Aquatic organism environment sampling method, system, equipment and medium
By dividing the aquatic biological environment into functional zones and merging redundant sensors, the problem of sensor redundancy was solved, data quality and sampling efficiency were improved, and system complexity and cost were reduced.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-31
- Publication Date
- 2026-03-13
AI Technical Summary
In traditional aquatic biological environmental monitoring, the deployment of sensors lacks adaptability, leading to an increase in redundant data and affecting data quality and management decision-making efficiency.
Functional zones are divided by acquiring hydrogeomorphological and functional information, high-density zones are selected, sensor water quality parameters and spatial locations are compared, redundant sensors are merged, and sensor deployment is optimized.
It improves the rationality of sensor deployment, reduces resource waste, ensures data quality and coverage, reduces system complexity and cost, and improves the overall efficiency of environmental sampling.
Smart Images
Figure CN121656512A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of biological sampling technology, and more specifically, to a method, system, device, and medium for sampling aquatic organisms in the environment. Background Technology
[0002] Aquatic biological environmental sampling is a technique that obtains biological samples such as plankton, benthic organisms, and attached algae in water bodies through systematic site selection, standardized collection and preservation procedures. Sampling points are scientifically set according to hydrological characteristics and ecological functional zoning, sampling equipment is selected to ensure the representativeness and integrity of the samples, and strict fixation, refrigeration and transportation are carried out to ultimately provide reliable evidence for aquatic ecosystem health assessment and biodiversity research.
[0003] In traditional aquatic biological environmental monitoring networks, sensors are mostly deployed in a fixed manner, lacking adaptive optimization to the spatial heterogeneity and dynamic functional changes of aquatic areas. This easily leads to over-dense sensor deployment in key sensitive areas or ecological hotspots, resulting in a high spatial concentration of adjacent or functionally overlapping monitoring nodes. This causes multiple sensors to collect highly similar or even duplicate water quality parameter data in close proximity, significantly increasing the spatiotemporal redundancy of monitoring data. This not only reduces the effective information content per unit of data but also unnecessarily increases the load on data storage, transmission, and computational processing. Furthermore, redundant data can mask true environmental gradient changes, increasing the complexity of subsequent data cleaning and analysis, ultimately affecting the accuracy of environmental assessments and the efficiency of management decisions. Therefore, how to achieve redundant merging of sensors in aquatic biological environmental sampling has become a challenge for the industry. Summary of the Invention
[0004] This application provides a method, system, device and medium for sampling aquatic organisms in the environment, which can realize the redundancy merging of sensors in the sampling of aquatic organisms in the environment.
[0005] Firstly, this application provides a method for sampling the environment of aquatic organisms, including: Obtain hydrogeomorphic and functional information of the target water body, and divide the sampling area of the target water body into multiple functional zones based on the type distribution of the hydrogeomorphic information and the functional distribution of the functional information. The sampling density of each functional zone is determined by the sampling frequency and the number of spatially deployed points of the sensors in each functional zone, and high-density zones are selected from the target water body based on each sampling density. The sampling information of each sensor in the high-density partition is obtained, the water quality parameters in each sampling information are compared to obtain the water quality parameter similarity between each sensor, the data redundancy of each sensor in the high-density partition is determined by the water quality parameter similarity and the spatial proximity between each sensor, and multiple redundant sensors are selected from the high-density partition using the data redundancy. Based on the similarity of various water quality parameters and the proximity of various spatial locations, all redundant sensors are deployed and merged, and then the merged sensors are used to perform environmental sampling on the target water body.
[0006] In some embodiments, dividing the sampling area of the target water body into multiple functional zones based on the type distribution of the hydrogeomorphic information and the functional distribution of the functional information specifically includes: Extract the type distribution map of the target water body from the hydrogeomorphic information and the functional distribution map of the target water body from the functional information; The various type regions in the type distribution map and the various functional regions in the function distribution map are merged and clustered to obtain multiple composite cluster regions; Each composite cluster region is mapped to a functional partition, resulting in multiple functional partitions.
[0007] In some embodiments, extracting the type distribution map of the target water body from the hydrogeomorphological information and the functional distribution map of the target water body from the functional information specifically includes: Multiple types of zones of the target water body are obtained from the hydrogeomorphological information; Determine the type distribution map of the target water body by identifying all type zones; Multiple functional zones of the target water body are obtained from the aforementioned functional information; The functional distribution map of the target water body is determined by all functional zones.
[0008] In some embodiments, determining the sampling density of each functional zone by the sampling frequency of the sensors and the number of spatially deployed points in each functional zone specifically includes: For each functional zone, obtain the sampling frequency and the number of spatially deployed sensor points in the functional zone; The sampling density of the functional zones is determined by the sampling frequency and the number of spatially deployed points, thereby obtaining the sampling density of each functional zone.
[0009] In some embodiments, comparing water quality parameters in various sampling information to obtain the water quality parameter similarity between various sensors specifically includes: Obtain multi-parameter vectors of water quality parameters from various sampling information; Calculate the vector distance between each multi-parameter vector to obtain the water quality parameter similarity between each sensor.
[0010] In some embodiments, determining the data redundancy of each sensor in the high-density partition by the similarity of various water quality parameters and the spatial proximity between various sensors specifically includes: For each sensor in the high-density zone, calculate the spatial proximity between the sensor and other sensors, and obtain the water quality parameter similarity between the sensor and other sensors; The spatial redundancy value of the sensor is determined by the proximity of all spatial locations; The data redundancy value of the sensor is determined by the similarity of all water quality parameters; The data redundancy of the sensor is determined based on the spatial redundancy value and the data redundancy value, thereby obtaining the data redundancy of each sensor in the high-density partition.
[0011] In some embodiments, the deployment and merging of all redundant sensors based on the similarity of various water quality parameters and the proximity of various spatial locations specifically includes: Based on the similarity of various water quality parameters and the proximity of various spatial locations, all redundant sensors are clustered to obtain multiple redundant sensor clusters. The target deployment points in each redundant sensor cluster are determined, thereby completing the deployment and merging of all redundant sensors.
[0012] Secondly, this application provides an aquatic biological environment sampling system, comprising: The acquisition module is used to acquire hydrological and geomorphological information and functional information of the target water body, and divide the sampling area of the target water body into multiple functional zones based on the type distribution of the hydrological and geomorphological information and the functional distribution of the functional information. The processing module is used to determine the sampling density of each functional zone by the sampling frequency and the number of spatially deployed points of the sensors in each functional zone, and to screen high-density zones from the target water body based on each sampling density. The processing module is also used to acquire sampling information from each sensor in the high-density partition, compare the water quality parameters in each sampling information to obtain the similarity of water quality parameters between each sensor, determine the data redundancy of each sensor in the high-density partition by the similarity of each water quality parameter and the spatial proximity between each sensor, and use the data redundancy to filter out multiple redundant sensors from the high-density partition. The execution module is used to deploy and merge all redundant sensors based on the similarity of various water quality parameters and the proximity of various spatial locations, and then use the deployed and merged sensors to perform environmental sampling on the target water body.
[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described aquatic biological environment sampling method.
[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described aquatic biological environment sampling method.
[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application provides a method, system, device, and medium for aquatic biological environmental sampling. The method involves acquiring hydrogeomorphic and functional information of a target water body. The sampling area of the target water body is divided into multiple functional zones based on the type distribution of the hydrogeomorphic information and the functional distribution of the functional information. The sampling density of each functional zone is determined by the sampling frequency and spatial deployment number of sensors within each zone. High-density zones are then selected from the target water body based on these sampling densities. Sampling information from each sensor in the high-density zones is acquired. Water quality parameters in each sampling information are compared to obtain the similarity between the sensors. Data redundancy of each sensor in the high-density zones is determined by the similarity of the water quality parameters and the spatial proximity between the sensors. Multiple redundant sensors are then selected from the high-density zones using these data redundancies. All redundant sensors are then deployed and merged based on the similarity of the water quality parameters and the spatial proximity, and the merged sensors are used to perform environmental sampling of the target water body.
[0016] Therefore, in this application, all redundant sensors are deployed and merged based on the similarity of various water quality parameters and the proximity of various spatial locations. The merged sensors are then used to sample the target water body. First, identifying high-density zones reveals areas with high sampling density in the target water body, providing a clear scope for subsequent redundant sensor screening. Screening redundant sensors within high-density zones avoids unnecessary redundancy analysis in areas with low sampling density, improving the efficiency and accuracy of redundancy merging, making sensor deployment more rational, reducing resource waste, and ensuring the data quality and coverage of aquatic biological environmental sampling. Then, identifying redundant sensors allows for the determination of areas with high sampling density within the target water body. Sensors with high data redundancy within a partition provide a basis for sensor deployment and merging. By analyzing the similarity of water quality parameters and spatial proximity among sensors, sensors with highly similar collected data and close locations can be accurately identified. These sensors may provide a significant degree of overlap in the information they provide in aquatic biological environment sampling. Screening out and merging all redundant sensors can effectively reduce the number of sensors, lower system complexity and cost, and avoid excessive data redundancy from interfering with subsequent data analysis and processing, thereby improving the overall efficiency and data quality of aquatic biological environment sampling. In summary, the above scheme can achieve redundant sensor merging in aquatic biological environment sampling. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is an exemplary flowchart of an aquatic organism environmental sampling method according to some embodiments of this application; Figure 2 This is a flowchart illustrating the process of determining data redundancy according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of an aquatic biological environment sampling system according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device for implementing an aquatic biological environment sampling method according to some embodiments of this application. Detailed Implementation
[0019] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.
[0020] refer to Figure 1 The figure is an exemplary flowchart of an aquatic organism environmental sampling method according to some embodiments of this application. The aquatic organism environmental sampling method mainly includes the following steps: In step 101, hydrogeomorphic information and functional information of the target water body are obtained, and the sampling area of the target water body is divided into multiple functional zones by the type distribution of the hydrogeomorphic information and the functional distribution of the functional information.
[0021] It should be noted that, in this application, hydrogeomorphological information describes the natural morphology and dynamic characteristics of water bodies, including but not limited to the type of water body, water depth, flow velocity, flow direction, shoreline morphology and sediment composition; functional information describes the social use attributes and environmental protection requirements of water bodies, including but not limited to drinking water source protection areas, aquaculture areas, industrial drainage areas, and landscape and recreational water areas.
[0022] In practice, this involves obtaining hydrological and geomorphological data such as the spatial range, flow characteristics, and topography of the target water body through on-site surveys, remote sensing image interpretation, and the collection and integration of authoritative data and atlases released by water conservancy, environmental protection, and planning departments. At the same time, it clarifies the various planning functions assigned to the target water body in socio-economic development. Thus, the collection of hydrological and geomorphological data is used as the hydrological and geomorphological information of the target water body, and the collection of all planning functions is used as the functional information of the target water body.
[0023] In some embodiments, dividing the sampling area of the target water body into multiple functional zones based on the type distribution of the hydrogeomorphological information and the functional distribution of the functional information can be achieved using the following steps: Extract the type distribution map of the target water body from the hydrogeomorphic information and the functional distribution map of the target water body from the functional information; The various type regions in the type distribution map and the various functional regions in the function distribution map are merged and clustered to obtain multiple composite cluster regions; Each composite cluster region is mapped to a functional partition, resulting in multiple functional partitions.
[0024] It should be noted that in this application, the functional zoning is a management zoning with unified internal functional characteristics and clear boundaries; the type distribution map is a thematic map that can intuitively display the spatial location, geographical range and corresponding type attribute characteristics of all type zones within the target water body; the functional distribution map is a thematic map that can intuitively display the spatial location, geographical range and corresponding functional attribute characteristics of all functional zones within the target water body; and the composite cluster area is a new spatial unit formed by merging type zones with similar hydrogeomorphic characteristics and functional zones with consistent dominant functions.
[0025] In specific implementation, firstly, the type distribution map of the target water body in the hydrogeomorphological information and the functional distribution map of the target water body in the functional information are extracted; then, the various type areas in the type distribution map and the various functional areas in the functional distribution map are merged and clustered to obtain multiple composite cluster areas. This can be achieved in the following way: in the geographic information system platform, the type distribution map and the functional distribution map are spatially overlaid and analyzed. The small patches generated after overlay are merged according to their spatial adjacency and attribute similarity. Areas with conflicting attributes are manually identified and their attributes merged according to the principle of ecological protection priority. Finally, the overlay results are output as a series of composite cluster areas. The composite clusters are defined by their natural endowments and functional positioning. Finally, each composite cluster is mapped to a functional zone, resulting in multiple functional zones. This can be achieved in the following way: For each composite cluster, a clear and unified environmental management objective and monitoring level are assigned to the composite cluster by comprehensively considering its hydrogeomorphological characteristics and social functions. For example, the area formed by combining the "main river area" and the "drinking water source protection area" is formally designated as the "core protection zone for drinking water sources". The attributes of each composite cluster can be redefined in this way, and all composite clusters that have undergone attribute redefinition can be used as functional zones to guide subsequent sampling deployment, thus creating multiple functional zones.
[0026] In the above embodiments, extracting the type distribution map of the target water body from the hydrogeomorphological information and the functional distribution map of the target water body from the functional information can be achieved by the following steps: Multiple types of zones of the target water body are obtained from the hydrogeomorphological information; Determine the type distribution map of the target water body by identifying all type zones; Multiple functional zones of the target water body are obtained from the aforementioned functional information; The functional distribution map of the target water body is determined by all functional zones.
[0027] In specific implementation, firstly, obtaining multiple type zones of the target water body from the hydrological and geomorphological information can be achieved in the following way: based on water depth, flow velocity, bottom sediment, and shoreline tortuosity in the hydrological and geomorphological information, spatial clustering analysis is used to group continuous water areas with similar hydrological and geomorphological characteristics and spatial adjacency together to form an independent category. Ultimately, the target water body is spatially divided into several type zones with different hydrological and geomorphological characteristics. These type zones are spatially relatively homogeneous units with internal properties, defined based on the inherent differences in the hydrodynamic conditions and morphological characteristics of the water body. For example, river main stream areas, river tributary areas, open lake areas, and still water bay areas are defined based on characteristics such as flow velocity, water depth, and shoreline morphology. Secondly, determining the type distribution map of the target water body through all type zones can be achieved in the following way: the spatial boundaries of each type zone are digitally delineated, and each polygonal area is assigned its corresponding type attribute. This map is then represented using geographic information system software and used as the target water body's map. The distribution map of the water body types is then obtained. Next, multiple functional zones of the target water body can be acquired from the functional information in the following way: spatial delineation information regarding the functional positioning of the water body is obtained from the functional information; the management boundaries and dominant functions of various functional zones are clarified; ultimately, the target water body is divided into several functional zones undertaking different dominant functions. These functional zones are spatial units with clearly defined dominant functions, delineated on the target water body based on government-issued regulations, plans, or actual management needs to meet socio-economic or ecological protection purposes. Examples include: drinking water source protection areas, fishery waters, industrial water areas, and landscape / recreational water areas. Finally, the functional distribution map of the target water body can be determined through all functional zones in the following way: the management boundaries of each functional zone are digitally depicted, and each polygonal area is assigned its corresponding functional attributes. Geographic information system software is used for symbolic representation and cartographic output to generate standardized thematic maps. These thematic maps are then used as the functional distribution map of the target water body.
[0028] In step 102, the sampling density of each functional zone is determined by the sampling frequency and the number of spatially deployed points of the sensors in each functional zone, and high-density zones are selected from the target water body based on each sampling density.
[0029] In some embodiments, determining the sampling density of each functional zone by the sampling frequency of the sensors and the number of spatially deployed points in each functional zone can be achieved by the following steps: For each functional zone, obtain the sampling frequency and the number of spatially deployed sensor points in the functional zone; The sampling density of the functional zones is determined by the sampling frequency and the number of spatially deployed points, thereby obtaining the sampling density of each functional zone.
[0030] It should be noted that in this application, sampling density is a composite index used to quantitatively evaluate the overall monitoring resource input level of functional zones; sampling frequency is a quantitative value used to characterize the temporal density of data acquisition from a single sensor; and the number of spatially deployed points is a quantitative index used to characterize the spatial coverage density of the monitoring network.
[0031] In specific implementation, firstly, for each functional zone, the sampling frequency and spatial deployment point of the sensors in the functional zone can be obtained in the following way: For each functional zone, the equipment management database of the environmental monitoring network is retrieved, and the geographical location information of all effective sensors within the functional zone is counted and recorded for the spatial range of the functional zone. At the same time, the actual working cycle and sampling number of each sensor within a unified specified time period (default is 1 week) are extracted from the system operation log. Finally, the total number of sensors obtained is taken as the spatial deployment point number, and the average sampling number per unit time is taken as the sampling frequency. Then, the sampling density of the functional zone is determined by the sampling frequency and the spatial deployment point number. The sampling density of each functional zone can be obtained in the following way: Multiply the spatial deployment point number of the functional zone by the average sampling frequency to obtain the total sampling number of the functional zone per unit time. Then divide this total sampling number by the surface area of the functional zone to eliminate the influence of the difference in zone area on the monitoring intensity assessment. Finally, the sampling intensity value per unit area per unit time is taken as the sampling density of the functional zone. The sampling density of each functional zone can be obtained in the above way.
[0032] In some embodiments, the screening of high-density zones from the target water body based on each sampling density can be achieved as follows: calculate the average and standard deviation of the sampling density of all functional zones, and determine the functional zones with sampling density values greater than the average plus one standard deviation as high-density zones; in other embodiments, a sampling density threshold can also be set to 1.5 times the median value of the sampling density of all functional zones, and determine the functional zones with sampling density values greater than this threshold as high-density zones; or a clustering algorithm can be used to cluster all functional zones according to their sampling density values, and determine the functional zones contained in the category with the highest density in the clustering results as high-density zones; there is no limitation here; wherein, a high-density zone represents the set of functional zones that need to be sensor merged.
[0033] In step 103, the sampling information of each sensor in the high-density partition is obtained, the water quality parameters in each sampling information are compared to obtain the water quality parameter similarity between each sensor, the data redundancy of each sensor in the high-density partition is determined by the water quality parameter similarity and the spatial proximity between each sensor, and multiple redundant sensors are selected from the high-density partition using the data redundancy.
[0034] In some embodiments, the sampling information of each sensor in the high-density zone can be obtained in the following way: through a preset sensor data acquisition interface, according to a unified communication protocol, the raw monitoring data containing all target water quality parameters recorded within a specified time period is read from the data storage module of each sensor in the high-density zone as sampling information, thereby obtaining the sampling information of each sensor in the high-density zone. The sampling information contains the sampling values of multiple water quality parameters, including pH, dissolved oxygen, permanganate index, and ammonia nitrogen content.
[0035] In some embodiments, comparing water quality parameters in various sampling information to obtain the similarity of water quality parameters between different sensors can be achieved through the following steps: Obtain multi-parameter vectors of water quality parameters from various sampling information; Calculate the vector distance between each multi-parameter vector to obtain the water quality parameter similarity between each sensor.
[0036] It should be noted that in this application, water quality parameter similarity is an indicator used to quantify the degree of similarity in the overall condition of water quality measured by different sensors. Specifically, the multi-parameter vector of water quality parameters obtained from each sampling information can be implemented as follows: For each sampling information, the measured values of multiple pre-set water quality parameters are extracted from the sampling information. These measured values are then arranged according to the sampling time sequence to form a numerical sequence containing multiple dimensions. Finally, this ordered numerical sequence is used as the multi-parameter vector of water quality parameters in the sampling information. Through the above method, the multi-parameter vector of water quality parameters in each sampling information can be obtained. The parameter vector, which is a multi-parameter vector, consists of the values of multiple water quality parameters measured by the same sensor within the same sampling period. These water quality parameters include pH, dissolved oxygen, permanganate index, and ammonia nitrogen content. Then, the vector distance between each multi-parameter vector is calculated to obtain the similarity of water quality parameters between each sensor. This can be achieved by using the cosine similarity between each multi-parameter vector as the vector distance. This vector distance is a mathematical measure used to quantify the degree of difference between two multi-parameter vectors, which corresponds to the similarity of water quality parameters between the corresponding sensors. Thus, the similarity of water quality parameters between each sensor can be obtained.
[0037] In some embodiments, the data redundancy of each sensor in the high-density partition is determined by the similarity of various water quality parameters and the spatial proximity between various sensors, with reference to... Figure 2 The diagram is a flowchart illustrating the determination of data redundancy in some embodiments of this application. In this embodiment, the determination of data redundancy can be achieved using the following steps: In step 1031, for each sensor in the high-density partition, the spatial proximity between the sensor and other sensors is calculated, and the water quality parameter similarity between the sensor and other sensors is obtained. In step 1032, the spatial redundancy value of the sensor is determined by the proximity of all spatial locations; In step 1033, the data redundancy value of the sensor is determined by the similarity of all water quality parameters; In step 1034, the data redundancy of the sensor is determined based on the spatial redundancy value and the data redundancy value, thereby obtaining the data redundancy of each sensor in the high-density partition.
[0038] It should be noted that, in this application, data redundancy is a quantitative indicator used to determine the degree to which sensor monitoring data can be replaced; spatial proximity is an indicator used to quantify the degree to which two sensors are geographically close to each other; spatial redundancy value is a quantitative indicator used to characterize the degree to which the location information value of a sensor is reduced due to its spatial aggregation with multiple other sensors in a high-density zone; and data redundancy value is a quantitative indicator used to characterize the degree to which the data information value of a sensor is reduced due to its similarity to the water quality data monitored by multiple other sensors in a high-density zone.
[0039] In specific implementation, firstly, for each sensor in the high-density zone, the spatial proximity between the sensor and other sensors is calculated, and the similarity of water quality parameters between the sensor and other sensors is obtained. This can be achieved in the following way: For each sensor in the high-density zone, the straight-line distance between the sensor and other sensors is statistically calculated using the geographic coordinates of all sensors in the high-density zone, and this straight-line distance is converted into a standardized proximity value using a distance decay function (e.g., reciprocal or negative exponential function) as the spatial proximity, thus obtaining the spatial proximity between the sensor and other sensors; simultaneously, the similarity of water quality parameters between the sensor and other sensors is obtained. Secondly, the spatial redundancy value of the sensor is determined through all spatial proximity values, which can be achieved in the following way: The average of all spatial proximity is used as the spatial redundancy value of the sensor. Then, the data redundancy value of the sensor can be determined by the similarity of all water quality parameters, which can be achieved by using the average of all water quality parameter similarities as the data redundancy value of the sensor. Finally, the data redundancy of the sensor is determined based on the spatial redundancy value and the data redundancy value, and the data redundancy of each sensor in the high-density partition can be obtained by normalizing the spatial redundancy value and the data redundancy value to eliminate the influence of dimensions, obtaining the preset weight coefficients from the sensor's control console and performing a weighted sum. The weight coefficients can be preset through historical experience. Finally, the calculated weighted sum is used as the data redundancy of the sensor. The data redundancy of each sensor can be obtained through the above methods.
[0040] In some embodiments, selecting multiple redundant sensors from the high-density partition using various data redundancy levels can be achieved by setting a data redundancy threshold for the sensors based on historical experience, and identifying sensors with a data redundancy greater than or equal to the data redundancy threshold as redundant sensors. In other embodiments, all sensors can be sorted in descending order of data redundancy, and the top K (default is 10% of the total number of sensors) sensors can be identified as redundant sensors, which is not limited here. Here, a redundant sensor refers to a monitoring device that can be effectively replaced by a neighboring sensor.
[0041] In step 104, all redundant sensors are deployed and merged based on the similarity of each water quality parameter and the proximity of each spatial location, and then the deployed and merged sensors are used to perform environmental sampling on the target water body.
[0042] In some embodiments, the deployment and merging of all redundant sensors based on the similarity of various water quality parameters and the proximity of various spatial locations can be achieved by the following steps: Based on the similarity of various water quality parameters and the proximity of various spatial locations, all redundant sensors are clustered to obtain multiple redundant sensor clusters. The target deployment points in each redundant sensor cluster are determined, thereby completing the deployment and merging of all redundant sensors.
[0043] In specific implementation, firstly, all redundant sensors are clustered based on the similarity of various water quality parameters and the proximity of various spatial locations to obtain multiple redundant sensor clusters. This can be achieved by treating each redundant sensor as a sample, with the similarity of water quality parameters and the proximity of spatial locations of each redundant sensor constituting the clustering features. Hierarchical clustering or density-based clustering algorithms can be used to automatically group redundant sensors that are spatially close and have similar water quality data characteristics into the same group. Finally, all redundant sensors are divided into several redundant sensor clusters with high internal similarity, thus obtaining multiple redundant sensor clusters. Then, the target deployment points in each redundant sensor cluster are determined, and the deployment and merging of all redundant sensors are completed. This can be achieved by calculating the geometric center of the geographic coordinates of all sensors within each redundant sensor cluster, or by using the sensor point with the highest comprehensive similarity of water quality parameters and spatial proximity as the new location representing the cluster for continuous monitoring. All original sensor points within the cluster are removed in the network deployment scheme, and finally, the deployment points of the redundant sensor clusters are replaced with a single target deployment point. The deployment and merging of each redundant sensor cluster can be completed in the above way.
[0044] In another aspect, in some embodiments, this application provides an aquatic organism environmental sampling system, referring to... Figure 3 The figure is a schematic diagram of the structure of an aquatic biological environment sampling system according to some embodiments of this application. The aquatic biological environment sampling system includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire the hydrological and geomorphological information and functional information of the target water body, and to divide the sampling area of the target water body into multiple functional zones by the type distribution of the hydrological and geomorphological information and the functional distribution of the functional information. Processing module 202, in this application, is used to determine the sampling density of each functional zone by the sampling frequency and the number of spatially deployed points of the sensors in each functional zone, and to screen high-density zones from the target water body based on each sampling density. It should be noted that the processing module 202 is also used to obtain the sampling information of each sensor in the high-density partition, compare the water quality parameters in each sampling information to obtain the water quality parameter similarity between each sensor, determine the data redundancy of each sensor in the high-density partition by the water quality parameter similarity and the spatial proximity between each sensor, and use the data redundancy to filter out multiple redundant sensors from the high-density partition. The execution module 203 in this application is mainly used to deploy and merge all redundant sensors based on the similarity of various water quality parameters and the proximity of various spatial locations, and then use the deployed and merged sensors to perform environmental sampling on the target water body.
[0045] The foregoing has detailed examples of aquatic biological environment sampling methods, systems, devices, and media provided in the embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0046] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described aquatic biological environment sampling method.
[0047] In some embodiments, reference Figure 4 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the structure of a computer device for implementing an aquatic biological environment sampling method according to an embodiment of this application. The aquatic biological environment sampling method described in the above embodiments can be achieved through… Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.
[0048] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.
[0049] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.
[0050] For example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.
[0051] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.
[0052] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.
[0053] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.
[0054] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.
[0055] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described aquatic biological environment sampling method.
[0056] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.
[0057] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. A method for environmental sampling of aquatic organisms, characterized in that, Includes the following steps: Obtain hydrogeomorphic and functional information of the target water body, and divide the sampling area of the target water body into multiple functional zones based on the type distribution of the hydrogeomorphic information and the functional distribution of the functional information. The sampling density of each functional zone is determined by the sampling frequency and the number of spatially deployed points of the sensors in each functional zone, and high-density zones are selected from the target water body based on each sampling density. The sampling information of each sensor in the high-density partition is obtained, the water quality parameters in each sampling information are compared to obtain the water quality parameter similarity between each sensor, the data redundancy of each sensor in the high-density partition is determined by the water quality parameter similarity and the spatial proximity between each sensor, and multiple redundant sensors are selected from the high-density partition using the data redundancy. Based on the similarity of various water quality parameters and the proximity of various spatial locations, all redundant sensors are deployed and merged, and then the merged sensors are used to perform environmental sampling on the target water body.
2. The method as described in claim 1, characterized in that, Based on the type distribution of the hydrogeomorphological information and the functional distribution of the functional information, the sampling area of the target water body is divided into multiple functional zones, specifically including: Extract the type distribution map of the target water body from the hydrogeomorphic information and the functional distribution map of the target water body from the functional information; The various type regions in the type distribution map and the various functional regions in the function distribution map are merged and clustered to obtain multiple composite cluster regions; Each composite cluster region is mapped to a functional partition, resulting in multiple functional partitions.
3. The method as described in claim 2, characterized in that, Extracting the type distribution map of the target water body from the hydrogeomorphological information and the functional distribution map of the target water body from the functional information specifically includes: Multiple types of zones of the target water body are obtained from the hydrogeomorphological information; Determine the type distribution map of the target water body by identifying all type zones; Multiple functional zones of the target water body are obtained from the aforementioned functional information; The functional distribution map of the target water body is determined by all functional zones.
4. The method as described in claim 1, characterized in that, The sampling density of each functional zone is determined by the sampling frequency of the sensors and the number of spatially deployed points in each zone. Specifically, this includes: For each functional zone, obtain the sampling frequency and the number of spatially deployed sensor points in the functional zone; The sampling density of the functional zones is determined by the sampling frequency and the number of spatially deployed points, thereby obtaining the sampling density of each functional zone.
5. The method as described in claim 1, characterized in that, By comparing the water quality parameters in each sampling data point, the similarity of water quality parameters between the various sensors is obtained, specifically including: Obtain multi-parameter vectors of water quality parameters from various sampling information; Calculate the vector distance between each multi-parameter vector to obtain the water quality parameter similarity between each sensor.
6. The method as described in claim 1, characterized in that, Determining the data redundancy of each sensor in the high-density partition by considering the similarity of various water quality parameters and the spatial proximity between various sensors specifically includes: For each sensor in the high-density zone, calculate the spatial proximity between the sensor and other sensors, and obtain the water quality parameter similarity between the sensor and other sensors; The spatial redundancy value of the sensor is determined by the proximity of all spatial locations; The data redundancy value of the sensor is determined by the similarity of all water quality parameters; The data redundancy of the sensor is determined based on the spatial redundancy value and the data redundancy value, thereby obtaining the data redundancy of each sensor in the high-density partition.
7. The method as described in claim 1, characterized in that, Based on the similarity of various water quality parameters and the proximity of various spatial locations, all redundant sensors are deployed and merged, specifically including: Based on the similarity of various water quality parameters and the proximity of various spatial locations, all redundant sensors are clustered to obtain multiple redundant sensor clusters. The target deployment points in each redundant sensor cluster are determined, thereby completing the deployment and merging of all redundant sensors.
8. An aquatic organism environmental sampling system, characterized in that, include: The acquisition module is used to acquire hydrological and geomorphological information and functional information of the target water body, and divide the sampling area of the target water body into multiple functional zones based on the type distribution of the hydrological and geomorphological information and the functional distribution of the functional information. The processing module is used to determine the sampling density of each functional zone by the sampling frequency and the number of spatially deployed points of the sensors in each functional zone, and to screen high-density zones from the target water body based on each sampling density. The processing module is also used to acquire sampling information from each sensor in the high-density partition, compare the water quality parameters in each sampling information to obtain the similarity of water quality parameters between each sensor, determine the data redundancy of each sensor in the high-density partition by the similarity of each water quality parameter and the spatial proximity between each sensor, and use the data redundancy to filter out multiple redundant sensors from the high-density partition. The execution module is used to deploy and merge all redundant sensors based on the similarity of various water quality parameters and the proximity of various spatial locations, and then use the deployed and merged sensors to perform environmental sampling on the target water body.
9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory being used to store a computer program, and the processor being used to call and run the computer program from the memory, causing the computer device to perform the aquatic biological environment sampling method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to perform the aquatic biological environment sampling method as described in any one of claims 1 to 7.