state change trend curve, the second sample animal state change trend curve, and so on until the Nth sample animal state change trend curve.

[0053] In a preferred embodiment, the proximity evaluation function is constructed as:

[0054] ,

[0055] in, Characterize the proximity. The proximity evaluation function comprehensively considers the proximity of the feed feeding plan, environmental control plan, and the initial state of the target animal. By comparing the relationship between the indicators of each dimension and the threshold, the proximity value of 0 or 1 is calculated.

[0056] Specifically, in the diet feeding regimen proximity analysis, Indicates the weight percentage of the i-th feed ingredient of the sample to be evaluated, that is, the usage ratio of the i-th raw material (such as corn, soybean meal, etc.) in the feed formula used in the sample; Indicates the weight of the i-th feed ingredient in the feed feeding plan used in the current breeding process; is the allowable deviation threshold of the weight of the i-th feed ingredient, reflecting the tolerance for the difference in the dosage of this ingredient. The size of the component is set according to its importance, and important components are set smaller , to improve the matching accuracy; Q is the total number of ingredients considered in the feed formula, including various energy feeds, protein feeds, vitamins, minerals, etc. For the sample to be evaluated and the current feed feeding plan, first, determine whether the deviation of the weight of each component between the sample to be evaluated and the current feed feeding plan is within the corresponding Within the threshold, calculate whether the proportion of the number of components that meet the condition to the total number of components Q is not less than the preset proportion (e.g. 80%), to determine whether the two feed solutions are close in overall proportion; secondly, calculate the maximum value of the weight deviation of all ingredients to determine whether it does not exceed the preset overall deviation threshold a. Only when these two conditions are met at the same time, the sample to be evaluated is considered to be close to the current solution in terms of feed feeding, and the function value is 1, otherwise it is 0.

[0057] In the environmental control scheme proximity analysis, It represents the characteristic value of the sample to be evaluated on the i-th environmental indicator, that is, the actual value of the i-th environmental indicator (such as temperature, humidity, light intensity, etc.) in the historical breeding process corresponding to the sample; It represents the target characteristic value of the environmental control scheme on the i-th indicator in the current breeding process, that is, according to the current environmental control scheme, it is hoped that the i-th environmental indicator will be controlled at the target level; is the allowable deviation threshold of the characteristic value of the i-th environmental indicator, which reflects the tolerance of the actual value of the indicator to deviate from the target value. To ensure that the actual environmental conditions are closer to the expected target, Q is the total number of environmental indicators considered, including temperature, humidity, light, air quality and other aspects. For the sample to be evaluated and the current environmental control plan, first compare the deviation of each environmental indicator characteristic value one by one to see if it is within the set Within the threshold range, count the number of indicators that meet the condition, calculate their proportion to the total number of indicators Q, and determine whether the proportion is greater than or equal to the preset proportion threshold , in order to measure the overall similarity of the two sets of environmental control schemes in the settings of various indicators; secondly, find the maximum value in the characteristic value deviation of each indicator, and determine whether the maximum deviation is less than or equal to the preset overall deviation upper limit a. Only when these two conditions are met at the same time, the environmental conditions of the sample to be evaluated are judged to be close to the currently set target environmental conditions, and the proximity value is assigned 1, otherwise the proximity value is 0.

[0058] In the initial state proximity analysis of the target animal, It represents the characteristic value of the i-th status indicator of the target animal in the sample to be evaluated when it enters the breeding process, such as initial weight, age, health status, etc., which reflects the development level and physical condition of the animal in the historical sample when it starts to be farmed; It represents the actual characteristic value of the i-th state indicator of the target animal in the initial stage of the current breeding process, that is, the indicator values ​​actually measured or evaluated when the animal group currently prepared for breeding enters the breeding process; is the allowable deviation threshold of the characteristic value of the i-th state indicator, reflecting the tolerance for the difference between the historical value and the current actual value of the indicator; Q is the total number of initial state indicators considered, including multiple indicators such as weight and age that reflect the initial development level and health status. For the sample to be evaluated and the current farmed animal population, first compare the deviation of each initial state indicator characteristic value one by one to see if it is within the set Within the threshold range, count the number of indicators that meet the condition, calculate their proportion to the total number of indicators Q, and determine whether the proportion is greater than or equal to the preset proportion threshold , in order to measure the overall similarity of the two animal groups in various initial state indicators; secondly, find the maximum value in the characteristic value deviation of each indicator, and determine whether the maximum deviation is less than or equal to the preset overall deviation upper limit a. Only when these two conditions are met at the same time, the animal group in the sample to be evaluated is judged to be adjacent to the current animal group in the initial state, and the proximity value is assigned 1, otherwise the proximity value is 0.

[0059] Through the proximity evaluation function and sample screening rules, the growth status prediction module 12 quickly finds the reference sample that best matches the current breeding scenario from massive historical data, and extracts its growth process data to construct a state change trend curve, providing high-quality analysis samples for subsequent growth trend predictions. The data-driven sample retrieval method can maximize the use of historical breeding experience, reduce dependence on expert experience, and improve the objectivity and applicability of the prediction.

[0060] Furthermore, the embodiment of the present application also includes:

[0061] The first sample animal state change trend curve includes a first sample first animal state indicator change trend curve;

[0062] The second sample animal state change trend curve includes a second sample first animal state indicator change trend curve;

[0063] Distribute the first sample first animal state indicator change trend curve and the second sample first animal state indicator change trend curve in a two-dimensional coordinate system, interconnect the first sample first animal state indicator change trend curve and the second sample first animal state indicator change trend curve head to head, and interconnect the second sample first animal state indicator change trend curve tail to tail, and count the inverse of the area of ​​the enclosed graph, which is set as the similarity of the first animal state indicator change trend curves of the two samples;

[0064] The similarity of the change trend curves of the first animal state index of the two samples is added to the similarity coefficient of the change trend curves of the animal state of the several samples.

[0065] In a preferred embodiment, first, determine the animal state indicators included in the first sample animal state change trend curve. Taking the first animal state indicator as an example, extract the change trend curve of the first sample animal on the indicator, recorded as the first sample first animal state indicator change trend curve, reflecting the dynamic change process of the first state indicator of the first sample animal during the entire growth cycle. Among them, the first animal state indicator is an indicator reflecting the growth and development of the animal, such as weight, body length, fat thickness, etc. Then, determine the animal state indicators included in the second sample animal state change trend curve. Similar to extracting the first sample first animal state indicator change trend curve, taking the first animal state indicator as an example, extract the change trend curve of the second sample animal on the indicator, recorded as the second sample first animal state indicator change trend curve, reflecting the dynamic change process of the first state indicator of the second sample animal during the entire growth cycle.

[0066] Subsequently, the trend curve of the change of the first animal state indicator of the first sample and the trend curve of the change of the first animal state indicator of the second sample are plotted in the same two-dimensional coordinate system, with the horizontal axis representing time and the vertical axis representing the value of the first state indicator. Then, the starting point and the end point of the two curves are connected respectively to form a closed geometric shape. The area of ​​the shape is calculated, and its reciprocal is taken as the similarity measure of the two curves, which is recorded as the similarity of the trend curves of the change of the first animal state indicator of the first sample and the second sample. The similarity value ranges from 0 to positive infinity. The larger the value, the closer the two curves are, and the smaller the value, the more the two curves deviate. Next, the calculated similarity of the trend curves of the change of the first animal state indicator of the first sample and the second sample is used as the similarity component of the first sample and the second sample on the indicator, and is added to the similarity coefficients of the trend curves of the change of the animal state of several samples.

[0067] By calculating the inverse of the area enclosed by the two curves, the similarity of the two curves over the entire growth cycle is quantitatively evaluated. Since growth curves are usually nonlinear and non-stationary, traditional curve similarity metrics (such as Euclidean distance, Manhattan distance, etc.) are difficult to accurately describe their dynamic change characteristics. By comprehensively considering the accumulated differences of the curves at various time points through the inverse of the area, the shape characteristics of the two curves can be compared more comprehensively and meticulously, thereby more accurately judging the similarity of growth trends.

[0068] Furthermore, the embodiment of the present application also includes:

[0069] Obtaining a plurality of first animal state indicator change trend curve similarity coefficients of the plurality of sample animal state change trend curve similarity coefficients;

[0070] Based on the similarity coefficients of the plurality of first animal state indicator change trend curves, traverse the plurality of first animal state indicator change trend curves and perform distribution density analysis by using the LOF algorithm to obtain the distribution density of the plurality of first animal state indicator change trend curves;

[0071] The maximum values ​​of the distribution densities of the plurality of first animal state indicator change trend curves are extracted, set as the first animal state indicator change trend prediction curve, and added into the target animal state change trend curve.

[0072] In a preferred embodiment, a subset of similarity coefficients related to the first animal state index is extracted from the obtained similarity coefficients of the trend curves of the animal state change of several samples, and several similarity coefficients of the trend curves of the first animal state index are formed, which reflects the similarity between the trend of change of different historical samples on the first animal state index. Then, the similarity coefficients of the trend curves of the first animal state index are used as input to perform distribution density analysis on the corresponding trend curves of the first animal state index. Specifically, the LOF algorithm is used to measure the typicality or abnormality of the curve in the entire curve set by calculating the density deviation of each curve relative to other curves in its neighborhood. The LOF algorithm can effectively find isolated curves in sparsely distributed areas and clustered curves in densely distributed areas, and characterize the overall distribution characteristics of the curve set. By traversing all curves and calculating their LOF values, the distribution density of several first animal state index trend curves is obtained. Afterwards, among the distribution densities of several first animal state index trend curves, the curve with the largest density is extracted as the change trend prediction result of the first animal state index, which is recorded as the first animal state index change trend prediction curve. The maximum density means that the curve is the most representative in the entire curve set and has the highest degree of similarity with other curves. Therefore, it can be used as a typical sample reflecting the overall change law of the indicator. The extracted first animal state indicator change trend prediction curve is added to the target animal state change trend curve.

[0073] Through the local outlier factor algorithm, the most representative curve is further mined from the results of similarity analysis as the preferred object for predicting the changing trend of the indicator. Through the curve screening method based on density analysis, the interference of noise and abnormal samples is effectively suppressed, thereby improving the robustness and reliability of monitoring data processing.

[0074] Furthermore, the embodiment of the present application also includes:

[0075] Construct a consistency evaluation function:

[0076] ,

[0077] ,

[0078] in, Characterize the first evaluation parameter, Characterize the second evaluation parameter, The j-th moment characteristic value of a certain indicator representing the target animal state change trend curve, The j-th moment characteristic value of a certain indicator that represents the expected animal state change trend curve, represents a small constant, H represents the total number of moments;

[0079] By using the consistency evaluation function, evaluating several pairs of animal state index change trend curves of the target animal state change trend curve and the expected animal state change trend curve, to obtain several first evaluation parameters and several second evaluation parameters;

[0080] When the plurality of first evaluation parameters are all greater than or equal to the first evaluation parameter threshold, and the plurality of second evaluation parameters are all less than or equal to the second evaluation parameter threshold, it is considered that the target animal state change trend curve is consistent with the expected animal state change trend curve;

[0081] Otherwise, it is considered that the target animal state change trend curve is inconsistent with the expected animal state change trend curve.

[0082] In a preferred embodiment, in order to quantitatively evaluate the degree of agreement between the target animal state change trend curve and the expected animal state change trend curve, two consistency evaluation parameters are defined: and .in, Characterizes the average similarity of the two curves over the entire growth period, Characterizes the discrete degree of similarity between two curves at each moment. Specifically, let It represents the characteristic value of a certain indicator at the jth moment on the target animal state change trend curve. represents the characteristic value of the same indicator at the jth moment on the expected animal state change trend curve, H represents the total number of moments in the growth cycle, and b represents a small constant, then and The calculation formula is as follows:

[0083] ,

[0084] ,

[0085] in, The value range of is from 0 to 1, and the larger the value, the higher the average similarity of the two curves; The value range of is from 0 to positive infinity. The smaller the value, the more stable the similarity of the two curves at each moment. The small constant b is introduced to prevent the denominator from being zero and causing calculation anomalies.

[0086] Subsequently, the constructed consistency evaluation function is used to evaluate several pairs of animal state index change trend curves of the target animal state change trend curve and the expected animal state change trend curve, and the first evaluation parameter corresponding to each pair of index curves is calculated. and the second evaluation parameter Among them, animal status indicators include weight, body length, feed conversion rate and other indicators that reflect the growth status of animals. The evaluation results of each pair of indicator curves reflect the degree of consistency between the expected growth trend and the actual growth trend of the target animal on this indicator. Then, for each animal status indicator, the first evaluation parameter The second evaluation parameter is compared with the preset first evaluation parameter threshold. Compare with the preset second evaluation parameter threshold. are greater than or equal to the first evaluation parameter threshold, and all indicators If both are less than or equal to the second evaluation parameter threshold, it is determined that the target animal state change trend curve is consistent with the expected animal state change trend curve as a whole, the actual growth trend of the animal is in line with expectations, and there is no need to adjust the current feed feeding plan and environmental control plan. Less than the first evaluation parameter threshold, or any indicator When it is greater than the second evaluation parameter threshold, it is determined that the target animal state change trend curve is inconsistent with the expected animal state change trend curve as a whole, and the actual growth trend of the animal deviates from the expected target. The current feed feeding plan and environmental control plan need to be optimized and adjusted to improve the growth state of the animal and bring it closer to the expected target trend.

[0087] By utilizing two quantitative evaluation parameters, the consistency between the target animal state change trend curve and the expected animal state change trend curve is comprehensively evaluated from the two perspectives of average similarity and similarity stability, and it is automatically determined whether the feeding management measures need to be optimized and adjusted, so as to maximize the regulatory effect of feed formula and environmental conditions on animal growth, continuously improve breeding performance, and enhance breeding benefits.

[0088] Furthermore, the embodiment of the present application also includes:

[0089] When the target animal state change trend curve is inconsistent with the expected animal state change trend curve, the feed feeding plan and the environmental control plan are adjusted to obtain an updated feed feeding plan and an updated environmental control plan;

[0090] Through the growth status prediction module, the growth status of the target animal type is predicted based on the updated feed feeding plan, the updated environmental control plan and the initial state of the target animal to obtain an updated target animal state change trend curve;

[0091] When the updated target animal state change trend curve is consistent with the expected animal state change trend curve, the recommended feed feeding plan and the recommended environmental control plan are obtained.

[0092] In a feasible implementation, when the feed feeding program optimization module 13 determines through consistency evaluation that the target animal state change trend curve is inconsistent with the expected animal state change trend curve, the optimization adjustment mechanism is triggered. Specifically, the feed feeding program optimization module 13 generates one or more updated feed feeding programs and environmental control programs based on the current feed feeding program and environmental control program according to a certain adjustment strategy and adjustment range. Among them, the adjustment strategy is based on the knowledge of breeding experts, historical optimization experience, mathematical optimization algorithms, etc., and targeted modifications are made to the key factors affecting animal growth (such as feed formula, feeding time, temperature and humidity settings, etc.) in order to improve the growth status of the animal and make it closer to the expected target trend; the adjustment range controls the strength of each modification, which must be large enough to produce obvious optimization effects, but small enough to avoid impacting animal growth.

[0093] Then, for the updated feed feeding scheme and the updated environmental control scheme, the feed feeding scheme optimization module 13 calls the growth state prediction module 12, and re-executes a complete growth state prediction process. Specifically, the updated feed feeding scheme and the updated environmental control scheme and the original target animal initial state are used as inputs, and after a series of steps such as adjacent sample retrieval, similarity analysis, and curve fitting, the growth trend of the target animal type on each animal state index under the breeding conditions of the updated feed feeding scheme and the updated environmental control scheme is predicted, and the corresponding updated target animal state change trend curve is generated. After that, the feed feeding scheme optimization module 13 calls the constructed consistency evaluation function again, and each updated target animal state change trend curve is compared with the expected animal state change trend curve one by one, and the consistency evaluation parameters between them are calculated, and it is judged whether the consistency condition is met. When the updated target animal state change trend curve is consistent with the expected animal state change trend curve, the updated feed feeding scheme and the updated environmental control scheme are determined as the final optimization adjustment results, and the recommended feed feeding scheme and the recommended environmental control scheme are generated. If the updated target animal state change trend curve does not reach consistency with the expected animal state change trend curve, adjustment and optimization are performed again until the optimal solution that meets the consistency requirements is found.

[0094] Through iterative optimization, dynamic matching and adaptive calibration between the feed feeding plan and environmental control plan and the expected growth status of animals are achieved. Through growth status prediction and consistency evaluation, the plan that best meets the expected goals is screened out.

[0095] Furthermore, the embodiment of the present application also includes:

[0096] Performing proximity analysis on the feed feeding monitoring information and the recommended feed feeding plan to obtain a feed feeding proximity analysis result;

[0097] Performing proximity analysis on the environmental control monitoring information and the recommended environmental control solution to obtain an environmental control proximity analysis result;

[0098] When the feed feeding proximity analysis result is 1, the feed feeding scheme abnormality coefficient is equal to 0, and when the feed feeding scheme abnormality coefficient is 0, the feed feeding scheme abnormality coefficient is equal to 1;

[0099] When the environmental control proximity analysis result is 1, the environmental control abnormality coefficient is equal to 0, and when the environmental control proximity analysis result is 0, the environmental control abnormality coefficient is equal to 1.

[0100] In a preferred embodiment, the monitoring data processing module 15 first performs a proximity analysis on the feed feeding monitoring information collected by the breeding equipment monitoring module 14 and the recommended feed feeding scheme generated by the feed feeding scheme optimization module 13. Specifically, a proximity evaluation function is used to compare the deviations of the feed feeding monitoring information and the recommended feed feeding scheme in various key parameters, such as feed composition ratio, feeding time and frequency, and comprehensively judge the overall consistency of the two, and obtain a proximity analysis result of 0 or 1. The result of 1 indicates that the actual feed feeding execution is basically consistent with the recommended scheme, and the result of 0 indicates that there is a large deviation between the two. At the same time, the monitoring data processing module 15 performs a proximity analysis on the environmental control monitoring information collected by the breeding equipment monitoring module 14 and the recommended environmental control scheme generated by the feed feeding scheme optimization module 13. By comparing the deviations of the environmental control monitoring information and the recommended environmental control scheme in key environmental indicators such as temperature, humidity, and air quality, the overall consistency of the two is comprehensively judged, and a proximity analysis result of 0 or 1 is obtained. The result of 1 indicates that the actual environmental control execution is basically consistent with the recommended scheme, and the result of 0 indicates that there is a large deviation between the two.

[0101] Subsequently, according to the feed feeding proximity analysis result, the monitoring data processing module 15 determines the value of the feed feeding scheme abnormal coefficient. When the feed feeding proximity analysis result is 1, it means that the actual implementation is consistent with the recommended feed feeding scheme and the feed feeding process is normal, so the feed feeding scheme abnormal coefficient is assigned to 0; when the feed feeding proximity analysis result is 0, it means that the actual implementation deviates from the recommended feed feeding scheme and the feed feeding process is abnormal, so the feed feeding scheme abnormal coefficient is assigned to 1. At the same time, according to the environmental control proximity analysis result, the monitoring data processing module 15 determines the value of the environmental control abnormal coefficient. When the environmental control proximity analysis result is 1, it means that the actual implementation is consistent with the recommended scheme and the environmental control process is normal, so the environmental control abnormal coefficient is assigned to 0; when the environmental control proximity analysis result is 0, it means that the actual implementation deviates from the recommended scheme and the environmental control process is abnormal, so the environmental control abnormal coefficient is assigned to 1.

[0102] By collecting real-time monitoring information during the operation of breeding equipment, the implementation effect of the recommended feeding plan and the recommended environmental control plan can be quantitatively evaluated, and abnormal situations that deviate from the optimal plan can be discovered in time. Compared with manual inspections and subjective judgments, the sensitivity, accuracy and consistency of monitoring data processing can be greatly improved, and the interference of human factors can be reduced.

[0103] The farming equipment monitoring data processing system provided by the embodiment of the present invention has at least the following technical effects:

[0104] The user end is used to communicate with the breeding equipment to receive the breeding control information before the breeding begins. The breeding control information includes the feed feeding plan, the environmental control plan, the target animal type and the initial state of the target animal, which lays the foundation for the subsequent growth state prediction, breeding plan optimization, abnormal analysis and early warning, and ensures the pertinence and effectiveness of data processing. The growth state prediction module is used to receive the feed feeding plan, the environmental control plan, and the initial state of the target animal to predict the growth state of the target animal type, obtain the target animal state change trend curve, provide an objective basis for optimizing the breeding plan, and ensure the scientificity and rationality of the breeding process. The feed feeding plan optimization module is used to optimize the feed feeding plan and the environmental control plan when the target animal state change trend curve is inconsistent with the expected animal state change trend curve, obtain the recommended feed feeding plan and the recommended environmental control plan, and generate the optimal breeding plan, ensuring that the breeding process is always in the best state and improving the breeding efficiency and success rate. The breeding equipment monitoring module is used to collect the feed feeding monitoring information and environmental control monitoring information of the breeding equipment after the breeding begins, realize the real-time monitoring and data collection of the breeding process, provide reliable data support for the subsequent abnormal analysis and early warning, and ensure the comprehensiveness and accuracy of the monitoring. The monitoring data processing module is used to perform abnormal analysis on the recommended feed feeding plan and the recommended environmental control plan based on the feed feeding monitoring information and the environmental control monitoring information, obtain the feed feeding plan abnormal coefficient and the environmental control abnormal coefficient, realize the timely discovery and quantitative evaluation of abnormal situations in the breeding process, provide a basis for early warning, and ensure the controllability and traceability of abnormal situations. The monitoring and early warning module is used to generate an abnormal warning signal for breeding equipment and alarm at the user end when the feed feeding plan abnormal coefficient is greater than or equal to the first abnormal coefficient threshold, or / and the environmental control abnormal coefficient is greater than or equal to the second abnormal coefficient threshold, realize early warning and rapid response to risk factors in the breeding process, minimize the impact of abnormal situations on breeding efficiency and success rate, and improve breeding efficiency and success rate.

[0105] Embodiment 2:

[0106] See also Figure 2 , Figure 2 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention proposes an electronic device 500, including a memory 510, a processor 520, and a computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 511, a breeding equipment monitoring data processing system is implemented.

[0107] Embodiment 4:

[0108] See also Figure 3 , Figure 3 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 3 As shown, this embodiment provides a computer-readable storage medium 600 on which a computer program 611 is stored. When the computer program 611 is executed by a processor, a breeding equipment monitoring data processing system is implemented.

[0109] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0110] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0114] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0115] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technology, the present invention is also intended to include these changes and variations.

Claims

1. A farming equipment monitoring data processing system, characterized in that: include: The user end is used to communicate with the farming equipment and receive farming control information before the start of farming, wherein the farming control information includes a feeding plan, an environmental control plan, a target animal type, and an initial state of the target animal; A growth status prediction module, used to predict the growth status of the target animal type according to the feed feeding plan, the environmental control plan, and the initial state of the target animal, and obtain a state change trend curve of the target animal; A feed feeding scheme optimization module, used for optimizing the feed feeding scheme and the environmental control scheme when the target animal state change trend curve is inconsistent with the expected animal state change trend curve, to obtain a recommended feed feeding scheme and a recommended environmental control scheme; Aquaculture equipment monitoring module, used to collect feed feeding monitoring information and environmental control monitoring information of aquaculture equipment after the start of aquaculture; A monitoring data processing module, configured to perform abnormal analysis on the recommended feed feeding scheme and the recommended environmental control scheme based on the feed feeding monitoring information and the environmental control monitoring information, and obtain an abnormal coefficient of the feed feeding scheme and an abnormal coefficient of the environmental control; A monitoring and early warning module, for generating an abnormal early warning signal for breeding equipment to alarm at the user end when the abnormal coefficient of the feed feeding scheme is greater than or equal to a first abnormal coefficient threshold, or / and the abnormal coefficient of the environmental control is greater than or equal to a second abnormal coefficient threshold; The steps performed by the growth status prediction module include: According to the feed feeding scheme, the environmental control scheme, and the initial state of the target animal, a neighboring growth sample search is performed on the target animal type to obtain a first sample animal state change trend curve, a second sample animal state change trend curve, and finally an Nth sample animal state change trend curve; Performing pairwise similarity analysis on the first sample animal state change trend curve, the second sample animal state change trend curve, and the Nth sample animal state change trend curve to obtain similarity coefficients of several sample animal state change trend curves; Performing curve fitting analysis on the first sample animal state change trend curve, the second sample animal state change trend curve, and the Nth sample animal state change trend curve according to similarity coefficients of the plurality of sample animal state change trend curves to obtain the target animal state change trend curve; Among them, performing a neighboring growth sample search for the target animal type according to the feed feeding scheme, the environmental control scheme, and the initial state of the target animal to obtain a first sample animal state change trend curve, a second sample animal state change trend curve, and finally an Nth sample animal state change trend curve includes: Construct a proximity evaluation function: ， in, Characterize proximity; In the proximity analysis of the feeding regimen, The weight of the i-th feed ingredient representing the sample to be evaluated, The weight of the i-th feed ingredient characterizing the feed feeding regimen, represents the deviation threshold of the weight portion of the i-th feed component, Q represents the feed component dimension of the feed feeding plan, represents the preset ratio, a represents the preset feed component weight deviation threshold, () is the counting function; In the environmental control scheme proximity analysis, Characterizes the characteristic value of the i-th environmental control index of the sample to be evaluated, Characterize the characteristic value of the i-th environmental control index of the environmental control scheme, represents the deviation threshold of the characteristic value of the i-th environmental control indicator, Q represents the dimension of the environmental indicator of the environmental control scheme, represents the preset ratio, a represents the deviation threshold of the characteristic value of the preset environmental control index; In the initial state proximity analysis of the target animal, Characterize the characteristic value of the i-th animal state indicator of the sample to be evaluated, The characteristic value of the i-th animal state index representing the initial state of the target animal, represents the characteristic value deviation threshold of the i-th animal state indicator, Q represents the dimension of the animal state indicator of the initial state of the target animal, represents a preset ratio, a represents a preset animal status indicator characteristic value deviation threshold; When the proximity analysis results of the feeding plan, the environmental control plan and the target animal's initial state are all equal to 1, the selected samples are added according to the samples to be evaluated to construct the first sample animal state change trend curve, the second sample animal state change trend curve, and so on until the Nth sample animal state change trend curve.

2. The system according to claim 1, characterized in that Performing pairwise similarity analysis on the first sample animal state change trend curve, the second sample animal state change trend curve, and the Nth sample animal state change trend curve to obtain similarity coefficients of several sample animal state change trend curves, including: The first sample animal state change trend curve includes a first sample first animal state indicator change trend curve; The second sample animal state change trend curve includes a second sample first animal state indicator change trend curve; Distribute the first sample first animal state indicator change trend curve and the second sample first animal state indicator change trend curve in a two-dimensional coordinate system, interconnect the first sample first animal state indicator change trend curve and the second sample first animal state indicator change trend curve head to head, and interconnect the second sample first animal state indicator change trend curve tail to tail, and count the inverse of the area of ​​the enclosed graph, which is set as the similarity of the first animal state indicator change trend curves of the two samples; The similarity of the change trend curves of the first animal state index of the two samples is added to the similarity coefficient of the change trend curves of the animal state of the several samples.

3. The system according to claim 1, characterized in that Performing curve fitting analysis on the first sample animal state change trend curve, the second sample animal state change trend curve, and the Nth sample animal state change trend curve according to the similarity coefficients of the plurality of sample animal state change trend curves to obtain the target animal state change trend curve, including: Obtaining a plurality of first animal state indicator change trend curve similarity coefficients of the plurality of sample animal state change trend curve similarity coefficients; Based on the similarity coefficients of the plurality of first animal state indicator change trend curves, traverse the plurality of first animal state indicator change trend curves and perform distribution density analysis by using the LOF algorithm to obtain the distribution density of the plurality of first animal state indicator change trend curves; The maximum values ​​of the distribution densities of the plurality of first animal state indicator change trend curves are extracted, set as the first animal state indicator change trend prediction curve, and added into the target animal state change trend curve.

4. The system according to claim 1, characterized in that The steps performed by the feed feeding program optimization module include: Construct a consistency evaluation function: ， ， in, Characterize the first evaluation parameter, Characterize the second evaluation parameter, The j-th moment characteristic value of a certain indicator representing the target animal state change trend curve, The j-th moment characteristic value of a certain indicator that represents the expected animal state change trend curve, represents a small constant, H represents the total number of moments, () represents the standard deviation function; By using the consistency evaluation function, evaluating several pairs of animal state index change trend curves of the target animal state change trend curve and the expected animal state change trend curve, to obtain several first evaluation parameters and several second evaluation parameters; When the plurality of first evaluation parameters are all greater than or equal to the first evaluation parameter threshold, and the plurality of second evaluation parameters are all less than or equal to the second evaluation parameter threshold, it is considered that the target animal state change trend curve is consistent with the expected animal state change trend curve; Otherwise, it is considered that the target animal state change trend curve is inconsistent with the expected animal state change trend curve.

5. The system according to claim 1, wherein: The steps performed by the feed feeding program optimization module include: When the target animal state change trend curve is inconsistent with the expected animal state change trend curve, the feed feeding plan and the environmental control plan are adjusted to obtain an updated feed feeding plan and an updated environmental control plan; Through the growth status prediction module, the growth status of the target animal type is predicted based on the updated feed feeding plan, the updated environmental control plan and the initial state of the target animal to obtain an updated target animal state change trend curve; When the updated target animal state change trend curve is consistent with the expected animal state change trend curve, the recommended feed feeding plan and the recommended environmental control plan are obtained.

6. The system according to claim 1, wherein: The steps performed by the monitoring data processing module include: Performing proximity analysis on the feed feeding monitoring information and the recommended feed feeding plan to obtain a feed feeding proximity analysis result; Performing proximity analysis on the environmental control monitoring information and the recommended environmental control solution to obtain an environmental control proximity analysis result; When the feed feeding proximity analysis result is 1, the feed feeding scheme abnormality coefficient is equal to 0, and when the feed feeding scheme abnormality coefficient is 0, the feed feeding scheme abnormality coefficient is equal to 1; When the environmental control proximity analysis result is 1, the environmental control abnormality coefficient is equal to 0, and when the environmental control proximity analysis result is 0, the environmental control abnormality coefficient is equal to 1.

7. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby realizing a breeding equipment monitoring data processing system as described in any one of claims 1-6.

8. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, it implements a breeding equipment monitoring data processing system as described in any one of claims 1-6.

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