A method and system for monitoring the growth environment of pearl oysters

Abstract

This invention relates to a method and system for monitoring the growth environment of pearl oysters, belonging to the field of environmental monitoring. The method includes: classifying abnormal environmental parameters from multiple sets of environmental parameters to obtain first and second abnormality ratios; obtaining a third abnormality ratio based on the first and second abnormality ratios; configuring the identification scale for abnormalities in the pearl oyster environment and the pearl environment; identifying environmental parameter anomalies to obtain the abnormality levels of the pearl oyster and pearl environments; and performing a weighted calculation of the abnormality levels of the pearl oyster and pearl environments based on the first and third abnormality ratios to obtain the abnormality level of the growth environment as the environmental monitoring result. This invention solves the technical problem of existing methods lacking a comprehensive analysis of the interaction between the growth environment of pearl oysters and pearl quality, leading to insufficient accuracy and reliability in environmental monitoring. It can significantly improve the accuracy and reliability of environmental monitoring, comprehensively and accurately reflecting the health status of pearl oysters and changes in pearl quality.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring, and in particular to a method and system for monitoring the growth environment of pearl oysters. Background Technology

[0002] In the process of pearl oyster farming and pearl production, monitoring and optimization of the growth environment are crucial to ensuring the healthy growth of pearl oysters and the formation of high-quality pearls. The health status of pearl oysters is closely related to the quality of pearls, and the growth environment of pearl oysters has a direct impact on the uniformity and quality of pearl formation.

[0003] However, existing environmental monitoring systems typically treat the pearl oyster's growth environment and pearl quality as two separate monitoring objects, lacking a comprehensive analysis of the interaction between the two. This fails to accurately reveal the full impact of environmental factors on pearl formation, resulting in insufficient accuracy and reliability of environmental monitoring. Summary of the Invention

[0004] This invention addresses the technical problem that existing pearl oyster growth environment monitoring methods lack comprehensive analysis of the interaction between the pearl oyster growth environment and pearl quality, resulting in insufficient accuracy and reliability of environmental monitoring. It provides a method and system for monitoring the pearl oyster growth environment to solve this problem.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] In a first aspect, the present invention provides a method for monitoring the growth environment of pearl oysters, comprising: during the growth process of pearl oysters, monitoring and collecting environmental parameters of multiple categories to obtain multiple sets of environmental parameters, wherein the multiple categories of environmental parameters include water temperature, salinity, and dissolved oxygen; using a pearl oyster anomaly classifier and a pearl anomaly classifier respectively to classify the multiple sets of environmental parameters into abnormal environmental parameters, obtaining a first anomaly ratio and a second anomaly ratio, wherein the pearl oyster anomaly classifier and the pearl anomaly classifier include multiple anomaly classification paths corresponding to the multiple categories of environmental parameters; based on the first anomaly ratio and the second anomaly ratio, correcting and obtaining a third anomaly ratio, configuring the pearl oyster environment anomaly identification scale and the pearl environment anomaly identification scale, and performing environmental parameter anomaly identification on the multiple sets of environmental parameters respectively to obtain a pearl oyster environment anomaly level and a pearl environment anomaly level; and performing a weighted calculation on the pearl oyster environment anomaly level and the pearl environment anomaly level based on the first anomaly ratio and the third anomaly ratio to obtain a growth environment anomaly level, which is used as the monitoring result of the pearl oyster growth environment.

[0007] Furthermore, during the growth process of pearl oysters, multiple environmental parameter categories were monitored and collected to obtain multiple sets of environmental parameters, including:

[0008] During the growth of pearl oysters, environmental parameters at the first location in the growth environment are collected according to multiple environmental parameter categories, including water temperature, salinity, and dissolved oxygen.

[0009] Continue collecting environmental parameter sets from multiple locations to obtain multiple environmental parameter sets.

[0010] Furthermore, a pearl oyster anomaly classifier and a pearl anomaly classifier are used respectively to classify the multiple environmental parameter sets into abnormal environmental parameters, obtaining a first anomaly ratio and a second anomaly ratio, including:

[0011] The multiple sets of environmental parameters are divided according to the multiple categories of environmental parameters to obtain sets of water temperature, salinity, and dissolved oxygen.

[0012] The water temperature set, salinity set, and dissolved oxygen set are respectively input into the pearl oyster anomaly classifier to classify abnormal environmental parameters and obtain the first water temperature anomaly ratio, the first salinity anomaly ratio, and the first dissolved oxygen anomaly ratio. The first anomaly ratio is calculated. The pearl oyster anomaly classifier is constructed based on the normal environmental parameters of pearl oyster growth over a historical period and includes the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path.

[0013] Based on the normal environmental parameters of pearl cultivation over a historical period, a pearl anomaly classifier is constructed, wherein the pearl anomaly classifier includes a second water temperature anomaly classification path, a second salinity anomaly classification path, and a second dissolved oxygen anomaly classification path.

[0014] The water temperature set, salinity set, and dissolved oxygen set are respectively input into the second water temperature anomaly classification path, the second salinity anomaly classification path, and the second dissolved oxygen anomaly classification path to classify abnormal environmental parameters, obtain the second water temperature anomaly ratio, the second salinity anomaly ratio, and the second dissolved oxygen anomaly ratio, and calculate the second anomaly ratio.

[0015] Further, the water temperature set, salinity set, and dissolved oxygen set are respectively input into the pearl oyster anomaly classifier to classify abnormal environmental parameters, obtain the first water temperature anomaly ratio, the first salinity anomaly ratio, and the first dissolved oxygen anomaly ratio, and calculate the first anomaly ratio, including:

[0016] The normal growth environment parameters of pearl oysters within a preset historical time range are obtained and divided into normal water temperature set, normal salinity set and normal dissolved oxygen set;

[0017] The normal water temperature set, normal salinity set, and normal dissolved oxygen set are used to construct binary classification nodes to obtain the first water temperature division node set, the first salinity division node set, and the first dissolved oxygen division node set, and to construct the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path.

[0018] The water temperature set, salinity set, and dissolved oxygen set are respectively input into the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path for classification. The proportions of the classification into a single water temperature parameter, a single salinity parameter, and a single dissolved oxygen parameter are filtered to obtain the first water temperature anomaly proportion, the first salinity anomaly proportion, and the first dissolved oxygen anomaly proportion.

[0019] The average of the first abnormal water temperature ratio, the first abnormal salinity ratio, and the first abnormal dissolved oxygen ratio is calculated to obtain the first abnormal ratio.

[0020] Further, based on the first and second anomaly ratios, a third anomaly ratio is obtained, and the scale for identifying anomalies in the pearl oyster environment and the scale for identifying anomalies in the pearl environment are configured. Anomalies in multiple sets of environmental parameters are then identified to obtain the anomaly level of the pearl oyster environment and the anomaly level of the pearl environment, including:

[0021] The sum of 1 and the first abnormality ratio is used as a correction coefficient, which is then multiplied by the second abnormality ratio to obtain the third abnormality ratio.

[0022] Obtain the maximum anomaly detection scale, wherein the maximum anomaly detection scale includes the maximum number of integrated anomaly detections;

[0023] The first and third anomaly ratios are multiplied by the maximum number of integrated anomalies identified and then rounded to obtain the number of integrated anomalies identified in pearl oysters and the number of integrated anomalies identified in pearls, which are used as the scale of anomaly identification in the pearl oyster environment and the scale of anomaly identification in the pearl environment.

[0024] Based on the maximum number of integrated anomaly recognitions, and using ensemble learning, a pearl oyster environment anomaly recognizer including multiple first environment anomaly recognition branches and a pearl environment anomaly recognizer including multiple second environment anomaly recognition branches are constructed.

[0025] The multiple sets of environmental parameters are respectively input into the first environmental anomaly identification branch of the pearl oyster integrated anomaly identification quantity and the second environmental anomaly identification branch of the pearl integrated anomaly identification quantity. Multiple sets of pearl oyster environmental anomaly levels and multiple sets of pearl environmental anomaly levels are output. The average value is calculated to obtain the pearl oyster environmental anomaly level and the pearl environmental anomaly level.

[0026] Furthermore, based on ensemble learning, and according to the maximum number of integrated anomaly recognitions, a pearl oyster environment anomaly recognizer comprising multiple first environmental anomaly recognition branches and a pearl environment anomaly recognizer comprising multiple second environmental anomaly recognition branches are constructed, including:

[0027] Based on historical growth environment data of pearl oysters and pearls, multiple sets of sample environmental parameters were collected, and the abnormality levels of the pearl oyster environment and the pearl environment were identified respectively, resulting in a set of abnormality levels of sample pearl oyster environment and a set of abnormality levels of sample pearl environment.

[0028] According to the maximum number of integrated anomaly identifications, the multiple sets of sample environmental parameters, the set of sample pearl oyster environmental anomaly levels, and the set of sample pearl environmental anomaly levels are divided to obtain multiple sets of input data, multiple sets of first output data, and multiple sets of second output data.

[0029] Using the multiple input data, the multiple first output data and multiple second output data are combined respectively to train multiple first environmental anomaly recognition branches and multiple second environmental anomaly recognition branches;

[0030] By combining the multiple first environmental anomaly recognition branches and the multiple second environmental anomaly recognition branches respectively, a pearl oyster environmental anomaly recognizer and a pearl environmental anomaly recognizer are obtained.

[0031] Further, based on the first and third anomaly ratios, a weighted calculation is performed on the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment to obtain the abnormality level of the growth environment, which serves as the monitoring result of the pearl oyster growth environment, including:

[0032] Configure the pearl oyster weight and pearl weight based on the first and third abnormality ratios.

[0033] Using the pearl oyster weight and pearl weight, the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment are weighted and calculated to obtain the abnormality level of the growth environment, which is used as the monitoring result of the growth environment of the pearl oyster.

[0034] Secondly, the present invention provides a pearl oyster growth environment monitoring system, comprising: an environmental parameter monitoring module, used to monitor and collect environmental parameters of multiple categories during the growth process of pearl oysters, obtaining multiple sets of environmental parameters, wherein the multiple categories of environmental parameters include water temperature, salinity, and dissolved oxygen; an abnormal environmental parameter classification module, used to classify the multiple sets of environmental parameters into abnormal environmental parameters using a pearl oyster abnormality classifier and a pearl abnormality classifier, respectively, to obtain a first abnormality ratio and a second abnormality ratio, wherein the pearl oyster abnormality classifier and the pearl abnormality classifier include multiple abnormal classification paths corresponding to the multiple categories of environmental parameters; an environmental parameter abnormality identification module, used to correct and obtain a third abnormality ratio based on the first abnormality ratio and the second abnormality ratio, configure the pearl oyster environmental abnormality identification scale and the pearl environmental abnormality identification scale, and perform environmental parameter abnormality identification on the multiple sets of environmental parameters respectively, to obtain a pearl oyster environmental abnormality level and a pearl environmental abnormality level; and an environmental monitoring result obtaining module, used to perform a weighted calculation of the pearl oyster environmental abnormality level and the pearl environmental abnormality level based on the first abnormality ratio and the third abnormality ratio, to obtain a growth environment abnormality level, which serves as the monitoring result of the pearl oyster growth environment.

[0035] Thirdly, the present invention also provides an electronic device, comprising:

[0036] At least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method described in any one of the first aspects above.

[0037] Fourthly, a computer-readable storage medium storing a computer program that, when executed, implements the steps of the method described in any one of the first aspects above.

[0038] The beneficial effects of this invention are as follows: During the growth of pearl oysters, multiple environmental parameter sets are obtained by monitoring and collecting various environmental parameter categories, including water temperature, salinity, and dissolved oxygen. Then, a pearl oyster anomaly classifier and a pearl anomaly classifier are used to classify the multiple environmental parameter sets into anomalous environmental parameters, obtaining a first anomaly ratio and a second anomaly ratio. The pearl oyster anomaly classifier and the pearl anomaly classifier include multiple anomaly classification paths corresponding to the various environmental parameter categories. Finally, based on the first and second anomaly ratios, a third anomaly ratio is obtained, and the scale for identifying pearl oyster environmental anomalies and pearl environmental anomalies is configured. By differentiating scales, multiple sets of environmental parameters are used to identify environmental anomalies, obtaining the anomaly levels of the pearl oyster environment and the pearl environment. Finally, based on the first and third anomaly ratios, the anomaly levels of the pearl oyster environment and the pearl environment are weighted and calculated to obtain the growth environment anomaly level, which serves as the monitoring result of the pearl oyster growth environment. In other words, through comprehensive analysis of multidimensional environmental parameters and the interaction analysis of the pearl oyster growth environment and pearl quality, the accuracy and reliability of pearl oyster growth environment monitoring can be significantly improved, reflecting the health status of the pearl oyster and changes in pearl quality more comprehensively and accurately, thus providing a more scientific and refined environmental regulation basis for pearl quality control. Attached Figure Description

[0039] Figure 1 A flowchart illustrating a method for monitoring the growth environment of pearl oysters provided by this invention;

[0040] Figure 2 This invention provides a schematic diagram of the structure of a pearl oyster growth environment monitoring system.

[0041] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention;

[0042] Figure 4 This is a schematic diagram of the structure of a computer-readable storage medium provided by the present invention.

[0043] The components represented by each number in the attached diagram are explained below:

[0044] The system includes an environmental parameter monitoring module 01, an abnormal environmental parameter classification module 02, an abnormal environmental parameter identification module 03, an environmental monitoring result acquisition module 04, an electronic device 500, a memory 510, a processor 520, a first computer program 511, a computer-readable storage medium 600, and a second computer program 611. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0047] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0048] Example 1, as Figure 1 As shown in the figure, this embodiment of the invention provides a method for monitoring the growth environment of pearl oysters, which specifically includes the following steps:

[0049] S100: During the growth of pearl oysters, multiple environmental parameter categories are monitored and collected to obtain multiple sets of environmental parameters, including water temperature, salinity, and dissolved oxygen.

[0050] Furthermore, step S100 of the present invention further includes:

[0051] S110: During the growth of pearl oysters, environmental parameter sets are collected at the first location within the growth environment according to multiple environmental parameter categories, wherein the multiple environmental parameter categories include water temperature, salinity, and dissolved oxygen; S120: Continue to collect environmental parameter sets at multiple locations to obtain multiple environmental parameter sets.

[0052] Specifically, the growth environment of pearl oysters plays a decisive role in pearl farming and pearl production. The growth of pearl oysters and the formation of pearls are jointly influenced by environmental parameters such as water temperature, salinity, and dissolved oxygen. These parameters not only individually affect the growth of the oysters but also have complex interactions with each other, thus affecting the quality and uniformity of the pearls. Therefore, accurate monitoring and analysis of changes in environmental parameters are crucial for optimizing the growth environment of pearl oysters and improving pearl quality.

[0053] The study identifies multiple categories of environmental parameters that influence the growth process of pearl oysters. These categories include water temperature, salinity, and dissolved oxygen, which significantly affect oyster growth and pearl quality. For example, water temperature affects the metabolism and growth rate of oysters; salinity affects the adaptability of oysters and the pearl formation process; and dissolved oxygen directly affects the respiration and metabolic activities of oysters, while also influencing the uniformity of pearl texture.

[0054] Pearl oysters are typically distributed across multiple locations within the aquaculture water, and the environmental parameters at each location may differ. To accurately understand the impact of the entire aquaculture environment on pearl oysters, environmental parameter data needs to be collected at multiple locations to reflect the environmental conditions such as water temperature, salinity, and dissolved oxygen at different locations. First, multiple environmental parameter collection locations are established based on the actual pearl oyster aquaculture scenario. These locations can be different areas within the water body (such as near the shore, deep water, different water depths, etc.) to obtain more extensive and comprehensive environmental data. Next, one location is randomly selected from these multiple collection locations as the first location, and environmental parameter sets within the growth environment of this first location are collected according to the aforementioned multiple environmental parameter categories. Using the same method, environmental parameter sets (water temperature data, salinity data, and dissolved oxygen data) are collected from multiple locations, resulting in multiple environmental parameter sets, where each environmental parameter set corresponds one-to-one with the collection location. By establishing multiple environmental parameter collection locations in the actual pearl oyster aquaculture scenario and collecting multiple environmental parameters such as water temperature, salinity, and dissolved oxygen, changes in the pearl oyster growth environment can be comprehensively and accurately reflected, providing a basis for subsequent data analysis and anomaly detection.

[0055] S200: Using a pearl oyster anomaly classifier and a pearl anomaly classifier respectively, the multiple sets of environmental parameters are classified as abnormal environmental parameters to obtain a first anomaly ratio and a second anomaly ratio. The pearl oyster anomaly classifier and the pearl anomaly classifier include multiple anomaly classification paths corresponding to the multiple environmental parameter categories.

[0056] Furthermore, step S200 of the present invention further includes:

[0057] S210: Divide the multiple sets of environmental parameters according to the multiple categories of environmental parameters to obtain a set of water temperature, a set of salinity, and a set of dissolved oxygen.

[0058] Specifically, the multiple sets of environmental parameters are divided according to the multiple categories of environmental parameters (water temperature, salinity, and dissolved oxygen), that is, the water temperature data, salinity data, and dissolved oxygen data from each collection location are grouped into one category to obtain the water temperature set, salinity set, and dissolved oxygen set.

[0059] S220: Input the water temperature set, salinity set, and dissolved oxygen set into the pearl oyster anomaly classifier respectively to classify abnormal environmental parameters, obtain the first water temperature anomaly ratio, the first salinity anomaly ratio, and the first dissolved oxygen anomaly ratio, and calculate the first anomaly ratio. The pearl oyster anomaly classifier is constructed based on the normal environmental parameters of pearl oyster growth over a historical period, including the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path.

[0060] Furthermore, step S220 of the present invention further includes:

[0061] S221: Obtain the normal growth environment parameters of pearl oysters within a preset historical time range and divide them into normal water temperature set, normal salinity set, and normal dissolved oxygen set; S222: Construct binary classification nodes using the normal water temperature set, normal salinity set, and normal dissolved oxygen set respectively to obtain the first water temperature classification node set, the first salinity classification node set, and the first dissolved oxygen classification node set, and construct the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path; S223: Input the water temperature set, salinity set, and dissolved oxygen set into the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path respectively for classification, filter the proportion of classification into a single water temperature parameter, a single salinity parameter, and a single dissolved oxygen parameter, and obtain the first water temperature anomaly proportion, the first salinity anomaly proportion, and the first dissolved oxygen anomaly proportion; S224: Calculate the average of the first water temperature anomaly proportion, the first salinity anomaly proportion, and the first dissolved oxygen anomaly proportion to obtain the first anomaly proportion.

[0062] Specifically, firstly, data on the normal growth environment of pearl oysters is collected within a preset historical timeframe (e.g., the past day, week, or month). This means setting a normal environmental range that matches the actual aquaculture scenario and the growth needs of the pearl oysters. For example, the normal water temperature range for pearl oysters is 20 to 28°C, which is considered the most suitable temperature range for their growth; the normal salinity range is generally 15 to 30 parts per thousand, as salinity fluctuations directly affect the adaptability and growth rate of the oysters; and the normal dissolved oxygen range is 4 to 8 mg / L, as dissolved oxygen concentration is crucial for the growth of the oysters. The physiological activities of the species are crucial; then, based on the normal growth environment data within the historical time range, normal water temperature set, normal salinity set, and normal dissolved oxygen set are obtained. Among them, the normal water temperature set refers to all environmental data that meet the water temperature conditions (20℃ to 28℃) within the historical time range. The normal salinity set includes all salinity data within the range of 1.5% to 3.0% within the historical time range. The normal dissolved oxygen set refers to all environmental data with dissolved oxygen concentrations within the range of 4 to 8 mg / L within the historical time range.

[0063] Next, binary classification nodes are constructed using the normal water temperature set, normal salinity set, and normal dissolved oxygen set, respectively. These binary classification nodes are threshold points that divide environmental parameter data into two categories. A boundary is set based on normal environmental parameter data to determine whether the parameter exceeds the normal range, resulting in the first water temperature classification node set, the first salinity classification node set, and the first dissolved oxygen classification node set. Furthermore, a first water temperature anomaly classification path is constructed based on the first water temperature classification node set. This path determines whether the water temperature is abnormal through a series of judgment conditions and further classifies abnormal water temperatures into different levels. For example, at node 1, if the water temperature is less than 20℃, it is directly judged as an abnormal water temperature and enters the "too low water temperature" anomaly category; at node 2, if the water temperature is between 20℃ and 28℃, it is judged as a normal water temperature, requiring no further classification, and the classification path ends; at node 3, if the water temperature is greater than 28℃, it is judged as an abnormal water temperature and enters the "too high water temperature" anomaly category. By constructing the first water temperature anomaly classification path, it is possible to quickly determine whether the water temperature data is within the normal range each time it is collected. On the other hand, using the same method, a first salinity anomaly classification path is constructed based on the first salinity partitioning node set, and a first dissolved oxygen anomaly classification path is constructed based on the first dissolved oxygen partitioning node set.

[0064] Then, the water temperature set is input into the first water temperature anomaly classification path for division, that is, binary classification is performed using the Isolation Forest algorithm or other classification methods (e.g., less than 20℃ and greater than or equal to 20℃). Based on the input water temperature data, it is determined whether it deviates from the normal range. When using the Isolation Forest algorithm to classify the environmental parameter set, isolated data points (i.e., outliers) are identified as isolated individual data points, while normal data are grouped into relatively dense clusters. That is, the Isolation Forest algorithm discovers outliers by randomly dividing the data feature space and "isolating" data points, and obtains the proportion of a single water temperature parameter. In other words, the Isolation Forest algorithm identifies individual water temperature data points in the water temperature set that are judged to be abnormal (e.g., abnormal water temperature 19℃), calculates the proportion of these abnormal data points, which is the proportion of a single water temperature parameter (the ratio of abnormal water temperature data to the total amount of water temperature data), and sets the proportion of the single water temperature parameter as the first water temperature anomaly proportion, where the first water temperature anomaly proportion represents the percentage of abnormal water temperature data in all water temperature data.

[0065] On the other hand, the salinity set is input into the first salinity anomaly classification path for partitioning, and the isolated forest algorithm is used to identify anomaly data, obtaining the proportion of a single salinity parameter (the ratio of anomalous salinity data to the total salinity data). This proportion of a single salinity parameter is set as the first salinity anomaly proportion, representing the percentage of anomalous salinity data in all salinity data. Similarly, the dissolved oxygen set is input into the first dissolved oxygen anomaly classification path for partitioning, obtaining the proportion of a single dissolved oxygen parameter (the ratio of anomalous dissolved oxygen data to the total dissolved oxygen data). This proportion of a single dissolved oxygen parameter is set as the first dissolved oxygen anomaly proportion, representing the percentage of anomalous dissolved oxygen data in all dissolved oxygen data.

[0066] Furthermore, the above scheme only describes one method for calculating the first abnormal water temperature ratio, the first abnormal salinity ratio, and the first abnormal dissolved oxygen ratio. Those skilled in the art can also choose other suitable methods to calculate the abnormality ratio according to the actual situation. For example, the normal water temperature range for pearl oysters is set to 20 to 28°C, the normal salinity range to 1.5 to 3.0%, and the normal dissolved oxygen range to 4 to 8 mg / L. Then, based on the normal water temperature range, normal salinity range, and normal dissolved oxygen range, the water temperature set, salinity set, and dissolved oxygen set are judged, and environmental parameters that are not within the range are set as abnormal parameters (e.g., a water temperature of 19.5°C is an abnormal water temperature). The proportion of abnormal parameters (the ratio of the number of abnormal parameters to the total number of parameters) is counted to obtain the abnormal proportion of environmental parameters. For example, if there are 10 abnormal water temperature data points and 100 water temperature data points in the water temperature set, then the first abnormal water temperature ratio is 10 / 100 = 10%.

[0067] The average values ​​of the first abnormal water temperature ratio, the first abnormal salinity ratio, and the first abnormal dissolved oxygen ratio are further calculated, and the average calculation result is set as the first abnormal ratio. The first abnormal ratio reflects the overall abnormality of environmental parameters such as water temperature, salinity, and dissolved oxygen in the pearl oyster's growth environment.

[0068] S230: Construct a pearl anomaly classifier based on normal environmental parameters of pearl cultivation over a historical period. The pearl anomaly classifier includes a second water temperature anomaly classification path, a second salinity anomaly classification path, and a second dissolved oxygen anomaly classification path. S240: Input the water temperature set, salinity set, and dissolved oxygen set into the second water temperature anomaly classification path, the second salinity anomaly classification path, and the second dissolved oxygen anomaly classification path, respectively, to classify the abnormal environmental parameters, obtain the second water temperature anomaly ratio, the second salinity anomaly ratio, and the second dissolved oxygen anomaly ratio, and calculate the second anomaly ratio.

[0069] Specifically, based on the same method used to construct the pearl oyster anomaly classifier (including the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path), a second normal water temperature set, a second normal salinity set, and a second normal dissolved oxygen set are obtained according to the normal environmental parameters of pearl cultivation over a historical period. Then, binary classification nodes are constructed using the second normal water temperature set, the second normal salinity set, and the second normal dissolved oxygen set to obtain the second water temperature anomaly classification path, the second salinity anomaly classification path, and the second dissolved oxygen anomaly classification path. Finally, the water temperature set, salinity set, and dissolved oxygen set are respectively input into the second water temperature anomaly classification path, the second salinity anomaly classification path, and the second dissolved oxygen anomaly classification path to classify abnormal environmental parameters, obtaining the second water temperature anomaly ratio, the second salinity anomaly ratio, and the second dissolved oxygen anomaly ratio. Then, the average of the second water temperature anomaly ratio, the second salinity anomaly ratio, and the second dissolved oxygen anomaly ratio is calculated, and the result of the average calculation is set as the second anomaly ratio. The second anomaly ratio reflects the overall abnormality of environmental parameters such as water temperature, salinity, and dissolved oxygen in the pearl growth environment.

[0070] S300: Based on the first and second abnormality ratios, a third abnormality ratio is obtained by adjusting the ratio, configuring the scale for identifying abnormalities in the pearl oyster environment and the scale for identifying abnormalities in the pearl environment, and performing environmental parameter anomaly identification on multiple sets of environmental parameters to obtain the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment.

[0071] Furthermore, step S300 of the present invention also includes:

[0072] S310: Use the sum of 1 and the first anomaly ratio as a correction coefficient, multiply it by the second anomaly ratio to obtain the third anomaly ratio; S320: Obtain the maximum anomaly identification scale, wherein the maximum anomaly identification scale includes the maximum number of integrated anomaly identifications; S330: Multiply the first anomaly ratio and the third anomaly ratio by the maximum number of integrated anomaly identifications and round them to obtain the number of integrated anomaly identifications of pearl oysters and the number of integrated anomaly identifications of pearls, as the anomaly identification scale of pearl oyster environment and the anomaly identification scale of pearl environment.

[0073] Specifically, during the growth of pearl oysters, the growth environment (such as water temperature, salinity, and dissolved oxygen) has a significant impact on the uniformity and quality of pearls. If the growth environment of pearl oysters changes abnormally, the pearl formation process may also be affected, leading to uneven texture or decreased quality of the pearls. First, the first abnormality ratio (1) is added to the second abnormality ratio, and the sum of the two is used as a correction coefficient. That is, in order to comprehensively consider the interaction between the growth environment of pearl oysters and pearl quality, the second abnormality ratio needs to be corrected. This correction coefficient reflects the degree of abnormality in the pearl oyster environment. If the abnormality ratio of the pearl oyster's growth environment is large, the correction coefficient will increase accordingly, thereby amplifying the abnormal impact on the pearl. Then, the correction coefficient is multiplied by the second abnormality ratio, and the product of the two is used as the third abnormality ratio. By introducing the correction coefficient, the impact of the pearl oyster's growth environment on pearl quality can be effectively considered comprehensively, improving the accuracy of environmental monitoring.

[0074] The maximum anomaly identification scale is obtained, which includes the maximum number of integrated anomaly identifications. To ensure the efficiency and accuracy of environmental anomaly detection, a maximum anomaly identification scale is set, representing the maximum number of anomalies that can be processed simultaneously. This maximum number of integrated anomaly identifications can be determined based on various factors such as historical data, laboratory capacity, and computing resources. For example, the maximum number of integrated anomaly identifications can be set to 10. Further, the first anomaly ratio is multiplied by the maximum number of integrated anomaly identifications and rounded to obtain the number of integrated anomaly identifications for pearl oysters, which serves as the pearl oyster environmental anomaly identification scale. The third anomaly ratio is multiplied by the maximum number of integrated anomaly identifications and rounded to obtain the number of integrated pearl anomaly identifications, which serves as the pearl environmental anomaly identification scale.

[0075] By setting an appropriate number of anomaly identifications based on the anomaly ratio, the matching degree between the number of anomaly identifications and the actual anomaly state can be improved, thereby improving the accuracy of anomaly identification, avoiding over- or under-calculation, and thus improving analysis efficiency and resource utilization. This ensures that the monitoring system can efficiently and accurately identify anomalies under different environmental conditions.

[0076] S340: Based on ensemble learning, construct a pearl oyster environment anomaly identifier that includes multiple first environment anomaly identification branches and a pearl environment anomaly identifier that includes multiple second environment anomaly identification branches, according to the maximum number of integrated anomaly identifications.

[0077] Furthermore, step S340 of the present invention further includes:

[0078] S341: Based on historical growth environment data of pearl oysters and pearls, collect multiple sample environmental parameter sets, and identify the abnormality levels of the pearl oyster environment and the pearl environment respectively, to obtain sample pearl oyster environmental abnormality level sets and sample pearl environment abnormality level sets; S342: According to the maximum number of integrated anomaly recognitions, divide the multiple sample environmental parameter sets, sample pearl oyster environmental abnormality level sets, and sample pearl environment abnormality level sets to obtain multiple sets of input data, multiple sets of first output data, and multiple sets of second output data; S343: Using the multiple sets of input data, combine the multiple sets of first output data and multiple sets of second output data respectively to train multiple first environmental anomaly recognition branches and multiple second environmental anomaly recognition branches; S344: Combine the multiple first environmental anomaly recognition branches and multiple second environmental anomaly recognition branches respectively to obtain a pearl oyster environmental anomaly recognizer and a pearl environmental anomaly recognizer.

[0079] Specifically, based on historical growth environment data of pearl oysters and pearls, multiple sets of sample environmental parameters are collected. Then, the abnormality levels of the pearl oyster environment and the pearl environment under different sets of sample environmental parameters are identified. For example, for the environmental data of each sample, it is identified as normal, slightly abnormal, moderately abnormal, or severely abnormal according to the test results. These levels reflect the health status and stability of the pearl oyster growth environment. Through anomaly detection, the abnormality level of pearl quality is identified. These abnormality levels reflect the impact of the environment on pearl quality, thus obtaining the set of abnormality levels of sample pearl oyster environment and the set of abnormality levels of sample pearl environment.

[0080] Next, according to the maximum number of integrated anomaly identifications (e.g., 10), the multiple sets of sample environmental parameters, the set of sample pearl oyster environmental anomaly levels, and the set of sample pearl environmental anomaly levels are divided. The set of sample environmental parameters is used as input data, the set of sample pearl oyster environmental anomaly levels is set as the first output data, and the set of sample pearl environmental anomaly levels is set as the second output data, resulting in multiple sets of input data, multiple sets of first output data, and multiple sets of second output data. Each set of input data includes several sets of sample environmental parameters, each set of first output data includes several sets of sample pearl oyster environmental anomaly levels, and each set of second output data includes several sets of sample pearl environmental anomaly levels.

[0081] A first environmental anomaly identification branch and a second environmental anomaly identification branch are constructed based on a backpropagation (BP) neural network. The first and second environmental anomaly identification branches are BP neural network models that can be iteratively optimized in machine learning. The first environmental anomaly identification branch includes an input layer, multiple hidden layers, and an output layer. The input data of its input layer is a set of sample environmental parameters, and the output data of its output layer is the environmental anomaly level of the sample pearl oyster. The second environmental anomaly identification branch also includes an input layer, multiple hidden layers, and an output layer. The input data of its input layer is a set of sample environmental parameters, and the output data of its output layer is the environmental anomaly level of the sample pearl.

[0082] Further, the multiple input data sets are combined with the multiple first output data sets to conduct supervised training on multiple first environmental anomaly recognition branches. For example, the first input data set (a set of environmental parameters for several samples) and the first first input data set (anomaly levels of pearl oysters for several samples) are used to conduct supervised training on the first environmental anomaly recognition branch. First, the input data and target output are paired using a training algorithm, and the error between each input data set and the target output is calculated (e.g., using cross-entropy loss function or mean squared error). Then, an optimization algorithm (such as gradient descent) is used to minimize the error. During training, the model adjusts its parameters according to different combinations of input features to improve prediction accuracy. Iterative training is performed, and in each round, the model adjusts the weights and parameters according to the error feedback until the error converges to a small value (e.g., less than the expected error index), indicating that the model has learned the relationship between input features and anomaly levels, and the trained first environmental anomaly recognition branch is obtained. On the other hand, the multiple input data are combined with the multiple second output data to perform supervised training on multiple second environmental anomaly recognition branches. The training method of the second environmental anomaly recognition branches is the same as the training method of the first environmental anomaly recognition branches, and will not be described in detail here.

[0083] Finally, the multiple first environmental anomaly recognition branches are combined to construct a pearl oyster environmental anomaly recognizer, and the multiple second environmental anomaly recognition branches are combined to construct a pearl environmental anomaly recognizer.

[0084] S350: Input the multiple sets of environmental parameters into the first environmental anomaly identification branch of the pearl oyster integrated anomaly identification quantity and the second environmental anomaly identification branch of the pearl integrated anomaly identification quantity respectively, and output multiple sets of pearl oyster environmental anomaly levels and multiple sets of pearl environmental anomaly levels. Calculate the average value to obtain the pearl oyster environmental anomaly level and the pearl environmental anomaly level respectively.

[0085] Specifically, based on the number of integrated anomaly identifications in the pearl oyster, a first environmental anomaly identification branch is randomly selected from the pearl oyster environmental anomaly identifier. For example, assuming the pearl oyster environmental anomaly identifier includes 10 first environmental anomaly identification branches and the number of integrated anomaly identifications in the pearl oyster is 3, then any 3 first environmental anomaly identification branches are randomly selected from the 10 first environmental anomaly identification branches. Next, the multiple sets of environmental parameters are input into the first environmental anomaly identification branches of the number of integrated anomaly identifications in the pearl oyster to obtain multiple sets of pearl oyster environmental anomaly levels; then, the average of the multiple sets of pearl oyster environmental anomaly levels is calculated to obtain the pearl oyster environmental anomaly level. On the other hand, the multiple sets of environmental parameters are input into the second environmental anomaly identification branches of the number of integrated pearl anomaly identifications to obtain multiple sets of pearl environmental anomaly levels, and the average of the multiple sets of pearl environmental anomaly levels is calculated to obtain the pearl environmental anomaly level.

[0086] S400: Based on the first abnormality ratio and the third abnormality ratio, the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment are weighted and calculated to obtain the abnormality level of the growth environment, which is used as the monitoring result of the growth environment of the pearl oyster.

[0087] Furthermore, step S400 of the present invention further includes:

[0088] S410: Configure the pearl oyster weight and pearl weight according to the first abnormality ratio and the third abnormality ratio; S420: Use the pearl oyster weight and pearl weight to perform a weighted calculation on the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment to obtain the abnormality level of the growth environment, which is used as the monitoring result of the growth environment of the pearl oyster.

[0089] Specifically, firstly, based on the first and third anomaly ratios, the weights of the pearl oysters and the pearl are configured. The first anomaly ratio represents the degree of abnormality in the growth environment of the pearl oysters. The larger the ratio, the more abnormal the growth environment of the pearl oysters, and the more significant the impact on the growth of the pearl oysters. Therefore, its weight should be appropriately increased. The weight of the pearl oysters can be set to a certain proportional coefficient of the first anomaly ratio, or the first anomaly ratio can be directly used as the weight. The third anomaly ratio represents the degree to which the quality of the pearl is affected by environmental anomalies. The weight of the pearl can be set to a certain proportional coefficient of the third anomaly ratio, or the third anomaly ratio can be directly used as the weight. Thus, the weights of the pearl oysters and the pearl are obtained.

[0090] Finally, the pearl oyster weight and pearl weight are used to perform a weighted calculation on the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment. The weighted calculation result is set as the abnormality level of the growth environment, and the abnormality level of the growth environment is used as the monitoring result of the growth environment of the pearl oyster.

[0091] The method for monitoring the growth environment of pearl oysters provided in this invention has at least the following technical effects:

[0092] 1. By comprehensively analyzing multidimensional environmental parameters and analyzing the interaction between the growth environment of pearl oysters and pearl quality, the accuracy and reliability of monitoring the growth environment of pearl oysters can be significantly improved, and the health status of pearl oysters and changes in pearl quality can be reflected more comprehensively and accurately, thus providing a more scientific and refined basis for environmental regulation for pearl quality control.

[0093] 2. By setting an appropriate number of anomalies to be identified based on the anomaly ratio, the matching degree between the number of anomalies identified and the actual anomaly state can be improved, thereby improving the accuracy of anomaly identification, avoiding over- or under-calculation, and thus improving analysis efficiency and resource utilization. This ensures that the monitoring system can efficiently and accurately identify anomalies under different environmental conditions.

[0094] Example 2, as Figure 2 As shown, based on the same inventive concept as the pearl oyster growth environment monitoring method provided in Embodiment 1, this embodiment of the invention also provides a pearl oyster growth environment monitoring system, including:

[0095] The environmental parameter monitoring module 01 is used to monitor and collect environmental parameters of multiple categories during the growth process of pearl oysters, obtaining multiple sets of environmental parameters, including water temperature, salinity, and dissolved oxygen. The abnormal environmental parameter classification module 02 is used to classify the multiple sets of environmental parameters into abnormal categories using a pearl oyster abnormality classifier and a pearl abnormality classifier, respectively, to obtain a first abnormality ratio and a second abnormality ratio. The pearl oyster abnormality classifier and the pearl abnormality classifier include multiple abnormality classification paths corresponding to the multiple environmental parameter categories. The environmental parameter abnormality identification module 03 is used to correct and obtain a third abnormality ratio based on the first and second abnormality ratios, configure the pearl oyster environmental abnormality identification scale and the pearl environmental abnormality identification scale, and perform environmental parameter abnormality identification on the multiple sets of environmental parameters to obtain the pearl oyster environmental abnormality level and the pearl environmental abnormality level. The environmental monitoring result acquisition module 04 is used to perform a weighted calculation of the pearl oyster environmental abnormality level and the pearl environmental abnormality level based on the first and third abnormality ratios to obtain the growth environment abnormality level, which serves as the monitoring result of the pearl oyster growth environment.

[0096] Furthermore, the pearl oyster growth environment monitoring system further includes: during the pearl oyster growth process, collecting a set of environmental parameters at a first location within the growth environment according to multiple environmental parameter categories, wherein the multiple environmental parameter categories include water temperature, salinity, and dissolved oxygen; and continuing to collect environmental parameter sets at multiple locations to obtain multiple environmental parameter sets.

[0097] Furthermore, the pearl oyster growth environment monitoring system further includes: dividing the multiple sets of environmental parameters according to the multiple environmental parameter categories to obtain a water temperature set, a salinity set, and a dissolved oxygen set; inputting the water temperature set, salinity set, and dissolved oxygen set into a pearl oyster anomaly classifier to classify abnormal environmental parameters, obtaining a first water temperature anomaly ratio, a first salinity anomaly ratio, and a first dissolved oxygen anomaly ratio, and calculating a first anomaly ratio, wherein the pearl oyster anomaly classifier is constructed based on the normal environmental parameters of pearl oyster growth over a historical period, including a first water temperature anomaly classification path, a first salinity anomaly classification path, and a first salinity anomaly classification path. The system employs a normal classification path and a first dissolved oxygen anomaly classification path. Based on the normal environmental parameters of pearl cultivation over a historical period, a pearl anomaly classifier is constructed. This pearl anomaly classifier includes a second water temperature anomaly classification path, a second salinity anomaly classification path, and a second dissolved oxygen anomaly classification path. The water temperature set, salinity set, and dissolved oxygen set are respectively input into the second water temperature anomaly classification path, the second salinity anomaly classification path, and the second dissolved oxygen anomaly classification path to classify abnormal environmental parameters, obtain the second water temperature anomaly ratio, the second salinity anomaly ratio, and the second dissolved oxygen anomaly ratio, and calculate the second anomaly ratio.

[0098] Furthermore, the pearl oyster growth environment monitoring system further includes: acquiring normal growth environment parameters of pearl oysters within a preset historical time range, and dividing them into a normal water temperature set, a normal salinity set, and a normal dissolved oxygen set; constructing binary classification nodes using the normal water temperature set, normal salinity set, and normal dissolved oxygen set respectively to obtain a first water temperature classification node set, a first salinity classification node set, and a first dissolved oxygen classification node set, and constructing a first water temperature anomaly classification path, a first salinity anomaly classification path, and a first dissolved oxygen anomaly classification path; inputting the water temperature set, salinity set, and dissolved oxygen set into the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path respectively for classification, filtering the proportions of classification into a single water temperature parameter, a single salinity parameter, and a single dissolved oxygen parameter, and obtaining a first water temperature anomaly proportion, a first salinity anomaly proportion, and a first dissolved oxygen anomaly proportion; calculating the average of the first water temperature anomaly proportion, the first salinity anomaly proportion, and the first dissolved oxygen anomaly proportion to obtain a first anomaly proportion.

[0099] Furthermore, the pearl oyster growth environment monitoring system further includes: using the sum of 1 and the first anomaly ratio as a correction coefficient, multiplying it by the second anomaly ratio to obtain a third anomaly ratio; obtaining the maximum anomaly identification scale, wherein the maximum anomaly identification scale includes the maximum integrated anomaly identification quantity; multiplying the first anomaly ratio and the third anomaly ratio by the maximum integrated anomaly identification quantity and rounding them to obtain the pearl oyster integrated anomaly identification quantity and the pearl integrated anomaly identification quantity, as the pearl oyster environment anomaly identification scale and the pearl environment anomaly identification scale; constructing a pearl oyster environment anomaly identifier including multiple first environment anomaly identification branches and a pearl environment anomaly identifier including multiple second environment anomaly identification branches based on ensemble learning according to the maximum integrated anomaly identification quantity; inputting the multiple environmental parameter sets into the first environment anomaly identification branches of the pearl oyster integrated anomaly identification quantity and the second environment anomaly identification branches of the pearl integrated anomaly identification quantity, respectively, outputting multiple pearl oyster environment anomaly level sets and multiple pearl environment anomaly level sets, and calculating the average values ​​to obtain the pearl oyster environment anomaly level and the pearl environment anomaly level.

[0100] Furthermore, the pearl oyster growth environment monitoring system further includes: collecting multiple sample environmental parameter sets based on historical growth environment data of pearl oysters and pearls, identifying the abnormality levels of the pearl oyster environment and the pearl environment respectively, and obtaining sample pearl oyster environmental abnormality level sets and sample pearl environment abnormality level sets; dividing the multiple sample environmental parameter sets, sample pearl oyster environmental abnormality level sets, and sample pearl environment abnormality level sets according to the maximum integrated abnormality recognition number, obtaining multiple sets of input data, multiple sets of first output data, and multiple sets of second output data; using the multiple sets of input data, combining the multiple sets of first output data and multiple sets of second output data respectively, training multiple first environmental abnormality recognition branches and multiple second environmental abnormality recognition branches; combining the multiple first environmental abnormality recognition branches and multiple second environmental abnormality recognition branches respectively, obtaining a pearl oyster environmental abnormality recognizer and a pearl environment abnormality recognizer.

[0101] Furthermore, the pearl oyster growth environment monitoring system further includes: configuring pearl oyster weight and pearl weight according to the first abnormality ratio and the third abnormality ratio; using the pearl oyster weight and pearl weight, performing a weighted calculation on the pearl oyster environment abnormality level and the pearl environment abnormality level to obtain the growth environment abnormality level, which is used as the pearl oyster growth environment monitoring result.

[0102] Example 3, please refer to Figure 3 , Figure 3 A schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 3As shown, this embodiment of the invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, it performs the following steps: during the growth process of pearl oysters, monitoring and collecting environmental parameters of multiple categories to obtain multiple sets of environmental parameters, wherein the multiple categories of environmental parameters include water temperature, salinity, and dissolved oxygen; using a pearl oyster anomaly classifier and a pearl anomaly classifier respectively, classifying the multiple sets of environmental parameters into abnormal environmental parameters to obtain a first anomaly ratio and a second anomaly ratio. The ratio, wherein the pearl oyster anomaly classifier and the pearl anomaly classifier include multiple anomaly classification paths corresponding to the multiple environmental parameter categories; based on the first anomaly ratio and the second anomaly ratio, a third anomaly ratio is obtained, the pearl oyster environmental anomaly identification scale and the pearl environmental anomaly identification scale are configured, and environmental parameter anomalies are identified for multiple environmental parameter sets respectively to obtain the pearl oyster environmental anomaly level and the pearl environmental anomaly level; based on the first anomaly ratio and the third anomaly ratio, the pearl oyster environmental anomaly level and the pearl environmental anomaly level are weighted and calculated to obtain the growth environment anomaly level, which is used as the monitoring result of the pearl oyster growth environment.

[0103] Example 4, please refer to Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 4 As shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, it performs the following steps: During the growth of pearl oysters, environmental parameters of multiple categories are monitored and collected to obtain multiple sets of environmental parameters, wherein the multiple categories of environmental parameters include water temperature, salinity, and dissolved oxygen; a pearl oyster anomaly classifier and a pearl anomaly classifier are used to classify the multiple sets of environmental parameters into abnormal environmental parameters to obtain a first anomaly ratio and a second anomaly ratio, wherein the pearl oyster anomaly classifier and the pearl anomaly classifier include multiple anomaly classification paths corresponding to the multiple categories of environmental parameters; based on the first anomaly ratio and the second anomaly ratio, a third anomaly ratio is obtained; the scale for identifying anomalies in the pearl oyster environment and the scale for identifying anomalies in the pearl environment are configured; environmental parameter anomalies are identified in the multiple sets of environmental parameters to obtain the pearl oyster environment anomaly level and the pearl environment anomaly level; based on the first anomaly ratio and the third anomaly ratio, the pearl oyster environment anomaly level and the pearl environment anomaly level are weighted and calculated to obtain the growth environment anomaly level, which is used as the monitoring result of the pearl oyster growth environment.

[0104] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0105] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0110] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring the growth environment of pearl oysters, characterized in that the method... include: During the growth of pearl oysters, multiple environmental parameters of various categories are monitored and collected to obtain multiple sets of environmental parameters, including water temperature, salinity, and dissolved oxygen. The pearl oyster anomaly classifier and the pearl anomaly classifier are used respectively to classify the multiple sets of environmental parameters into abnormal environmental parameters, and obtain a first anomaly ratio and a second anomaly ratio. The pearl oyster anomaly classifier and the pearl anomaly classifier include multiple anomaly classification paths corresponding to the multiple environmental parameter categories. Based on the first and second anomaly ratios, a third anomaly ratio is obtained by adjusting the ratio. The scale for identifying anomalies in the pearl oyster environment and the scale for identifying anomalies in the pearl environment are configured. Anomalies in multiple environmental parameter sets are then identified to obtain the anomaly levels in the pearl oyster environment and the pearl environment, including: The sum of 1 and the first abnormality ratio is used as a correction coefficient, which is then multiplied by the second abnormality ratio to obtain the third abnormality ratio. Obtain the maximum anomaly detection scale, wherein the maximum anomaly detection scale includes the maximum number of integrated anomaly detections; The first and third anomaly ratios are multiplied by the maximum number of integrated anomalies identified and then rounded to obtain the number of integrated anomalies identified in pearl oysters and the number of integrated anomalies identified in pearls, which are used as the scale of anomaly identification in the pearl oyster environment and the scale of anomaly identification in the pearl environment. Based on the maximum number of integrated anomaly recognitions, and using ensemble learning, a pearl oyster environment anomaly recognizer including multiple first environment anomaly recognition branches and a pearl environment anomaly recognizer including multiple second environment anomaly recognition branches are constructed. The multiple sets of environmental parameters are respectively input into the first environmental anomaly identification branch of the number of integrated anomalies identified in pearl oysters and the second environmental anomaly identification branch of the number of integrated anomalies identified in pearls, and multiple sets of environmental anomaly levels of pearl oysters and multiple sets of environmental anomaly levels of pearls are output. The average values ​​are calculated to obtain the environmental anomaly level of pearl oysters and the environmental anomaly level of pearls. Based on the first and third abnormality ratios, the abnormality levels of the pearl oyster environment and the pearl environment are weighted and calculated to obtain the abnormality level of the growth environment, which serves as the monitoring result of the pearl oyster growth environment.

2. The method for monitoring the growth environment of pearl oysters according to claim 1, characterized in that, During the growth process of pearl oysters, multiple environmental parameters of various categories were monitored and collected, resulting in multiple sets of environmental parameters, including: During the growth of pearl oysters, environmental parameters at the first location in the growth environment are collected according to multiple environmental parameter categories, including water temperature, salinity, and dissolved oxygen. Continue collecting environmental parameter sets from multiple locations to obtain multiple environmental parameter sets.

3. The method for monitoring the growth environment of pearl oysters according to claim 1, characterized in that, The multiple sets of environmental parameters are classified into abnormal environmental parameters using a pearl oyster anomaly classifier and a pearl anomaly classifier, respectively, to obtain a first anomaly ratio and a second anomaly ratio, including: The multiple sets of environmental parameters are divided according to the multiple categories of environmental parameters to obtain sets of water temperature, salinity, and dissolved oxygen. The water temperature set, salinity set, and dissolved oxygen set are respectively input into the pearl oyster anomaly classifier to classify abnormal environmental parameters and obtain the first water temperature anomaly ratio, the first salinity anomaly ratio, and the first dissolved oxygen anomaly ratio. The first anomaly ratio is calculated. The pearl oyster anomaly classifier is constructed based on the normal environmental parameters of pearl oyster growth over a historical period and includes the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path. Based on the normal environmental parameters of pearl cultivation over a historical period, a pearl anomaly classifier is constructed, wherein the pearl anomaly classifier includes a second water temperature anomaly classification path, a second salinity anomaly classification path, and a second dissolved oxygen anomaly classification path. The water temperature set, salinity set, and dissolved oxygen set are respectively input into the second water temperature anomaly classification path, the second salinity anomaly classification path, and the second dissolved oxygen anomaly classification path to classify abnormal environmental parameters, obtain the second water temperature anomaly ratio, the second salinity anomaly ratio, and the second dissolved oxygen anomaly ratio, and calculate the second anomaly ratio.

4. The method for monitoring the growth environment of pearl oysters according to claim 3, characterized in that, The water temperature set, salinity set, and dissolved oxygen set are respectively input into the pearl oyster anomaly classifier to classify abnormal environmental parameters, obtain the first water temperature anomaly ratio, the first salinity anomaly ratio, and the first dissolved oxygen anomaly ratio, and calculate the first anomaly ratio, including: The normal growth environment parameters of pearl oysters within a preset historical time range are obtained and divided into normal water temperature set, normal salinity set and normal dissolved oxygen set; The normal water temperature set, normal salinity set, and normal dissolved oxygen set are used to construct binary classification nodes to obtain the first water temperature division node set, the first salinity division node set, and the first dissolved oxygen division node set, and to construct the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path. The water temperature set, salinity set, and dissolved oxygen set are respectively input into the first water temperature anomaly classification path, the first salinity anomaly classification path, and the first dissolved oxygen anomaly classification path for classification. The proportions of the classification into a single water temperature parameter, a single salinity parameter, and a single dissolved oxygen parameter are filtered to obtain the first water temperature anomaly proportion, the first salinity anomaly proportion, and the first dissolved oxygen anomaly proportion. The average of the first abnormal water temperature ratio, the first abnormal salinity ratio, and the first abnormal dissolved oxygen ratio is calculated to obtain the first abnormal ratio.

5. The method for monitoring the growth environment of pearl oysters according to claim 1, characterized in that, Based on the maximum number of integrated anomaly recognitions, and using ensemble learning, a pearl oyster environment anomaly recognizer comprising multiple first environmental anomaly recognition branches and a pearl environment anomaly recognizer comprising multiple second environmental anomaly recognition branches are constructed, including: Based on historical growth environment data of pearl oysters and pearls, multiple sets of sample environmental parameters were collected, and the abnormality levels of the pearl oyster environment and the pearl environment were identified respectively, resulting in a set of abnormality levels of sample pearl oyster environment and a set of abnormality levels of sample pearl environment. According to the maximum number of integrated anomaly identifications, the multiple sets of sample environmental parameters, the set of sample pearl oyster environmental anomaly levels, and the set of sample pearl environmental anomaly levels are divided to obtain multiple sets of input data, multiple sets of first output data, and multiple sets of second output data. Using the multiple input data, the multiple first output data and multiple second output data are combined respectively to train multiple first environmental anomaly recognition branches and multiple second environmental anomaly recognition branches; By combining the multiple first environmental anomaly recognition branches and the multiple second environmental anomaly recognition branches respectively, a pearl oyster environmental anomaly recognizer and a pearl environmental anomaly recognizer are obtained.

6. The method for monitoring the growth environment of pearl oysters according to claim 1, characterized in that, Based on the first and third anomaly ratios, a weighted calculation is performed on the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment to obtain the abnormality level of the growth environment, which serves as the monitoring result of the pearl oyster growth environment, including: Configure the pearl oyster weight and pearl weight based on the first and third abnormality ratios. Using the pearl oyster weight and pearl weight, the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment are weighted and calculated to obtain the abnormality level of the growth environment, which is used as the monitoring result of the growth environment of the pearl oyster.

7. A monitoring system for the growth environment of pearl oysters, characterized in that, The steps for implementing a method for monitoring the growth environment of pearl oysters according to any one of claims 1 to 6 include: An environmental parameter monitoring module is used to monitor and collect multiple environmental parameter categories during the growth process of pearl oysters, and obtain multiple sets of environmental parameters, wherein the multiple environmental parameter categories include water temperature, salinity and dissolved oxygen; An abnormal environmental parameter classification module is used to classify the multiple sets of environmental parameters into abnormal environmental parameters using a pearl oyster abnormal classifier and a pearl abnormal classifier, respectively, to obtain a first abnormal ratio and a second abnormal ratio. The pearl oyster abnormal classifier and the pearl abnormal classifier include multiple abnormal classification paths corresponding to the multiple environmental parameter categories. An environmental parameter anomaly identification module is used to correct and obtain a third anomaly ratio based on the first and second anomaly ratios, configure the pearl oyster environment anomaly identification scale and the pearl environment anomaly identification scale, and perform environmental parameter anomaly identification on multiple sets of environmental parameters respectively to obtain the pearl oyster environment anomaly level and the pearl environment anomaly level, including: The sum of 1 and the first abnormality ratio is used as a correction coefficient, which is then multiplied by the second abnormality ratio to obtain the third abnormality ratio. Obtain the maximum anomaly detection scale, wherein the maximum anomaly detection scale includes the maximum number of integrated anomaly detections; The first and third anomaly ratios are multiplied by the maximum number of integrated anomalies identified and then rounded to obtain the number of integrated anomalies identified in pearl oysters and the number of integrated anomalies identified in pearls, which are used as the scale of anomaly identification in the pearl oyster environment and the scale of anomaly identification in the pearl environment. Based on the maximum number of integrated anomaly recognitions, and using ensemble learning, a pearl oyster environment anomaly recognizer including multiple first environment anomaly recognition branches and a pearl environment anomaly recognizer including multiple second environment anomaly recognition branches are constructed. The multiple sets of environmental parameters are respectively input into the first environmental anomaly identification branch of the number of integrated anomalies identified in pearl oysters and the second environmental anomaly identification branch of the number of integrated anomalies identified in pearls, and multiple sets of environmental anomaly levels of pearl oysters and multiple sets of environmental anomaly levels of pearls are output. The average values ​​are calculated to obtain the environmental anomaly level of pearl oysters and the environmental anomaly level of pearls. The environmental monitoring results acquisition module is used to perform a weighted calculation of the abnormality level of the pearl oyster environment and the abnormality level of the pearl environment based on the first abnormality ratio and the third abnormality ratio, so as to obtain the abnormality level of the growth environment, which is used as the monitoring result of the growth environment of the pearl oyster.

8. An electronic device, characterized in that, include: Memory, used to store computer software programs; A processor is configured to read and execute the computer software program, thereby implementing the steps of the pearl oyster growth environment monitoring method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores a computer software program, which, when executed by a processor, implements the steps of a method for monitoring the growth environment of pearl oysters as described in any one of claims 1 to 6.

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