Shrimp population health condition assessment method based on deep clustering analysis

Abstract

The invention relates to the technical field of data processing, in particular to a shrimp group health condition assessment method based on deep clustering analysis, and the method comprises the steps: monitoring health parameters; determining a shrimp group dispersion grid; determining a temporary grid; identifying a non-shrimp group abnormal grid; calculating an actual health index; and adjusting the grid step length and evaluating health. According to the method, the health condition of the shrimp group can be accurately monitored and evaluated by combining physiological and behavior data such as real-time density, saturation and activeness, abnormal grids are eliminated step by step, more representative grids are screened out, evaluation deviation caused by environmental fluctuation or measurement errors is avoided, and the evaluation accuracy is improved. The dynamic monitoring of the health condition of the prawn group is realized, and the grid step length is automatically adjusted by comparing the ideal health index with the actual health index, so that the health assessment precision and reliability are improved, and the problem of inaccurate health assessment result caused by unstable environmental parameters is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a shrimp population health status assessment method based on deep cluster analysis. Background Art

[0002] With the growing demand for biological asset management, how to achieve efficient, accurate and real-time assessment of the health status of biological populations has become an urgent problem to be solved. Especially in the face of complex environmental changes and the influence of various internal and external factors, how to accurately monitor and evaluate the health status of biological populations through automation technology is an important challenge to achieve refined management and optimize resource allocation.

[0003] Patent document with publication number CN117636146A discloses a system and method for intelligent identification and evaluation of shrimp live asset inventory, the method comprising: capturing shrimp images under various lighting conditions; processing and identifying shrimp activity images through micro-organism body recognition and counting algorithms to achieve accurate identification of shrimp shape, size, color, species and quantity information; evaluating shrimp live assets based on shrimp shape, size, color, species, quantity information and environmental parameters; issuing an alarm to the user when the health status or quantity of the shrimp is abnormal; integrating, analyzing and storing shrimp information and shrimp live asset evaluation results.

[0004] It can be seen that the intelligent identification and evaluation method for inventory of live shrimp assets has the following problems: the method relies on capturing shrimp images under various lighting conditions. Changes in lighting affect the image quality, resulting in recognition errors or inaccurate evaluation results. In low-light or strong-light environments, the accuracy of image processing is significantly affected, resulting in data deviations. When evaluating live shrimp assets, this method relies on environmental parameters such as water quality and temperature, which causes the evaluation results to be greatly affected by environmental changes. In particular, it is difficult to obtain stable and consistent evaluation results under extreme environmental conditions. Summary of the invention

[0005] To this end, the present invention provides a shrimp population health status assessment method based on deep cluster analysis, which is used to overcome the problem of inaccurate health assessment results caused by unstable environmental parameters in the prior art through deep cluster analysis.

[0006] To achieve the above object, the present invention provides a shrimp population health status assessment method based on deep cluster analysis, comprising: Monitor the real-time density, real-time saturation and real-time activity of shrimp populations in each grid to be evaluated within the monitoring area constructed based on the preset grid step size; Determine a number of shrimp swarm dispersion grids according to the real-time density; Determine a number of temporary grids according to the real-time saturation of the shrimp swarm dispersion grid within a preset determined time period; Determine a number of non-shrimp group abnormal grids according to the real-time activity and the real-time density of any two adjacent temporary grids; Determine an actual shrimp population health index according to the real-time density and the real-time saturation of the non-shrimp population abnormal grid; Determine an ideal shrimp population health index according to a preset cluster analysis model, the real-time density, the real-time saturation, and the real-time activity of each grid to be evaluated; Adjust the preset grid step size according to the ideal shrimp health index and the actual shrimp health index; The actual shrimp population health index is determined based on the grid step size to form a shrimp population health assessment value.

[0007] Further, the determining of a number of non-shrimp group abnormal grids according to the real-time activity and the real-time density of any two adjacent temporary grids comprises: Calculating the difference between the two real-time densities to form a density change value; Calculate the difference between the two real-time activities to form an activity change value; Determining a change consistency according to the density change value and the activity change value; Determine a number of shrimp group abnormal grids according to the change consistency and the real-time density; The shrimp group abnormal grid is excluded from all the grids to be evaluated to determine a number of non-shrimp group abnormal grids.

[0008] Further, determining the change consistency according to the density change value and the activity change value includes: Draw a density change curve according to the density change value within a preset drawing time; Draw an activity change curve according to the activity change value within the preset drawing time; The cosine similarity of the density change curve and the activity change curve is calculated to form a change consistency.

[0009] Further, the determining of a number of shrimp group abnormal grids according to the change consistency and the real-time density includes: When the change consistency is less than a preset consistency threshold, determining that the two adjacent temporary grids are difference grids, and forming a plurality of difference grids; A plurality of shrimp group abnormal grids are determined according to the real-time density of the difference grids.

[0010] Further, the determining of a number of shrimp group abnormal grids according to the real-time density of the difference grids comprises: Calculating the standard deviation of the difference between the real-time densities of any two adjacent difference grids to form a difference density fluctuation value; When the difference density fluctuation value is greater than a preset difference fluctuation threshold, the difference grid is determined to be the shrimp group abnormal grid, and a plurality of shrimp group abnormal grids are formed.

[0011] Further, determining the actual shrimp population health index according to the real-time density and the real-time saturation of the non-shrimp population abnormal grid includes: Standardizing the real-time density to form a standard density; Standardizing the real-time saturation to form a standard saturation; The standard density, the standard saturation, the preset density weight and the preset saturation weight are weighted and summed to form an actual shrimp population health index.

[0012] Further, adjusting the preset grid step size according to the ideal shrimp health index and the actual shrimp health index includes: Calculating the relative deviation between the ideal shrimp population health index and the actual shrimp population health index to form an index deviation; The preset grid step size is adjusted according to the exponential deviation.

[0013] Further, adjusting the preset grid step size according to the exponential deviation includes: Calculate the standard deviation of the index deviation within the preset adjustment period to form a deviation fluctuation value; When the deviation fluctuation value is greater than a preset deviation fluctuation value threshold, the preset grid step size is reduced according to a relative deviation between the deviation fluctuation value and the preset deviation fluctuation value threshold and a preset adjustment coefficient.

[0014] Further, the determining of a plurality of shrimp swarm dispersion grids according to the real-time density comprises: When the real-time density is less than a preset density threshold, the grid to be evaluated is determined to be a shrimp swarm dispersion grid, and a plurality of shrimp swarm dispersion grids are formed.

[0015] Further, the determining of a plurality of temporary grids according to the real-time saturation of the shrimp swarm dispersion grids comprises: Calculating the standard deviation of the real-time saturation to form a saturation fluctuation value; When the saturation fluctuation value is greater than a preset saturation fluctuation value threshold, the shrimp swarm dispersion grid is determined to be the temporary grid, and a plurality of temporary grids are formed.

[0016] Compared with the prior art, the beneficial effect of the present invention lies in that, by combining physiological and behavioral data such as real-time density, saturation and activity, it can accurately monitor and evaluate the health status of shrimp populations, and by eliminating abnormal grids step by step and screening out more representative grids, it avoids evaluation deviations caused by environmental fluctuations or measurement errors. It not only realizes dynamic monitoring of the health status of shrimp populations, but also automatically adjusts the grid step size by comparing the ideal health index with the actual health index, thereby improving the accuracy and reliability of health assessment. In addition, the adaptive model based on deep clustering analysis can handle complex and changeable monitoring data, has good robustness and flexibility, and effectively solves the problem of inaccurate health assessment results due to unstable environmental parameters.

[0017] Furthermore, by calculating the density and activity differences of adjacent grids and combining the judgment of change consistency, we can accurately identify abnormal grids that do not meet the health standards. This method can effectively exclude abnormal data and ensure the accuracy of health assessment.

[0018] Furthermore, by calculating the cosine similarity of the density and activity change curves, abnormal situations with poor synchronization of changes can be effectively identified. By dynamically comparing the changes in density and activity, potential abnormal conditions can be captured more sensitively, providing higher reliability and response speed for shrimp health monitoring.

[0019] Furthermore, through dynamic judgment of the consistency of changes and analysis of the real-time density of difference grids, areas with abnormal health conditions can be accurately identified, which improves the accuracy of determining abnormal grids and avoids misjudgment caused by occasional fluctuations.

[0020] Furthermore, by calculating the differential density fluctuation values, the grid areas with abnormal density fluctuations can be effectively identified, thereby accurately capturing shrimp groups with large fluctuations in health status, helping to eliminate misjudgments caused by natural fluctuations or short-term changes, and improving the accuracy of health assessment and the sensitivity of the system.

[0021] Furthermore, the health status of the shrimp population is comprehensively evaluated through standardization and weighting, which effectively avoids the errors caused by the fluctuation of a single indicator. At the same time, it can comprehensively consider the effects of density and saturation to provide a more comprehensive and accurate health index.

[0022] Furthermore, by adjusting the preset grid step size according to the exponential deviation, the grid division can be dynamically optimized to improve the sensitivity and accuracy of health assessment.

[0023] Furthermore, by dynamically adjusting the preset grid step size, it is possible to effectively respond to health index fluctuations, reduce instabilities caused by environmental changes or measurement errors, ensure more refined grid division, and make health assessment results more accurate.

[0024] Furthermore, by determining the shrimp dispersion grid based on the real-time density, the areas with lower density can be effectively identified, thereby accurately dividing the shrimp dispersion grid, which helps to optimize the health assessment process, ensure more accurate assessment results, and avoid low-density areas affecting the overall health judgment.

[0025] Furthermore, by introducing the judgment of saturation fluctuation value, grids with large fluctuations in health status can be effectively identified, and then the grid division can be adjusted to ensure the accuracy and meticulousness of the assessment, which helps to improve the timeliness and accuracy of shrimp health monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of the shrimp population health status assessment method based on deep cluster analysis in this embodiment; Figure 2 The decision logic diagram for determining the shrimp group abnormal grid in this embodiment; Figure 3 A decision logic diagram for adjusting the preset grid step size for this embodiment; Figure 4 This is a decision logic diagram for determining the shrimp swarm dispersion grid in this embodiment. DETAILED DESCRIPTION

[0027] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0029] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0030] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0031] See also Figure 1 As shown, it is a flow chart of the shrimp population health status assessment method based on deep cluster analysis in this embodiment; This embodiment provides a shrimp population health status assessment method based on deep cluster analysis, comprising: Monitor the real-time density, real-time saturation and real-time activity of shrimp populations in each grid to be evaluated within the monitoring area constructed based on the preset grid step size; Determine a number of shrimp swarm dispersion grids according to the real-time density; Determine a number of temporary grids according to the real-time saturation of the shrimp swarm dispersion grid within a preset determined time period; Determine a number of non-shrimp group abnormal grids according to the real-time activity and the real-time density of any two adjacent temporary grids; Determine an actual shrimp population health index according to the real-time density and the real-time saturation of the non-shrimp population abnormal grid; Determine an ideal shrimp population health index according to a preset cluster analysis model, the real-time density, the real-time saturation, and the real-time activity of each grid to be evaluated; Adjust the preset grid step size according to the ideal shrimp health index and the actual shrimp health index; The actual shrimp population health index is determined based on the grid step size to form a shrimp population health assessment value.

[0032] The preset grid step size refers to the step size for dividing each grid in the monitoring area, which is used to refine the distribution and sampling density of the monitoring area. It depends on the scale of the monitoring area, the resolution of the monitoring equipment and the required evaluation accuracy. It is usually set between 1 meter and 10 meters. In this embodiment, it is set to 5 meters, which can take into account both accuracy and computational efficiency, ensuring that the dynamic changes of the shrimp population can be effectively captured, while avoiding excessive calculations caused by over-refinement.

[0033] The preset duration refers to the time window set during the monitoring process to obtain stable health assessment data. It depends on the activity cycle of the shrimp population, the frequency of health changes and the frequency of monitoring data collection. It is usually set between 1 hour and 24 hours. In this embodiment, it is set to 6 hours, which can balance the timeliness and accuracy of the data, effectively reflect the health change trend of the shrimp population, reduce the interference caused by short-term fluctuations, and improve the stability of the assessment results.

[0034] Real-time density refers to the number density of shrimps in each monitoring grid. The location and number of shrimps in the monitoring area are captured in real time by an underwater camera or laser scanning device. The number of shrimps in each grid is identified and calculated through image processing algorithms, and the density value is calculated based on the area of ​​the grid.

[0035] Real-time saturation reflects the density or spatial occupancy of shrimp at a certain point in time, which is usually obtained through optical sensors or sonar systems. The saturation index is calculated by analyzing the spatial distribution of shrimp in the grid area. Saturation is usually expressed as the ratio of occupied area to grid area.

[0036] Real-time activity measures the activity frequency or movement intensity of shrimps in a specified time period. It is achieved by sampling a number of shrimps in each grid on the water, using video monitoring combined with motion detection algorithms. The activity of shrimps is calculated by analyzing their movement speed and change trajectory. The activity value is usually positively correlated with the swimming frequency and speed of the shrimps.

[0037] The preset cluster analysis model used in this embodiment is the K-means clustering model. This model divides each grid to be evaluated into multiple clusters by analyzing the real-time density, saturation and activity of each monitoring grid, and quickly identifies shrimp groups with different health conditions. The K-means model is suitable for processing large-scale data, and can efficiently classify the status of shrimp groups in real-time monitoring, and has a fast calculation speed, which is convenient for rapid response to health assessment. Through the preset number of clusters K and thresholds, the healthy, abnormal and other shrimp group states can be identified according to the clustering results, thereby providing a basis for health assessment.

[0038] The actual shrimp health index is a comprehensive indicator obtained by real-time monitoring and calculation of key health parameters such as density, saturation and activity of shrimp in a specific monitoring area. It reflects the health status of the shrimp population in the current environment, taking into account the impact of abnormal grids (non-shrimp abnormal grids), and performing weighted calculations based on the real-time density and saturation of these grids to obtain an accurate health assessment result.

[0039] The shrimp health assessment value is used to comprehensively measure the health status of the shrimp population, helping managers to identify abnormal situations and intervene in time. The assessment value quantifies the health status of the shrimp population by analyzing key parameters such as real-time density, saturation, and activity, combined with a cluster analysis model.

[0040] By monitoring the density, saturation, activity and other physiological and behavioral characteristics of shrimp swarms in real time, and using deep cluster analysis to evaluate the health of shrimp swarms. First, the shrimp swarm is divided into dispersed grids according to the real-time density, and the temporary grids and abnormal grids are further screened by saturation fluctuations and activity differences. Then, the actual shrimp swarm health index is calculated by combining the density and saturation of the non-shrimp swarm abnormal grids. At the same time, based on the preset clustering model, the ideal health index is calculated through the real-time data of each grid. Finally, the difference between the actual and ideal health indexes is compared, and the grid step size is adjusted to optimize the accuracy of the health assessment to form the final shrimp swarm health assessment value.

[0041] By combining physiological and behavioral data such as real-time density, saturation and activity, the health status of shrimp populations can be accurately monitored and evaluated. By eliminating abnormal grids step by step and screening out more representative grids, evaluation biases caused by environmental fluctuations or measurement errors can be avoided. Not only can dynamic monitoring of the health status of shrimp populations be achieved, but also the grid step size can be automatically adjusted by comparing the ideal health index with the actual health index, thereby improving the accuracy and reliability of health assessment. In addition, the adaptive model based on deep clustering analysis can handle complex and changeable monitoring data, has good robustness and flexibility, and effectively solves the problem of inaccurate health assessment results due to unstable environmental parameters.

[0042] Specifically, the determining of a number of non-shrimp group abnormal grids according to the real-time activity and the real-time density of any two adjacent temporary grids includes: Calculating the difference between the two real-time densities to form a density change value; Calculate the difference between the two real-time activities to form an activity change value; Determining a change consistency according to the density change value and the activity change value; Determine a number of shrimp group abnormal grids according to the change consistency and the real-time density; The shrimp group abnormal grid is excluded from all the grids to be evaluated to determine a number of non-shrimp group abnormal grids.

[0043] By calculating the difference between the real-time density and real-time activity of any two adjacent temporary grids, the density change value and the activity change value are formed. Subsequently, the change consistency is calculated based on these two change values ​​to reflect the degree of coordination between the density and activity changes. Based on the relationship between the change consistency and the real-time density, the shrimp abnormal grids are further identified. Finally, the abnormal grids are excluded from all the grids to be evaluated, and the non-shrimp abnormal grids are finally determined, which mark the areas that do not meet the health status.

[0044] By calculating the density and activity differences of adjacent grids and combining the judgment of change consistency, we can accurately identify abnormal grids that do not meet health standards. This method can effectively exclude abnormal data and ensure the accuracy of health assessment.

[0045] Specifically, determining the change consistency according to the density change value and the activity change value includes: Draw a density change curve according to the density change value within a preset drawing time; Draw an activity change curve according to the activity change value within the preset drawing time; The cosine similarity of the density change curve and the activity change curve is calculated to form a change consistency.

[0046] The preset drawing duration refers to the time window selected during the health assessment process for drawing density and activity change curves. It depends on the monitoring frequency, the periodicity of data changes, and the time scale of changes in the health status of the shrimp population. It is usually set between 10 minutes and 1 hour to ensure that sufficient change information can be captured. In this embodiment, it is set to 30 minutes, which can effectively reflect the dynamic changes in the health status of the shrimp population and will not be disturbed by short-term fluctuations, ensuring the stability and reliability of the health assessment.

[0047] Within a preset time period, the change curves of density change value and activity change value are drawn respectively. Then, the cosine similarity of these two change curves is calculated to obtain the change consistency. Cosine similarity reflects the coordination between density change and activity change, and is used to evaluate whether the two change synchronously. Through this process, the relationship between density and activity can be quantitatively analyzed, and the health status of the shrimp population can be further judged.

[0048] By calculating the cosine similarity of the density and activity change curves, abnormal situations with poor synchronization of changes can be effectively identified. By dynamically comparing the changes in density and activity, potential abnormal conditions can be captured more sensitively, providing higher reliability and response speed for shrimp health monitoring.

[0049] Please continue reading Figure 2 As shown, it is a decision logic diagram for determining the shrimp group abnormal grid in this embodiment; Specifically, determining a number of shrimp group abnormal grids according to the change consistency and the real-time density includes: When the change consistency is less than a preset consistency threshold, determining that the two adjacent temporary grids are difference grids, and forming a plurality of difference grids; A plurality of shrimp group abnormal grids are determined according to the real-time density of the difference grids.

[0050] The preset consistency threshold is a standard value used to determine whether the density and activity changes of adjacent grids are consistent. It depends on the natural fluctuation range of shrimp activity in the monitoring area and the sensitivity of health status changes. It is usually set between 0.8 and 0.95. In this embodiment, it is set to 0.9 to ensure that obvious health anomalies can be accurately detected, while reducing misjudgment of short-term fluctuations and improving the reliability and stability of health assessments.

[0051] When the consistency of the change is less than the preset consistency threshold, the two adjacent temporary grids are determined to be difference grids, forming several difference grids. Next, the shrimp population abnormal grids are further screened out according to the real-time density of these difference grids. These abnormal grids mark areas with large differences in health assessment, which may be abnormal shrimp population health.

[0052] By dynamically judging the consistency of changes and analyzing the real-time density of difference grids, areas with abnormal health conditions can be accurately identified, improving the accuracy of determining abnormal grids and avoiding misjudgments caused by occasional fluctuations.

[0053] Specifically, determining a number of shrimp group abnormal grids according to the real-time density of the difference grid includes: Calculating the standard deviation of the difference between the real-time densities of any two adjacent difference grids to form a difference density fluctuation value; When the difference density fluctuation value is greater than a preset difference fluctuation threshold, the difference grid is determined to be the shrimp group abnormal grid, and a plurality of shrimp group abnormal grids are formed.

[0054] The preset difference fluctuation threshold is a standard value used to determine whether the density fluctuation of the difference grid is abnormal, which depends on the density fluctuation range of the shrimp population in the monitoring area and the sensitivity of the system to abnormal changes. It is usually set between 0.05 and 0.15. In this embodiment, it is set to 0.1, which can better capture larger fluctuations in health while avoiding oversensitivity to small changes, thereby improving the stability and accuracy of the evaluation results.

[0055] First, the standard deviation of the real-time density difference between any adjacent difference grids is calculated to form a difference density fluctuation value. Then, if the difference density fluctuation value is greater than the preset difference fluctuation threshold, these difference grids are determined to be shrimp abnormal grids. These abnormal grids may indicate that there are large fluctuations or abnormalities in the health status of the shrimp population, so they are further excluded or marked as areas that need to be monitored.

[0056] By calculating the differential density fluctuation values, the grid areas with abnormal density fluctuations can be effectively identified, thereby accurately capturing shrimp groups with large fluctuations in health status. This helps to eliminate misjudgments caused by natural fluctuations or short-term changes, and improves the accuracy of health assessments and the sensitivity of the system.

[0057] Specifically, determining the actual shrimp health index according to the real-time density and the real-time saturation of the non-shrimp abnormal grid includes: Standardizing the real-time density to form a standard density; Standardizing the real-time saturation to form a standard saturation; The standard density, the standard saturation, the preset density weight and the preset saturation weight are weighted and summed to form an actual shrimp population health index.

[0058] The preset density weight refers to the weight ratio of density in the total assessment when calculating the shrimp health index. It depends on the impact of density on shrimp health and the sensitivity of density changes to the health index in the actual breeding environment. It is usually set between 0.3 and 0.7. In this embodiment, it is set to 0.5. It can maintain its reasonable weighting effect when density has a greater impact on the health index, while avoiding over-reliance on a single factor.

[0059] The preset saturation weight refers to the weight ratio of saturation to the total assessment when calculating the shrimp health index. It depends on the impact of saturation on the health of the shrimp population and the indicative effect of saturation fluctuations on the health status in the actual breeding environment. It is usually set between 0.3 and 0.7. In this embodiment, it is set to 0.5, which can fully reflect the contribution of saturation changes to health assessment and ensure the accuracy of the assessment results.

[0060] First, the real-time density and real-time saturation of the non-shrimp abnormal grids are standardized to obtain the standard density and standard saturation. Then, combined with the preset density weight and saturation weight, the standard density and standard saturation are weighted and summed to form the actual shrimp health index. In this way, the density and saturation information of the shrimp population is quantified, and its health status is evaluated by weight, ensuring that the evaluation results can fully reflect the overall health level of the shrimp population.

[0061] Through standardization and weighting, the health status of the shrimp population is comprehensively evaluated, which effectively avoids the errors caused by the fluctuation of a single indicator. At the same time, it can comprehensively consider the effects of density and saturation to provide a more comprehensive and accurate health index.

[0062] Specifically, adjusting the preset grid step size according to the ideal shrimp health index and the actual shrimp health index includes: Calculating the relative deviation between the ideal shrimp population health index and the actual shrimp population health index to form an index deviation; The preset grid step size is adjusted according to the exponential deviation.

[0063] According to the difference between the ideal shrimp health index and the actual shrimp health index, the relative deviation between the two is calculated to form an index deviation. The deviation reflects the accuracy of the evaluation result under the current grid step size and indicates the difference between the actual health status and the ideal status. According to the deviation, the preset grid step size is adjusted to optimize the health evaluation process, improve the accuracy of grid division, ensure that the subsequent evaluation results are more accurate, and timely reflect the health changes of the shrimp population.

[0064] By adjusting the preset grid step size according to the exponential deviation, the grid division can be dynamically optimized to improve the sensitivity and accuracy of health assessment.

[0065] Please continue reading Figure 3 As shown, it is a decision logic diagram for adjusting the preset grid step size in this embodiment; Specifically, adjusting the preset grid step size according to the exponential deviation includes: Calculate the standard deviation of the index deviation within the preset adjustment period to form a deviation fluctuation value; When the deviation fluctuation value is greater than a preset deviation fluctuation value threshold, the preset grid step size is reduced according to a relative deviation between the deviation fluctuation value and the preset deviation fluctuation value threshold and a preset adjustment coefficient.

[0066] The preset deviation fluctuation value threshold is a standard value used to determine whether the deviation fluctuation value exceeds the normal range. It depends on the stability of the breeding environment, the accuracy requirements of the health assessment, and the expected shrimp population health fluctuation range. It is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3. It can effectively avoid excessive adjustment of the grid step size within a reasonable fluctuation range of the health index change, and ensure timely adjustment in the case of large fluctuations, thereby improving the accuracy and stability of the evaluation results.

[0067] First, the standard deviation within the preset adjustment time is calculated to obtain the deviation fluctuation value. This value reflects the fluctuation of the deviation and indicates the variation between the health index and the ideal value. When the deviation fluctuation value is greater than the preset deviation fluctuation value threshold, it indicates that the stability of the evaluation result is poor. At this time, according to the relative deviation between the deviation fluctuation value and the preset deviation fluctuation value threshold and the preset adjustment coefficient, the preset grid step size is reduced and adjusted to improve the accuracy and reliability of the evaluation.

[0068] By dynamically adjusting the preset grid step size, it is possible to effectively respond to health index fluctuations, reduce instabilities caused by environmental changes or measurement errors, ensure more refined grid division, and make health assessment results more accurate.

[0069] Please continue reading Figure 4 As shown, it is a decision logic diagram for determining the shrimp group dispersion grid in this embodiment; Specifically, determining a plurality of shrimp swarm dispersion grids according to the real-time density includes: When the real-time density is less than a preset density threshold, the grid to be evaluated is determined to be a shrimp swarm dispersion grid, and a plurality of shrimp swarm dispersion grids are formed.

[0070] The preset density threshold refers to the real-time density standard value used to judge the shrimp dispersion grid. It depends on the normal growth density range of the shrimp, the capacity of the breeding environment and the impact on the health of the shrimp. It is usually set between 0.2 and 1.0. In this embodiment, it is set to 0.5, which can effectively distinguish areas with too low density, avoid misjudgment due to too low density, and ensure reasonable division of shrimp distribution.

[0071] First, the density of each grid to be evaluated is monitored. When the real-time density of some grids is lower than the preset density threshold, the system determines these grids as shrimp dispersion grids. Through this process, those areas with low density and more dispersed shrimp can be effectively identified and divided into shrimp dispersion grids for further analysis and adjustment.

[0072] By determining the shrimp dispersion grid based on real-time density, low-density areas can be effectively identified, thereby accurately dividing the shrimp dispersion grid, which helps to optimize the health assessment process, ensure more accurate assessment results, and avoid low-density areas affecting overall health judgments.

[0073] Specifically, determining a number of temporary grids according to the real-time saturation of the shrimp swarm dispersion grids includes: Calculating the standard deviation of the real-time saturation to form a saturation fluctuation value; When the saturation fluctuation value is greater than a preset saturation fluctuation value threshold, the shrimp swarm dispersion grid is determined to be the temporary grid, and a plurality of temporary grids are formed.

[0074] The preset saturation fluctuation value threshold refers to the critical value used to judge whether the saturation fluctuation is significant during the evaluation process. It depends on the degree of influence of saturation on the health of the shrimp population and the normal range of saturation fluctuation in the breeding environment. It is usually set between 0.05 and 0.2 to ensure that the grids with obvious changes in health status can be captured. In this embodiment, it is set to 0.1, which can effectively screen out grids with significant fluctuations and make timely health assessment adjustments to avoid oversensitivity or insensitivity, ensuring the rationality and accuracy of the evaluation results.

[0075] The saturation fluctuation value is calculated according to the standard deviation of the real-time saturation. When the fluctuation value exceeds the preset saturation fluctuation value threshold, the corresponding shrimp swarm dispersion grid is judged as a temporary grid, thereby forming a plurality of temporary grids. This process utilizes the fluctuation of saturation to identify potential health problem areas.

[0076] By introducing the judgment of saturation fluctuation value, the grids with large fluctuations in health status can be effectively identified, and then the grid division can be adjusted to ensure the accuracy and meticulousness of the assessment, which helps to improve the timeliness and accuracy of shrimp health monitoring.

[0077] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A shrimp population health status assessment method based on deep cluster analysis, characterized in that: include: Monitor the real-time density, real-time saturation and real-time activity of shrimp populations in each grid to be evaluated within the monitoring area constructed based on the preset grid step size; Determine a number of shrimp swarm dispersion grids according to the real-time density; Determine a number of temporary grids according to the real-time saturation of the shrimp swarm dispersion grid within a preset determined time period; Determine a number of non-shrimp group abnormal grids according to the real-time activity and the real-time density of any two adjacent temporary grids; Determine an actual shrimp population health index according to the real-time density and the real-time saturation of the non-shrimp population abnormal grid; Determine an ideal shrimp population health index according to a preset cluster analysis model, the real-time density, the real-time saturation, and the real-time activity of each grid to be evaluated; Adjust the preset grid step size according to the ideal shrimp health index and the actual shrimp health index; The actual shrimp population health index is determined based on the grid step size to form a shrimp population health assessment value.

2. The shrimp population health status assessment method based on deep cluster analysis according to claim 1, characterized in that: The step of determining a number of non-shrimp group abnormal grids according to the real-time activity and the real-time density of any two adjacent temporary grids comprises: Calculating the difference between the two real-time densities to form a density change value; Calculate the difference between the two real-time activities to form an activity change value; Determining a change consistency according to the density change value and the activity change value; Determine a number of shrimp group abnormal grids according to the change consistency and the real-time density; The shrimp group abnormal grid is excluded from all the grids to be evaluated to determine a number of non-shrimp group abnormal grids.

3. The shrimp population health status assessment method based on deep cluster analysis according to claim 2 is characterized in that: Determining the change consistency according to the density change value and the activity change value includes: Draw a density change curve according to the density change value within a preset drawing time; Draw an activity change curve according to the activity change value within the preset drawing time; The cosine similarity of the density change curve and the activity change curve is calculated to form a change consistency.

4. The shrimp population health status assessment method based on deep cluster analysis according to claim 3 is characterized in that: Determining a number of shrimp group abnormal grids according to the change consistency and the real-time density includes: When the change consistency is less than a preset consistency threshold, determining that the two adjacent temporary grids are difference grids, and forming a plurality of difference grids; A plurality of shrimp group abnormal grids are determined according to the real-time density of the difference grids.

5. The shrimp population health status assessment method based on deep cluster analysis according to claim 4, characterized in that: The step of determining a plurality of shrimp group abnormal grids according to the real-time density of the difference grids comprises: Calculating the standard deviation of the difference between the real-time densities of any two adjacent difference grids to form a difference density fluctuation value; When the difference density fluctuation value is greater than a preset difference fluctuation threshold, the difference grid is determined to be the shrimp group abnormal grid, and a plurality of shrimp group abnormal grids are formed.

6. The shrimp population health status assessment method based on deep cluster analysis according to claim 5, characterized in that: Determining the actual shrimp health index according to the real-time density and the real-time saturation of the non-shrimp abnormal grid includes: Standardizing the real-time density to form a standard density; Standardizing the real-time saturation to form a standard saturation; The standard density, the standard saturation, the preset density weight and the preset saturation weight are weighted and summed to form an actual shrimp population health index.

7. The shrimp population health status assessment method based on deep cluster analysis according to claim 6, characterized in that: The adjusting the preset grid step size according to the ideal shrimp health index and the actual shrimp health index comprises: Calculating the relative deviation between the ideal shrimp population health index and the actual shrimp population health index to form an index deviation; The preset grid step size is adjusted according to the exponential deviation.

8. The shrimp population health status assessment method based on deep cluster analysis according to claim 7, characterized in that: The adjusting the preset grid step size according to the exponential deviation comprises: Calculate the standard deviation of the index deviation within the preset adjustment period to form a deviation fluctuation value; When the deviation fluctuation value is greater than a preset deviation fluctuation value threshold, the preset grid step size is reduced according to a relative deviation between the deviation fluctuation value and the preset deviation fluctuation value threshold and a preset adjustment coefficient.

9. The shrimp population health status assessment method based on deep cluster analysis according to claim 8, characterized in that: Determining a plurality of shrimp swarm dispersion grids according to the real-time density comprises: When the real-time density is less than a preset density threshold, the grid to be evaluated is determined to be a shrimp swarm dispersion grid, and a plurality of shrimp swarm dispersion grids are formed.

10. The shrimp population health status assessment method based on deep cluster analysis according to claim 9, characterized in that: The determining of a plurality of temporary grids according to the real-time saturation of the shrimp swarm dispersion grids comprises: Calculating the standard deviation of the real-time saturation to form a saturation fluctuation value; When the saturation fluctuation value is greater than a preset saturation fluctuation value threshold, the shrimp swarm dispersion grid is determined to be the temporary grid, and a plurality of temporary grids are formed.

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