Microbial Population Monitoring-Based Regulation Method and Platform for Complementary Fishing and Photovoltaic

Abstract

The present invention discloses a method and platform for complementary regulation of fishery and photovoltaic based on microbial population monitoring, which relates to the field of intelligent regulation technology. The method includes: performing multi-dimensional identification and analysis on multi-point water body microbial sample sets respectively to obtain multi-point microbial identification data stream sets, and performing statistical integration processing on them to generate a microbial population change report; matching the microbial population change report with the complementary regulation strategy of fishery and photovoltaic to obtain a target complementary regulation strategy of fishery and photovoltaic, and constructing a parameter space for the complementary regulation strategy of fishery and photovoltaic; performing regulation analysis on the microbial population change report based on the parameter space for the complementary regulation strategy of fishery and photovoltaic to obtain a library of fishery-light balance regulation parameter schemes for performing complementary regulation of fishery and photovoltaic on the target water body. By monitoring the changes in the microbial population in the water body at multiple points, the parameter coordination and optimization of fishery and photovoltaic power generation are realized, the accuracy and rationality of the complementary regulation parameters are improved, and further the complementary regulation effect of fishery and photovoltaic is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent regulation, and particularly to a complementary regulation method and platform for fishing and photovoltaic power generation based on microbial population monitoring. Background Art

[0002] Fishing and photovoltaic power generation complementarity is a new energy utilization model that combines a photovoltaic power generation system with fishery farming, realizing a new power generation model of "generating electricity above and raising fish below". However, in practical applications, due to the complexity of the water environment, there are mutual influences between fishery farming and photovoltaic power generation. How to reasonably coordinate and optimize the relationship between the two has become an urgent problem to be solved in order to achieve intelligent regulation of fishery farming and photovoltaic power generation. Summary of the Invention

[0003] This application provides a complementary regulation method and platform for fishing and photovoltaic power generation based on microbial population monitoring, solving the technical problem in the prior art that the regulation accuracy and rationality of fishing and photovoltaic power generation complementarity are relatively low due to the complexity of the water environment, achieving the technical effect of coordinating and optimizing the parameters of fishery and photovoltaic power generation by multi-point monitoring of the changes in microbial populations in the water body, improving the accuracy and rationality of complementary regulation parameters, and further ensuring the regulation effect of fishing and photovoltaic power generation complementarity.

[0004] In view of the above problems, the present invention provides a complementary regulation method and platform for fishing and photovoltaic power generation based on microbial population monitoring.

[0005] In a first aspect, this application provides a complementary regulation method for fishing and photovoltaic power generation based on microbial population monitoring. The method includes: obtaining a microbial population monitoring device, where the microbial population monitoring device includes a microbial sampler, a microbial identifier, and a data processor; performing sampling point analysis on a target water body to determine multiple key sampling points, and collecting water samples at multiple times at the multiple key sampling points through the microbial sampler to obtain a multi-point water body microbial sample set; using the microbial identifier to perform multi-dimensional identification and analysis on the multi-point water body microbial sample set respectively to obtain a multi-point microbial identification data stream set; performing statistical integration processing on the multi-point microbial identification data stream set based on the data processor to generate a microbial population change report; constructing a complementary regulation strategy for fishing and photovoltaic power generation balance, matching the microbial population change report with the complementary regulation strategy for fishing and photovoltaic power generation balance to obtain a target complementary regulation strategy for fishing and photovoltaic power generation, and constructing a complementary regulation strategy parameter space for fishing and photovoltaic power generation based on the target complementary regulation strategy for fishing and photovoltaic power generation; performing regulation analysis on the microbial population change report based on the complementary regulation strategy parameter space for fishing and photovoltaic power generation to obtain a fishing and photovoltaic power generation balance regulation parameter scheme library, and performing complementary regulation of fishing and photovoltaic power generation on the target water body through the fishing and photovoltaic power generation balance regulation parameter scheme library.

[0006] On the other hand, the present application also provides a fishery-photovoltaic complementary regulation platform based on microbial population monitoring. The platform includes: a monitoring device acquisition module for acquiring a microbial population monitoring device, which includes a microbial sampler, a microbial identifier, and a data processor; a sampling point analysis module for analyzing sampling points of a target water body to determine a plurality of key sampling points, and performing multi-time water body collection on the plurality of key sampling points through the microbial sampler to obtain a multi-point water body microbial sample set; a multi-dimensional identification analysis module for performing multi-dimensional identification analysis on the multi-point water body microbial sample set respectively using the microbial identifier to obtain a multi-point microbial identification data stream set; a population change report generation module for statistically integrating and processing the multi-point microbial identification data stream set based on the data processor to generate a microbial population change report; a strategy parameter space construction module for constructing a fishery-photovoltaic complementary equilibrium regulation strategy, matching the microbial population change report with the fishery-photovoltaic complementary equilibrium regulation strategy to obtain a target fishery-photovoltaic complementary regulation strategy, and constructing a fishery-photovoltaic complementary regulation strategy parameter space according to the target fishery-photovoltaic complementary regulation strategy; a fishery-photovoltaic complementary regulation module for performing regulation analysis on the microbial population change report based on the fishery-photovoltaic complementary regulation strategy parameter space to obtain a fishery equilibrium regulation parameter scheme library, and performing fishery-photovoltaic complementary regulation on the target water body through the fishery equilibrium regulation parameter scheme library.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] Due to the technical solution of analyzing sampling points of a target water body to determine a plurality of key sampling points, performing multi-time water body collection on the plurality of key sampling points through the microbial sampler to obtain a multi-point water body microbial sample set, performing multi-dimensional identification analysis on it respectively using a microbial identifier to obtain a multi-point microbial identification data stream set, statistically integrating and processing the multi-point microbial identification data stream set based on a data processor to generate a microbial population change report, then matching the microbial population change report with a fishery-photovoltaic complementary equilibrium regulation strategy to obtain a target fishery-photovoltaic complementary regulation strategy, and constructing a fishery-photovoltaic complementary regulation strategy parameter space according to the target fishery-photovoltaic complementary regulation strategy, and thereby performing regulation analysis on the microbial population change report to obtain a fishery equilibrium regulation parameter scheme library for performing fishery-photovoltaic complementary regulation on the target water body. Thus, the technical effect of realizing the parameter coordination and optimization of fishery and photovoltaic power generation by monitoring the changes of microbial populations in the water body at multiple points, improving the accuracy and rationality of complementary regulation parameters, and further ensuring the fishery-photovoltaic complementary regulation effect is achieved.

[0009] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific implementation manners of this application are hereinafter specifically exemplified. Description of the Drawings

[0010] Figure 1 It is a schematic flowchart of the fishery-photovoltaic complementary regulation method based on microbial population monitoring of this application;

[0011] Figure 2 It is a schematic flowchart of determining multiple key sampling points in the fishery-photovoltaic complementary regulation method based on microbial population monitoring of this application;

[0012] Figure 3 It is a schematic structural diagram of the fishery-photovoltaic complementary regulation platform based on microbial population monitoring of this application.

[0013] Description of the reference numerals in the drawings: Monitoring device acquisition module 11, sampling point analysis module 12, multi-dimensional identification and analysis module 13, population change report generation module 14, strategy parameter space construction module 15, fishery-photovoltaic complementary regulation module 16. Specific Embodiment

[0014] This application provides a fishery-photovoltaic complementary regulation method and platform based on microbial population monitoring, solving the technical problem that the regulation accuracy and rationality of the existing fishery-photovoltaic complementary regulation are relatively low due to the complexity of the water environment, achieving the technical effect of realizing the coordinated optimization of the parameters of fishery and photovoltaic power generation by monitoring the changes of microbial populations at multiple points in the water body, improving the accuracy and rationality of the complementary regulation parameters, and further ensuring the effect of fishery-photovoltaic complementary regulation.

[0015] In order to make the purpose, technical solution and advantages of this application clearer, the following further details this application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0016] The following describes this application in combination with the drawings in this application.

[0017] Embodiment 1

[0018] As Figure 1 shown, this application provides a fishery-photovoltaic complementary regulation method based on microbial population monitoring, and the method includes:

[0019] Step S1: Obtain a microbial population monitoring device, and the microbial population monitoring device includes a microbial sampler, a microbial identifier and a data processor.

[0020] Specifically, to achieve a comprehensive monitoring of the water body microbial population, first obtain a microbial population monitoring device. The microbial population monitoring device is a device used to monitor and evaluate the quantity, species, and dynamic changes of the microbial population in a specific environment, including a microbial sampler, which is a special tool for collecting microbial samples from the water body. It can automatically or manually collect microbial samples from the water body and maintain them under specific temperature and environment conditions to ensure the integrity and accuracy of the samples; a microbial identifier, which is an instrument used to automatically identify and classify microorganisms; and a data processor, which is a new generation of processor for achieving high-performance data processing, integrating complete data center functions on a single chip. Through the microbial population monitoring device, sampling and monitoring of the water body are carried out to achieve a comprehensive monitoring of the water body microbial population.

[0021] Step S2: Analyze the sampling points of the target water body to determine multiple key sampling points, and use the microbial sampler to collect water samples at multiple times from the multiple key sampling points to obtain a set of multi-point water body microbial samples.

[0022] Such as Figure 2 shown, further, for the determination of the multiple key sampling points, the steps of this application further include:

[0023] Obtain information on fishery water body attribute factors, where the fishery water body attribute factor information includes water body geographical characteristics, water quality environmental conditions, fishery farming situations, and pollution source distributions; based on the fishery water body attribute factor information, perform multi-dimensional parameter collection on the target water body to obtain a set of fishery water body multi-dimensional factor parameters; based on the set of fishery water body multi-dimensional factor parameters, perform spatial overlay analysis to obtain a set of fishery water body spatial feature layers; identify key areas for the set of fishery water body spatial feature layers to obtain a set of key fishery water body areas, and perform coverage analysis on the set of key fishery water body areas to determine the multiple key sampling points.

[0024] Further, for the determination of the multiple key sampling points, the steps of this application further include:

[0025] Use the recursive feature elimination algorithm to extract influencing features from the set of key fishery water body areas to obtain a set of fishery water body influencing features; based on the set of fishery water body influencing features, perform feature marking on the set of key fishery water body areas to obtain a set of fishery water body area influencing features; based on the set of fishery water body area influencing features, perform coverage analysis on the set of key fishery water body areas to obtain a set of initial sampling points for the fishery water body areas; perform clustering analysis on the set of initial sampling points for the fishery water body areas to obtain a clustering result of water body sampling, and determine the multiple key sampling points according to the clustering result of water body sampling.

[0026] Furthermore, for the step of obtaining the set of fishery water body impact characteristics in this application, it further includes:

[0027] Based on the set of multi-dimensional factor parameters of the fishery water body, extract the correlation characteristics of the set of key fishery water body regions to obtain the set of fishery water body correlation characteristics; conduct historical data mining on the set of fishery water body correlation characteristics to obtain the data set of the distribution characteristics of fishery water body microorganisms; use random forest to perform microbial population prediction training on the data set of the distribution characteristics of fishery water body microorganisms to obtain the microbial population prediction model of the fishery water body; obtain the meta-transformer according to the recursive feature elimination algorithm, and through the meta-transformer, perform iterative feature removal on the set of fishery water body correlation characteristics, and conduct training evaluation on the microbial population prediction model of the fishery water body after feature removal, and screen to obtain the set of fishery water body impact characteristics.

[0028] Specifically, to ensure the comprehensiveness of the sampling of the microbial population in the water body, analyze the sampling points of the target water body to be regulated. First, obtain the information on the attribute factors of the fishery water body, and the information on the attribute factors of the fishery water body is the type of index that affects the change of the microbial population in the water body, including the geographical characteristics of the water body, the water quality environment status, the fishery farming situation, and the distribution of pollution sources, etc. Based on the information on the attribute factors of the fishery water body, collect multi-dimensional parameters of the target water body to obtain the set of multi-dimensional factor parameters of the fishery water body, and the set of multi-dimensional factor parameters of the fishery water body is the multi-factor attribute parameters such as the geographical characteristics of the water body, the water quality environment status, the fishery farming situation, and the distribution of pollution sources of this target water body. Use GIS technology to perform spatial overlay analysis on the set of multi-dimensional factor parameters of the fishery water body, overlay data layers such as water quality parameters, aquaculture distribution, topography, etc., to obtain the set of fishery water body spatial feature layers, and the set of fishery water body spatial feature layers is used to comprehensively display the situation of the water body spatial distribution parameters.

[0029] Identify key areas from the set of spatial feature layers of the fishery water body according to the requirements of water body microorganism collection, and obtain key areas where the distribution of microorganism populations varies greatly, namely the set of key fishery water body areas. For example, intensive aquaculture areas, near pollution discharge outlets, areas with slow water flow, etc. Then conduct a coverage analysis on the set of key fishery water body areas. Since there are many multi-dimensional factor parameters in fishery water bodies, in order to improve the processing accuracy and efficiency, it is necessary to select features that have an important impact on the distribution of microorganism populations. Specifically: Use the recursive feature elimination algorithm to extract the influencing features from the set of key fishery water body areas. First, based on the multi-dimensional factor parameter set of the fishery water body, extract the associated features from the set of key fishery water body areas to obtain the fishery water body associated feature set. The fishery water body associated feature set includes all features in the multi-dimensional factor parameters of the fishery water body that are related to the distribution of microorganism populations, such as water body area, water depth, water flow conditions, aquaculture species, aquaculture density, etc. Then conduct historical data mining on the fishery water body associated feature set to obtain the fishery water body microorganism distribution feature data set. The fishery water body microorganism distribution feature data set includes historical fishery water body associated feature data and corresponding data such as the types and quantities of microorganism populations distributed.

[0030] Use random forest to conduct microorganism population prediction training on the fishery water body microorganism distribution feature data set until the preset convergence condition is reached, and obtain the fishery water body microorganism population prediction model. The fishery water body microorganism population prediction model is used to predict the distribution of microorganism populations according to the fishery water body associated features. Obtain the meta-transformer according to the recursive feature elimination algorithm. The meta-transformer is the feature selection optimizer in the recursive feature elimination algorithm. Through the meta-transformer, iterative feature removal is performed on the fishery water body associated feature set. In each iteration, the meta-transformer will remove the feature that is considered the least important in the fishery water body associated feature set, and re-train and evaluate the fishery water body microorganism population prediction model after feature removal. After each iteration, evaluate the performance of the model, and score the importance of the removed feature accordingly to represent the contribution degree of the feature to the model performance. Record the removed features and their importance scores. Through multiple iterations, the meta-transformer will gradually reduce the number of features. According to the model performance and feature importance scores, determine a suitable feature number threshold. Then, screen and retain those features that have the greatest impact on the model performance according to the importance scores to obtain the fishery water body influencing feature set that has an important impact on the distribution of microorganism populations, such as temperature, dissolved oxygen, pH value, nutrient salt concentration, etc. Through recursive feature elimination, accurately select the features that have an important impact on the distribution of microorganism populations, reduce the influence of redundant and irrelevant features on the selection of sampling points, and thus improve the data processing efficiency.

[0031] Based on the set of fishery water body impact characteristics, perform feature marking on the set of key fishery water body regions to obtain the corresponding fishery water body region impact characteristic sets for each key fishery water body region after feature parameter marking. Based on the set of fishery water body region impact characteristics, conduct a coverage analysis on the set of key fishery water body regions to cover the regions with different levels of characteristics within each key fishery water body region, and mark sampling points for each region with different levels of characteristics to obtain a corresponding large set of initial sampling points for the fishery water body region. Conduct a clustering analysis on the set of initial sampling points for the fishery water body region, set a similarity threshold using clustering algorithms such as K-means and hierarchical clustering, cluster the sampling points with similar characteristics into one category, and obtain the calculated water body sampling clustering result. And according to the water body sampling clustering result, interpret each cluster as a specific region or sub-region in the fishery water body. The most representative sampling point can be selected from each clustering result based on the sampling point position. For example, select the sampling point with the eigenvalue closest to the cluster center in the cluster to determine multiple key sampling points. And use the fully automatic microbial sampler to collect water samples at multiple times for the multiple key sampling points to obtain a set of multi-point water body microbial samples to ensure the integrity and accuracy of the samples. Use feature extraction analysis and clustering algorithms to identify the most representative sampling points for the characteristics of each region in the fishery water body, ensure that the sampling points can comprehensively reflect the microbial population distribution and changes in the fishery water body, and thus achieve multi-point comprehensive monitoring of the microbial population in the fishery water body.

[0032] Step S3: Use the microbial identifier to perform multi-dimensional identification analysis on the set of multi-point water body microbial samples respectively to obtain a set of multi-point microbial identification data streams.

[0033] Specifically, using the microbial identifier to perform multi-dimensional identification analysis on the set of multi-point water body microbial samples respectively includes identification characteristics such as colony morphology, color, and growth rate. Among them, the microbial identifier consists of parts such as a biochemical reaction plate, an instrument, and an identification database. Through the software inside the instrument combined with the existing database for automatic query, the result closest to the genus of a strain is given to obtain a set of multi-point microbial identification data streams. The set of multi-point microbial identification data streams is compared and identified to obtain the quantity and types of microbial populations in the corresponding different collected samples, improving the comprehensiveness and efficiency of microbial identification.

[0034] Step S4: Based on the data processor, perform statistical integration processing on the set of multi-point microbial identification data streams to generate a report on the changes in the microbial population.

[0035] Furthermore, for the generation of the report on the changes in the microbial population, the steps of this application further include:

[0036] Perform microbial parameter statistics on the multi-point microbial identification data stream set to obtain a multi-point microbial parameter set, where the multi-point microbial parameter set includes microbial species, quantity, and population proportion; integrate the multi-point microbial parameter set according to the time series to obtain a multi-point microbial population time series distribution parameter set; use the regression analysis method to perform trend fitting on the multi-point microbial population time series distribution parameter set to generate a microbial population distribution change trend curve set; perform multi-point network connection processing on the microbial population distribution change trend curve set according to the sampling point sequence to generate the microbial population change report.

[0037] Specifically, based on the data processor, perform statistical integration processing on the multi-point microbial identification data stream set. Among them, the data processor can achieve high-performance and fast processing of microbial data. First, perform microbial parameter statistics on the multi-point microbial identification data stream set to obtain the corresponding multi-point microbial parameter set, where the multi-point microbial parameter set includes microbial species, quantity, and population proportion. Integrate the multi-point microbial parameter set according to the time series to obtain a multi-point microbial population time series distribution parameter set in which the multi-point microbial parameter set is arranged in ascending order of collection time from early to late.

[0038] Use the regression analysis method to perform trend fitting on the multi-point microbial population time series distribution parameter set, analyze the change trend of the microbial population quantity with the change of the time series, and generate a microbial population distribution change trend curve set through regression analysis fitting. The microbial population distribution change trend curve set is the time change trend of the microbial population quantity. Perform multi-point network connection processing on the microbial population distribution change trend curve set according to the sampling point distribution position sequence, and record and generate a microbial population change report. The microbial population change report includes information such as microbial species, quantity, and change trend. Improve the comprehensiveness and accuracy of the record of the microbial population change report, ensure a comprehensive reflection of the microbial population distribution and change trend in the fishery water body, and further improve the analysis accuracy of the subsequent fishery-light complementary regulation parameters.

[0039] Step S5: Construct a fishery-light complementary equilibrium regulation strategy, match the microbial population change report with the fishery-light complementary equilibrium regulation strategy to obtain a target fishery-light complementary regulation strategy, and construct a fishery-light complementary regulation strategy parameter space according to the target fishery-light complementary regulation strategy.

[0040] Specifically, the regulation strategies for fishery and photovoltaic power generation need to meet the purpose of balanced regulation, including resource utilization, environmental impact, economic benefits, etc. The expert group constructs a complementary fishing and photovoltaic balanced regulation strategy through the experience of complementary fishing and photovoltaic regulation and the purpose of balanced regulation. The complementary fishing and photovoltaic balanced regulation strategy is a set of regulation strategies for the balanced development of complementary fishing and photovoltaic formulated according to the microbial population situation. Based on the similarity matching between the microbial population change report and the complementary fishing and photovoltaic balanced regulation strategy, the target complementary fishing and photovoltaic regulation strategy matching the change of the microbial population is obtained. And according to the target complementary fishing and photovoltaic regulation strategy, big data mining is carried out to construct a complementary fishing and photovoltaic regulation strategy parameter space. The complementary fishing and photovoltaic regulation strategy parameter space is historical complementary fishing and photovoltaic regulation data associated with the target complementary fishing and photovoltaic regulation strategy, including fishing and light balance regulation parameters and corresponding complementary fishing and photovoltaic regulation effect data, so as to serve as the optimization range of subsequent fishing and light balance regulation parameters, thereby improving the efficiency of parameter optimization analysis.

[0041] Step S6: Based on the complementary fishing and photovoltaic regulation strategy parameter space, conduct regulation analysis on the microbial population change report to obtain a fishing and light balance regulation parameter scheme library, and conduct complementary fishing and photovoltaic regulation on the target water body through the fishing and light balance regulation parameter scheme library.

[0042] Furthermore, for obtaining the fishing and light balance regulation parameter scheme library, the steps of this application also include:

[0043] Obtain a set of complementary fishing and photovoltaic regulation effect index, fit the set of complementary fishing and photovoltaic regulation effect index based on the complementary fishing and photovoltaic regulation strategy parameter space to construct a complementary fishing and photovoltaic regulation effect fitness function; conduct a correlation analysis on the complementary fishing and photovoltaic regulation strategy parameter space based on the microbial population change report to determine the selection threshold of the regulation strategy parameters; orthogonally select multiple groups of regulation strategy parameters within the selection threshold of the regulation strategy parameters, and use the complementary fishing and photovoltaic regulation effect fitness function to evaluate the fitness of the multiple groups of regulation strategy parameters to obtain the fitness of multiple groups of parameter regulation effects; select the parameter cluster center according to the fitness of multiple groups of parameter regulation effects, and conduct an optimization analysis on the multiple groups of regulation strategy parameters based on the parameter cluster center to optimize and obtain the fishing and light balance regulation parameter scheme library.

[0044] Furthermore, for optimizing and obtaining the fishing and light balance regulation parameter scheme library, the steps of this application also include:

[0045] Set a learning factor, and according to the learning factor, update the remaining regulation parameters in the multiple groups of regulation strategy parameters by learning towards the parameter cluster center to obtain a regulation strategy learning parameter cluster; use the genetic algorithm to perform population iterative optimization within the regulation strategy learning parameter cluster to obtain a regulation strategy optimization parameter cluster, and conduct proportional screening optimization on the regulation strategy optimization parameter cluster to obtain the fishing and light balance regulation parameter scheme library.

[0046] Specifically, based on the parameter space of the fishery-photovoltaic complementary regulation strategy, the regulation analysis of the microbial population change report is carried out. First, a set of fishery-photovoltaic complementary regulation effect indicators is formulated. The set of fishery-photovoltaic complementary regulation effect indicators is used for multi-dimensional evaluation of the fishery-photovoltaic complementary regulation effect, including photovoltaic energy utilization efficiency, fishery income, and consumption cost, etc. The regression analysis method is used to evaluate and fit the fishery-photovoltaic complementary regulation effect data and the set of fishery-photovoltaic complementary regulation effect indicators in the parameter space of the fishery-photovoltaic complementary regulation strategy, and a fishery-photovoltaic complementary regulation effect fitness function is constructed. The fishery-photovoltaic complementary regulation effect fitness function is a regression function about the set of fishery-photovoltaic complementary regulation effect indicators, and is used to evaluate the fishery-photovoltaic complementary regulation effect according to the fishery-light balance regulation parameters. The greater the fitness, the better the fishery-photovoltaic complementary regulation effect.

[0047] Based on the microbial population change report, a correlation analysis of the parameter space of the fishery-photovoltaic complementary regulation strategy is carried out to determine the selection threshold of the regulation strategy parameters that matches the population quantity change value in the microbial population change report. The selection threshold of the regulation strategy parameters is the selectable range of the fishery-photovoltaic complementary regulation. Orthogonal arrangement of parameters is carried out within the selection threshold of the regulation strategy parameters, and multiple groups of regulation strategy parameters are selected therefrom. The fishery-photovoltaic complementary regulation effect fitness function is used to evaluate the fitness of the multiple groups of regulation strategy parameters, and the corresponding multiple groups of parameter regulation effect fitness are obtained. According to the multiple groups of parameter regulation effect fitness, the regulation strategy parameter with the largest fitness is selected as the center of the parameter cluster.

[0048] Based on the center of the parameter cluster, an optimization analysis of the multiple groups of regulation strategy parameters is carried out. First, a learning factor is set through the parameter optimization accuracy requirement. The learning factor is the parameter learning step size. According to the learning factor, the remaining regulation parameters in the multiple groups of regulation strategy parameters are updated by learning towards the center of the parameter cluster, so that the fitness of the remaining regulation parameters approaches the center of the parameter cluster, and the regulation strategy parameter with the largest fitness after each learning is used as the center of the parameter cluster instead, and the regulation strategy learning parameter cluster after multiple learning updates is obtained. The genetic algorithm is used to perform population iterative optimization within the regulation strategy learning parameter cluster. According to the fitness values of the parameters within the cluster, a part of the parameter individuals are selected from the current population as the parent generation to generate the next generation population. The selected parent individuals are crossed to generate new offspring individuals, and at the same time, the offspring individuals are mutated to increase the diversity of the population until the preset termination condition is reached, and the optimized regulation strategy parameter cluster after population iterative optimization is obtained.

[0049] Perform proportional screening and optimization on the optimized parameter clusters of the regulation strategy according to the fitness values of the intra-cluster parameters. For example, screen the fishery-solar equilibrium regulation parameters with the top 5% fitness values to form a library of fishery-solar equilibrium regulation parameter solutions. Then, perform fishery-solar complementary regulation on the target water body through the library of fishery-solar equilibrium regulation parameter solutions. First, use the optimal parameter solution in the library of fishery-solar equilibrium regulation parameter solutions for complementary regulation. When the regulation effect does not meet the expectation, the sub-optimal parameter solution in the library of fishery-solar equilibrium regulation parameter solutions can be directly applied for regulation, reducing the time for optimizing and analyzing the regulation parameters. Achieve the coordinated optimization of the parameters of fishery and photovoltaic power generation, improve the accuracy and rationality of the complementary regulation parameters, and ensure the timeliness of the fishery-solar complementary regulation and the fishery-solar complementary regulation effect.

[0050] In summary, the fishery-solar complementary regulation method based on microbial population monitoring provided by this application has the following technical effects:

[0051] Due to the technical solution of analyzing sampling points of the target water body to determine multiple key sampling points, collecting water samples at multiple times at the multiple key sampling points through the microbial sampler to obtain a set of multi-point water body microbial samples, performing multi-dimensional identification and analysis on them respectively using a microbial identifier to obtain a set of multi-point microbial identification data streams, statistically integrating and processing the set of multi-point microbial identification data streams based on a data processor to generate a microbial population change report, then matching the microbial population change report with the fishery-solar complementary equilibrium regulation strategy to obtain a target fishery-solar complementary regulation strategy, and constructing a parameter space for the fishery-solar complementary regulation strategy according to the target fishery-solar complementary regulation strategy, and then performing regulation analysis on the microbial population change report to obtain a library of fishery-solar equilibrium regulation parameter solutions for performing fishery-solar complementary regulation on the target water body. Furthermore, it achieves the technical effect of realizing the coordinated optimization of the parameters of fishery and photovoltaic power generation by monitoring the changes of microbial populations in the water body at multiple points, improving the accuracy and rationality of the complementary regulation parameters, and thus ensuring the fishery-solar complementary regulation effect.

[0052] Embodiment 2

[0053] Based on the same inventive concept as the fishery-solar complementary regulation method based on microbial population monitoring in the foregoing embodiment, the present invention also provides a fishery-solar complementary regulation platform based on microbial population monitoring, as Figure 3 shown, the platform includes:

[0054] The monitoring device acquisition module 11 is used to acquire a microbial population monitoring device, which includes a microbial sampler, a microbial identifier, and a data processor; the sampling point analysis module 12 is used to analyze the sampling points of the target water body to determine multiple key sampling points, and collect water samples at multiple times from the multiple key sampling points through the microbial sampler to obtain a set of multi-point water body microbial samples; the multi-dimensional identification and analysis module 13 is used to perform multi-dimensional identification and analysis on the set of multi-point water body microbial samples respectively using the microbial identifier to obtain a set of multi-point microbial identification data streams; the population change report generation module 14 is used to perform statistical integration processing on the set of multi-point microbial identification data streams based on the data processor to generate a microbial population change report; the strategy parameter space construction module 15 is used to construct a complementary fishing and solar energy balance regulation strategy, match the microbial population change report with the complementary fishing and solar energy balance regulation strategy to obtain a target complementary fishing and solar energy regulation strategy, and construct a complementary fishing and solar energy regulation strategy parameter space according to the target complementary fishing and solar energy regulation strategy; the complementary fishing and solar energy regulation module 16 is used to perform regulation analysis on the microbial population change report based on the complementary fishing and solar energy regulation strategy parameter space to obtain a library of fishing and solar energy balance regulation parameter solutions, and perform complementary fishing and solar energy regulation on the target water body through the library of fishing and solar energy balance regulation parameter solutions.

[0055] Further, the sampling point analysis module 12 is also used for:

[0056] Obtain information on fishery water body attribute factors, where the fishery water body attribute factors information includes water body geographical characteristics, water quality environmental conditions, fishery farming conditions, and pollution source distribution; perform multi-dimensional parameter collection on the target water body based on the fishery water body attribute factors information to obtain a set of fishery water body multi-dimensional factor parameters; perform spatial overlay analysis on the set of fishery water body multi-dimensional factor parameters to obtain a set of fishery water body spatial feature layers; identify key areas from the set of fishery water body spatial feature layers to obtain a set of key fishery water body areas, and perform coverage analysis on the set of key fishery water body areas to determine the multiple key sampling points.

[0057] Further, the sampling point analysis module 12 is also used for:

[0058] Use the recursive feature elimination algorithm to extract the impact features of the set of key fishery water body regions, and obtain the set of fishery water body impact features; based on the set of fishery water body impact features, perform feature marking on the set of key fishery water body regions to obtain the set of fishery water body region impact features; based on the set of fishery water body region impact features, perform coverage analysis on the set of key fishery water body regions to obtain the initial sampling point set of fishery water body regions; perform clustering analysis on the initial sampling point set of fishery water body regions, obtain the water body sampling clustering result, and determine the multiple key sampling points according to the water body sampling clustering result.

[0059] Further, the sampling point analysis module 12 is also used for:

[0060] Extract the correlation features of the set of key fishery water body regions based on the set of multi-dimensional factor parameters of the fishery water body to obtain the set of fishery water body correlation features; perform historical data mining on the set of fishery water body correlation features to obtain the data set of the distribution characteristics of fishery water body microorganisms; use random forest to perform microbial population prediction training on the data set of the distribution characteristics of fishery water body microorganisms to obtain the microbial population prediction model of the fishery water body; obtain the meta-transformer according to the recursive feature elimination algorithm, and use the meta-transformer to perform iterative feature removal on the set of fishery water body correlation features, and perform training evaluation on the microbial population prediction model of the fishery water body after feature removal, and screen to obtain the set of fishery water body impact features.

[0061] Further, the population change report generation module 14 is also used for:

[0062] Perform microbial parameter statistics on the set of multi-point microbial identification data streams to obtain the set of multi-point microbial parameters, where the set of multi-point microbial parameters includes the types, quantities, and population proportions of microorganisms; integrate the set of multi-point microbial parameters according to the time series to obtain the set of time series distribution parameters of the multi-point microbial population; use the regression analysis method to perform trend fitting on the set of time series distribution parameters of the multi-point microbial population to generate a set of microbial population distribution change trend curves; perform multi-point network connection processing on the set of microbial population distribution change trend curves according to the sampling point sequence to generate the microbial population change report.

[0063] Further, the fishery-photo complementary regulation module 16 is also used for:

[0064] Obtain the set of fishery-photovoltaic complementary regulation effect indicators, fit the set of fishery-photovoltaic complementary regulation effect indicators based on the fishery-photovoltaic complementary regulation strategy parameter space, and construct a fishery-photovoltaic complementary regulation effect fitness function; perform a correlation analysis on the fishery-photovoltaic complementary regulation strategy parameter space based on the microbial population change report to determine the selection threshold of the regulation strategy parameters; orthogonally select multiple groups of regulation strategy parameters within the regulation strategy parameter selection threshold, and use the fishery-photovoltaic complementary regulation effect fitness function to evaluate the fitness of the multiple groups of regulation strategy parameters to obtain the fitness of multiple groups of parameter regulation effects; select the parameter cluster center according to the fitness of multiple groups of parameter regulation effects, and perform an optimization analysis on the multiple groups of regulation strategy parameters based on the parameter cluster center to optimize and obtain the fishery-light balance regulation parameter solution library.

[0065] Further, the fishery-photovoltaic complementary regulation module 16 is further configured to:

[0066] Set a learning factor, and update the remaining regulation parameters in the multiple groups of regulation strategy parameters towards the parameter cluster center according to the learning factor to obtain a regulation strategy learning parameter cluster; use a genetic algorithm to perform population iterative optimization within the regulation strategy learning parameter cluster to obtain a regulation strategy optimization parameter cluster, and perform proportional screening optimization on the regulation strategy optimization parameter cluster to obtain the fishery-light balance regulation parameter solution library.

[0067] The foregoing Figure 1 All the various change methods and specific examples of the fishery-photovoltaic complementary regulation method based on microbial population monitoring in the first embodiment are equally applicable to the fishery-photovoltaic complementary regulation platform based on microbial population monitoring in this embodiment. Through the foregoing detailed description of the fishery-photovoltaic complementary regulation method based on microbial population monitoring, those skilled in the art can clearly know the implementation method of the fishery-photovoltaic complementary regulation platform based on microbial population monitoring in this embodiment. Therefore, for the sake of brevity of the specification, it will not be elaborated here.

[0068] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for regulating fishery-light complementation based on microbial population monitoring, characterized in that: The method comprises: Obtaining a microbial population monitoring device, wherein the microbial population monitoring device includes a microbial sampler, a microbial identification instrument, and a data processor; Perform sampling point analysis on the target water body to determine multiple key sampling points, and use the microbial sampler to collect water samples at multiple key sampling points at multiple times to obtain a multi-point water body microbial sample set; Use the microbial identification instrument to perform multi-dimensional identification analysis on the multi-point water body microbial sample sets to obtain a multi-point microbial identification data stream set; Based on the data processor, the multi-point microbial identification data stream set is statistically integrated and processed to generate a microbial population change report; Constructing a balanced regulation strategy for fishery-photovoltaic complementarity, matching the report on the change of microbial populations with the balanced regulation strategy for fishery-photovoltaic complementarity, obtaining a target fishery-photovoltaic complementarity regulation strategy, and constructing a parameter space for the fishery-photovoltaic complementarity regulation strategy according to the target fishery-photovoltaic complementarity regulation strategy; Based on the parameter space of the fish-light complementary regulation strategy, the microbial population change report is regulated and analyzed to obtain a fish-light balanced regulation parameter solution library, and the target water body is regulated by the fish-light balanced regulation parameter solution library; The method of obtaining a fish-light balance control parameter solution library includes: Acquire a set of fishery-photovoltaic complementation regulation effect indicators, fit the set of fishery-photovoltaic complementation regulation effect indicators based on the parameter space of the fishery-photovoltaic complementation regulation strategy, and construct a fishery-photovoltaic complementation regulation effect fitness function; Based on the microbial population change report, a correlation analysis is performed on the parameter space of the fishery-photovoltaic complementary control strategy to determine a control strategy parameter selection threshold; orthogonally selecting multiple groups of control strategy parameters within the control strategy parameter selection threshold, and using the fishery-photovoltaic complementary control effect fitness function to evaluate the fitness of the multiple groups of control strategy parameters to obtain the fitness of the control effects of the multiple groups of parameters; The center of a parameter cluster is selected according to the fitness of the multiple groups of parameter control effects, and the multiple groups of control strategy parameters are optimized and analyzed based on the center of the parameter cluster to obtain the fish-light balance control parameter solution library.

2. The method for regulating fishery-light complementarity based on microbial population monitoring according to claim 1, characterized in that: The determining of multiple key sampling points includes: Acquire fishery water attribute factor information, wherein the fishery water attribute factor information includes water body geographical characteristics, water quality and environmental conditions, fishery breeding conditions, and pollution source distribution; Based on the fishery water body attribute factor information, multi-dimensional parameter collection is performed on the target water body to obtain a multi-dimensional factor parameter set of the fishery water body; Based on the multidimensional factor parameter set of the fishery water body, a spatial superposition analysis is performed to obtain a spatial characteristic layer set of the fishery water body; The key areas of the fishery water body spatial characteristic layer set are identified to obtain a key fishery water body area set, and the key fishery water body area set is analyzed for coverage to determine the multiple key sampling points.

3. The method for regulating fishery-light complementation based on microbial population monitoring according to claim 2, characterized in that: The determining of the plurality of key sampling points comprises: Using a recursive feature elimination algorithm to extract impact features from the key fishery water body area set, to obtain a fishery water body impact feature set; Based on the fishery water body impact feature set, feature marking is performed on the key fishery water body area set to obtain a fishery water body area impact feature set; Based on the fishery water area impact feature set, a coverage analysis is performed on the key fishery water area set to obtain an initial sampling point set for the fishery water area; A cluster analysis is performed on the initial sampling point set of the fishery water area to obtain a water body sampling clustering result, and the multiple key sampling points are determined based on the water body sampling clustering result.

4. The method for regulating fishery-light complementation based on microbial population monitoring according to claim 3, characterized in that: The fishery water body impact feature set obtained includes: Extracting correlation features of the key fishery water body area set based on the fishery water body multidimensional factor parameter set to obtain a fishery water body correlation feature set; Performing historical data mining on the fishery water body associated feature set to obtain a fishery water body microbial distribution feature data set; Using random forest to perform microbial population prediction training on the fishery water microbial distribution characteristic dataset to obtain a fishery water microbial population prediction model; A meta-transformer is obtained according to a recursive feature elimination algorithm, and the fishery water body associated feature set is iteratively removed through the meta-transformer. The fishery water body microbial population prediction model after feature removal is trained and evaluated to screen and obtain the fishery water body influencing feature set.

5. The method for regulating fishery-light complementation based on microbial population monitoring according to claim 1, characterized in that: The generating of the microbial population change report includes: Performing microbial parameter statistics on the multi-point microbial identification data stream set to obtain a multi-point microbial parameter set, wherein the multi-point microbial parameter set includes microbial species, quantity, and population proportion; Integrating the multi-point microbial parameter set according to the time series to obtain a multi-point microbial population time series distribution parameter set; Using regression analysis to perform trend fitting on the multi-point microbial population time series distribution parameter set to generate a set of microbial population distribution change trend curves; The microbial population distribution change trend curve set is processed by multi-point networking according to the sampling point sequence to generate the microbial population change report.

6. The method for regulating fishery-light complementation based on microbial population monitoring according to claim 1, characterized in that: The optimization step to obtain the fish-light balance control parameter solution library includes: Setting a learning factor, and learning and updating the remaining control parameters in the multiple groups of control strategy parameters toward the center of the parameter cluster according to the learning factor to obtain a control strategy learning parameter cluster; A genetic algorithm is used to perform population iterative optimization within the control strategy learning parameter cluster to obtain a control strategy optimization parameter cluster, and the control strategy optimization parameter cluster is screened and optimized in proportion to obtain the fish-light balance control parameter solution library.

7. A fishery-photovoltaic complementary regulation platform based on microbial population monitoring, characterized in that: The platform is used to implement the method for regulating fishery-light complementation based on microbial population monitoring according to any one of claims 1 to 6, wherein the platform comprises: A monitoring device acquisition module, used to acquire a microbial population monitoring device, wherein the microbial population monitoring device includes a microbial sampler, a microbial identification instrument and a data processor; A sampling point analysis module is used to perform sampling point analysis on the target water body, determine multiple key sampling points, collect water samples at multiple key sampling points at multiple times through the microbial sampler, and obtain a multi-point water body microbial sample set; A multi-dimensional identification and analysis module, used to use the microbial identification instrument to perform multi-dimensional identification and analysis on the multi-point water body microbial sample sets respectively, to obtain a multi-point microbial identification data stream set; A population change report generating module, used for performing statistical integration processing on the multi-point microbial identification data stream set based on the data processor to generate a microbial population change report; A strategy parameter space construction module is used to construct a balanced regulation strategy for fishery-photovoltaic complementarity, match the report on the change of microbial populations with the balanced regulation strategy for fishery-photovoltaic complementarity, obtain a target fishery-photovoltaic complementarity regulation strategy, and construct a parameter space for the fishery-photovoltaic complementarity regulation strategy according to the target fishery-photovoltaic complementarity regulation strategy; The fish-light complementary regulation module is used to regulate and analyze the microbial population change report based on the fish-light complementary regulation strategy parameter space, obtain the fish-light balanced regulation parameter solution library, and perform fish-light complementary regulation on the target water body through the fish-light balanced regulation parameter solution library.

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