Red water river aquatic organism species sharing information management system

Abstract

The invention, which relates to the field of biological species data management, discloses a shared information management system for aquatic organism species in a red water river, comprising a data acquisition module, a data processing module, a database module, a data analysis module, an information sharing module and a visual display module. The data acquisition module is used for acquiring aquatic organism population data, environment variables and man-made interference data; the data processing module performs cleaning, normalization and standardization processing on the multi-source data through a spatio-temporal data fusion algorithm; the database module adopts a multi-source heterogeneous data storage technology and manages aquatic ecology data in combination with a spatio-temporal index and a species knowledge graph; the data analysis module evaluates and predicts the ecological health state and the aquatic organism population trend of the watershed through a population dynamic prediction model and an ecological health evaluation model; the information sharing module realizes data security storage and traceability based on a block chain technology; and the visual display module dynamically displays the aquatic organism distribution and the ecological health state based on a GIS (Geographic Information System) technology.

Description

Technical Field

[0001] The present invention relates to the technical field of biological species data management, and in particular to a Chishui River aquatic biological species sharing information management system. Background Art

[0002] The Chishui River is an important tributary of the upper reaches of the Yangtze River and is known as an "important model river of ecological civilization". Its ecological environment directly affects the overall health of the Yangtze River system. However, in recent years, with the intensification of human activities and the rapid development of industry and agriculture, the ecosystem of the Chishui River basin faces the following significant problems:

[0003] 1. Water pollution: There are a large number of industrial enterprises and agricultural irrigation areas along the Chishui River Basin. The large-scale discharge of industrial wastewater and agricultural non-point source pollutants (such as fertilizer and pesticide residues) has led to the deterioration of river water quality, the reduction of dissolved oxygen in the water body, and the excessive nitrogen and phosphorus content. Some waters have even experienced eutrophication. This situation directly threatens the living environment of aquatic organisms.

[0004] 2. Sharp decline in aquatic species: The number of unique aquatic species (such as some endangered fish and amphibians) living in the basin has decreased significantly. The main reasons for the sharp decline in population include:

[0005] Habitat destruction: River regulation projects, large dam construction, etc. have damaged river connectivity and destroyed the natural habitats of organisms; Human fishing: Overfishing and illegal fishing practices have had devastating effects on certain aquatic species.

[0006] 3. Decline in health of watershed ecosystem: The Chishui River watershed ecosystem faces serious degradation problems, which are manifested in the following aspects: The aquatic biodiversity of rivers has been significantly reduced; Vegetation coverage has declined, coastal wetlands have been encroached upon, and soil erosion has become increasingly serious; The accumulation of pollution in the watershed has led to a decline in the self-purification capacity of the ecosystem and a deterioration in its health.

[0007] 4. Insufficiency of existing monitoring and governance methods: Although the river basin management agency has deployed some water quality monitoring equipment, the existing technology has the following deficiencies: The data source is single, and the monitoring content is mostly focused on water quality parameters, while the monitoring of aquatic biological populations and their habitats is relatively lacking; Insufficient data processing capabilities and lack of support for spatiotemporal fusion algorithms result in the inability to effectively integrate multi-source data; The lack of scientific ecological health assessment and trend prediction models makes it difficult to provide a scientific basis for ecological management of the watershed; There is a lack of linkage between monitoring results and governance measures, making it impossible to achieve dynamic optimization and feedback.

[0008] To this end, this application aims to provide a Chishui River aquatic biological species sharing information management system to solve the above problems. Summary of the invention

[0009] The purpose of the present invention is to solve the technical problems raised in the above-mentioned background technology and to provide a Chishui River aquatic biological species sharing information management system.

[0010] The above-mentioned object of the present invention is achieved by the following technical solutions: A Chishui River aquatic species sharing information management system, including: data acquisition module, data processing module, database module, data analysis module, information sharing module and visual display module, the data acquisition module is used to collect aquatic species data , Environmental data and human interference data The data processing module uses spatiotemporal data fusion algorithm to , , Perform cleaning, normalization and standardization; The database module is used to store , , Establish spatiotemporal index and species knowledge graph based on multi-source heterogeneous data; The data analysis module analyzes and predicts trends in the database through population dynamics prediction models and ecological health assessment models; The information sharing module stores key data based on blockchain technology and enables secure data sharing and traceability; Visualization display module based on GIS technology dynamic display , and ecological health.

[0011] Furthermore, the database module includes: a multi-source heterogeneous database: MySQL is used to store structured data, MongoDB is used to store semi-structured data, and GeoMesa is used to manage spatiotemporal data; Spatiotemporal database: using quadtree and R-tree to store geographic data Perform spatial indexing and use B-tree to index time series data , Perform time segment indexing; Species knowledge graph database: The relationship network between aquatic species is stored through a graph database. : In spatial position and time The thermal value of population distribution at the moment; Geospatial coordinates.

[0012] Furthermore, the spatiotemporal database supports the following data models: Time series data model: used to store population dynamics data and environment variables in, Time point; time The corresponding monitoring values ​​include , , ; Geospatial data model: habitat distribution heat map data It is expressed as: in, : space coordinates; Time point; Location The population density value.

[0013] Furthermore, the species knowledge graph database is constructed by the following steps: Construction of nodes and edges: Nodes represent species, and edges represent the relationship between species, which is defined as: in, species collection; Ecological relationship set; Relationship reasoning: Species relationship reasoning is realized based on graph convolutional network. The model formula is: in, node In the Feature representation of the layer; :node The set of neighbors of ; Weight matrix; Activation function.

[0014] Furthermore, the database module and the information sharing module are integrated through the following synchronization mechanism: when the database is updated, the event monitoring module is triggered to generate a hash value of the key data, and the key data include: in, : The hash value of the previous block; : Ecological health index.

[0015] Furthermore, the Ecological Health Index The calculation formula is: in, Ecological health score; : Environment variables The health weight of Standardized environment variables; : Normalized intensity of human disturbance.

[0016] Furthermore, the formula of the population dynamics prediction model is: in, The natural growth rate of a population; Environmental carrying capacity, calculate: : Environment variables The bearing capacity weight of Reduction factor of human interference Furthermore, the visualization module displays species distribution heat maps based on GIS technology. , the formula is: in, : The standardized population size; : Space point geographical suitability factors.

[0017] Furthermore, the data analysis module uses population dynamics to predict data and Ecological Health Index , combined with the habitat restoration goal, define the optimization objective function for: Solved by genetic algorithm, the genetic algorithm process is: initialize the population Calculate the fitness of each individual Perform crossover, mutation, and selection operations; stop optimization when the fitness increment is lower than the preset threshold.

[0018] Furthermore, the constraints for optimizing the objective function include: in For time The optimization process ensures that the disturbance value is non-negative and minimizes the negative impact on the ecosystem.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention provides a Chishui River aquatic species sharing information management system, which realizes the comprehensive monitoring and unified management of Chishui River basin aquatic population data, environmental variables and human disturbance data through the collection and spatiotemporal fusion of multi-source heterogeneous data. The system adopts advanced data processing technology and ecological health assessment models, which can accurately predict the dynamics of aquatic populations and quantify the ecological health index of the basin, providing a reliable basis for scientific decision-making.

[0020] 2. The system of the present invention generates the optimal governance strategy for ecological protection by optimizing the objective function and combining the genetic algorithm, and dynamically adjusts the amount of human disturbance I(t), thereby ensuring the recovery of the aquatic biological population and improving the overall health level of the basin ecosystem. The visualization display module based on GIS technology intuitively displays the ecological health heat map and dynamic changes of population distribution in the basin, greatly improving the efficiency and scientificity of ecological governance.

[0021] 3. The system of the present invention introduces blockchain technology to ensure the safe sharing and traceability of data, promotes multi-departmental collaboration and transparent management of ecological data, and comprehensively improves the scientific and technological level of ecological protection in the Chishui River Basin. The system can not only effectively protect the aquatic ecosystem of the Chishui River, but also has strong scalability and can be promoted and applied to the ecological protection and sustainable development management of other river basins. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 This is a system block diagram of a Chishui River aquatic species sharing information management system. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The following is combined with Figure 1 , the specific implementation methods of the present invention are described in detail.

[0026] The solution of the present invention provides a Chishui River aquatic species sharing information management system, which includes: a data acquisition module, a data processing module, a database module, a data analysis module, an information sharing module and a visual display module, wherein: the data acquisition module is used to collect aquatic biological population data , Environmental data and human interference data The data processing module uses spatiotemporal data fusion algorithm to , , Perform cleaning, normalization and standardization; The database module includes: multi-source heterogeneous database: MySQL is used to store structured data, MongoDB is used to store semi-structured data, and GeoMesa is used to manage spatiotemporal data; Spatiotemporal database: using quadtree and R-tree to store geographic data Perform spatial indexing and use B-tree to index time series data , Perform time segment indexing; Species knowledge graph database: The relationship network between aquatic species is stored through a graph database. Indicates the position in space and time The thermal value of population distribution at the moment; The geographic coordinates.

[0027] The spatiotemporal database supports the following data models: Time series data model: used to store population dynamics data and environment variables in, : time point; :time The corresponding monitoring values ​​include , , ; Geospatial data model: habitat distribution heat map data It is expressed as: in, : space coordinates; Time point; Location The population density value.

[0028] The species knowledge graph database is constructed through the following steps: Construction of nodes and edges: Nodes represent species, and edges represent the relationship between species, which is defined as: in, for species collection; is a collection of ecological relationships; Relationship reasoning: Species relationship reasoning is realized based on graph convolutional network. The model formula is: in, node In the Feature representation of the layer; :node The set of neighbors of ; Weight matrix; Activation function.

[0029] The database module is used to store , , Establish spatiotemporal index and species knowledge graph based on multi-source heterogeneous data; The data analysis module analyzes and predicts the trend of the data in the database through the population dynamics prediction model and the ecological health assessment model; the formula of the population dynamics prediction model is: in, The natural growth rate of a population; Environmental carrying capacity, calculate: : Environment variables The bearing capacity weight of : Reduction factor of human interference Data analysis module uses population dynamics to predict data and Ecological Health Index , combined with the habitat restoration goal, define the optimization objective function for: Solved by genetic algorithm, the genetic algorithm process is: initialize the population Calculate the fitness of each individual Perform crossover, mutation, and selection operations; stop optimization when the fitness increment is lower than the preset threshold.

[0030] The information sharing module stores key data based on blockchain technology and realizes data security sharing and traceability; the database module and the information sharing module are integrated through the following synchronization mechanism: when the database is updated, the event monitoring module is triggered to generate the hash value of the key data, and the key data include: in, : The hash value of the previous block; : Ecological health index; Ecological Health Index The calculation formula is: in, Ecological health score; : Environment variables The health weight of Standardized environment variables; : Normalized intensity of human disturbance.

[0031] The constraints for optimizing the objective function include: in For time The optimization process ensures that the disturbance value is non-negative and minimizes the negative impact on the ecosystem. Visualization display module based on GIS technology dynamic display , The visualization module displays species distribution heat maps based on GIS technology. , the formula is:

[0032] in, : The standardized population size; : Space point geographical suitability factors.

[0033] Example: Implementation process of ecological health monitoring and restoration strategy in Chishui River Basin: Due to long-term industrial pollution and human activities (such as agricultural irrigation and industrial wastewater discharge), the Chishui River Basin has led to a significant reduction in the number of aquatic species and a continuous deterioration in the health of the basin ecosystem. To this end, the Chishui River Basin Management Agency decided to use the "Chishui River Aquatic Biological Species Sharing Information Management System" of this invention to conduct real-time monitoring and evaluation of the ecological health of the basin and formulate a scientific restoration strategy.

[0034] Implementation process: 1. Data collection; 1.1 Deployment of collection equipment: Water quality monitoring sensors are installed in the main ecologically sensitive areas and near pollution sources in the Chishui River Basin to collect real-time environmental parameters such as dissolved oxygen, nitrogen and phosphorus concentrations, and pH values. Underwater cameras and biological monitoring equipment were deployed at the confluence of the Chishui River tributaries and the main stream to record the number of aquatic species. Through drone patrols, the vegetation coverage rate on both sides of the river, the river area and human disturbance activities are collected. (such as factory discharge points and agricultural irrigation discharge areas).

[0035] 1.2 Data collection frequency: Sensor data is uploaded every hour; UAV monitoring data is collected once a week; manual sampling data is recorded once a month to calibrate the accuracy of sensor and UAV data.

[0036] 2. Data processing; 2.1 Data cleaning and outlier processing: Input the collected data into the data processing module to remove outliers. Outlier detection formula:

[0037] like , the data is an outlier and is replaced by the mean of the adjacent time points.

[0038] 2.2 Data standardization and normalization: Unify different data types into the range of [0, 1] through the formula: 2.3 Spatiotemporal data fusion: Use GeoMesa tools to build an index of time and space dimensions. Fusion of aquatic populations, environmental variables, and disturbance data into a unified spatiotemporal data model:

[0039] 3. Data storage: The processed data is stored in layers through database modules: MySQL stores water quality parameters and population numbers (structured data). MongoDB stores drone images and underwater camera videos (semi-structured data). GeoMesa stores spatial distribution and temporal dynamic information, supporting fast queries based on time and space.

[0040] 4. Data analysis; 4.1 Population dynamics prediction: predicting future population size based on formula in, The natural growth rate of the population is taken as 0.03; : Environmental carrying capacity, determined by environmental parameters Sure; .

[0041] 4.2 Ecological health assessment: Calculation of ecological health index : in, : The weight of environmental variables (e.g., the weight of dissolved oxygen is 0.4, and the weight of nitrogen and phosphorus concentration is 0.3); .

[0042] 5. Optimize recovery strategy; 5.1 Daily standard function: Combining population prediction data and health index, define the optimization objective function: and are the start and end time of the strategy implementation respectively.

[0043] Initial population generation: Randomly generate several interference adjustment strategies . Fitness calculation: through the objective function Evaluate the fitness of each strategy.

[0044] Genetic operations: perform crossover and mutation operations to generate the next generation of strategies.

[0045] Termination condition: When the fitness increment is lower than the set threshold, the optimization is stopped.

[0046] 5.3 Constraints: The interference adjustment amount is non-negative and minimized: 6. Visual display; 6.1 Heat map generation: Use the GIS module to generate a heat map of the ecological health status of the Chishui River Basin, showing the health index of different areas and population density .

[0047] Heat map formula: 6.2 Display of restoration effects: Display comparison charts before and after restoration on the GIS platform to intuitively show changes in population size and ecological health status.

[0048] 7. Implementation of restoration measures: Based on the optimization strategy, the following measures will be implemented: establish buffer zones near pollution sources to reduce industrial emissions; prohibit some highly polluting agricultural activities and promote eco-friendly agriculture; release specific aquatic organisms (such as filter-feeding fish) to control water pollution; establish a community participation mechanism and regularly clean up garbage in the watershed.

[0049] 8. Implementation results: Monitoring shows that after three months of implementation, the number of aquatic species in the Chishui River Basin has Increased by 15%, Ecological Health Index The heat map shows that the ecological health status near the pollution source has improved from "unhealthy" to "sub-healthy". Through this embodiment, the real-time monitoring, scientific evaluation and effective restoration of the ecological health of the Chishui River Basin have been successfully achieved, providing a scientific decision-making basis for basin management.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A Chishui River aquatic species sharing information management system, including: Data collection module, data processing module, database module, data analysis module, information sharing module and visual display module, characterized in that: the data collection module is used to collect aquatic biological population data , Environmental data and human interference data ; The data processing module uses spatiotemporal data fusion algorithm to , , Perform cleaning, normalization and standardization; The database module is used to store , , Establish spatiotemporal index and species knowledge graph based on multi-source heterogeneous data; The data analysis module analyzes and predicts trends in the database through population dynamics prediction models and ecological health assessment models; The information sharing module stores key data based on blockchain technology and enables secure data sharing and traceability; Visualization display module based on GIS technology dynamic display , and ecological health.

2. The Chishui River aquatic species sharing information management system according to claim 1 is characterized in that: The database module includes: multi-source heterogeneous database: MySQL is used to store structured data, MongoDB is used to store semi-structured data, and GeoMesa is used to manage spatiotemporal data; Spatiotemporal database: using quadtree and R-tree to store geographic data Perform spatial indexing and use B-tree to index time series data , Perform time segment indexing; Species knowledge graph database: The relationship network between aquatic species is stored through a graph database. : In spatial position and time The thermal value of population distribution at the moment; Geospatial coordinates.

3. The Chishui River aquatic species sharing information management system according to claim 2 is characterized in that: The spatiotemporal database supports the following data models: Time series data model: used to store population dynamics data and environment variables ; in, Time point; time The corresponding monitoring values ​​include , , ; Geospatial data model: habitat distribution heat map data It is expressed as: ; in, : spatial coordinates; Time point; Location The population density value.

4. The Chishui River aquatic species sharing information management system according to claim 3 is characterized in that: The species knowledge graph database is constructed by the following steps: Construction of nodes and edges: Nodes represent species, and edges represent the relationship between species, which is defined as: ; in, species collection; Ecological relationship set; Relationship reasoning: Species relationship reasoning is realized based on graph convolutional network. The model formula is: ; in, node In the Feature representation of the layer; :node The set of neighbors of ; Weight matrix; Activation function.

5. The Chishui River aquatic species sharing information management system according to claim 1 is characterized in that: The database module and the information sharing module are integrated through the following synchronization mechanism: when the database is updated, the event monitoring module is triggered to generate the hash value of the key data, and the key data include: ； in, : The hash value of the previous block; : Ecological health index.

6. The Chishui River aquatic species sharing information management system according to claim 5 is characterized in that: Ecological Health Index The calculation formula is: ; in, ; Ecological health score; : Environment variables health weight; Standardized environment variables; : Normalized intensity of human disturbance.

7. The Chishui River aquatic species sharing information management system according to claim 1 is characterized in that: The formula of the population dynamics prediction model is: ; in, The natural growth rate of a population; Environmental carrying capacity, calculate: ; : Environment variables The bearing capacity weight of Reduction factor of human interference.

8. The Chishui River aquatic species sharing information management system according to claim 1 is characterized in that: The visualization module displays species distribution heat maps based on GIS technology. , the formula is: ; in, : The standardized population size; : Spatial point geographical suitability factors.

9. The Chishui River aquatic species sharing information management system according to claim 1 is characterized in that: Data analysis module uses population dynamics to predict data and Ecological Health Index , combined with the habitat restoration goal, define the optimization objective function for: ; Solved by genetic algorithm, the genetic algorithm process is: initialize the population Calculate the fitness of each individual ; Perform crossover, mutation, and selection operations; stop optimization when the fitness increment is lower than the preset threshold.

10. The Chishui River aquatic species sharing information management system according to claim 9 is characterized in that: The constraints for optimizing the objective function include: ; in For time The optimization process ensures that the disturbance value is non-negative and minimizes the negative impact on the ecosystem.

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