Monitoring management method and system for oyster breeding

Abstract

The invention discloses a monitoring management method and system for oyster breeding, and particularly relates to the field of breeding monitoring management. The method comprises the following steps: dividing the activeness of a fresh oyster population, and identifying the high-activeness fresh oyster population and the low-activeness fresh oyster population; respectively evaluating the growth behavior consistency and the environmental adaptability of the low-activity oyster population, and judging whether the early warning needs to be performed on the oyster breeding strategy or not; and evaluating the demand degree of the low-activity fresh oyster population for the diversity resources of the breeding area environment, and comprehensively analyzing the health state of the fresh oyster population in combination with the growth behavior consistency and environmental adaptability of the low-activity fresh oyster population. When health risks occur, the oyster breeding strategy is adjusted, the stability and production efficiency of oyster breeding can be improved, resource utilization is optimized, and the method is suitable for oyster farms of different scales.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture monitoring and management, and more specifically, to a monitoring and management method and system for oyster aquaculture. Background Art

[0002] Traditional oyster farming management mainly relies on manual monitoring and empirical judgment, which cannot accurately evaluate and adjust the growth status, environmental adaptability and resource demand of oyster populations. Existing farming management methods cannot timely and accurately predict and respond to health risks of oyster populations, which may lead to problems such as uneven growth, and usually lack segmentation and targeted management of oyster population activity.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a monitoring and management method and system for oyster farming to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A monitoring and management method for oyster farming comprises the following steps: Analyze the physiological behavior of oyster groups in the oyster farming area and divide the oyster populations in the farming area into high-activity oyster populations and low-activity oyster populations; Analyze the growth status of low-activity oyster populations in the culture area and evaluate the consistency of growth behavior of low-activity oyster populations; Collect data on the growth status of low-activity oyster populations; Use time series analysis methods to analyze the growth data of low-activity populations and identify persistent growth patterns; Calculate the standard deviation of individual growth states, assess the degree of variation in growth states within a population, and determine the variability of growth behavior; The results of the variance analysis were used to evaluate the consistency of the growth behavior of the low-activity oyster population; The growth consistency coefficient of active oyster populations is calculated as follows: ;in, represents the growth consistency coefficient of low-activity oyster populations; represents the total number of oyster individuals in the low-activity oyster population; represents the maximum value of variability; Analyze the dynamic changes of physical and chemical indicators of water bodies in the aquaculture area and evaluate the adaptability of low-activity oyster populations to the aquaculture area environment; Collect physical and chemical index data of water bodies in aquaculture areas; Model the dynamic changes of the collected water index data to reveal the time series change trend of the water index data; Calculate the environmental pressure on low-activity oyster populations based on the time series trend of water body indicator data; Based on the analysis results of the behavioral and physiological changes of low-activity oysters under different environmental pressures, the adaptability coefficient of low-activity oyster populations to the breeding area environment was evaluated; The adaptability coefficient of low-activity oyster population to the aquaculture environment is defined as , the calculation formula is: ;in, It indicates the adaptability coefficient of low-activity oyster population to the breeding area environment; represents the total number of oyster individuals in the low-activity oyster population; Based on the consistency of the growth behavior of low-activity oyster populations and their adaptability to the aquaculture environment, the growth stability of low-activity oyster populations is evaluated, and it is determined whether an early warning is needed for oyster aquaculture strategies; When early warning of oyster farming strategies is needed, the demand for diverse resources in the farming area environment by low-activity oyster populations is evaluated by analyzing the differences in consumption and growth responses of low-activity oyster populations to diverse resources in the farming area environment; Collect resource consumption data of low-activity oyster populations, combine it with growth status data of low-activity oyster populations, and establish a data set; Based on the data set, the diversity deviation values ​​of growth status and resource consumption are calculated; Based on the correlation between diversity deviation and resource demand, key resources affecting oyster growth were identified; Based on the growth response function, diversity deviation value and resource demand coefficient, the demand degree of low-activity oyster population for the diversity resources of the aquaculture area environment was evaluated; Defining the Demand Intensity Index , the calculation formula is: ;in, It represents the demand intensity index, reflecting the demand of low-activity oyster populations for the diverse resources of the breeding area environment; is a very small positive number; By conducting a comprehensive analysis of the consistency of the growth behavior of low-activity oyster populations, the adaptability of low-activity oyster populations to the environment of the breeding area, and the degree of demand for diverse resources in the breeding area environment, the health status of the oyster population can be predicted and the oyster breeding strategy can be adjusted when health risks arise.

[0006] In a preferred embodiment, the physiological behavior of the oyster population in the oyster culture area is analyzed, and the oyster population in the culture area is divided into a high-activity oyster population and a low-activity oyster population, specifically: Collect physiological and behavioral data of oyster populations in oyster farming areas and build a behavioral database; Preprocess the behavior database to extract effective physiological and behavioral characteristics of oyster populations; Through the behavioral characteristic clustering algorithm, the activity of oysters is analyzed in layers: based on the extracted effective physiological and behavioral characteristics, the clustering algorithm is used to analyze the oyster population in layers; the pre-processed effective behavioral characteristic data is input into the clustering algorithm to identify oyster populations with different activity levels; in the clustering process, the oyster individuals are divided into different activity levels according to the differences in the oysters' activity paths, shell opening and closing frequencies, and oxygen consumption; Based on the activity stratification results, the oyster population is divided into high-activity oyster population and low-activity oyster population.

[0007] In a preferred embodiment, based on the consistency of the growth behavior of the low-activity oyster population and the adaptability of the low-activity oyster population to the breeding area environment, the growth stability of the low-activity oyster population is evaluated, and it is determined whether an early warning is needed for the oyster breeding strategy, specifically: A growth consistency coefficient threshold is set, and the growth consistency coefficient is compared with the growth consistency coefficient threshold: when the growth consistency coefficient is less than the growth consistency coefficient threshold, an early warning is required for the breeding strategy; Set an adaptability coefficient threshold, and compare the adaptability coefficient with the adaptability coefficient threshold: when the adaptability coefficient is less than the adaptability coefficient threshold, an early warning of the breeding strategy is required; When the growth consistency coefficient is less than the growth consistency coefficient threshold and the adaptability coefficient is less than the adaptability coefficient threshold, an early warning is required for the breeding strategy.

[0008] In a preferred embodiment, the health status of the oyster population is predicted by comprehensively analyzing the consistency of the growth behavior of the low-activity oyster population, the adaptability of the low-activity oyster population to the breeding area environment, and the demand of the low-activity oyster population for the diverse resources of the breeding area environment, and when health risks occur, the oyster breeding strategy is adjusted, specifically: The growth consistency coefficient, adaptability coefficient and demand intensity index corresponding to the consistency of the growth behavior of the low-activity oyster population, the adaptability of the low-activity oyster population to the aquaculture area environment and the demand degree of the low-activity oyster population for the diverse resources of the aquaculture area environment were standardized to calculate the health status index of the oyster population. The calculation formula is: ;in, An index indicating the health status of the oyster population; are weighted coefficients, representing the influence weights of demand intensity index, adaptability coefficient and growth consistency coefficient on the health status of oysters.

[0009] Set a health status index threshold and compare the health status index with the health status index threshold: When the health status index is greater than or equal to the health status index threshold, the current breeding strategy does not need to be adjusted.

[0010] When the health status index is less than the health status index threshold, it indicates that the health status of the low-activity oyster population is at health risk and the breeding strategy needs to be adjusted.

[0011] On the other hand, the present invention provides a monitoring and management system for oyster farming, including: an activity classification module, a consistency evaluation module, an adaptability evaluation module, a farming strategy early warning module, a resource demand degree evaluation module and a farming strategy adjustment module.

[0012] Activity classification module: Analyze the physiological behavior of oyster groups in the oyster farming area and divide the oyster population in the farming area into high-activity oyster population and low-activity oyster population; Consistency assessment module: Analyze the growth status of low-activity oyster populations in the aquaculture area and evaluate the consistency of the growth behavior of low-activity oyster populations; Adaptability assessment module: Analyze the dynamic changes of physical and chemical indicators of water bodies in the aquaculture area to assess the adaptability of low-activity oyster populations to the aquaculture area environment; Farming strategy early warning module: Based on the consistency of the growth behavior of low-activity oyster populations and the adaptability of low-activity oyster populations to the farming area environment, the growth stability of low-activity oyster populations is evaluated, and it is determined whether an early warning of oyster farming strategies is needed; Resource demand assessment module: When early warning of oyster farming strategies is needed, the demand for diverse resources in the farming area by low-activity oyster populations is assessed by analyzing the differences in consumption and growth responses of low-activity oyster populations to diverse resources in the farming area. Farming strategy adjustment module: Through comprehensive analysis of the consistency of growth behavior of low-activity oyster populations, the adaptability of low-activity oyster populations to the farming area environment, and the degree of demand for diverse resources in the farming area environment, the health status of oyster populations is predicted, and oyster farming strategies are adjusted when health risks arise.

[0013] Technical effects and advantages of a monitoring and management method and system for oyster farming of the present invention: By dividing oyster populations into high-activity and low-activity populations, it is helpful to manage oyster populations with different activity levels; by evaluating the consistency of growth behavior of low-activity oyster populations and their adaptability to the breeding area environment, we can fully understand the growth status of oysters and their responsiveness to the environment, and evaluate the growth stability of low-activity oyster populations to determine whether early warning of oyster breeding strategies is needed; by evaluating the demand for diverse resources of low-activity oyster populations, combined with the consistency of growth behavior of low-activity oyster populations and their adaptability to the breeding area environment, the health status of oyster populations can be predicted. When the health status of oyster populations is not good, the breeding strategy can be adjusted to avoid waste of resources while improving the growth efficiency of oysters. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of a monitoring and management method for oyster farming according to the present invention; Figure 2 The present invention is a schematic structural diagram of a monitoring and management system for oyster farming. DETAILED DESCRIPTION

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

[0016] Example 1

[0017] Figure 1 The present invention provides a monitoring and management method for oyster farming, which comprises the following steps: Analyze the physiological behavior of oyster groups in the oyster farming area and divide the oyster populations in the farming area into high-activity oyster populations and low-activity oyster populations; Analyze the growth status of low-activity oyster populations in the culture area and evaluate the consistency of growth behavior of low-activity oyster populations; Analyze the dynamic changes of physical and chemical indicators of water bodies in the aquaculture area and evaluate the adaptability of low-activity oyster populations to the aquaculture area environment; Based on the consistency of the growth behavior of low-activity oyster populations and their adaptability to the aquaculture environment, the growth stability of low-activity oyster populations is evaluated, and it is determined whether an early warning is needed for oyster aquaculture strategies; When early warning of oyster farming strategies is needed, the consumption and growth responses of high-activity oyster populations and low-activity oyster populations to the diversity resources of the farming area environment are analyzed to assess the degree of demand for the diversity resources of the farming area environment by high-activity oyster populations and low-activity oyster populations; By conducting a comprehensive analysis of the consistency of the growth behavior of low-activity oyster populations, the adaptability of low-activity oyster populations to the environment of the breeding area, and the degree of demand for diverse resources in the breeding area environment, the health status of the oyster population can be predicted and the oyster breeding strategy can be adjusted when health risks arise.

[0018] The physiological behavior of oyster groups in the oyster farming area was analyzed, and the oyster populations in the farming area were divided into high-activity oyster populations and low-activity oyster populations, specifically: Collect physiological and behavioral data of oyster groups in oyster farming areas and build a behavioral database: In oyster farming areas, use a variety of sensors to collect physiological and behavioral data of oyster groups. These sensors include temperature sensors, dissolved oxygen sensors, pH sensors, and biosensors, which monitor the physiological behavior of oysters in real time, such as activity paths, shell opening and closing frequency, and oxygen consumption. A behavioral database is built based on the collected physiological and behavioral data of oyster groups. In order to improve the diversity and comprehensiveness of data, data collection should include different environmental conditions to ensure that the physiological and behavioral changes of oysters in different environments can be fully recorded.

[0019] Use data preprocessing algorithms to preprocess the behavior database and extract effective physiological and behavioral characteristics of oyster groups: After the behavior database is established, the collected data needs to be preprocessed. Data preprocessing uses a combination of multiple algorithms, including outlier detection, data smoothing, and noise filtering. Through outlier detection algorithms, such as the triple standard deviation method, deviated abnormal data points are identified and removed. Use data smoothing algorithms, such as the moving average algorithm, to handle changes caused by sensor fluctuations or external environmental interference to ensure data stability and accuracy. Use a low-pass filter to filter noise to extract effective physiological and behavioral characteristics of oyster groups, such as activity paths, shell opening and closing frequencies, and oxygen consumption.

[0020] The activity of oysters is analyzed in layers through the behavioral characteristic clustering algorithm: Based on the extracted effective physiological and behavioral characteristics, the clustering algorithm is used to analyze the oyster population in layers. The pre-processed effective behavioral characteristic data is input into the clustering algorithm to identify oyster populations with different activity levels. In the clustering process, the oyster individuals are divided into different activity levels according to the differences in the oysters' activity paths, shell opening and closing frequencies, and oxygen consumption. Specifically, the clustering algorithm will calculate different cluster centers based on the distribution of effective physiological and behavioral characteristics to form multiple activity levels.

[0021] Based on the results of activity stratification, the oyster population is divided into high-activity oyster population and low-activity oyster population: Based on the results of activity stratification, the oyster population in the breeding area is divided into high-activity oyster population and low-activity oyster population. Among them, the high-activity oyster population refers to the oyster population of high-activity oysters; the low-activity oyster population refers to the oyster population of low-activity oysters.

[0022] The growth status of low-activity oyster populations in the aquaculture area was analyzed to evaluate the consistency of the growth behavior of low-activity oyster populations, specifically: Collect growth status data of low-activity oyster populations, specifically: In the oyster farming area, a variety of sensor devices are used to monitor the growth status of low-activity oyster populations in real time. The collected data include changes in oyster shell thickness and weight growth, expressed as symbols. Indicates that The number that indicates the individual oyster.

[0023] In order to ensure that the growth status data of different oyster individuals are comparable, the data needs to be standardized. The standardized growth data is .

[0024] Using time series analysis methods, we conduct trend analysis on the growth data of low-activity populations and identify sustained growth patterns, specifically: In order to understand the growth trend of low-activity oyster populations, time series analysis was performed on the standardized growth data. Represents individual oysters At the point in time The standard growth trend data is calculated as follows: ;in, Oyster individual At the point in time Standard growth trend data for Functions representing time series analysis, used to extract growth trend information.

[0025] Use the moving average method to smooth the time series data and obtain the smoothed growth trend data , the calculation formula is: ;in, Indicates time point Smoothed growth trend data; Indicates the window size of the moving average; Indicates time point The original trend data.

[0026] By smoothing, short-term fluctuations in growth data can be filtered out, revealing the long-term growth trend of low-activity populations. Trend data reflects the consistent growth pattern of individual oysters over a period of time.

[0027] Calculate the standard deviation of individual growth states, assess the degree of difference in growth states within the population, and determine the variability of growth behavior, specifically: The growth state of a low-activity population may vary between individuals, and this difference can be quantified by calculating variability. For oyster individuals The growth state variability is calculated as follows: ;in, Represents individual oysters variability in growth status; Indicates the total length of the time period; Indicates time point Smoothed growth trend data; Represents individual oysters Average smoothed growth trend over the time period.

[0028] Variability The size of reflects the degree of fluctuation in the growth status of individual oysters. The greater the variability, the more unstable the growth behavior of individual oysters; the smaller the variability, the more consistent the growth behavior of individual oysters.

[0029] The results of the variation analysis were used to evaluate the consistency of the growth behavior of the low-activity oyster population, specifically: Variability among individual oysters , further evaluate the growth consistency of low-activity oyster populations. The growth consistency coefficient of active oyster populations is defined as , the calculation formula is: ;in, It represents the growth consistency coefficient of low-activity oyster populations, ranging from 0 to 1; represents the total number of oyster individuals in the low-activity oyster population; Represents the maximum value of variability.

[0030] The value of the growth consistency coefficient reflects the stability of the growth behavior of individual oysters within a low-activity oyster population. When the value of the growth consistency coefficient is close to 1, the growth behavior of individual oysters is similar, the growth trend and pattern change is small, and the oyster population shows a high consistency, indicating that the current conditions are suitable for the overall growth of the oyster population. When the value of the growth consistency coefficient is close to 0, it means that the growth behavior of individual oysters within the oyster population is quite different, and the growth rate and behavior pattern of different individual oysters fluctuate greatly.

[0031] The dynamic changes of physical and chemical indicators of the water in the aquaculture area were analyzed to evaluate the adaptability of the low-activity oyster population to the aquaculture area environment. Specifically: Collect physical and chemical index data of water bodies in aquaculture areas, specifically: In the oyster farming area, the water temperature, salinity, dissolved oxygen, pH value and ammonia nitrogen content are collected in real time. The collected water index data are: ,in Indicates the water body index type. The sampling time point of water body index data is , then at time point The collected water index data are expressed as The median filter method is used to smooth the collected water index data to obtain the smoothed data. .

[0032] The collected water index data are dynamically modeled to reveal the time series change trend of the water index data, specifically: The dynamic change model of water body index data is defined as , used to describe water body indicators at a point in time The dynamic change model expression of water body index data is: ;in, Is the water body index at the time point ; Represents the weight coefficient of the change of water body indicators, which is used to adjust the sensitivity of the model; It is the basic change value of water body indicators, indicating the baseline of water body indicators in the initial state.

[0033] Based on the time series trend of water body index data, the environmental pressure on low-activity oyster populations is calculated, specifically: Based on the dynamic changes of physical and chemical indicators of water bodies, the environmental pressure of low-activity oyster populations was evaluated. The environmental pressure value was defined as , the calculation formula is: ;in, Indicates time point The environmental pressure value at ; Indicates the total number of physical and chemical indicators of water bodies; The pressure weight coefficient of the water body index is used to indicate the degree of impact of each index on the environmental pressure of oysters.

[0034] When setting the pressure weights of water indicators, it is necessary to optimize them according to the physiological sensitivity of oysters. For example, dissolved oxygen has a greater impact on the growth of oysters, so the corresponding pressure weight coefficient is set to a larger value; salinity changes have a smaller impact on oysters, so the corresponding pressure weight coefficient is set to a smaller value.

[0035] The environmental pressure value reflects the degree of abnormality of the physical and chemical indicators of the water body. The higher the environmental pressure value, the higher the degree of abnormality of the physical and chemical indicators of the water body, indicating that the environmental pressure faced by oysters is greater; the smaller the environmental pressure value, the lower the degree of abnormality of the physical and chemical indicators of the water body, indicating that the environment faced by oysters is more stable.

[0036] The behavioral and physiological changes of low-activity oysters under different environmental stresses were analyzed, specifically: Based on the changes in environmental pressure, the physiological behavior of low-activity oyster populations is monitored in real time, including changes in shell thickness, feeding frequency, and activity frequency. The adaptive response value of individual oysters is set to , the calculation formula is: ;in, represents the adaptive response value of individual oysters; Represents individual oysters At the point in time The length of the activity path; Represents individual oysters At the point in time The frequency of opening and closing shells; Represents individual oysters At the point in time Oxygen consumption; , , are the response weight coefficients for activity path, shell opening and closing frequency, and oxygen consumption, respectively.

[0037] Adaptive response values ​​are expressed at time points When the oyster individual The comprehensive response ability to the current environmental pressure. The higher the adaptive response value, the stronger the adaptability of the oyster individual to the current environment; the lower the adaptive response value, the physiological behavior of the oyster individual is not adapted to the environmental pressure.

[0038] Based on the analysis results of the behavioral and physiological changes of low-activity oysters under different environmental pressures, the adaptability coefficient of low-activity oyster populations to the aquaculture area environment was evaluated, specifically: Based on the adaptive response value, the overall environmental adaptability of the low-activity oyster population was evaluated. The adaptability coefficient of the low-activity oyster population to the aquaculture area environment was defined as , the calculation formula is: ;in, It indicates the adaptability coefficient of low-activity oyster population to the breeding area environment; Represents the total number of oyster individuals in the low-activity oyster population.

[0039] The adaptability coefficient is used to reflect the overall adaptability of low-activity oyster populations to the dynamic changes in the environment within the breeding area. The higher the value of the adaptability coefficient, the better the adaptability of the low-activity oyster population to the current environmental conditions; the lower the value of the adaptability coefficient, the worse the adaptability of the low-activity oyster population in the current environment.

[0040] Based on the consistency of the growth behavior of low-activity oyster populations and their adaptability to the aquaculture environment, the growth stability of low-activity oyster populations is evaluated, and it is determined whether an early warning is needed for oyster aquaculture strategies. Specifically: A growth consistency coefficient threshold is set. The growth consistency coefficient threshold is set by professional and technical personnel in this field based on other actual conditions such as the changing trend of growth data of low-activity oyster populations.

[0041] Compare the growth consistency coefficient to the growth consistency coefficient threshold: When the growth consistency coefficient is greater than or equal to the growth consistency coefficient threshold, it indicates that the growth behavior of the low-activity oyster population is highly consistent and the growth pattern is stable, which means that the current breeding strategy is relatively effective and there is no need for early warning of the breeding strategy.

[0042] When the growth consistency coefficient is less than the growth consistency coefficient threshold, it means that the growth behavior consistency of the low-activity oyster population is poor and the growth pattern is unstable, indicating that the growth conditions among individuals in the oyster population are uneven and an early warning of the breeding strategy may be needed.

[0043] The adaptability coefficient threshold is set by professional and technical personnel in this field based on other actual conditions such as the impact of low-activity oyster populations on the environmental conditions of the breeding area on the growth of oyster populations.

[0044] Compare the fitness coefficient to the fitness coefficient threshold: When the adaptability coefficient is greater than or equal to the adaptability coefficient threshold, it indicates that the low-activity oyster population has a strong adaptability to the current breeding environment and can better cope with environmental pressure, which means that the low-activity oyster population grows stably in the current environment and there is no need for early warning of the breeding strategy.

[0045] When the fitness coefficient is less than the fitness coefficient threshold, it indicates that the low-activity oyster population has a weak ability to adapt to the aquaculture environment and may experience growth stagnation or other adverse reactions, which may require early warning of the aquaculture management strategy.

[0046] When the growth consistency coefficient is less than the growth consistency coefficient threshold and the adaptability coefficient is less than the adaptability coefficient threshold, it indicates that the growth behavior of the low-activity oyster population fluctuates greatly and has poor adaptability to the environment, and faces a higher growth risk. At this time, the growth stability of the low-activity oyster population is poor, and an early warning for the breeding strategy is required.

[0047] When early warning of oyster farming strategies is needed, the consumption and growth response differences of low-activity oyster populations to the diversity resources of the farming area environment are analyzed to assess the demand of low-activity oyster populations for the diversity resources of the farming area environment, specifically: The resource consumption data of low-activity oyster populations were collected and combined with the growth status data of low-activity oyster populations to establish a data set, specifically: In the oyster farming area, the consumption of diverse resources such as microalgae, plankton, and bait by low-activity oyster populations is collected through sensor equipment to establish a complete data set. Among them, the consumption of diverse resources by low-activity oyster populations is defined as .in, Indicates The consumption of resources, Represents the total number of resource species; the growth status data of low-activity oyster populations is defined as .in, Indicates Growth indicators, Indicates the total number of growth indicators. Combine the consumption of diversity resources by low-activity oyster populations and the growth status data of low-activity oyster populations to obtain a data set.

[0048] Based on the data set, the diversity deviation values ​​of growth status and resource consumption are calculated, specifically: In order to quantify the deviation of growth response of low-activity oyster populations under different diversity resource consumption, the growth response function was defined , which is used to indicate the contribution of specific resource consumption to the growth index. By calculating the changes in the growth response function under different resource combinations, the diversity deviation value is obtained. , the calculation formula is: ;in, is the average value of the growth response function under all resource combinations. Diversity deviation value It reflects the growth volatility of oysters under various resource consumption. The larger the diversity deviation value, the more mismatched the growth status is with resource consumption.

[0049] Based on the correlation between diversity deviation and resource requirements, the key resources affecting oyster growth were identified, specifically: Defining resource requirement factors , which is used to quantify the intensity of demand for specific resources by low-activity oyster populations. The resource demand coefficient is calculated as: ;in, is the resource demand coefficient, indicating the The proportion of a resource in total resource demand reflects the relative importance of the resource to growth.

[0050] Using the correlation coefficient The correlation between the diversity deviation value and the resource demand coefficient was analyzed. If the correlation coefficient was greater than 0.5, it indicated that the specific resource had a greater impact on the growth of oysters and was a key resource affecting the growth of oysters.

[0051] Based on the growth response function, diversity deviation value and resource demand coefficient, the demand degree of low-activity oyster population for the diversity resources of the aquaculture area environment is evaluated, specifically: Based on the growth response function, diversity deviation value and resource demand coefficient, the demand intensity index is defined , the calculation formula is: ;in, It represents the demand intensity index, reflecting the demand of low-activity oyster populations for the diverse resources of the breeding area environment; is a very small positive number.

[0052] The larger the demand intensity index, the higher the overall demand of low-activity oyster populations for diverse resources in the aquaculture area, which means that the diverse resources in the current aquaculture area cannot meet the growth needs of oysters and the growth state of oysters is poor. A higher demand intensity index usually reflects a mismatch between resource consumption and growth response, indicating that oysters have a stronger demand for certain key resources, but these key resources may be unevenly distributed.

[0053] Through a comprehensive analysis of the consistency of the growth behavior of low-activity oyster populations, the adaptability of low-activity oyster populations to the breeding area environment, and the degree of demand for the diversity of resources in the breeding area environment, the health status of the oyster population is predicted, and when health risks occur, the oyster farming strategy is adjusted, specifically: The growth consistency coefficient, adaptability coefficient and demand intensity index corresponding to the consistency of the growth behavior of the low-activity oyster population, the adaptability of the low-activity oyster population to the aquaculture area environment and the demand degree of the low-activity oyster population for the diverse resources of the aquaculture area environment were standardized to calculate the health status index of the oyster population. The calculation formula is: ;in, An index indicating the health status of the oyster population; are weighted coefficients, representing the influence weights of demand intensity index, adaptability coefficient and growth consistency coefficient on the health status of oysters.

[0054] Set a health status index threshold, which is set by professionals in the field based on the expected health status of the low-activity oyster population, and compare the health status index with the health status index threshold: When the health status index is greater than or equal to the health status index threshold, it means that the low-activity oyster population is in a healthy or relatively stable state, and the current breeding strategy and resource allocation can meet the growth needs of oysters and no adjustment is needed.

[0055] When the health status index is less than the health status index threshold, it indicates that the health status of the low-activity oyster population is at health risk, and the aquaculture strategy needs to be adjusted to improve the growth environment and resource allocation of oysters. For example, the supply of key resources can be increased, water quality parameters can be optimized, and aquaculture space can be reconfigured.

[0056] Example 2 The difference between Example 2 of the present invention and Example 1 is that this example introduces a monitoring and management system for oyster farming.

[0057] Figure 2 A structural schematic diagram of a monitoring and management system for oyster farming of the present invention is given, which includes: an activity division module, a consistency evaluation module, an adaptability evaluation module, a farming strategy early warning module, a resource demand degree evaluation module and a farming strategy adjustment module.

[0058] Activity classification module: Analyze the physiological behavior of oyster groups in the oyster farming area and divide the oyster population in the farming area into high-activity oyster population and low-activity oyster population; Consistency assessment module: Analyze the growth status of low-activity oyster populations in the aquaculture area and evaluate the consistency of the growth behavior of low-activity oyster populations; Adaptability assessment module: Analyze the dynamic changes of physical and chemical indicators of water bodies in the aquaculture area to assess the adaptability of low-activity oyster populations to the aquaculture area environment; Farming strategy early warning module: Based on the consistency of the growth behavior of low-activity oyster populations and the adaptability of low-activity oyster populations to the farming area environment, the growth stability of low-activity oyster populations is evaluated, and it is determined whether an early warning of oyster farming strategies is needed; Resource demand assessment module: When early warning of oyster farming strategies is needed, the demand for diverse resources in the farming area by low-activity oyster populations is assessed by analyzing the differences in consumption and growth responses of low-activity oyster populations to diverse resources in the farming area. Farming strategy adjustment module: Through comprehensive analysis of the consistency of growth behavior of low-activity oyster populations, the adaptability of low-activity oyster populations to the farming area environment, and the degree of demand for diverse resources in the farming area environment, the health status of oyster populations is predicted, and oyster farming strategies are adjusted when health risks arise.

[0059] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0060] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0061] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0062] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0063] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0064] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0066] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0067] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0068] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A monitoring and management method for oyster farming, characterized in that: The specific steps include: Analyze the physiological behavior of oyster groups in the oyster farming area and divide the oyster populations in the farming area into high-activity oyster populations and low-activity oyster populations; Analyze the growth status of low-activity oyster populations in the culture area and evaluate the consistency of growth behavior of low-activity oyster populations; Collect data on the growth status of low-activity oyster populations; Use time series analysis methods to analyze the growth data of low-activity populations and identify persistent growth patterns; Calculate the standard deviation of individual growth states, assess the degree of variation in growth states within a population, and determine the variability of growth behavior; The results of the variance analysis were used to evaluate the consistency of the growth behavior of the low-activity oyster population; The growth consistency coefficient of active oyster populations is calculated as follows: ;in, represents the growth consistency coefficient of low-activity oyster populations; represents the total number of oyster individuals in the low-activity oyster population; represents the maximum value of variability; Analyze the dynamic changes of physical and chemical indicators of water bodies in the aquaculture area and evaluate the adaptability of low-activity oyster populations to the aquaculture area environment; Collect physical and chemical index data of water bodies in aquaculture areas; Model the dynamic changes of the collected water index data to reveal the time series change trend of the water index data; Calculate the environmental pressure on low-activity oyster populations based on the time series trend of water body indicator data; Based on the analysis results of the behavioral and physiological changes of low-activity oysters under different environmental pressures, the adaptability coefficient of low-activity oyster populations to the breeding area environment was evaluated; The adaptability coefficient of low-activity oyster population to the aquaculture environment is defined as , the calculation formula is: ;in, It indicates the adaptability coefficient of low-activity oyster population to the breeding area environment; represents the total number of oyster individuals in the low-activity oyster population; Based on the consistency of the growth behavior of low-activity oyster populations and their adaptability to the aquaculture environment, the growth stability of low-activity oyster populations is evaluated, and it is determined whether an early warning is needed for oyster aquaculture strategies; When early warning of oyster farming strategies is needed, the demand for diverse resources in the farming area environment by low-activity oyster populations is evaluated by analyzing the differences in consumption and growth responses of low-activity oyster populations to diverse resources in the farming area environment; Collect resource consumption data of low-activity oyster populations, combine it with growth status data of low-activity oyster populations, and establish a data set; Based on the data set, the diversity deviation values ​​of growth status and resource consumption are calculated; Based on the correlation between diversity deviation and resource demand, key resources affecting oyster growth were identified; Based on the growth response function, diversity deviation value and resource demand coefficient, the demand degree of low-activity oyster population for the diversity resources of the aquaculture area environment was evaluated; Defining the Demand Intensity Index , the calculation formula is: ;in, It represents the demand intensity index, reflecting the demand of low-activity oyster populations for the diverse resources of the breeding area environment; is a very small positive number; By conducting a comprehensive analysis of the consistency of the growth behavior of low-activity oyster populations, the adaptability of low-activity oyster populations to the environment of the breeding area, and the degree of demand for diverse resources in the breeding area environment, the health status of the oyster population can be predicted and the oyster breeding strategy can be adjusted when health risks arise.

2. A monitoring and management method for oyster farming according to claim 1, characterized in that: The physiological behavior of oyster groups in the oyster farming area was analyzed, and the oyster populations in the farming area were divided into high-activity oyster populations and low-activity oyster populations, including: Collect physiological and behavioral data of oyster populations in oyster farming areas and build a behavioral database; Preprocess the behavior database to extract effective physiological and behavioral characteristics of oyster populations; Through the behavioral characteristic clustering algorithm, the activity of oysters is analyzed in layers: based on the extracted effective physiological and behavioral characteristics, the clustering algorithm is used to analyze the oyster population in layers; the pre-processed effective behavioral characteristic data is input into the clustering algorithm to identify oyster populations with different activity levels; in the clustering process, the oyster individuals are divided into different activity levels according to the differences in the oysters' activity paths, shell opening and closing frequencies, and oxygen consumption; Based on the activity stratification results, the oyster population is divided into high-activity oyster population and low-activity oyster population.

3. A monitoring and management method for oyster farming according to claim 2, characterized in that: Based on the consistency of the growth behavior of low-activity oyster populations and their adaptability to the aquaculture environment, the growth stability of low-activity oyster populations is evaluated, and it is determined whether an early warning is needed for oyster aquaculture strategies, including: A growth consistency coefficient threshold is set, and the growth consistency coefficient is compared with the growth consistency coefficient threshold: when the growth consistency coefficient is less than the growth consistency coefficient threshold, an early warning is required for the breeding strategy; Set an adaptability coefficient threshold, and compare the adaptability coefficient with the adaptability coefficient threshold: when the adaptability coefficient is less than the adaptability coefficient threshold, an early warning of the breeding strategy is required; When the growth consistency coefficient is less than the growth consistency coefficient threshold and the adaptability coefficient is less than the adaptability coefficient threshold, an early warning is required for the breeding strategy.

4. A monitoring and management method for oyster farming according to claim 3, characterized in that: Through comprehensive analysis of the consistency of the growth behavior of low-activity oyster populations, the adaptability of low-activity oyster populations to the aquaculture environment, and the degree of demand for diverse resources in the aquaculture environment, the health status of oyster populations can be predicted, and when health risks occur, oyster aquaculture strategies can be adjusted, including: The growth consistency coefficient, adaptability coefficient and demand intensity index corresponding to the consistency of the growth behavior of the low-activity oyster population, the adaptability of the low-activity oyster population to the aquaculture area environment and the demand degree of the low-activity oyster population for the diverse resources of the aquaculture area environment were standardized to calculate the health status index of the oyster population. The calculation formula is: ;in, An index indicating the health status of the oyster population; are weighted coefficients, representing the influence weights of demand intensity index, adaptability coefficient and growth consistency coefficient on the health status of oysters; Set a health status index threshold and compare the health status index with the health status index threshold: When the health status index is greater than or equal to the health status index threshold, the current breeding strategy does not need to be adjusted; When the health status index is less than the health status index threshold, it indicates that the health status of the low-activity oyster population is at health risk and the breeding strategy needs to be adjusted.

5. A monitoring and management system for oyster farming, used to implement the monitoring and management method for oyster farming according to any one of claims 1 to 4, characterized in that: include: Activity classification module, consistency assessment module, adaptability assessment module, breeding strategy early warning module, resource demand assessment module and breeding strategy adjustment module; Activity classification module: Analyze the physiological behavior of oyster groups in the oyster farming area and divide the oyster population in the farming area into high-activity oyster population and low-activity oyster population; Consistency assessment module: Analyze the growth status of low-activity oyster populations in the aquaculture area and evaluate the consistency of the growth behavior of low-activity oyster populations; Adaptability assessment module: Analyze the dynamic changes of physical and chemical indicators of water bodies in the aquaculture area to assess the adaptability of low-activity oyster populations to the aquaculture area environment; Farming strategy early warning module: Based on the consistency of the growth behavior of low-activity oyster populations and the adaptability of low-activity oyster populations to the farming area environment, the growth stability of low-activity oyster populations is evaluated, and it is determined whether an early warning of oyster farming strategies is needed; Resource demand assessment module: When early warning of oyster farming strategies is needed, the demand for diverse resources in the farming area by low-activity oyster populations is assessed by analyzing the differences in consumption and growth responses of low-activity oyster populations to diverse resources in the farming area. Farming strategy adjustment module: Through comprehensive analysis of the consistency of growth behavior of low-activity oyster populations, the adaptability of low-activity oyster populations to the farming area environment, and the degree of demand for diverse resources in the farming area environment, the health status of oyster populations is predicted, and oyster farming strategies are adjusted when health risks arise.

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