Method and system for managing automatic breeding equipment for snails

Abstract

The invention discloses a management method and system for automatic snail breeding equipment, and the method comprises the steps: obtaining the environment change data and snail behavior data of historical snail breeding, and employing an association rule mining method to recognize the influence characteristics of the environment on snail behaviors; and based on the influence characteristics, evaluating the growth suitability of the snails under different environmental parameters. Meanwhile, the snail behavior of the target snail breeding equipment is monitored through camera equipment, and the behavior state of the target snail breeding equipment is captured. And in combination with the influence characteristics and the snail behavior state, evaluating the snail growth suitability in the current environment, and generating growth suitability evaluation data. And finally, adjusting operation parameters of the automatic snail breeding equipment according to the evaluation data, and realizing intelligent management of the snail breeding environment. According to the method, environmental data and behavior monitoring are fully utilized, the snail breeding efficiency is optimized, the growth suitability is improved, and an innovative management means is provided for the snail breeding industry.

Description

Technical Field

[0001] The present invention relates to the technical field of snail breeding equipment management, and in particular to a snail automated breeding equipment management method and system. Background Art

[0002] As part of the aquaculture sector, snail farming faces limitations in traditional management methods, including low farming efficiency and difficulty controlling growth suitability. Traditional farming methods are often based on empirical methods, which make it difficult to fully understand the complex behaviors and needs of snails under different environmental conditions.

[0003] The current management of snail farming mainly relies on manual observation and manual adjustment, and fails to make full use of advanced technologies to improve production efficiency. Although the application of sensor technology is gradually increasing, most of them still only focus on a single parameter and fail to provide a deep understanding of snail behavior and environmental changes.

[0004] The present invention is based on an in-depth study of snail farming, combining comprehensive analysis of environmental data and snail behavior data. The association rule mining method is introduced to systematically identify the impact characteristics of environmental changes on snail behavior, thereby more accurately evaluating the growth suitability of snails under different environmental parameters. The snail behavior is monitored in real time through video equipment to accurately capture its state. This advanced management method combines data science with snail farming, providing a new solution for improving farming efficiency and realizing intelligent management. Summary of the invention

[0005] In order to solve at least one of the above technical problems, the present invention proposes a method and system for managing automated snail breeding equipment.

[0006] The first aspect of the present invention provides a method for managing automatic snail breeding equipment, comprising:

[0007] Obtain historical snail farming environmental change data and snail behavior data, and identify the impact of environmental changes on snail behavior through association rule mining methods;

[0008] The growth suitability of snails under different environmental parameters is evaluated according to the impact characteristics;

[0009] The behavior of snails in the target snail breeding equipment is monitored by video equipment to capture the behavior state of the snails;

[0010] According to the influencing characteristics and the behavioral state of the snails, the growth suitability of the snails in the current environment is evaluated to obtain growth suitability evaluation data;

[0011] The operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment plan.

[0012] In this scheme, the environmental change data and snail behavior data of historical snail farming are obtained, and the impact characteristics of environmental changes on snail behavior are identified through association rule mining methods, specifically:

[0013] Obtaining environmental change data during the historical snail farming process, wherein the environmental change data includes temperature, pH value, dissolved oxygen, and light conditions of the farming environment;

[0014] Obtaining video data of the snail breeding process according to the camera equipment, and observing the behavior data of the snail according to the video data, wherein the behavior data includes movement, foraging, and resting behaviors;

[0015] Aligning the environmental change data and the behavior data based on the time series, introducing the Apriori algorithm, obtaining the data processing format of the Apriori algorithm, and converting the environmental change data and the behavior data into data formats according to the data processing format;

[0016] Initialize the environmental change data into a data item set through the Apriori algorithm, extract high-frequency data items, and extract the behavior data corresponding to the high-frequency data items at the same time to obtain the behavior corresponding to the high-frequency data items;

[0017] Based on the correlation analysis of high-frequency data items and the corresponding behaviors of high-frequency data items, association rules are generated, and the relationship between environmental changes and snail behaviors is analyzed according to the association rules to obtain relational data;

[0018] The impact characteristics of environmental changes on snail behavior are identified based on the relationship data, and the impact characteristics include movement impact characteristics, eating impact characteristics, and resting state impact characteristics.

[0019] In this scheme, the growth suitability of snails under different environmental parameters is evaluated according to the influencing characteristics, specifically:

[0020] Obtain the influence of the changes in the influencing characteristics on the growth of snails using big data technology;

[0021] Assign an impact weight to each impact feature according to the impact situation, and obtain impact weight data of the impact feature on the growth of snails;

[0022] The optimum growth environment parameter variation range of snails is obtained, and the growth suitability of snails under different environmental parameters is evaluated according to the impact weight data and the optimum growth environment parameter variation range, so as to obtain a growth suitability environmental parameter comparison table.

[0023] In this solution, the behavior of the snails in the target snail breeding equipment is monitored by a camera device to capture the behavior state of the snails, specifically:

[0024] Based on the camera device, the snail farming video image data in the target snail automated farming equipment is acquired in real time, and the video frame image data is extracted according to the video image data;

[0025] Acquire a historical snail image dataset, perform snail boundary box annotation on the historical snail image dataset, and perform pixel value normalization processing on the annotated historical snail image dataset to obtain a preprocessed dataset;

[0026] A snail recognition model is built based on the YOLO algorithm, and the preprocessed dataset is imported into the snail recognition model to extract and train the image features in the annotated bounding box;

[0027] Importing the video frame image data into the snail recognition algorithm to identify the snails in the image, and representing the snails with bounding boxes to obtain the snail recognition results;

[0028] The coordinates of a preset number of snails are randomly extracted from the video frame image data according to the snail recognition result, and the video frame images are sorted according to the time series of the video frame image data, and the coordinates of the randomly extracted snails are subjected to trajectory continuity analysis to obtain activity trajectory information within a preset time period;

[0029] Obtain standard activity trajectory data of various behavior states of historical snails, and compare the activity trajectory information with the standard activity trajectory data to obtain the current behavior state of the snail.

[0030] In this scheme, the growth suitability of snails in the current environment is evaluated according to the influencing characteristics and the behavioral state of the snails to obtain growth suitability evaluation data, specifically:

[0031] A growth fitness assessment model was constructed based on the regression analysis algorithm, and the relationship data was imported into the growth fitness assessment model for regression analysis to obtain the linear regression characteristics of environmental data and snail behavior.

[0032] The growth fitness under different environmental parameters and the linear regression characteristics of environmental data and snail behavior are introduced into the growth fitness evaluation model as the evaluation criteria for snail growth fitness for learning and training;

[0033] The environmental parameters of the current snail breeding equipment are obtained, and the environmental parameters and the current behavior state of the snails are introduced into a growth suitability assessment model to assess the suitability of the current environment, thereby obtaining growth suitability assessment data of the snails under the environmental parameters of the current breeding equipment.

[0034] In this solution, the operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment plan, which is specifically:

[0035] According to the growth suitability evaluation data, if the growth suitability is greater than a preset value, maintaining the operating parameters of the current snail automated breeding equipment;

[0036] If the growth suitability is less than the preset threshold, extract the environmental suitability data and behavioral suitability data; if the environmental suitability data deviates from the range of variation of the optimal growth environment parameters, adjust the breeding environment parameters of the breeding equipment;

[0037] According to the behavior suitability data, if the behavior suitability is less than the preset behavior suitability, the snail behavior is induced by automatically placing bait through the breeding equipment;

[0038] If the environmental suitability and behavioral suitability are both outside the suitability range, first adjust the environmental parameters to determine whether the growth suitability of the adjusted environmental parameters is greater than the preset value. If not, induce the snail behavior until the growth suitability is greater than the preset value, and obtain the equipment operation parameter adjustment plan.

[0039] The second aspect of the present invention further provides a snail automated breeding equipment management system, the system comprising: a memory, a processor, the memory comprising a snail automated breeding equipment management method program, the snail automated breeding equipment management method program when executed by the processor, the following steps are implemented:

[0040] Obtain historical snail farming environmental change data and snail behavior data, and identify the impact of environmental changes on snail behavior through association rule mining methods;

[0041] The growth suitability of snails under different environmental parameters is evaluated according to the impact characteristics;

[0042] The behavior of snails in the target snail breeding equipment is monitored by video equipment to capture the behavior state of the snails;

[0043] According to the influencing characteristics and the behavioral state of the snails, the growth suitability of the snails in the current environment is evaluated to obtain growth suitability evaluation data;

[0044] The operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment plan.

[0045] In this scheme, the growth suitability of snails under different environmental parameters is evaluated according to the influencing characteristics, specifically:

[0046] Obtain the influence of the changes in the influencing characteristics on the growth of snails using big data technology;

[0047] Assign an impact weight to each impact feature according to the impact situation, and obtain impact weight data of the impact feature on the growth of snails;

[0048] The optimum growth environment parameter variation range of snails is obtained, and the growth suitability of snails under different environmental parameters is evaluated according to the impact weight data and the optimum growth environment parameter variation range, so as to obtain a growth suitability environmental parameter comparison table.

[0049] In this solution, the behavior of the snails in the target snail breeding equipment is monitored by a camera device to capture the behavior state of the snails, specifically:

[0050] Based on the camera device, the snail farming video image data in the target snail automated farming equipment is acquired in real time, and the video frame image data is extracted according to the video image data;

[0051] Acquire a historical snail image dataset, perform snail boundary box annotation on the historical snail image dataset, and perform pixel value normalization processing on the annotated historical snail image dataset to obtain a preprocessed dataset;

[0052] A snail recognition model is built based on the YOLO algorithm, and the preprocessed dataset is imported into the snail recognition model to extract and train the image features in the annotated bounding box;

[0053] Importing the video frame image data into the snail recognition algorithm to identify the snails in the image, and representing the snails with bounding boxes to obtain the snail recognition results;

[0054] The coordinates of a preset number of snails are randomly extracted from the video frame image data according to the snail recognition result, and the video frame images are sorted according to the time series of the video frame image data, and the coordinates of the randomly extracted snails are subjected to trajectory continuity analysis to obtain activity trajectory information within a preset time period;

[0055] Obtain standard activity trajectory data of various behavior states of historical snails, and compare the activity trajectory information with the standard activity trajectory data to obtain the current behavior state of the snail.

[0056] In this scheme, the growth suitability of snails in the current environment is evaluated according to the influencing characteristics and the behavioral state of the snails to obtain growth suitability evaluation data, specifically:

[0057] A growth fitness assessment model was constructed based on the regression analysis algorithm, and the relationship data was imported into the growth fitness assessment model for regression analysis to obtain the linear regression characteristics of environmental data and snail behavior.

[0058] The growth fitness under different environmental parameters and the linear regression characteristics of environmental data and snail behavior are introduced into the growth fitness evaluation model as the evaluation criteria for snail growth fitness for learning and training;

[0059] The environmental parameters of the current snail breeding equipment are obtained, and the environmental parameters and the current behavior state of the snails are introduced into a growth suitability assessment model to assess the suitability of the current environment, thereby obtaining growth suitability assessment data of the snails under the environmental parameters of the current breeding equipment.

[0060] The present invention discloses a management method and system for automatic snail breeding equipment. By acquiring environmental change data and snail behavior data of historical snail breeding, an association rule mining method is used to identify the impact characteristics of the environment on the behavior of snails. Based on the impact characteristics, the growth suitability of snails under different environmental parameters is evaluated. At the same time, the snail behavior of the target snail breeding equipment is monitored by a camera device to capture its behavior state. Combined with the impact characteristics and the snail behavior state, the growth suitability of snails in the current environment is evaluated to generate growth suitability evaluation data. Finally, the operating parameters of the automatic snail breeding equipment are adjusted according to the evaluation data to realize the intelligent management of the snail breeding environment. This method optimizes the snail breeding efficiency and improves the growth suitability by making full use of environmental data and behavior monitoring, thereby providing an innovative management means for the snail breeding industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flow chart of a method for managing automatic snail breeding equipment according to the present invention is shown;

[0062] Figure 2 A flow chart showing the behavior state of catching snails according to the present invention;

[0063] Figure 3 A flow chart showing the method of obtaining growth suitability assessment data;

[0064] Figure 4 A block diagram of a snail automated breeding equipment management system of the present invention is shown. DETAILED DESCRIPTION

[0065] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0067] Figure 1 A flow chart of a method for managing automated snail breeding equipment according to the present invention is shown.

[0068] like Figure 1 As shown, the first aspect of the present invention provides a method for managing automatic snail breeding equipment, comprising:

[0069] S102, obtaining historical environmental change data and snail behavior data of snail farming, and identifying the impact characteristics of environmental changes on snail behavior through association rule mining methods;

[0070] S104, evaluating the growth suitability of snails under different environmental parameters according to the impact characteristics;

[0071] S106, monitoring the behavior of the snails in the target snail breeding equipment by using a camera device to capture the behavior state of the snails;

[0072] S108, evaluating the growth suitability of snails in the current environment according to the influencing characteristics and the behavior state of the snails to obtain growth suitability evaluation data;

[0073] S110, adjusting the operating parameters of the snail automated breeding equipment according to the growth suitability data to obtain an equipment operating parameter adjustment plan.

[0074] It should be noted that the impact characteristics of environmental changes on snail behavior are identified through association rule mining methods, and the growth suitability of snails under different parameters is evaluated based on the impact characteristics. Then, the growth suitability of snails in the current environment is evaluated by capturing the behavioral state of snails. The operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability, which realizes multi-faceted consideration of the suitability of snail growth. The operating parameters of the snail automated breeding equipment are adjusted based on multi-faceted data to optimize the snail breeding environment, thereby improving the breeding efficiency and product quality. The use of association rule mining methods can identify the impact characteristics of environmental changes on snail behavior, which helps to reveal the correlation between environmental factors and snail behavior, and provides a basis for subsequent growth suitability evaluation.

[0075] According to an embodiment of the present invention, the environmental change data and snail behavior data of historical snail farming are obtained, and the impact characteristics of environmental changes on snail behavior are identified by an association rule mining method, specifically:

[0076] Obtaining environmental change data during the historical snail farming process, wherein the environmental change data includes temperature, pH value, dissolved oxygen, and light conditions of the farming environment;

[0077] Obtaining video data of the snail breeding process according to the camera equipment, and observing the behavior data of the snail according to the video data, wherein the behavior data includes movement, foraging, and resting behaviors;

[0078] Aligning the environmental change data and the behavior data based on the time series, introducing the Apriori algorithm, obtaining the data processing format of the Apriori algorithm, and converting the environmental change data and the behavior data into data formats according to the data processing format;

[0079] Initialize the environmental change data into a data item set through the Apriori algorithm, extract high-frequency data items, and extract the behavior data corresponding to the high-frequency data items at the same time to obtain the behavior corresponding to the high-frequency data items;

[0080] Based on the correlation analysis of high-frequency data items and the corresponding behaviors of high-frequency data items, association rules are generated, and the relationship between environmental changes and snail behaviors is analyzed according to the association rules to obtain relational data;

[0081] The impact characteristics of environmental changes on snail behavior are identified based on the relationship data, and the impact characteristics include movement impact characteristics, eating impact characteristics, and resting state impact characteristics.

[0082] It should be noted that the Apriori algorithm is an association rule mining algorithm for discovering frequent item sets and association rules in a data set; the association rule describes the association relationship between different items in the data set, and the association rule usually adopts the "If-Then" form, indicating that an event or thing may occur when certain conditions are met. In the present application, the environmental change data represents the conditions in the association rules, and the snail behavior data represents the event; the impact feature is the impact feature on the snail behavior when the environmental parameters change, for example, the movement impact feature is the feature of whether the movement frequency of the snail increases or decreases when the ambient temperature drops.

[0083] According to an embodiment of the present invention, the growth suitability of snails under different environmental parameters is evaluated according to the influencing characteristics, specifically:

[0084] Obtain the influence of the changes in the influencing characteristics on the growth of snails using big data technology;

[0085] Assign an impact weight to each impact feature according to the impact situation, and obtain impact weight data of the impact feature on the growth of snails;

[0086] The optimum growth environment parameter variation range of snails is obtained, and the growth suitability of snails under different environmental parameters is evaluated according to the impact weight data and the optimum growth environment parameter variation range, so as to obtain a growth suitability environmental parameter comparison table.

[0087] It should be noted that by assigning weights to different influencing characteristics, the system can quantitatively express the relative influence of each influencing characteristic on the growth of snails; using the impact weight data and the range of variation of the optimal growth environment parameters, the growth suitability of snails under different environmental parameters is evaluated, and a growth suitability environmental parameter comparison table is obtained. Based on previous big data analysis and weight assignment, a growth suitability reference table can be generated for different environmental parameter combinations, providing a scientific basis for subsequent adjustments to the breeding environment.

[0088] Figure 2 A flow chart showing the behavior state of catching snails according to the present invention.

[0089] According to an embodiment of the present invention, the behavior monitoring of the snails in the target snail breeding equipment by using a camera device to capture the behavior state of the snails is specifically as follows:

[0090] S202, acquiring snail farming video image data in the target snail automated farming equipment in real time based on a camera device, and extracting video frame image data according to the video image data;

[0091] S204, obtaining a historical snail image dataset, annotating the historical snail image dataset with a snail boundary box, and performing pixel value normalization processing on the annotated historical snail image dataset to obtain a preprocessed dataset;

[0092] S206, building a snail recognition model based on the YOLO algorithm, importing the preprocessed data set into the snail recognition model to extract and train image features in the marked bounding box;

[0093] S208, importing the video frame image data into the snail recognition algorithm to recognize the snails in the image, and representing the snails with bounding boxes to obtain snail recognition results;

[0094] S210, randomly extracting the coordinates of a preset number of snails from the video frame image data according to the snail recognition result, sorting the video frame images according to the time sequence of the video frame image data, performing trajectory continuity analysis on the randomly extracted coordinates of the snails, and obtaining activity trajectory information within a preset time period;

[0095] S212, obtaining standard activity trajectory data of various behavior states of historical snails, and comparing the activity trajectory information with the standard activity trajectory data to obtain the current behavior state of the snail.

[0096] It should be noted that a snail recognition model is constructed by the YOLO algorithm, and a historical snail image dataset is imported into the model for training. The YOLO algorithm can accurately identify snails in video image data. The YOLO algorithm is a target detection algorithm; the various behavioral states include moving state, foraging state, reproduction state, resting state, escape state, etc.

[0097] Figure 3 A flow chart of obtaining growth suitability assessment data according to the present invention is shown.

[0098] According to an embodiment of the present invention, the growth suitability of snails in the current environment is evaluated according to the influencing characteristics and the behavioral state of the snails to obtain growth suitability evaluation data, specifically:

[0099] S302, constructing a growth fitness assessment model based on a regression analysis algorithm, importing the relationship data into the growth fitness assessment model for regression analysis, and obtaining linear regression characteristics of environmental data and snail behavior;

[0100] S304, importing the growth suitability under different environmental parameters and the linear regression characteristics of environmental data and snail behavior into a growth suitability evaluation model as an evaluation standard for snail growth suitability for learning and training;

[0101] S306, obtaining the environmental parameters of the current snail breeding equipment, importing the environmental parameters and the current behavior state of the snail into a growth suitability assessment model to assess the suitability of the current environment, and obtaining growth suitability assessment data of the snails under the environmental parameters of the current breeding equipment.

[0102] It should be noted that, when snails grow, not only the environment will affect the growth of snails, but also the behavioral state of snails will affect the growth of snails. In addition, different behavioral states of snails under different environmental parameters will also affect the growth of snails. For example, when the temperature of the breeding environment is higher than a certain threshold, the escape behavior of snails can promote the formation and metabolism of snails. Therefore, by considering the growth suitability of snails from multiple factors through regression analysis, the understanding of the growth habits of snails can be greatly improved. Therefore, the environmental parameters and the behavioral state of snails in the current breeding environment can be judged according to the growth habits of snails, and the growth fitness of snails can be evaluated at the same time, which greatly improves the accuracy and practicality of the evaluation and avoids inaccurate data evaluation due to the arbitrariness of single condition judgment.

[0103] According to an embodiment of the present invention, the operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment scheme, specifically:

[0104] According to the growth suitability evaluation data, if the growth suitability is greater than a preset value, maintaining the operating parameters of the current snail automated breeding equipment;

[0105] If the growth suitability is less than the preset threshold, extract the environmental suitability data and behavioral suitability data; if the environmental suitability data deviates from the range of variation of the optimal growth environment parameters, adjust the breeding environment parameters of the breeding equipment;

[0106] According to the behavior suitability data, if the behavior suitability is less than the preset behavior suitability, the snail behavior is induced by automatically placing bait through the breeding equipment;

[0107] If the environmental suitability and behavioral suitability are both outside the suitability range, first adjust the environmental parameters to determine whether the growth suitability of the adjusted environmental parameters is greater than the preset value. If not, induce the snail behavior until the growth suitability is greater than the preset value, and obtain the equipment operation parameter adjustment plan.

[0108] It should be noted that through intelligent operating parameter adjustment strategies, the operating status of breeding equipment can be optimized in a timely and effective manner under different circumstances, thereby improving the growth suitability of snails, optimizing breeding effects, and ultimately achieving the goal of automated management; automated management can reduce human intervention, improve breeding efficiency, and ensure that snails achieve optimal growth under suitable environmental conditions.

[0109] According to an embodiment of the present invention, it also includes:

[0110] Obtaining snail growth data in multiple snail breeding equipment in a target area, wherein the growth data includes snail growth rate, health status, and individual size data of snails;

[0111] Based on the t-test method, the difference analysis of snail growth data in each snail breeding equipment was performed to obtain snail growth difference data;

[0112] Evaluate the breeding effect of each snail breeding equipment according to the snail growth difference data to obtain breeding effect evaluation data;

[0113] Extract the historical operating parameter data and snail behavior status data of the snail breeding equipment with the best breeding effect;

[0114] The correlation change data of the historical operating parameter data and the snail behavior state data are analyzed by the correlation coefficient analysis method to obtain the correlation change data of the historical parameter data changing with the snail behavior state;

[0115] The equipment operating parameter adjustment plan is optimized according to the correlation change data and the historical operating parameter data.

[0116] It should be noted that among the multiple snail breeding equipment in the target area, the breeding effect of snails in each breeding equipment may be different. By analyzing the correlation between the changes in operating parameters of the breeding equipment with the best breeding effect and the behavior state of snails, the correlation change data of the operating parameters changing with the behavior state of snails is analyzed, and finally the correlation change data and the historical operating parameter data of the breeding equipment with the best breeding effect are used as reference plans for other breeding equipment, the equipment operating parameter adjustment plan is optimized, and the adjustment of the equipment operating parameter adjustment plan in actual application is realized, so that the equipment operating parameter adjustment plan is more in line with actual breeding needs, thereby improving the breeding effect of breeding equipment on snails and improving the snail breeding efficiency.

[0117] Figure 4 A block diagram of a snail automated breeding equipment management system of the present invention is shown.

[0118] The second aspect of the present invention further provides a snail automated breeding equipment management system 4, the system comprising: a memory 41, a processor 42, the memory comprising a snail automated breeding equipment management method program, the snail automated breeding equipment management method program when executed by the processor, the following steps are implemented:

[0119] Obtain historical snail farming environmental change data and snail behavior data, and identify the impact of environmental changes on snail behavior through association rule mining methods;

[0120] The growth suitability of snails under different environmental parameters is evaluated according to the impact characteristics;

[0121] The behavior of snails in the target snail breeding equipment is monitored by video equipment to capture the behavior state of the snails;

[0122] According to the influencing characteristics and the behavioral state of the snails, the growth suitability of the snails in the current environment is evaluated to obtain growth suitability evaluation data;

[0123] The operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment plan.

[0124] It should be noted that the impact characteristics of environmental changes on snail behavior are identified through association rule mining methods, and the growth suitability of snails under different parameters is evaluated based on the impact characteristics. Then, the growth suitability of snails in the current environment is evaluated by capturing the behavioral state of snails. The operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability, which realizes multi-faceted consideration of the suitability of snail growth. The operating parameters of the snail automated breeding equipment are adjusted based on multi-faceted data to optimize the snail breeding environment, thereby improving the breeding efficiency and product quality. The use of association rule mining methods can identify the impact characteristics of environmental changes on snail behavior, which helps to reveal the correlation between environmental factors and snail behavior, and provides a basis for subsequent growth suitability evaluation.

[0125] According to an embodiment of the present invention, the environmental change data and snail behavior data of historical snail farming are obtained, and the impact characteristics of environmental changes on snail behavior are identified by an association rule mining method, specifically:

[0126] Obtaining environmental change data during the historical snail farming process, wherein the environmental change data includes temperature, pH value, dissolved oxygen, and light conditions of the farming environment;

[0127] Obtaining video data of the snail breeding process according to the camera equipment, and observing the behavior data of the snail according to the video data, wherein the behavior data includes movement, foraging, and resting behaviors;

[0128] Aligning the environmental change data and the behavior data based on the time series, introducing the Apriori algorithm, obtaining the data processing format of the Apriori algorithm, and converting the environmental change data and the behavior data into data formats according to the data processing format;

[0129] Initialize the environmental change data into a data item set through the Apriori algorithm, extract high-frequency data items, and extract the behavior data corresponding to the high-frequency data items at the same time to obtain the behavior corresponding to the high-frequency data items;

[0130] Based on the correlation analysis of high-frequency data items and the corresponding behaviors of high-frequency data items, association rules are generated, and the relationship between environmental changes and snail behaviors is analyzed according to the association rules to obtain relational data;

[0131] The impact characteristics of environmental changes on snail behavior are identified based on the relationship data, and the impact characteristics include movement impact characteristics, eating impact characteristics, and resting state impact characteristics.

[0132] It should be noted that the Apriori algorithm is an association rule mining algorithm for discovering frequent item sets and association rules in a data set; the association rule describes the association relationship between different items in the data set, and the association rule usually adopts the "If-Then" form, indicating that an event or thing may occur when certain conditions are met. In the present application, the environmental change data represents the conditions in the association rules, and the snail behavior data represents the event; the impact feature is the impact feature on the snail behavior when the environmental parameters change, for example, the movement impact feature is the feature of whether the movement frequency of the snail increases or decreases when the ambient temperature drops.

[0133] According to an embodiment of the present invention, the growth suitability of snails under different environmental parameters is evaluated according to the influencing characteristics, specifically:

[0134] Obtain the influence of the changes in the influencing characteristics on the growth of snails using big data technology;

[0135] Assign an impact weight to each impact feature according to the impact situation, and obtain impact weight data of the impact feature on the growth of snails;

[0136] The optimum growth environment parameter variation range of snails is obtained, and the growth suitability of snails under different environmental parameters is evaluated according to the impact weight data and the optimum growth environment parameter variation range, so as to obtain a growth suitability environmental parameter comparison table.

[0137] It should be noted that by assigning weights to different influencing characteristics, the system can quantitatively express the relative influence of each influencing characteristic on the growth of snails; using the impact weight data and the range of variation of the optimal growth environment parameters, the growth suitability of snails under different environmental parameters is evaluated, and a growth suitability environmental parameter comparison table is obtained. Based on previous big data analysis and weight assignment, a growth suitability reference table can be generated for different environmental parameter combinations, providing a scientific basis for subsequent adjustments to the breeding environment.

[0138] According to an embodiment of the present invention, the behavior monitoring of the snails in the target snail breeding equipment by using a camera device to capture the behavior state of the snails is specifically as follows:

[0139] Based on the camera device, the snail farming video image data in the target snail automated farming equipment is acquired in real time, and the video frame image data is extracted according to the video image data;

[0140] Acquire a historical snail image dataset, perform snail boundary box annotation on the historical snail image dataset, and perform pixel value normalization processing on the annotated historical snail image dataset to obtain a preprocessed dataset;

[0141] A snail recognition model is built based on the YOLO algorithm, and the preprocessed dataset is imported into the snail recognition model to extract and train the image features in the annotated bounding box;

[0142] Importing the video frame image data into the snail recognition algorithm to identify the snails in the image, and representing the snails with bounding boxes to obtain the snail recognition results;

[0143] The coordinates of a preset number of snails are randomly extracted from the video frame image data according to the snail recognition result, and the video frame images are sorted according to the time series of the video frame image data, and the coordinates of the randomly extracted snails are subjected to trajectory continuity analysis to obtain activity trajectory information within a preset time period;

[0144] Obtain standard activity trajectory data of various behavior states of historical snails, and compare the activity trajectory information with the standard activity trajectory data to obtain the current behavior state of the snail.

[0145] It should be noted that a snail recognition model is constructed by the YOLO algorithm, and a historical snail image dataset is imported into the model for training. The YOLO algorithm can accurately identify snails in video image data. The YOLO algorithm is a target detection algorithm; the various behavioral states include moving state, foraging state, reproduction state, resting state, escape state, etc.

[0146] According to an embodiment of the present invention, the growth suitability of snails in the current environment is evaluated according to the influencing characteristics and the behavioral state of the snails to obtain growth suitability evaluation data, specifically:

[0147] A growth fitness assessment model was constructed based on the regression analysis algorithm, and the relationship data was imported into the growth fitness assessment model for regression analysis to obtain the linear regression characteristics of environmental data and snail behavior.

[0148] The growth fitness under different environmental parameters and the linear regression characteristics of environmental data and snail behavior are introduced into the growth fitness evaluation model as the evaluation criteria for snail growth fitness for learning and training;

[0149] The environmental parameters of the current snail breeding equipment are obtained, and the environmental parameters and the current behavior state of the snails are introduced into a growth suitability assessment model to assess the suitability of the current environment, thereby obtaining growth suitability assessment data of the snails under the environmental parameters of the current breeding equipment.

[0150] It should be noted that, when snails grow, not only the environment will affect the growth of snails, but also the behavioral state of snails will affect the growth of snails. In addition, different behavioral states of snails under different environmental parameters will also affect the growth of snails. For example, when the temperature of the breeding environment is higher than a certain threshold, the escape behavior of snails can promote the formation and metabolism of snails. Therefore, by considering the growth suitability of snails from multiple factors through regression analysis, the understanding of the growth habits of snails can be greatly improved. Therefore, the environmental parameters and the behavioral state of snails in the current breeding environment can be judged according to the growth habits of snails, and the growth fitness of snails can be evaluated at the same time, which greatly improves the accuracy and practicality of the evaluation and avoids inaccurate data evaluation due to the arbitrariness of single condition judgment.

[0151] According to an embodiment of the present invention, the operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment scheme, specifically:

[0152] According to the growth suitability evaluation data, if the growth suitability is greater than a preset value, maintaining the operating parameters of the current snail automated breeding equipment;

[0153] If the growth suitability is less than the preset threshold, extract the environmental suitability data and behavioral suitability data; if the environmental suitability data deviates from the range of variation of the optimal growth environment parameters, adjust the breeding environment parameters of the breeding equipment;

[0154] According to the behavior suitability data, if the behavior suitability is less than the preset behavior suitability, the snail behavior is induced by automatically placing bait through the breeding equipment;

[0155] If the environmental suitability and behavioral suitability are both outside the suitability range, first adjust the environmental parameters to determine whether the growth suitability of the adjusted environmental parameters is greater than the preset value. If not, induce the snail behavior until the growth suitability is greater than the preset value, and obtain the equipment operation parameter adjustment plan.

[0156] It should be noted that through intelligent operating parameter adjustment strategies, the operating status of breeding equipment can be optimized in a timely and effective manner under different circumstances, thereby improving the growth suitability of snails, optimizing breeding effects, and ultimately achieving the goal of automated management; automated management can reduce human intervention, improve breeding efficiency, and ensure that snails achieve optimal growth under suitable environmental conditions.

[0157] According to an embodiment of the present invention, it also includes:

[0158] Obtaining snail growth data in multiple snail breeding equipment in a target area, wherein the growth data includes snail growth rate, health status, and individual size data of snails;

[0159] Based on the t-test method, the difference analysis of snail growth data in each snail breeding equipment was performed to obtain snail growth difference data;

[0160] Evaluate the breeding effect of each snail breeding equipment according to the snail growth difference data to obtain breeding effect evaluation data;

[0161] Extract the historical operating parameter data and snail behavior status data of the snail breeding equipment with the best breeding effect;

[0162] The correlation change data of the historical operating parameter data and the snail behavior state data are analyzed by the correlation coefficient analysis method to obtain the correlation change data of the historical parameter data changing with the snail behavior state;

[0163] The equipment operating parameter adjustment plan is optimized according to the correlation change data and the historical operating parameter data.

[0164] It should be noted that among the multiple snail breeding equipment in the target area, the breeding effect of snails in each breeding equipment may be different. By analyzing the correlation between the changes in operating parameters of the breeding equipment with the best breeding effect and the behavior state of snails, the correlation change data of the operating parameters changing with the behavior state of snails is analyzed, and finally the correlation change data and the historical operating parameter data of the breeding equipment with the best breeding effect are used as reference plans for other breeding equipment, the equipment operating parameter adjustment plan is optimized, and the adjustment of the equipment operating parameter adjustment plan in actual application is realized, so that the equipment operating parameter adjustment plan is more in line with actual breeding needs, thereby improving the breeding effect of breeding equipment on snails and improving the snail breeding efficiency.

[0165] The present invention discloses a management method and system for automatic snail breeding equipment. By acquiring environmental change data and snail behavior data of historical snail breeding, an association rule mining method is used to identify the impact characteristics of the environment on the behavior of snails. Based on the impact characteristics, the growth suitability of snails under different environmental parameters is evaluated. At the same time, the snail behavior of the target snail breeding equipment is monitored by a camera device to capture its behavior state. Combined with the impact characteristics and the snail behavior state, the growth suitability of snails in the current environment is evaluated to generate growth suitability evaluation data. Finally, the operating parameters of the automatic snail breeding equipment are adjusted according to the evaluation data to realize the intelligent management of the snail breeding environment. This method optimizes the snail breeding efficiency and improves the growth suitability by making full use of environmental data and behavior monitoring, thereby providing an innovative management means for the snail breeding industry.

[0166] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0167] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0168] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0169] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0170] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0171] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for managing snail automated breeding equipment, characterized in that: The following steps are involved: Obtain historical snail farming environmental change data and snail behavior data, and identify the impact of environmental changes on snail behavior through association rule mining methods; The growth suitability of snails under different environmental parameters is evaluated according to the impact characteristics; The behavior of snails in the target snail breeding equipment is monitored by video equipment to capture the behavior state of the snails; According to the influencing characteristics and the behavioral state of the snails, the growth suitability of the snails in the current environment is evaluated to obtain growth suitability evaluation data; The operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment plan.

2. A method for managing automatic snail breeding equipment according to claim 1, characterized in that: The environmental change data and snail behavior data of historical snail farming are obtained, and the impact characteristics of environmental changes on snail behavior are identified through an association rule mining method, specifically: Obtaining environmental change data during the historical snail farming process, wherein the environmental change data includes temperature, pH value, dissolved oxygen, and light conditions of the farming environment; Obtaining video data of the snail breeding process according to the camera equipment, and observing the behavior data of the snail according to the video data, wherein the behavior data includes movement, foraging, and resting behaviors; Aligning the environmental change data and the behavior data based on the time series, introducing the Apriori algorithm, obtaining the data processing format of the Apriori algorithm, and converting the environmental change data and the behavior data into data formats according to the data processing format; Initialize the environmental change data into a data item set through the Apriori algorithm, extract high-frequency data items, and extract the behavior data corresponding to the high-frequency data items at the same time to obtain the behavior corresponding to the high-frequency data items; Based on the correlation analysis of high-frequency data items and the corresponding behaviors of high-frequency data items, association rules are generated, and the relationship between environmental changes and snail behaviors is analyzed according to the association rules to obtain relational data; The impact characteristics of environmental changes on snail behavior are identified based on the relationship data, and the impact characteristics include movement impact characteristics, eating impact characteristics, and resting state impact characteristics.

3. A method for managing snail automated breeding equipment according to claim 1, characterized in that: The method of evaluating the growth suitability of snails under different environmental parameters according to the influencing characteristics is specifically as follows: Obtain the influence of the changes in the influencing characteristics on the growth of snails using big data technology; Assign an impact weight to each impact feature according to the impact situation, and obtain impact weight data of the impact feature on the growth of snails; The optimum growth environment parameter variation range of snails is obtained, and the growth suitability of snails under different environmental parameters is evaluated according to the impact weight data and the optimum growth environment parameter variation range, so as to obtain a growth suitability environmental parameter comparison table.

4. The method for managing the automatic breeding equipment of snails according to claim 1, characterized in that: The behavior monitoring of the snails in the target snail breeding equipment is performed by using a camera device to capture the behavior state of the snails, specifically: Based on the camera device, the snail farming video image data in the target snail automated farming equipment is acquired in real time, and the video frame image data is extracted according to the video image data; Acquire a historical snail image dataset, perform snail boundary box annotation on the historical snail image dataset, and perform pixel value normalization processing on the annotated historical snail image dataset to obtain a preprocessed dataset; A snail recognition model is built based on the YOLO algorithm, and the preprocessed dataset is imported into the snail recognition model to extract and train the image features in the annotated bounding box; Importing the video frame image data into the snail recognition algorithm to identify the snails in the image, and representing the snails with bounding boxes to obtain the snail recognition results; The coordinates of a preset number of snails are randomly extracted from the video frame image data according to the snail recognition result, and the video frame images are sorted according to the time series of the video frame image data, and the coordinates of the randomly extracted snails are subjected to trajectory continuity analysis to obtain activity trajectory information within a preset time period; Obtain standard activity trajectory data of various behavior states of historical snails, and compare the activity trajectory information with the standard activity trajectory data to obtain the current behavior state of the snail.

5. The method for managing the automatic breeding equipment of snails according to claim 2, characterized in that: The growth suitability of snails in the current environment is evaluated according to the influencing characteristics and the behavior state of the snails to obtain growth suitability evaluation data, specifically: A growth fitness assessment model was constructed based on the regression analysis algorithm, and the relationship data was imported into the growth fitness assessment model for regression analysis to obtain the linear regression characteristics of environmental data and snail behavior. The growth fitness under different environmental parameters and the linear regression characteristics of environmental data and snail behavior are introduced into the growth fitness evaluation model as the evaluation criteria for snail growth fitness for learning and training; The environmental parameters of the current snail breeding equipment are obtained, and the environmental parameters and the current behavior state of the snails are introduced into a growth suitability assessment model to assess the suitability of the current environment, thereby obtaining growth suitability assessment data of the snails under the environmental parameters of the current breeding equipment.

6. The method for managing the automatic breeding equipment of snails according to claim 1, characterized in that: The operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment plan, which is specifically: According to the growth suitability evaluation data, if the growth suitability is greater than a preset value, maintaining the operating parameters of the current snail automated breeding equipment; If the growth suitability is less than the preset threshold, extract the environmental suitability data and behavioral suitability data; if the environmental suitability data deviates from the range of variation of the optimal growth environment parameters, adjust the breeding environment parameters of the breeding equipment; According to the behavior suitability data, if the behavior suitability is less than the preset behavior suitability, the snail behavior is induced by automatically placing bait through the breeding equipment; If the environmental suitability and behavioral suitability are both outside the suitability range, first adjust the environmental parameters to determine whether the growth suitability of the adjusted environmental parameters is greater than the preset value. If not, induce the snail behavior until the growth suitability is greater than the preset value, and obtain the equipment operation parameter adjustment plan.

7. A snail automated breeding equipment management system, characterized in that: The snail automated breeding equipment management system comprises a storage device and a processor, wherein the storage device comprises a snail automated breeding equipment management method program, and when the snail automated breeding equipment management method program is executed by the processor, the following steps are implemented: Obtain historical snail farming environmental change data and snail behavior data, and identify the impact of environmental changes on snail behavior through association rule mining methods; The growth suitability of snails under different environmental parameters is evaluated according to the impact characteristics; The behavior of snails in the target snail breeding equipment is monitored by video equipment to capture the behavior state of the snails; According to the influencing characteristics and the behavioral state of the snails, the growth suitability of the snails in the current environment is evaluated to obtain growth suitability evaluation data; The operating parameters of the snail automated breeding equipment are adjusted according to the growth suitability data to obtain an equipment operating parameter adjustment plan.

8. The snail automated breeding equipment management system according to claim 7, characterized in that: The method of evaluating the growth suitability of snails under different environmental parameters according to the influencing characteristics is specifically as follows: Obtain the influence of the changes in the influencing characteristics on the growth of snails using big data technology; Assign an impact weight to each impact feature according to the impact situation, and obtain impact weight data of the impact feature on the growth of snails; The optimum growth environment parameter variation range of snails is obtained, and the growth suitability of snails under different environmental parameters is evaluated according to the impact weight data and the optimum growth environment parameter variation range, so as to obtain a growth suitability environmental parameter comparison table.

9. The snail automated breeding equipment management system according to claim 7, characterized in that: The behavior monitoring of the snails in the target snail breeding equipment is performed by using a camera device to capture the behavior state of the snails, specifically: Based on the camera device, the snail farming video image data in the target snail automated farming equipment is acquired in real time, and the video frame image data is extracted according to the video image data; Acquire a historical snail image dataset, perform snail boundary box annotation on the historical snail image dataset, and perform pixel value normalization processing on the annotated historical snail image dataset to obtain a preprocessed dataset; A snail recognition model is built based on the YOLO algorithm, and the preprocessed dataset is imported into the snail recognition model to extract and train the image features in the annotated bounding box; Importing the video frame image data into the snail recognition algorithm to identify the snails in the image, and representing the snails with bounding boxes to obtain the snail recognition results; The coordinates of a preset number of snails are randomly extracted from the video frame image data according to the snail recognition result, and the video frame images are sorted according to the time series of the video frame image data, and the coordinates of the randomly extracted snails are subjected to trajectory continuity analysis to obtain activity trajectory information within a preset time period; Obtain standard activity trajectory data of various behavior states of historical snails, and compare the activity trajectory information with the standard activity trajectory data to obtain the current behavior state of the snail.

10. The snail automated breeding equipment management system according to claim 7, characterized in that: The growth suitability of snails in the current environment is evaluated according to the influencing characteristics and the behavior state of the snails to obtain growth suitability evaluation data, specifically: A growth fitness assessment model was constructed based on the regression analysis algorithm, and the relationship data was imported into the growth fitness assessment model for regression analysis to obtain the linear regression characteristics of environmental data and snail behavior. The growth fitness under different environmental parameters and the linear regression characteristics of environmental data and snail behavior are introduced into the growth fitness evaluation model as the evaluation criteria for snail growth fitness for learning and training; The environmental parameters of the current snail breeding equipment are obtained, and the environmental parameters and the current behavior state of the snails are introduced into a growth suitability assessment model to assess the suitability of the current environment, thereby obtaining growth suitability assessment data of the snails under the environmental parameters of the current breeding equipment.

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