A spatial optimization method and system for surface water environment monitoring network

Abstract

The present invention provides a spatial optimization method and system for a surface water environment monitoring network, and relates to the technical field of data processing. The method comprises: obtaining river information of a to-be-monitored area; establishing a river buffer zone based on the river information; extracting human activity intensity data in the river buffer zone; selecting multiple candidate monitoring sections based on the human activity intensity data, and determining the monitoring coverage of each section; merging all monitoring coverages, removing overlapping areas, and obtaining an accurate monitoring coverage; constructing a surface water environment monitoring network spatial optimization model with the goal of maximizing coverage of human activity intensity based on each candidate monitoring section and the accurate monitoring coverage; optimizing the model through an adaptive global optimization search algorithm, and outputting an optimal monitoring site layout plan; and arranging monitoring sites according to the optimal monitoring site layout plan, so as to perform spatial optimization of the surface water environment monitoring network.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a space optimization method and system for a surface water environment monitoring network. Background Art

[0002] Monitoring data provides a key basis for environmental governance, decision support and early warning, especially in the monitoring of water quality, air quality and ecological conditions. Surface water is an important water resource for human life and production, but its pollution problem is becoming increasingly serious, and the construction and optimization of surface water monitoring networks around the world still face challenges.

[0003] The existing surface water monitoring network mainly relies on traditional methods, and the construction of domestic monitoring networks focuses on administrative factors. For example, the setting of monitoring sections takes urban assessment as the main goal. In addition, scholars have also proposed different optimization objective functions to improve the monitoring network. One method proposes an estimation objective function based on water quality standards, water use supervision, pollution source monitoring, and water quality changes. Another proposed an objective function to minimize the average time of pollutant monitoring and maximize the reliability of the monitoring network.

[0004] However, the existing monitoring network has a small coverage area and fails to fully consider the actual ecological environment. It is difficult to achieve comprehensive monitoring of key areas and potential pollution sources, resulting in the monitoring results being unable to fully reflect the ecological status of the region. Secondly, the existing optimization methods rely on a variety of data, but since data is difficult to obtain in many areas, this limits the comprehensive coverage and precise layout of the monitoring network. Summary of the invention

[0005] In order to solve the problem that the existing monitoring network has a small coverage area, fails to fully consider the actual situation of the ecological environment, and is difficult to achieve comprehensive monitoring of key areas and potential pollution sources, resulting in the monitoring results being unable to fully reflect the ecological conditions of the region. Secondly, the existing optimization methods rely on a variety of data, but since data is difficult to obtain in many areas, this limits the technical problem of comprehensive coverage and precise layout of the monitoring network. The present invention provides a spatial optimization method and system for a surface water environment monitoring network.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides a method for spatial optimization of a surface water environment monitoring network, comprising:

[0009] S1: Obtain river information of the area to be monitored in the surface water environment monitoring network;

[0010] S2: Based on the river information, establishing a river buffer zone in the area to be monitored;

[0011] S3: extracting human activity intensity data within the river buffer zone;

[0012] S4: Based on the human activity intensity data, multiple candidate monitoring sections are selected, wherein each candidate monitoring section has a corresponding monitoring site;

[0013] S5: Determine the monitoring coverage of each candidate monitoring section;

[0014] S6: merging the monitoring coverage of each candidate monitoring section and removing the overlapping monitoring coverage to obtain an accurate monitoring coverage;

[0015] S7: Based on each candidate monitoring section and the precise monitoring coverage, a surface water environment monitoring network spatial optimization model is constructed with the goal of maximizing coverage of human activity intensity;

[0016] S8: Determine the objective function and constraint conditions of the surface water environment monitoring network space optimization model;

[0017] S9: Under the constraints of the constraints, with the goal of maximizing the objective function, the surface water environment monitoring network spatial optimization model is optimized through an adaptive global optimization search algorithm to output an optimal monitoring site layout plan;

[0018] S10: Deploy monitoring sites according to the optimal monitoring site deployment plan to perform spatial optimization of the surface water environment monitoring network.

[0019] Second aspect:

[0020] An embodiment of the present invention provides a spatial optimization system for a surface water environment monitoring network, comprising:

[0021] processor;

[0022] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the spatial optimization method for the surface water environment monitoring network as described in the first aspect is implemented.

[0023] The third aspect:

[0024] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the spatial optimization method of the surface water environment monitoring network as described in the first aspect is implemented.

[0025] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0026] (1) In an embodiment of the present invention, based on the human activity intensity data, multiple candidate monitoring sections are selected, the monitoring coverage of each candidate monitoring section is determined, the monitoring coverage of each candidate monitoring section is merged, and the overlapping monitoring coverage is removed to obtain an accurate monitoring coverage; thereby ensuring that the regional division of the monitoring network is closely related to the ecological environment, improving the coverage, and ensuring that the monitoring network can comprehensively and accurately reflect the ecological status of the region.

[0027] (2) In an embodiment of the present invention, based on each candidate monitoring section and the precise monitoring coverage, a spatial optimization model of a surface water environment monitoring network is constructed with the goal of maximizing coverage of human activity intensity. The spatial optimization model of the surface water environment monitoring network is optimized by an adaptive global optimization search algorithm, and an optimal monitoring site layout plan is output. By extracting human activity intensity data, the effective operation of the monitoring network in data-scarce areas is ensured, reducing dependence on difficult-to-obtain water quality and pollution source data, while enhancing the coverage and layout accuracy of the monitoring network. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A schematic flow chart of a method for spatial optimization of a surface water environment monitoring network provided by an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of selecting a candidate monitoring section provided by an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of monitoring section coverage simulation provided by an embodiment of the present invention;

[0032] Figure 4 A schematic structural diagram of a space optimization system for a surface water environment monitoring network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0034] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0035] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0036] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0037] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0038] Reference Manual Attached Figure 1 , showing a schematic flow chart of a spatial optimization method for a surface water environment monitoring network provided by an embodiment of the present invention.

[0039] Reference Manual Attached Figure 2 , showing a schematic diagram of selecting a candidate monitoring section provided by an embodiment of the present invention;

[0040] Reference Manual Attached Figure 3 , showing a schematic diagram of simulation of monitoring section coverage provided by an embodiment of the present invention;

[0041] The embodiment of the present invention provides a method for spatial optimization of a surface water environment monitoring network. The method can be implemented by a spatial optimization device of the surface water environment monitoring network. The spatial optimization device of the surface water environment monitoring network can be a terminal or a server. The processing flow of the method for spatial optimization of a surface water environment monitoring network can include the following steps:

[0042] S1: Obtain river information in the area to be monitored in the surface water environment monitoring network.

[0043] Among them, the surface water environment monitoring network is a system network used to monitor and evaluate the quality and ecological status of surface water bodies (reservoirs, rivers, lakes, wetlands, etc.). The network sets up monitoring stations to collect data such as water quality, flow rate, pollutant concentration, and ecological status, providing an important basis for water resource management, environmental protection, and pollution control.

[0044] It should be noted that river information includes: river centerline, river basin range, river tributaries and confluence points, and the impact of human activities on the river.

[0045] In the present invention, by obtaining information such as the river centerline, watershed range, tributaries and intersections, the geographical distribution and watershed range of the river can be fully understood, thereby providing a scientific basis for the subsequent deployment and optimization of monitoring stations.

[0046] S2: Based on river information, establish a river buffer zone in the area to be monitored.

[0047] It should be noted that the river buffer zone established in the area to be monitored is taken as the scope of influence of human activities.

[0048] Specifically, a number of studies have pointed out that a 5km riverbank buffer zone can well explain the impact of human activities on river water quality within the basin, and that the river bank is divided into 5km strips on both sides as the analysis range for the impact of human activities on river water quality.

[0049] S3: Extract human activity intensity data within the river buffer zone.

[0050] Among them, human activity intensity data is an important type of data that measures the impact of human activities on the environment and ecosystems. It is used to assess the spatial distribution of human behavior and the impact of these behaviors on the surrounding natural environment. It combines social, economic, urbanization and other factors, and quantifies the intensity and distribution of human activities through multiple data sources (such as remote sensing data, land use data, population density, industrial activities, etc.).

[0051] In a possible implementation, S3 specifically includes:

[0052] S301: Obtain a human activity intensity layer.

[0053] S302: Clipping the human activity intensity layer to determine a human activity intensity distribution map within the river buffer zone.

[0054] In the present invention, the human activity intensity layer is clipped to precisely limit the data within the river buffer zone, thereby improving the spatial correlation and accuracy of the data and ensuring that the results of monitoring and analysis are more in line with the actual situation.

[0055] S303: Extracting human activity intensity data from the human activity intensity distribution map.

[0056] Specifically, first, the river centerline is used as the analysis target, and the river buffer zone is established with a buffer distance of 5 km. Then, the human activity intensity layer is clipped with the buffer zone as the range to obtain the human activity intensity distribution map within 5 km of the river centerline, and then the human activity intensity data is extracted.

[0057] In the present invention, by extracting the human activity intensity data in the river buffer zone, the specific impact range of human activities on the river ecological environment can be clarified, helping to identify pollution sources or potential pollution source areas. At the same time, by extracting and analyzing the distribution map of human activity intensity in the buffer zone, it can help select those areas that are greatly affected by human activities for key monitoring, so as to optimize the layout of monitoring sites and improve monitoring effects and network coverage.

[0058] S4: Based on the human activity intensity data, multiple candidate monitoring sections are selected, where each candidate monitoring section has a corresponding monitoring site.

[0059] Among them, monitoring sections refer to specific locations or areas used to collect water samples and record environmental data in environmental monitoring, especially water quality monitoring. Monitoring sections are generally set at representative water locations and can reflect key characteristics of the water area, such as water quality, flow rate, and pollution. The data collected at the monitoring sections can effectively assess the status of the water body, the distribution of pollution sources, and their changing trends.

[0060] Monitoring sites refer to specific locations or facilities set up in environmental monitoring to collect, analyze and record environmental data, especially data on water quality, air quality, noise, etc. Monitoring sites collect samples or data in real time or regularly by installing corresponding monitoring equipment and sensors, providing a scientific basis for environmental assessment, pollution control, resource management, etc.

[0061] In a possible implementation manner, the candidate monitoring sections are selected in the following manner:

[0062] The river confluence and upstream locations were selected as candidate monitoring sections.

[0063] Specifically, when generating candidate monitoring sections, the present invention prioritizes river confluence points and main upstream locations as initial monitoring sections based on the principle of uniform distribution of river confluence points, upstream key locations and along the river sections. At the same time, when the river section length is sufficient, potential candidates are generated at fixed intervals of 10 km, and ultimately 495 candidate monitoring sections are generated.

[0064] In the present invention, by selecting the river confluence and upstream locations as candidate monitoring sections, it is possible to ensure that the monitoring sites cover key areas within the basin. At the same time, the river confluence is usually an important location for water flow interaction and pollutant exchange, while the upstream location may be the starting point of the pollution source. Such a selection can effectively capture the diffusion path of pollutants and changes in water quality.

[0065] Furthermore, selecting these key locations as monitoring sections helps to obtain the maximum information benefits under limited resources and budgets. By optimizing the layout of monitoring points, we can focus on monitoring the most influential areas and improve monitoring efficiency and effectiveness.

[0066] S5: Determine the monitoring coverage of each candidate monitoring section.

[0067] Among them, monitoring coverage refers to the geographical area or spatial scope that can be covered by setting certain monitoring sites and monitoring sections in environmental monitoring. In the fields of water quality monitoring and air quality monitoring, monitoring coverage is an important factor in determining the effectiveness and coverage quality of the monitoring network. It reflects whether the monitoring network can comprehensively and accurately collect the required environmental data and ensure that the monitoring results can represent a wide range of areas or samples.

[0068] In a possible implementation manner, the monitoring coverage of each candidate monitoring section in S5 is determined as follows:

[0069] The corresponding line features were generated 10 km upstream along the river channel of each candidate monitoring section. Based on each line feature, a buffer zone with a radius of 5 km was created as the monitoring coverage of each candidate monitoring section.

[0070] Among them, Line Feature is a spatial data type in Geographic Information System (GIS) used to represent geographic objects with linear forms. Line feature is a path composed of a series of connected points (called "nodes"), which can be used to represent actual objects with linear features, such as roads, rivers, railways, and boundaries.

[0071] In the present invention, by going up 10 kilometers along the river channel of the candidate monitoring section and creating a buffer zone, it can be ensured that the monitoring range covers the key parts of the river, including areas that may be polluted or spread. Going up a certain distance helps to capture changes in the upstream of the water body and identify potential sources of pollution or water quality problems. At the same time, setting up a buffer zone helps to expand the spatial scope of monitoring, allowing monitoring sites to cover a wider area, especially in a complex hydrological environment, ensuring that the monitoring results are more comprehensive and can reflect the overall status of the upstream and downstream of the river and the surrounding ecological environment.

[0072] S6: Merge the monitoring coverage of each candidate monitoring section and remove the overlapping monitoring coverage to obtain an accurate monitoring coverage.

[0073] Specifically, the present invention takes the monitoring section as the starting point, generates a line element 10 km upstream along the river, and creates a buffer zone with a radius of 5 km based on the line element as the coverage of the monitoring section. Considering that the coverage of adjacent sections may overlap, after generating the coverage of all sections, the section coverage is merged to determine the monitoring coverage of each candidate monitoring section.

[0074] In the present invention, after the section coverage is merged, it is possible to avoid missing important monitored areas and ensure that the entire monitoring network has a wider coverage. Especially in the case of complex terrain of rivers or uneven distribution of pollution sources, the merged coverage can ensure that no area is overlooked. At the same time, by accurately determining the coverage of each monitoring section, monitoring resources can be more effectively concentrated to ensure that key areas receive full attention. The process of merging and removing overlapping areas can optimize the layout of monitoring sites, ensuring that the monitoring network layout is more accurate and data collection is more representative.

[0075] Furthermore, by merging coverage and removing overlaps, the location arrangement of monitoring sites can be optimized, overlapping sampling areas can be reduced, and the overall efficiency of the monitoring network can be improved. This helps to achieve the best coverage effect under limited resources and improve the cost-effectiveness of monitoring work.

[0076] S7: Based on each candidate monitoring section and precise monitoring coverage, a surface water environment monitoring network spatial optimization model is constructed with the goal of maximizing coverage of human activity intensity.

[0077] It should be noted that the surface water environment monitoring network spatial optimization model is used to output the optimal monitoring site layout plan.

[0078] In the present invention, by constructing an optimization model with the goal of maximizing coverage of human activity intensity, monitoring sites can be intelligently selected according to the distribution of human activities, thereby ensuring that the monitoring network layout is more scientific and reasonable, and monitoring points can be set up in the most influential areas. By rationally arranging monitoring sites, the overall efficiency of the monitoring network is improved, and the resource investment in environmental governance is optimized. At the same time, by maximizing coverage of human activity intensity, the optimization model ensures that the monitoring sites can cover those areas that are greatly affected by human activities, and enhances the representativeness and accuracy of the monitoring data.

[0079] S8: Determine the objective function and constraints of the spatial optimization model of the surface water environment monitoring network.

[0080] In a possible implementation manner, the objective function is determined by:

[0081] S801: Determine a candidate monitoring section set of all candidate monitoring sections and a theoretical coverage range of each candidate monitoring section.

[0082] S802: Select multiple target monitoring sections from the candidate monitoring section set to form a target monitoring section set.

[0083] S803: Based on the theoretical coverage of each candidate monitoring section, determine the total coverage area composed of the theoretical coverage of each target monitoring section in the target monitoring section set:

[0084]

[0085] Among them, Z cover represents the total coverage area, Z represents the target monitoring section set, A i Represents the theoretical coverage of the i-th candidate monitoring section.

[0086] S804: Based on the total coverage area of ​​the target monitoring section set, determine a binary variable y used to indicate whether each human activity grid point is covered by the selected monitoring section j :

[0087]

[0088] Among them, y j A binary variable indicating whether the jth human activity grid point is covered by the selected monitoring section, y j = 1 means that the jth human activity grid point is covered by the selected monitoring section, y j =0 means that the jth human activity grid point is not covered by the selected monitoring section.

[0089] S805: Allocate the intensity of human activities in the coverage area to each target monitoring section.

[0090] S806: According to the binary variable y j And the intensity of human activities in each target candidate section, determine the objective function:

[0091]

[0092] Where f(Z) represents the objective function, ω j represents the intensity of human activity at the jth human activity grid point, and G represents the target monitoring section set.

[0093] In a possible implementation manner, the constraint conditions specifically include: a monitoring section quantity constraint and an integer constraint.

[0094] In a possible implementation, the monitoring section quantity constraint is specifically:

[0095]

[0096] Among them, x i Indicates whether the i-th monitoring section is selected, x i =1 means the i-th monitoring section is selected, x i =0 means that the i-th monitoring section is not selected, n represents the number of selected monitoring sections, and C represents the set of candidate monitoring sections.

[0097] The integer constraints are as follows:

[0098]

[0099] Among them, y j A binary variable indicating whether the jth human activity grid point is covered by the selected monitoring section, y j = 1 means that the jth human activity grid point is covered by the selected monitoring section, y j =0 means that the jth human activity grid point is not covered by the selected monitoring section, and G represents the target monitoring section set.

[0100] In the present invention, by constructing the objective function and constraints, monitoring sites can be scientifically selected to maximize coverage of human activity intensity. This method ensures that monitoring sites can focus on pollution sources and areas with dense human activities, improving the coverage efficiency and accuracy of the monitoring network. At the same time, by optimizing the objective function and constraints, the most representative monitoring sections can be selected under given resource constraints, avoiding waste of resources. This helps to reasonably allocate monitoring resources, so that the monitoring network can achieve maximum benefits under limited conditions.

[0101] Furthermore, the theoretical coverage of each monitoring section is clarified, and a binary variable is used to indicate whether the human activity grid point is covered, which can clearly define the target area for monitoring. This ensures the accuracy of the monitoring coverage and avoids excessive or insufficient monitoring.

[0102] S9: Under the constraints, with the goal of maximizing the objective function, the spatial optimization model of the surface water environment monitoring network is optimized through an adaptive global optimization search algorithm to output the optimal monitoring site layout plan.

[0103] Among them, the adaptive global optimization search algorithm is an optimization method used to find the global optimal solution, which is particularly suitable for complex, multi-peak, and high-dimensional optimization problems. It simulates the optimization process in nature or society, and gradually approaches the optimal solution through continuous search and iteration in the solution space. The "adaptive" feature of the algorithm usually means that it can dynamically adjust the strategy according to the specific characteristics of the problem and the search process to improve the search efficiency and avoid falling into the local optimal solution.

[0104] In a possible implementation, the objective function is used as the fitness function of the adaptive global optimization search algorithm.

[0105] In a possible implementation, the adaptive global optimization search algorithm specifically includes:

[0106] S901: Binary encode each section in the determined target candidate set to form a binary array whose degree is equal to the total number of candidate points.

[0107] S902: Initialize a population, where the population includes multiple individuals, each of which represents a feasible monitoring site deployment plan.

[0108] S903: randomly selecting a first preset number of target candidate points from the target candidate set, and setting the gene values ​​of the target candidate points to 1, and setting the gene values ​​of the remaining candidate points in the target candidate set to 0.

[0109] S904: Calculate the fitness value of each individual through the fitness function.

[0110] S905: Through the K-value tournament selection method, randomly select a second preset number of individuals from the group, compare the fitness values ​​of each individual, select the individual with the highest fitness to enter the next generation, and repeat the iteration until the number of individuals in the next generation generated is equal to the initial population.

[0111] Specifically, the present invention uses the K-value tournament selection method as the selection operator to perform individual selection. The specific steps are as follows: K individuals are randomly selected from the population for comparison, and the individuals with the highest fitness are selected to enter the next generation. This process is repeated until the same number of individuals as the initial population is generated to keep the population size constant. The K value is a key parameter of the tournament selection method, and its size directly affects the balance between selection pressure and population diversity.

[0112] It should be noted that a smaller K value can effectively maintain population diversity, but may reduce the selection intensity for excellent individuals; while a larger K value can increase the selection pressure and accelerate the spread of excellent genes, but may cause the population to converge to the local optimal solution prematurely. This paper combines the population size and optimization objectives, and experimentally verifies that choosing K=4 can achieve a good balance between exploring the solution space and obtaining high-quality solutions.

[0113] S906: With a preset crossover probability, a plurality of crossover points are randomly selected on the chromosomes of the individuals selected through the K-value tournament, and gene exchange is performed at these crossover points.

[0114] Specifically, after two parent individuals are selected through the tournament, according to the given crossover probability , multiple crossover points are randomly selected on the chromosome, and genes are exchanged at these crossover points to generate two new chromosomes. In order to meet the constraints, if there are repeated candidate site numbers in the chromosome, a repair step is performed, that is, the repeated numbers are removed and new sites are randomly selected from the candidate point set for replacement to ensure the validity of the chromosome and meet the constraints.

[0115] S907: With a preset mutation probability, randomly select a subset on the chromosome of the mutated individual, and perform a reverse operation on the gene sequence of the current subset.

[0116] Specifically, in this paper, the mutation operator adopts the subset inversion method. The specific steps are as follows: According to the given mutation probability P m , randomly select a subset on the chromosome and reverse its gene sequence. This method can effectively break the structure of the original solution and improve the algorithm's ability to explore new solution space.

[0117] S908: Repeat steps 904 to 907. When the fitness value of the individual is greater than the preset fitness value, stop the iteration and output the monitoring site layout plan represented by the optimal individual.

[0118] In the present invention, through iterative optimization, the genetic algorithm can gradually improve the layout of the monitoring sites, ensuring that the final layout plan can cover more key areas and potential pollution sources, thereby improving the coverage and monitoring efficiency of the monitoring network. At the same time, the use of the K-value tournament selection method and crossover mutation operation helps to balance the population diversity and the pressure of selecting excellent solutions during the optimization process. By adjusting the K value, the breadth of exploring the solution space and the depth of focusing on excellent individuals can be controlled, and a good balance can be achieved between the exploration of the solution space and high-quality solutions, avoiding falling into the local optimal solution too early, thereby improving the global search ability of the algorithm.

[0119] Furthermore, through the adaptive global optimization search algorithm, the optimal monitoring site layout plan can be found under the constraints. By binary encoding the candidate monitoring sections, fitness function evaluation, K-value tournament selection, crossover and mutation, etc., the solution space can be explored in a large range to ensure that the layout plan has the effect of maximizing the coverage of human activity intensity.

[0120] S10: Deploy monitoring sites according to the optimal monitoring site layout plan to optimize the space of the surface water environment monitoring network.

[0121] In the present invention, the site layout is carried out according to the optimal monitoring site layout plan, which can ensure that the selection and layout of the monitoring sites are carefully optimized, thereby maximizing the coverage of the monitoring network. This can ensure that all important areas, pollution sources and potential problem areas are fully monitored, thereby improving the monitoring effect.

[0122] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0123] (1) In an embodiment of the present invention, based on the human activity intensity data, multiple candidate monitoring sections are selected, the monitoring coverage of each candidate monitoring section is determined, the monitoring coverage of each candidate monitoring section is merged, and the overlapping monitoring coverage is removed to obtain an accurate monitoring coverage; thereby ensuring that the regional division of the monitoring network is closely related to the ecological environment, improving the coverage, and ensuring that the monitoring network can comprehensively and accurately reflect the ecological status of the region.

[0124] (2) In an embodiment of the present invention, based on each candidate monitoring section and the precise monitoring coverage, a spatial optimization model of a surface water environment monitoring network is constructed with the goal of maximizing coverage of human activity intensity. The spatial optimization model of the surface water environment monitoring network is optimized by an adaptive global optimization search algorithm, and an optimal monitoring site layout plan is output. By extracting human activity intensity data, the effective operation of the monitoring network in data-scarce areas is ensured, reducing dependence on difficult-to-obtain water quality and pollution source data, while enhancing the coverage and layout accuracy of the monitoring network.

[0125] Reference Manual Attached Figure 4 , showing a schematic structural diagram of a spatial optimization system for a surface water environment monitoring network provided by the present invention.

[0126] The present invention further provides a surface water environment monitoring network space optimization system 20, which is applied to the above-mentioned surface water environment monitoring network space optimization method, comprising:

[0127] Processor 201.

[0128] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the spatial optimization method of the surface water environment monitoring network in the method embodiment is implemented.

[0129] The spatial optimization system 20 for the surface water environment monitoring network provided by the present invention can execute the above-mentioned spatial optimization method for the surface water environment monitoring network and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0130] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0131] (1) In an embodiment of the present invention, based on the human activity intensity data, multiple candidate monitoring sections are selected, the monitoring coverage of each candidate monitoring section is determined, the monitoring coverage of each candidate monitoring section is merged, and the overlapping monitoring coverage is removed to obtain an accurate monitoring coverage; thereby ensuring that the regional division of the monitoring network is closely related to the ecological environment, improving the coverage, and ensuring that the monitoring network can comprehensively and accurately reflect the ecological status of the region.

[0132] (2) In an embodiment of the present invention, based on each candidate monitoring section and the precise monitoring coverage, a spatial optimization model of a surface water environment monitoring network is constructed with the goal of maximizing coverage of human activity intensity. The spatial optimization model of the surface water environment monitoring network is optimized by an adaptive global optimization search algorithm, and an optimal monitoring site layout plan is output. By extracting human activity intensity data, the effective operation of the monitoring network in data-scarce areas is ensured, reducing dependence on difficult-to-obtain water quality and pollution source data, while enhancing the coverage and layout accuracy of the monitoring network.

[0133] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0134] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0135] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0136] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0137] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0138] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0139] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0140] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0141] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0144] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0145] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the spatial optimization method for a surface water environment monitoring network as described in the method embodiment is implemented.

[0146] A computer-readable storage medium provided by the present invention can implement the steps and effects of the spatial optimization method of the surface water environment monitoring network of the above method embodiment. To avoid repetition, the present invention will not go into details.

[0147] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0148] (1) In an embodiment of the present invention, based on the human activity intensity data, multiple candidate monitoring sections are selected, the monitoring coverage of each candidate monitoring section is determined, the monitoring coverage of each candidate monitoring section is merged, and the overlapping monitoring coverage is removed to obtain an accurate monitoring coverage; thereby ensuring that the regional division of the monitoring network is closely related to the ecological environment, improving the coverage, and ensuring that the monitoring network can comprehensively and accurately reflect the ecological status of the region.

[0149] (2) In an embodiment of the present invention, based on each candidate monitoring section and the precise monitoring coverage, a spatial optimization model of a surface water environment monitoring network is constructed with the goal of maximizing coverage of human activity intensity. The spatial optimization model of the surface water environment monitoring network is optimized by an adaptive global optimization search algorithm, and an optimal monitoring site layout plan is output. By extracting human activity intensity data, the effective operation of the monitoring network in data-scarce areas is ensured, reducing dependence on difficult-to-obtain water quality and pollution source data, while enhancing the coverage and layout accuracy of the monitoring network.

[0150] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0151] There are a few points to note:

[0152] (1) The drawings of the embodiments of the present invention only involve structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0153] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0154] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0155] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A spatial optimization method for a surface water environment monitoring network, characterized in that: include: S1: Obtain river information of the area to be monitored in the surface water environment monitoring network; S2: Based on the river information, establishing a river buffer zone in the area to be monitored; S3: extracting human activity intensity data within the river buffer zone; S4: Based on the human activity intensity data, multiple candidate monitoring sections are selected, wherein each candidate monitoring section has a corresponding monitoring site; S5: Determine the monitoring coverage of each candidate monitoring section; S6: merging the monitoring coverage of each candidate monitoring section and removing the overlapping monitoring coverage to obtain an accurate monitoring coverage; S7: Based on each candidate monitoring section and the precise monitoring coverage, a surface water environment monitoring network spatial optimization model is constructed with the goal of maximizing coverage of human activity intensity; S8: Determine the objective function and constraint conditions of the surface water environment monitoring network space optimization model; S9: Under the constraints of the constraints, with the goal of maximizing the objective function, the surface water environment monitoring network spatial optimization model is optimized through an adaptive global optimization search algorithm to output an optimal monitoring site layout plan; S10: Deploy monitoring sites according to the optimal monitoring site deployment plan to perform spatial optimization of the surface water environment monitoring network.

2. The spatial optimization method of the surface water environment monitoring network according to claim 1 is characterized in that: The S3 specifically includes: S301: Obtaining a human activity intensity layer; S302: Clipping the human activity intensity layer to determine a human activity intensity distribution map within the river buffer zone; S303: Extracting the human activity intensity data from the human activity intensity distribution map.

3. The spatial optimization method of the surface water environment monitoring network according to claim 1 is characterized in that: The selection method of the candidate monitoring section is specifically as follows: The river confluence point and upstream location are used as the selection locations of the candidate monitoring sections.

4. The spatial optimization method of the surface water environment monitoring network according to claim 1 is characterized in that: The monitoring coverage of each candidate monitoring section in S5 is determined as follows: Corresponding line elements are generated 10 km upstream along the river channel of each candidate monitoring section. Based on each line element, a buffer zone with a radius of 5 km is created as the monitoring coverage range of each candidate monitoring section.

5. The spatial optimization method of the surface water environment monitoring network according to claim 1 is characterized in that: The objective function is determined by: S801: Determine a candidate monitoring section set of all candidate monitoring sections and a theoretical coverage range of each candidate monitoring section; S802: Selecting multiple target monitoring sections from the candidate monitoring section set to form a target monitoring section set; S803: Based on the theoretical coverage of each candidate monitoring section, determine the total coverage area composed of the theoretical coverage of each target monitoring section in the target monitoring section set: ； Among them, Z cover represents the total coverage area, Z represents the target monitoring section set, A i represents the theoretical coverage of the ith candidate monitoring section; S804: Based on the total coverage area of ​​the target monitoring section set, determine a binary variable y used to indicate whether each human activity grid point is covered by the selected monitoring section. j : ； Among them, y j A binary variable indicating whether the jth human activity grid point is covered by the selected monitoring section, y j = 1 means that the jth human activity grid point is covered by the selected monitoring section, y j =0 means that the jth human activity grid point is not covered by the selected monitoring section; S805: Allocating human activity intensity in the coverage area to each of the target monitoring sections; S806: According to the binary variable y j and the intensity of human activities at each of the target candidate sections, and determine the objective function: ； Where f(Z) represents the objective function, ω j represents the intensity of human activity at the jth human activity grid point, and G represents the target monitoring section set.

6. The spatial optimization method of the surface water environment monitoring network according to claim 1 is characterized in that: The constraint conditions specifically include: monitoring section quantity constraint and integer constraint.

7. The spatial optimization method of the surface water environment monitoring network according to claim 5 is characterized in that: The monitoring section quantity constraint is specifically: ； Among them, x i Indicates whether the i-th monitoring section is selected, x i =1 means the i-th monitoring section is selected, x i =0 means that the i-th monitoring section is not selected, n represents the number of selected monitoring sections, and C represents the set of candidate monitoring sections; The integer constraints are specifically: ； Among them, y j A binary variable indicating whether the jth human activity grid point is covered by the selected monitoring section, y j = 1 means that the jth human activity grid point is covered by the selected monitoring section, y j =0 means that the jth human activity grid point is not covered by the selected monitoring section, and G represents the target monitoring section set.

8. The spatial optimization method of the surface water environment monitoring network according to claim 1 is characterized in that: The objective function is used as the fitness function of the adaptive global optimization search algorithm.

9. The spatial optimization method of the surface water environment monitoring network according to claim 5, characterized in that: The adaptive global optimization search algorithm specifically includes: S901: Binary code each section in the determined target monitoring section combination to form a binary array with a degree equal to the total number of candidate points; S902: Initializing a population, wherein the population includes a plurality of individuals, each of which represents a feasible monitoring site deployment plan; S903: randomly selecting a first preset number of target candidate points from the target candidate set, and setting the gene values ​​of the target candidate points to 1, and setting the gene values ​​of the remaining candidate points in the target candidate set to 0; S904: Calculate the fitness value of each individual through the fitness function; S905: randomly selecting a second preset number of individuals from the population through the K-value tournament selection method, comparing the fitness values ​​of the individuals, selecting the individuals with the highest fitness to enter the next generation, and repeating the iteration until the number of individuals in the next generation generated is equal to that of the initial population; S906: randomly selecting multiple crossover points on the chromosomes of the individuals selected through the K-value tournament with a preset crossover probability, and performing gene exchange at these crossover points; S907: randomly selecting a subset on the chromosome of the mutated individual with a preset mutation probability, and performing a reverse operation on the gene sequence of the current subset; S908: Repeat steps 904 to 907. When the fitness value of the individual is greater than the preset fitness value, stop the iteration and output the monitoring site layout plan represented by the optimal individual.

10. A spatial optimization system for a surface water environment monitoring network, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the spatial optimization method for a surface water environment monitoring network as described in any one of claims 1 to 9 is implemented.

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