A method and system for managing an automatic cultivation device for freshwater clams

Abstract

The application discloses a kind of automatic cultivation equipment management method and system of clam, by obtaining historical clam cultivation environmental change data and clam behavior data, using association rule mining method to identify the influence characteristics of environment on clam behavior.Based on influence characteristics, assess the growth suitability of clam under different environmental parameters.At the same time, through camera equipment monitoring target clam cultivation equipment of clam behavior, capture its behavior state.Combined with influence characteristics and clam behavior state, assess the growth suitability of clam under current environment, generate growth suitability assessment data.Finally, according to the evaluation data, adjust the operating parameters of clam automatic cultivation equipment, realize the intelligent management of clam cultivation environment.This method optimizes the efficiency of clam cultivation by making full use of environmental data and behavior monitoring, improves the growth suitability, and provides an innovative management means for clam cultivation industry.

Description

Technical Field

[0001] This invention relates to the field of snail farming equipment management technology, and in particular to an automated snail farming equipment management method and system. Background Technology

[0002] As a part of aquaculture, snail farming faces limitations imposed by traditional management methods, resulting in low farming efficiency and difficulty in controlling growth suitability. Traditional farming methods are often based on experience and struggle to fully understand the complex behaviors and needs of snails under different environmental conditions.

[0003] Current snail farming management relies primarily on manual observation and adjustments, failing to fully utilize advanced technologies to improve production efficiency. While the application of sensor technology is gradually increasing, most still focus on single parameters, failing to provide a deep understanding of snail behavior and environmental changes.

[0004] This invention is based on in-depth research into snail farming, combining comprehensive analysis of environmental and snail behavioral data. It introduces association rule mining methods to systematically identify the impact characteristics of environmental changes on snail behavior, thereby more accurately assessing the snail's growth suitability under different environmental parameters. Real-time monitoring of snail behavior using camera equipment enables precise capture of its status. This advanced management method combines data science with snail farming, providing a novel solution for improving farming efficiency and achieving intelligent management. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this invention proposes an automated snail farming equipment management method and system.

[0006] The first aspect of this invention provides a method for managing automated snail farming equipment, comprising:

[0007] We acquire historical environmental change data and snail behavior data for snail farming, and use association rule mining methods to identify the impact characteristics of environmental changes on snail behavior;

[0008] The growth suitability of snails under different environmental parameters was assessed based on the aforementioned influencing characteristics.

[0009] The behavior of snails in the target snail farming equipment is monitored using video equipment to capture their behavioral status;

[0010] The growth suitability of snails in the current environment is evaluated based on the aforementioned influencing characteristics and the snails' behavioral state, resulting in growth suitability evaluation data.

[0011] Based on the growth suitability data, the operating parameters of the automated snail farming equipment are adjusted to obtain an equipment operating parameter adjustment plan.

[0012] In this solution, the acquisition of historical environmental change data and snail behavior data in snail farming, and the identification of the impact characteristics of environmental changes on snail behavior through association rule mining methods, specifically involves:

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

[0014] Video data of snail farming process is acquired using camera equipment, and the behavior data of snails is observed based on the video data, including movement, foraging, and resting behaviors.

[0015] The environmental change data and behavioral data are aligned based on time series, the Apriori algorithm is introduced, the data processing format of the Apriori algorithm is obtained, and the environmental change data and behavioral data are converted according to the data processing format.

[0016] The environmental change data is initialized into a data item set using the Apriori algorithm, high-frequency data items are extracted, and the corresponding behavioral data of the high-frequency data items at the same time are extracted to obtain the behavior corresponding to the high-frequency data items.

[0017] Correlation analysis is performed based on high-frequency data items and their corresponding behaviors to generate association rules. The relationship between environmental changes and snail behavior is then analyzed based on these association rules to obtain relational data.

[0018] Based on relational data, the influence characteristics of environmental changes on snail behavior are identified, including movement influence characteristics, feeding influence characteristics, and resting state influence characteristics.

[0019] In this scheme, the step of assessing the growth suitability of snails under different environmental parameters based on the aforementioned influencing characteristics specifically includes:

[0020] The impact of the changes in the aforementioned influencing characteristics on snail growth was obtained using big data technology;

[0021] Based on the aforementioned impact, an impact weight is assigned to each impact feature to obtain the impact weight data of the impact features on snail growth;

[0022] The optimal growth environment parameters for snails are obtained, and the suitability of snail growth under different environmental parameters is evaluated based on the influence weight data and the optimal growth environment parameter variation range, resulting in a growth suitability environmental parameter comparison table.

[0023] In this solution, the behavior monitoring of the snails in the target snail farming equipment using camera equipment to capture the snails' behavioral state specifically involves:

[0024] The system acquires real-time video image data of snail farming in the automated snail farming equipment based on camera equipment, and extracts video frame image data based on the video image data.

[0025] A historical snail image dataset is obtained, snail bounding boxes are annotated on the historical snail image dataset, and pixel values ​​are normalized on the annotated historical snail image dataset to obtain a preprocessed dataset;

[0026] A snail recognition model was constructed based on the YOLO algorithm. The preprocessed dataset was imported into the snail recognition model to extract and train the image features in the labeled bounding boxes.

[0027] The video frame image data is imported into the snail recognition algorithm to identify the snails in the image, and the snails are represented by bounding boxes to obtain the snail recognition results;

[0028] Based on the snail recognition results, the coordinates of a preset number of snails are randomly extracted from the video frame image data. The video frame images are sorted according to the time series of the video frame image data. The trajectory continuity analysis of the randomly extracted snail coordinates is performed to obtain the activity trajectory information within a preset time period.

[0029] Obtain standard activity trajectory data of various historical snail behavior states, 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 assessment of snail growth suitability in the current environment based on the influencing characteristics and the snail's behavioral state, to obtain growth suitability assessment data, specifically involves:

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

[0032] The linear regression characteristics of growth suitability under different environmental parameters and environmental data with snail behavior are imported into the growth suitability assessment model as the assessment standard for snail growth suitability for learning and training.

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

[0034] In this scheme, the adjustment of the operating parameters of the automated snail farming equipment based on the growth suitability data to obtain the equipment operating parameter adjustment scheme is as follows:

[0035] Based on the growth suitability assessment data, if the growth suitability is greater than the preset value, the current operating parameters of the automated snail farming equipment will be maintained.

[0036] If the growth suitability is less than the preset threshold, extract 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] Based on the behavioral suitability data, if the behavioral suitability is less than the preset behavioral suitability, the snails' behavior is induced by automatically dispensing feed through the aquaculture equipment.

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

[0039] A second aspect of the present invention also provides an automated snail farming equipment management system, the system comprising: a memory and a processor, wherein the memory includes a method program for managing the automated snail farming equipment, and when the processor executes the method program for managing the automated snail farming equipment, the following steps are implemented:

[0040] We acquire historical environmental change data and snail behavior data for snail farming, and use association rule mining methods to identify the impact characteristics of environmental changes on snail behavior;

[0041] The growth suitability of snails under different environmental parameters was assessed based on the aforementioned influencing characteristics.

[0042] The behavior of snails in the target snail farming equipment is monitored using video equipment to capture their behavioral status;

[0043] The growth suitability of snails in the current environment is evaluated based on the aforementioned influencing characteristics and the snails' behavioral state, resulting in growth suitability evaluation data.

[0044] Based on the growth suitability data, the operating parameters of the automated snail farming equipment are adjusted to obtain an equipment operating parameter adjustment plan.

[0045] In this scheme, the step of assessing the growth suitability of snails under different environmental parameters based on the aforementioned influencing characteristics specifically includes:

[0046] The impact of the changes in the aforementioned influencing characteristics on snail growth was obtained using big data technology;

[0047] Based on the aforementioned impact, an impact weight is assigned to each impact feature to obtain the impact weight data of the impact features on snail growth;

[0048] The optimal growth environment parameters for snails are obtained, and the suitability of snail growth under different environmental parameters is evaluated based on the influence weight data and the optimal growth environment parameter variation range, resulting in a growth suitability environmental parameter comparison table.

[0049] In this solution, the behavior monitoring of the snails in the target snail farming equipment using camera equipment to capture the snails' behavioral state specifically involves:

[0050] The system acquires real-time video image data of snail farming in the automated snail farming equipment based on camera equipment, and extracts video frame image data based on the video image data.

[0051] A historical snail image dataset is obtained, snail bounding boxes are annotated on the historical snail image dataset, and pixel values ​​are normalized on the annotated historical snail image dataset to obtain a preprocessed dataset;

[0052] A snail recognition model was constructed based on the YOLO algorithm. The preprocessed dataset was imported into the snail recognition model to extract and train the image features in the labeled bounding boxes.

[0053] The video frame image data is imported into the snail recognition algorithm to identify the snails in the image, and the snails are represented by bounding boxes to obtain the snail recognition results;

[0054] Based on the snail recognition results, the coordinates of a preset number of snails are randomly extracted from the video frame image data. The video frame images are sorted according to the time series of the video frame image data. The trajectory continuity analysis of the randomly extracted snail coordinates is performed to obtain the activity trajectory information within a preset time period.

[0055] Obtain standard activity trajectory data of various historical snail behavior states, 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 assessment of snail growth suitability in the current environment based on the influencing characteristics and the snail's behavioral state, to obtain growth suitability assessment data, specifically involves:

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

[0058] The linear regression characteristics of growth suitability under different environmental parameters and environmental data with snail behavior are imported into the growth suitability assessment model as the assessment standard for snail growth suitability for learning and training.

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

[0060] This invention discloses a management method and system for automated snail farming equipment. By acquiring historical environmental change data and snail behavior data from snail farming, it employs association rule mining to identify the influence characteristics of the environment on snail behavior. Based on these influence characteristics, the growth suitability of snails under different environmental parameters is assessed. Simultaneously, the snail behavior of the target snail farming equipment is monitored using camera equipment, capturing its behavioral state. Combining the influence characteristics and snail behavior state, the growth suitability of snails in the current environment is evaluated, generating growth suitability assessment data. Finally, the operating parameters of the automated snail farming equipment are adjusted based on the assessment data, achieving intelligent management of the snail farming environment. This method, by fully utilizing environmental data and behavioral monitoring, optimizes snail farming efficiency and improves growth suitability, providing an innovative management tool for the snail farming industry. Attached Figure Description

[0061] Figure 1 A flowchart of an automated snail farming equipment management method according to the present invention is shown;

[0062] Figure 2 A flowchart illustrating the behavioral states of snail capture according to the present invention is shown;

[0063] Figure 3 The flowchart illustrating the growth suitability assessment data obtained by this invention is shown.

[0064] Figure 4 A block diagram of an automated snail farming equipment management system according to the present invention is shown. Detailed Implementation

[0065] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0066] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0067] Figure 1 A flowchart of an automated snail farming equipment management method 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 automated snail farming equipment, comprising:

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

[0070] S104, Evaluate the growth suitability of snails under different environmental parameters based on the aforementioned influencing characteristics;

[0071] S106, using camera equipment to monitor the behavior of snails in the target snail farming equipment in order to capture the behavioral status of the snails;

[0072] S108, Based on the aforementioned influencing characteristics and the snail's behavioral state, the snail's growth suitability under the current environment is assessed to obtain growth suitability assessment data;

[0073] S110, Adjust the operating parameters of the automated snail farming equipment according to the growth suitability data to obtain the equipment operating parameter adjustment scheme.

[0074] It should be noted that the association rule mining method identifies the impact characteristics of environmental changes on snail behavior, assesses the snail's growth suitability under different parameters based on these characteristics, and then evaluates the snail's growth suitability in the current environment by capturing the snail's behavioral state. Based on this growth suitability, the operating parameters of the automated snail farming equipment are adjusted. This approach considers multiple aspects of snail growth suitability and optimizes the farming environment by adjusting the operating parameters of the automated snail farming equipment based on multi-faceted data, thereby improving farming efficiency and product quality. Furthermore, the association rule mining method's ability to identify the impact characteristics of environmental changes on snail behavior helps reveal the correlation between environmental factors and snail behavior, providing a basis for subsequent growth suitability assessments.

[0075] According to an embodiment of the present invention, the step of acquiring historical environmental change data and snail behavior data of snail farming, and identifying the influence characteristics of environmental changes on snail behavior through association rule mining methods, specifically includes:

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

[0077] Video data of snail farming process is acquired using camera equipment, and the behavior data of snails is observed based on the video data, including movement, foraging, and resting behaviors.

[0078] The environmental change data and behavioral data are aligned based on time series, the Apriori algorithm is introduced, the data processing format of the Apriori algorithm is obtained, and the environmental change data and behavioral data are converted according to the data processing format.

[0079] The environmental change data is initialized into a data item set using the Apriori algorithm, high-frequency data items are extracted, and the corresponding behavioral data of the high-frequency data items at the same time are extracted to obtain the behavior corresponding to the high-frequency data items.

[0080] Correlation analysis is performed based on high-frequency data items and their corresponding behaviors to generate association rules. The relationship between environmental changes and snail behavior is then analyzed based on these association rules to obtain relational data.

[0081] Based on relational data, the influence characteristics of environmental changes on snail behavior are identified, including movement influence characteristics, feeding influence characteristics, and resting state influence characteristics.

[0082] It should be noted that the Apriori algorithm is an association rule mining algorithm used to discover frequent itemsets and association rules in a dataset. The association rule describes the relationship between different items in the dataset. Association rules are usually in the form of "If-Then", indicating that an event or thing may occur under certain conditions. In this application, environmental change data represents the conditions in the association rule, and snail behavior data represents the event. The influence feature is the influence feature on snail behavior under the condition of environmental parameter changes. For example, the movement influence feature is the feature of whether the snail movement frequency increases or decreases when the ambient temperature decreases.

[0083] According to an embodiment of the present invention, the step of evaluating the growth suitability of snails under different environmental parameters based on the influencing characteristics specifically includes:

[0084] The impact of the changes in the aforementioned influencing characteristics on snail growth was obtained using big data technology;

[0085] Based on the aforementioned impact, an impact weight is assigned to each impact feature to obtain the impact weight data of the impact features on snail growth;

[0086] The optimal growth environment parameters for snails are obtained, and the suitability of snail growth under different environmental parameters is evaluated based on the influence weight data and the optimal growth environment parameter variation range, resulting in a growth suitability environmental parameter comparison table.

[0087] It should be noted that by assigning weights to different influencing characteristics, the system can quantitatively represent the relative impact of each influencing characteristic on snail growth. By utilizing the influence weight data and the range of variation of optimal growth environment parameters, the system can evaluate the suitability of snail growth under different environmental parameters and obtain a growth suitability environmental parameter comparison table. Based on previous big data analysis and weight assignment, the system can generate a growth suitability reference table for different combinations of environmental parameters, providing a scientific basis for subsequent adjustments to the aquaculture environment.

[0088] Figure 2 A flowchart illustrating the behavior of the present invention in capturing snails is shown.

[0089] According to an embodiment of the present invention, the step of monitoring the behavior of snails in the target snail farming equipment using a camera device to capture the behavioral state of the snails specifically involves:

[0090] S202, Based on the camera equipment, real-time video image data of snail farming in the target snail automated farming equipment is acquired, and video frame image data is extracted based on the video image data;

[0091] S204, obtain a historical snail image dataset, annotate the historical snail image dataset with snail bounding boxes, and normalize the pixel values ​​of the annotated historical snail image dataset to obtain a preprocessed dataset;

[0092] S206, a snail recognition model is constructed based on the YOLO algorithm. The preprocessed dataset is imported into the snail recognition model to extract and train the image features in the labeled bounding boxes.

[0093] S208: Import the video frame image data into the snail recognition algorithm to identify the snails in the image, and represent the snails with bounding boxes to obtain the snail recognition result;

[0094] S210, randomly extract the coordinates of a preset number of snails from the video frame image data based on the snail recognition results, sort the video frame images according to the time series of the video frame image data, and perform trajectory continuity analysis on the randomly extracted snail coordinates to obtain the activity trajectory information within a preset time period.

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

[0096] It should be noted that a snail recognition model is constructed using 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 movement, foraging, reproduction, resting, and escape states.

[0097] Figure 3 A flowchart illustrating the growth suitability assessment data obtained by this invention is shown.

[0098] According to an embodiment of the present invention, the assessment of snail growth suitability in the current environment based on the influencing characteristics and the snail's behavioral state to obtain growth suitability assessment data specifically includes:

[0099] S302, a growth suitability assessment model is constructed based on regression analysis algorithm. Relationship data is imported into the growth suitability assessment model for regression analysis to obtain the linear regression characteristics of environmental data and snail behavior.

[0100] S304, the linear regression characteristics of growth suitability under different environmental parameters and environmental data with snail behavior are imported into the growth suitability assessment model as the assessment standard for snail growth suitability for learning and training.

[0101] S306, Obtain the environmental parameters of the current snail farming equipment, import the environmental parameters and the current behavior status of the snails into the growth suitability assessment model to assess the suitability of the current environment, and obtain the growth suitability assessment data of the snails under the environmental parameters of the current farming equipment.

[0102] It should be noted that, in snail growth, not only the environment but also the snail's behavior affects its growth. Furthermore, different environmental parameters and varying snail behaviors can influence growth. For example, when the temperature in the breeding environment exceeds a certain threshold, snail escape behavior can promote snail metabolism. Therefore, regression analysis can be used to assess the suitability of snail growth for various factors, significantly improving our understanding of snail growth habits. This allows us to evaluate snail growth adaptability based on environmental parameters and snail behavior in the current breeding environment, greatly enhancing the accuracy and practicality of the assessment and avoiding inaccurate data evaluations due to the arbitrariness of judging from a single condition.

[0103] According to an embodiment of the present invention, the step of adjusting the operating parameters of the automated snail farming equipment based on the growth suitability data to obtain an equipment operating parameter adjustment scheme specifically includes:

[0104] Based on the growth suitability assessment data, if the growth suitability is greater than the preset value, the current operating parameters of the automated snail farming equipment will be maintained.

[0105] If the growth suitability is less than the preset threshold, extract 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] Based on the behavioral suitability data, if the behavioral suitability is less than the preset behavioral suitability, the snails' behavior is induced by automatically dispensing feed through the aquaculture equipment.

[0107] If both environmental suitability and behavioral suitability are outside the suitability range, first adjust the environmental parameters and determine whether the growth suitability of the adjusted environmental parameters is greater than the preset value. If it is not greater, then 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 aquaculture equipment can be optimized in a timely and effective manner under different conditions, thereby improving the growth suitability of snails, optimizing aquaculture results, and ultimately achieving the goal of automated management. Automated management can reduce human intervention, improve aquaculture efficiency, and ensure that snails achieve optimal growth under suitable environmental conditions.

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

[0110] Data on snail growth in multiple snail farming devices within a target area is obtained, including snail growth rate, health status, and individual snail size.

[0111] The difference data of snail growth in each snail farming device was analyzed by using the t-test method to obtain snail growth difference data;

[0112] The breeding effect of each snail breeding device is evaluated based on the snail growth difference data to obtain breeding effect evaluation data;

[0113] Extract historical operating parameter data and snail behavior data of the snail farming equipment with the best farming results;

[0114] The correlation coefficient analysis method was used to analyze the correlation changes between historical operating parameter data and snail behavior status data, and the correlation changes between historical parameter data and snail behavior status were obtained.

[0115] The equipment operating parameter adjustment scheme is optimized based on the correlation change data and the historical operating parameter data.

[0116] It should be noted that among multiple snail farming facilities in the target area, the farming effects may vary from facility to facility. By analyzing the correlation between changes in operating parameters and snail behavior of the facility with the best farming effect, and analyzing the correlation data of changes in operating parameters with changes in snail behavior, the historical operating parameter data of the facility with the best farming effect can be used as a reference scheme for other farming facilities. This optimizes the equipment operating parameter adjustment scheme, making the equipment operating parameter adjustment scheme more in line with actual farming needs, improving the farming effect of snail farming facilities, and increasing the efficiency of snail farming.

[0117] Figure 4 A block diagram of an automated snail farming equipment management system according to the present invention is shown.

[0118] A second aspect of the present invention also provides an automated snail farming equipment management system 4, which includes: a memory 41 and a processor 42. The memory includes a method program for managing the automated snail farming equipment. When the processor executes the method program, it performs the following steps:

[0119] We acquire historical environmental change data and snail behavior data for snail farming, and use association rule mining methods to identify the impact characteristics of environmental changes on snail behavior;

[0120] The growth suitability of snails under different environmental parameters was assessed based on the aforementioned influencing characteristics.

[0121] The behavior of snails in the target snail farming equipment is monitored using video equipment to capture their behavioral status;

[0122] The growth suitability of snails in the current environment is evaluated based on the aforementioned influencing characteristics and the snails' behavioral state, resulting in growth suitability evaluation data.

[0123] Based on the growth suitability data, the operating parameters of the automated snail farming equipment are adjusted to obtain an equipment operating parameter adjustment plan.

[0124] It should be noted that the association rule mining method identifies the impact characteristics of environmental changes on snail behavior, assesses the snail's growth suitability under different parameters based on these characteristics, and then evaluates the snail's growth suitability in the current environment by capturing the snail's behavioral state. Based on this growth suitability, the operating parameters of the automated snail farming equipment are adjusted. This approach considers multiple aspects of snail growth suitability and optimizes the farming environment by adjusting the operating parameters of the automated snail farming equipment based on multi-faceted data, thereby improving farming efficiency and product quality. Furthermore, the association rule mining method's ability to identify the impact characteristics of environmental changes on snail behavior helps reveal the correlation between environmental factors and snail behavior, providing a basis for subsequent growth suitability assessments.

[0125] According to an embodiment of the present invention, the step of acquiring historical environmental change data and snail behavior data of snail farming, and identifying the influence characteristics of environmental changes on snail behavior through association rule mining methods, specifically includes:

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

[0127] Video data of snail farming process is acquired using camera equipment, and the behavior data of snails is observed based on the video data, including movement, foraging, and resting behaviors.

[0128] The environmental change data and behavioral data are aligned based on time series, the Apriori algorithm is introduced, the data processing format of the Apriori algorithm is obtained, and the environmental change data and behavioral data are converted according to the data processing format.

[0129] The environmental change data is initialized into a data item set using the Apriori algorithm, high-frequency data items are extracted, and the corresponding behavioral data of the high-frequency data items at the same time are extracted to obtain the behavior corresponding to the high-frequency data items.

[0130] Correlation analysis is performed based on high-frequency data items and their corresponding behaviors to generate association rules. The relationship between environmental changes and snail behavior is then analyzed based on these association rules to obtain relational data.

[0131] Based on relational data, the influence characteristics of environmental changes on snail behavior are identified, including movement influence characteristics, feeding influence characteristics, and resting state influence characteristics.

[0132] It should be noted that the Apriori algorithm is an association rule mining algorithm used to discover frequent itemsets and association rules in a dataset. The association rule describes the relationship between different items in the dataset. Association rules are usually in the form of "If-Then", indicating that an event or thing may occur under certain conditions. In this application, environmental change data represents the conditions in the association rule, and snail behavior data represents the event. The influence feature is the influence feature on snail behavior under the condition of environmental parameter changes. For example, the movement influence feature is the feature of whether the snail movement frequency increases or decreases when the ambient temperature decreases.

[0133] According to an embodiment of the present invention, the step of evaluating the growth suitability of snails under different environmental parameters based on the influencing characteristics specifically includes:

[0134] The impact of the changes in the aforementioned influencing characteristics on snail growth was obtained using big data technology;

[0135] Based on the aforementioned impact, an impact weight is assigned to each impact feature to obtain the impact weight data of the impact features on snail growth;

[0136] The optimal growth environment parameters for snails are obtained, and the suitability of snail growth under different environmental parameters is evaluated based on the influence weight data and the optimal growth environment parameter variation range, resulting in a growth suitability environmental parameter comparison table.

[0137] It should be noted that by assigning weights to different influencing characteristics, the system can quantitatively represent the relative impact of each influencing characteristic on snail growth. By utilizing the influence weight data and the range of variation of optimal growth environment parameters, the system can evaluate the suitability of snail growth under different environmental parameters and obtain a growth suitability environmental parameter comparison table. Based on previous big data analysis and weight assignment, the system can generate a growth suitability reference table for different combinations of environmental parameters, providing a scientific basis for subsequent adjustments to the aquaculture environment.

[0138] According to an embodiment of the present invention, the step of monitoring the behavior of snails in the target snail farming equipment using a camera device to capture the behavioral state of the snails specifically involves:

[0139] The system acquires real-time video image data of snail farming in the automated snail farming equipment based on camera equipment, and extracts video frame image data based on the video image data.

[0140] A historical snail image dataset is obtained, snail bounding boxes are annotated on the historical snail image dataset, and pixel values ​​are normalized on the annotated historical snail image dataset to obtain a preprocessed dataset;

[0141] A snail recognition model was constructed based on the YOLO algorithm. The preprocessed dataset was imported into the snail recognition model to extract and train the image features in the labeled bounding boxes.

[0142] The video frame image data is imported into the snail recognition algorithm to identify the snails in the image, and the snails are represented by bounding boxes to obtain the snail recognition results;

[0143] Based on the snail recognition results, the coordinates of a preset number of snails are randomly extracted from the video frame image data. The video frame images are sorted according to the time series of the video frame image data. The trajectory continuity analysis of the randomly extracted snail coordinates is performed to obtain the activity trajectory information within a preset time period.

[0144] Obtain standard activity trajectory data of various historical snail behavior states, 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 using 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 movement, foraging, reproduction, resting, and escape states.

[0146] According to an embodiment of the present invention, the assessment of snail growth suitability in the current environment based on the influencing characteristics and the snail's behavioral state to obtain growth suitability assessment data specifically includes:

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

[0148] The linear regression characteristics of growth suitability under different environmental parameters and environmental data with snail behavior are imported into the growth suitability assessment model as the assessment standard for snail growth suitability for learning and training.

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

[0150] It should be noted that, in snail growth, not only the environment but also the snail's behavior affects its growth. Furthermore, different environmental parameters and varying snail behaviors can influence growth. For example, when the temperature in the breeding environment exceeds a certain threshold, snail escape behavior can promote snail metabolism. Therefore, regression analysis can be used to assess the suitability of snail growth for various factors, significantly improving our understanding of snail growth habits. This allows us to evaluate snail growth adaptability based on environmental parameters and snail behavior in the current breeding environment, greatly enhancing the accuracy and practicality of the assessment and avoiding inaccurate data evaluations due to the arbitrariness of judging from a single condition.

[0151] According to an embodiment of the present invention, the step of adjusting the operating parameters of the automated snail farming equipment based on the growth suitability data to obtain an equipment operating parameter adjustment scheme specifically includes:

[0152] Based on the growth suitability assessment data, if the growth suitability is greater than the preset value, the current operating parameters of the automated snail farming equipment will be maintained.

[0153] If the growth suitability is less than the preset threshold, extract 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] Based on the behavioral suitability data, if the behavioral suitability is less than the preset behavioral suitability, the snails' behavior is induced by automatically dispensing feed through the aquaculture equipment.

[0155] If both environmental suitability and behavioral suitability are outside the suitability range, first adjust the environmental parameters and determine whether the growth suitability of the adjusted environmental parameters is greater than the preset value. If it is not greater, then 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 aquaculture equipment can be optimized in a timely and effective manner under different conditions, thereby improving the growth suitability of snails, optimizing aquaculture results, and ultimately achieving the goal of automated management. Automated management can reduce human intervention, improve aquaculture efficiency, and ensure that snails achieve optimal growth under suitable environmental conditions.

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

[0158] Data on snail growth in multiple snail farming devices within a target area is obtained, including snail growth rate, health status, and individual snail size.

[0159] The difference data of snail growth in each snail farming device was analyzed by using the t-test method to obtain snail growth difference data;

[0160] The breeding effect of each snail breeding device is evaluated based on the snail growth difference data to obtain breeding effect evaluation data;

[0161] Extract historical operating parameter data and snail behavior data of the snail farming equipment with the best farming results;

[0162] The correlation coefficient analysis method was used to analyze the correlation changes between historical operating parameter data and snail behavior status data, and the correlation changes between historical parameter data and snail behavior status were obtained.

[0163] The equipment operating parameter adjustment scheme is optimized based on the correlation change data and the historical operating parameter data.

[0164] It should be noted that among multiple snail farming facilities in the target area, the farming effects may vary from facility to facility. By analyzing the correlation between changes in operating parameters and snail behavior of the facility with the best farming effect, and analyzing the correlation data of changes in operating parameters with changes in snail behavior, the historical operating parameter data of the facility with the best farming effect can be used as a reference scheme for other farming facilities. This optimizes the equipment operating parameter adjustment scheme, making the equipment operating parameter adjustment scheme more in line with actual farming needs, improving the farming effect of snail farming facilities, and increasing the efficiency of snail farming.

[0165] This invention discloses a management method and system for automated snail farming equipment. By acquiring historical environmental change data and snail behavior data from snail farming, it employs association rule mining to identify the influence characteristics of the environment on snail behavior. Based on these influence characteristics, the growth suitability of snails under different environmental parameters is assessed. Simultaneously, the snail behavior of the target snail farming equipment is monitored using camera equipment, capturing its behavioral state. Combining the influence characteristics and snail behavior state, the growth suitability of snails in the current environment is evaluated, generating growth suitability assessment data. Finally, the operating parameters of the automated snail farming equipment are adjusted based on the assessment data, achieving intelligent management of the snail farming environment. This method, by fully utilizing environmental data and behavioral monitoring, optimizes snail farming efficiency and improves growth suitability, providing an innovative management tool for the snail farming industry.

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

[0167] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0168] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0169] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0170] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0171] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A management method for automated snail farming equipment, characterized in that, Includes the following steps: We acquire historical environmental change data and snail behavior data for snail farming, and use association rule mining methods to identify the impact characteristics of environmental changes on snail behavior; The growth suitability of snails under different environmental parameters was assessed based on the aforementioned influencing characteristics. The behavior of snails in the target snail farming equipment is monitored using video equipment to capture their behavioral status; The growth suitability of snails in the current environment is evaluated based on the aforementioned influencing characteristics and the snails' behavioral state, resulting in growth suitability evaluation data. Based on the growth suitability data, the operating parameters of the automated snail farming equipment are adjusted to obtain an equipment operating parameter adjustment plan. The process involves acquiring historical environmental change data and snail behavior data related to snail farming, and then using association rule mining methods to identify the impact characteristics of environmental changes on snail behavior. Specifically: Obtain environmental change data during the historical snail farming process, including temperature, pH value, dissolved oxygen, and light conditions of the farming environment; Video data of snail farming process is acquired using camera equipment, and the behavior data of snails is observed based on the video data, including movement, foraging, and resting behaviors. The environmental change data and behavioral data are aligned based on time series, the Apriori algorithm is introduced, the data processing format of the Apriori algorithm is obtained, and the environmental change data and behavioral data are converted according to the data processing format. The environmental change data is initialized into a data item set using the Apriori algorithm, high-frequency data items are extracted, and the corresponding behavioral data of the high-frequency data items at the same time are extracted to obtain the behavior corresponding to the high-frequency data items. Correlation analysis is performed based on high-frequency data items and their corresponding behaviors to generate association rules. The relationship between environmental changes and snail behavior is then analyzed based on these association rules to obtain relational data. Based on relational data, the influence characteristics of environmental changes on snail behavior are identified, including movement influence characteristics, feeding influence characteristics, and resting state influence characteristics.

2. The method for managing automated snail farming equipment according to claim 1, characterized in that, The assessment of snail growth suitability under different environmental parameters based on the aforementioned influencing characteristics specifically involves: The impact of the changes in the aforementioned influencing characteristics on snail growth was obtained using big data technology; Based on the aforementioned impact, an impact weight is assigned to each impact feature to obtain the impact weight data of the impact features on snail growth; The optimal growth environment parameters for snails are obtained, and the suitability of snail growth under different environmental parameters is evaluated based on the influence weight data and the optimal growth environment parameter variation range, resulting in a growth suitability environmental parameter comparison table.

3. The method for managing automated snail farming equipment according to claim 1, characterized in that, The method of monitoring the behavior of snails in the target snail farming equipment using camera equipment to capture the behavioral state of the snails specifically includes: The system acquires real-time video image data of snail farming in the automated snail farming equipment based on camera equipment, and extracts video frame image data based on the video image data. A historical snail image dataset is obtained, snail bounding boxes are annotated on the historical snail image dataset, and pixel values ​​are normalized on the annotated historical snail image dataset to obtain a preprocessed dataset; A snail recognition model was constructed based on the YOLO algorithm. The preprocessed dataset was imported into the snail recognition model to extract and train the image features in the labeled bounding boxes. The video frame image data is imported into the snail recognition algorithm to identify the snails in the image, and the snails are represented by bounding boxes to obtain the snail recognition results; Based on the snail recognition results, the coordinates of a preset number of snails are randomly extracted from the video frame image data. The video frame images are sorted according to the time series of the video frame image data. The trajectory continuity analysis of the randomly extracted snail coordinates is performed to obtain the activity trajectory information within a preset time period. Obtain standard activity trajectory data of various historical snail behavior states, and compare the activity trajectory information with the standard activity trajectory data to obtain the current behavior state of the snail.

4. The method for managing automated snail farming equipment according to claim 1, characterized in that, The assessment of snail growth suitability in the current environment is performed based on the aforementioned influencing characteristics and snail behavior, yielding growth suitability assessment data, specifically as follows: A growth suitability assessment model was constructed based on regression analysis algorithm. Relationship data was imported into the growth suitability assessment model for regression analysis to obtain the linear regression characteristics of environmental data and snail behavior. The growth suitability under different environmental parameters and the linear regression characteristics of environmental data and snail behavior are imported into the growth suitability assessment model as the assessment standard for snail growth suitability for learning and training. The environmental parameters of the current snail farming equipment are obtained, and the environmental parameters and the current behavior status of the snails are imported into the growth suitability assessment model to assess the suitability of the current environment, thereby obtaining the growth suitability assessment data of the snails under the environmental parameters of the current farming equipment.

5. The method for managing automated snail farming equipment according to claim 1, characterized in that, The operating parameters of the automated snail farming equipment are adjusted based on the growth suitability data to obtain an equipment operating parameter adjustment scheme, specifically as follows: Based on the growth suitability assessment data, if the growth suitability is greater than the preset value, the current operating parameters of the automated snail farming equipment will be maintained. If the growth suitability is less than the preset threshold, extract 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. Based on the behavioral suitability data, if the behavioral suitability is less than the preset behavioral suitability, the snails' behavior is induced by automatically dispensing feed through the aquaculture equipment. If both environmental suitability and behavioral suitability are outside the suitability range, the environmental parameters are first adjusted to determine whether the growth suitability is greater than the preset value. If not, the snail behavior is induced until the growth suitability is greater than the preset value, thus obtaining the equipment operation parameter adjustment plan.

6. A management system for automated snail farming equipment, characterized in that, The automated snail farming equipment management system includes a storage unit and a processor. The storage unit includes a management method program for the automated snail farming equipment. When the processor executes the management method program for the automated snail farming equipment, it performs the following steps: We acquire historical environmental change data and snail behavior data for snail farming, and use association rule mining methods to identify the impact characteristics of environmental changes on snail behavior; The growth suitability of snails under different environmental parameters was assessed based on the aforementioned influencing characteristics. The behavior of snails in the target snail farming equipment is monitored using video equipment to capture their behavioral status; The growth suitability of snails in the current environment is evaluated based on the aforementioned influencing characteristics and the snails' behavioral state, resulting in growth suitability evaluation data. Based on the growth suitability data, the operating parameters of the automated snail farming equipment are adjusted to obtain an equipment operating parameter adjustment plan. The process involves acquiring historical environmental change data and snail behavior data related to snail farming, and then using association rule mining methods to identify the impact characteristics of environmental changes on snail behavior. Specifically: Obtain environmental change data during the historical snail farming process, including temperature, pH value, dissolved oxygen, and light conditions of the farming environment; Video data of snail farming process is acquired using camera equipment, and the behavior data of snails is observed based on the video data, including movement, foraging, and resting behaviors. The environmental change data and behavioral data are aligned based on time series, the Apriori algorithm is introduced, the data processing format of the Apriori algorithm is obtained, and the environmental change data and behavioral data are converted according to the data processing format. The environmental change data is initialized into a data item set using the Apriori algorithm, high-frequency data items are extracted, and the corresponding behavioral data of the high-frequency data items at the same time are extracted to obtain the behavior corresponding to the high-frequency data items. Correlation analysis is performed based on high-frequency data items and their corresponding behaviors to generate association rules. The relationship between environmental changes and snail behavior is then analyzed based on these association rules to obtain relational data. Based on relational data, the influence characteristics of environmental changes on snail behavior are identified, including movement influence characteristics, feeding influence characteristics, and resting state influence characteristics.

7. The automated snail farming equipment management system according to claim 6, characterized in that, The assessment of snail growth suitability under different environmental parameters based on the aforementioned influencing characteristics specifically involves: The impact of the changes in the aforementioned influencing characteristics on snail growth was obtained using big data technology; Based on the aforementioned impact, an impact weight is assigned to each impact feature to obtain the impact weight data of the impact features on snail growth; The optimal growth environment parameters for snails are obtained, and the suitability of snail growth under different environmental parameters is evaluated based on the influence weight data and the optimal growth environment parameter variation range, resulting in a growth suitability environmental parameter comparison table.

8. The automated snail farming equipment management system according to claim 6, characterized in that, The method of monitoring the behavior of snails in the target snail farming equipment using camera equipment to capture the behavioral state of the snails specifically includes: The system acquires real-time video image data of snail farming in the automated snail farming equipment based on camera equipment, and extracts video frame image data based on the video image data. A historical snail image dataset is obtained, snail bounding boxes are annotated on the historical snail image dataset, and pixel values ​​are normalized on the annotated historical snail image dataset to obtain a preprocessed dataset; A snail recognition model was constructed based on the YOLO algorithm. The preprocessed dataset was imported into the snail recognition model to extract and train the image features in the labeled bounding boxes. The video frame image data is imported into the snail recognition algorithm to identify the snails in the image, and the snails are represented by bounding boxes to obtain the snail recognition results; Based on the snail recognition results, the coordinates of a preset number of snails are randomly extracted from the video frame image data. The video frame images are sorted according to the time series of the video frame image data. The trajectory continuity analysis of the randomly extracted snail coordinates is performed to obtain the activity trajectory information within a preset time period. Obtain standard activity trajectory data of various historical snail behavior states, and compare the activity trajectory information with the standard activity trajectory data to obtain the current behavior state of the snail.

9. The automated snail farming equipment management system according to claim 6, characterized in that, The assessment of snail growth suitability in the current environment is performed based on the aforementioned influencing characteristics and snail behavior, yielding growth suitability assessment data, specifically as follows: A growth suitability assessment model was constructed based on regression analysis algorithm. Relationship data was imported into the growth suitability assessment model for regression analysis to obtain the linear regression characteristics of environmental data and snail behavior. The growth suitability under different environmental parameters and the linear regression characteristics of environmental data and snail behavior are imported into the growth suitability assessment model as the assessment standard for snail growth suitability for learning and training. The environmental parameters of the current snail farming equipment are obtained, and the environmental parameters and the current behavior status of the snails are imported into the growth suitability assessment model to assess the suitability of the current environment, thereby obtaining the growth suitability assessment data of the snails under the environmental parameters of the current farming equipment.

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