River basin water pollution tracing method, equipment and medium

Abstract

The invention belongs to the technical field of water pollution treatment, and particularly relates to a river basin water pollution tracing method and device and a medium. Performing water quality change feature clustering analysis on the preprocessed river data in the assessment section through a preset clustering analysis method, and analyzing dominant factors of water quality change features through a correlation analysis method; constructing a one-dimensional hydrological water quality model, calculating the water quality pollution contribution degree of each assessment section to the downstream assessment section through the one-dimensional hydrological water quality model, and determining the spatial position of a pollution source; and finally, calculating the pollution importance degree of each upstream inflow assessment section of the national examination section through the constructed random forest algorithm, and extracting the characteristics of the assessment section with the highest pollution importance degree on the national examination section. The method can solve the problem that in the prior art, the water quality pollutant problem of the river basin cannot be effectively analyzed.

Description

Technical Field

[0001] The present invention belongs to the technical field of water pollution treatment, and in particular relates to a method, equipment and medium for tracing the source of water pollution in a river basin. Background Art

[0002] Water pollution refers to the phenomenon that harmful substances enter natural water bodies, causing water quality to deteriorate, thus affecting human health and the ecological environment; the sources of water pollution are diverse, mainly including industrial wastewater, agricultural non-point source pollution, domestic sewage and accidental leakage; water pollution has a profound impact on the environment and social economy. On the one hand, it increases the threat to aquatic life and destroys the ecological balance; on the other hand, water pollution affects human health.

[0003] With the development and continuous improvement of sensor technology and data sharing technology, the acquisition of river basin water environment data has become more convenient and the amount of data has exploded. Therefore, there is an urgent need for water environment big data analysis to achieve accurate analysis of the health of river basin water quality, especially for the source analysis of water pollution in river basins, which can provide decision makers with a scientific basis for supervision and maintenance in river basins on the source of pollutants that affect water quality standards.

[0004] However, in actual operations, it is still difficult to effectively analyze the sources of pollutants that affect water quality compliance in river basins. Therefore, there is an urgent need to provide a river basin water pollution tracing technology to provide effective recommendations for achieving water quality standards. Summary of the invention

[0005] The technical problem solved by the present invention is to provide a method, equipment and medium for tracing the source of water pollution in a river basin, so as to solve the problem that the water quality pollutants in the river basin cannot be effectively analyzed in the prior art.

[0006] The basic solution provided by the present invention is a method for tracing the source of water pollution in a river basin, comprising:

[0007] S1: Divide the assessment sections of the river basin and obtain river data of each assessment section; the assessment sections include national assessment sections;

[0008] S2: preprocess the acquired river data;

[0009] S3: Perform cluster analysis on the water quality change characteristics of the pre-processed river data in the assessment section through the preset cluster analysis method, and analyze the dominant factors of the water quality change characteristics through the correlation analysis method;

[0010] S4: Construct a one-dimensional hydrological and water quality model, calculate the contribution of each assessment section to the water quality pollution of its downstream assessment section through the one-dimensional hydrological and water quality model, and determine the spatial location of the pollution source;

[0011] S5: The pollution importance of each upstream water assessment section of the national assessment section is calculated through the constructed random forest algorithm, and the characteristics of the assessment section with the highest pollution importance to the national assessment section are extracted.

[0012] Further, the S4 includes:

[0013] S4-1: Extract the side inflow location and pollution source location from the preprocessed river data, divide the target river basin into several sections, where the side inflow location and pollution source location are used as the section head, and the flow velocity and flow rate of each river basin are constant to generate a one-dimensional water quality model;

[0014] S4-2: Based on the one-dimensional water quality model, calculate the water pollution concentration from the upstream water to the downstream assessment section; the calculation formula is:

[0015]

[0016] Among them, X i-1 is the length of the river basin in the section, u i-1 is the average flow velocity of the river basin in the section, K is the pollutant attenuation coefficient, C 1,i represents the pollutant concentration of water flowing from the upstream assessment section to the downstream assessment section i, C 2,i-1 It represents the pollutant concentration of the river water flowing downstream from the previous section of section i;

[0017] S4-3: Obtain the pollutant concentration distribution map of each assessment section based on the calculation results and determine the spatial location of the pollution source.

[0018] Further, the S5 includes:

[0019] S5-1: Extract the water quality change characteristics X of each assessment section after correlation analysis j , generate the feature set M of each assessment section;

[0020] S5-2: Call the random forest algorithm to calculate the water quality change characteristics X in the characteristic set of each assessment section j The Gini index is calculated as follows:

[0021]

[0022] Among them, K means there are k categories, p mk Indicates the proportion of category k in node m;

[0023] S5-3: Calculate water quality change characteristics X j The importance of node m is calculated as:

[0024]

[0025] Among them, GI l Indicates water quality change characteristics X j The Gini index before branching at node m, GI r Indicates water quality change characteristics X j Gini index after branching at node m;

[0026] S5-4: Water quality change characteristics X j The node that appears in decision tree i is in the feature set M, and the water quality change feature X is calculated. j The importance of the i-th decision tree is calculated as follows:

[0027]

[0028] If there are n decision trees in total, then the water quality change feature X j The importance calculation formula is:

[0029]

[0030] Characteristics of water quality changes j The importance of is normalized, specifically:

[0031]

[0032] Among them, the denominator Represents the sum of the water quality variation characteristics of the entire data set.

[0033] Furthermore, the S5 further includes:

[0034] S5-5: Sort the water quality change characteristics in each assessment section in descending order according to the calculated importance;

[0035] S5-6: preset a rejection ratio, perform a rejection operation with a preset rejection ratio according to the importance of the water quality change characteristics, and generate a new feature set;

[0036] S5-7: preset the number of features to be retained, and repeat the operations S5-5 to S5-6 for the new feature set until a final feature set with the preset number of features to be retained remains;

[0037] S5-8: Select the feature set with the lowest Gini index in the final feature set of each assessment section as the assessment section feature set with the highest importance in affecting the water quality of the national assessment section.

[0038] Furthermore, the river data in S1 includes natural impact data and human impact data on water quality, the natural impact data includes geographical data, meteorological data and hydrological data, and the human impact data includes pollution source data, underground terrain data and socio-economic data.

[0039] Furthermore, the preprocessing in S2 includes data missing completion processing, data spatialization link processing and data spatiotemporal consistency processing.

[0040] Further, the S3 includes:

[0041] S3-1: Use K-means cluster analysis to cluster the river data of each assessment section in the river basin to analyze the water quality change characteristics;

[0042] S3-2: Based on the preprocessed river data, the correlation analysis method is used to analyze the correlation between water quality and the surrounding industries and rainfall in the river basin to obtain the dominant factors of water quality change characteristics.

[0043] A river basin water pollution source tracing device, applied to the above-mentioned river basin water pollution source tracing method, comprising:

[0044] Data acquisition module: used to acquire river data of each assessment section according to the assessment sections of the divided river basins; the assessment sections include national assessment sections;

[0045] Data preprocessing module: used to preprocess the acquired river data;

[0046] Water quality change characteristic analysis module: used to perform water quality change characteristic cluster analysis on the pre-processed river data in the assessment section according to the preset cluster analysis method to obtain water quality change characteristics;

[0047] Influencing factor analysis module: used to analyze the dominant factors of water quality change characteristics through correlation analysis;

[0048] Pollution contribution calculation module: used to calculate the contribution of each assessment section to the water quality pollution of its downstream assessment section through the constructed one-dimensional hydrological and water quality model, and determine the spatial location of the pollution source;

[0049] Pollution importance calculation module: used to calculate the pollution importance of each upstream water assessment section of the national examination section through the constructed random forest algorithm, and extract the characteristics of the assessment section with the highest pollution importance to the national examination section.

[0050] An electronic device includes a processor and a memory, wherein the memory stores programs or instructions, and the processor executes the above-mentioned method for tracing the source of water pollution in a river basin by calling the programs or instructions stored in the memory.

[0051] A computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute a method for tracing the source of water pollution in a river basin as described above.

[0052] The principles and advantages of the present invention are: in view of the problem that the existing technology cannot effectively analyze water pollution, the present invention first divides the river basin into assessment sections, and based on the divided river basin, according to the spatial logical chain of assessment section-upstream water supply station-national assessment section, constructs a characteristic analysis model of water quality anomalies in the river basin and a related analysis model of the dominant factors of anomalies, so as to obtain the main factors of water quality changes in each assessment section of the river basin; at the same time, the application uses a one-dimensional water quality model to obtain the upstream water pollution contribution of each assessment section station, determines the spatial location of the pollution source affecting the water quality in the river basin, and then uses the random forest algorithm to calculate the pollution importance of each upstream water supply assessment section of the national assessment section, so as to provide the Environmental Bureau with effective suggestions for water management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flowchart of an embodiment of the present invention;

[0054] Figure 2 is a functional block diagram of an embodiment of the present invention;

[0055] Figure 3 A schematic diagram of a one-dimensional water quality model according to an embodiment of the present invention;

[0056] Figure 4 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following is further described in detail through specific implementation methods:

[0058] The symbols in the drawings of the specification include: electronic device 400 , processor 401 , memory 402 , input device 403 , and output device 404 .

[0059] The embodiment is basically as shown in the attached Figure 1 As shown: A method for tracing the source of water pollution in a river basin, comprising:

[0060] S1: Divide the assessment section of the river basin and obtain the river data of each assessment section; the assessment section includes the national assessment section; in this embodiment, the assessment section division of the target river basin is generated through the geographic data of the river basin. Specifically, the DEM data, river network vector data, basin town and street data, land use type data, automatic monitoring station vector data, etc. of the target river basin are obtained, and the water collection units of the monitoring stations in the river basin are divided into assessment sections.

[0061] The river data of the river basin obtained in this application include natural impact data and human impact data on water quality. Specifically, in addition to the above-mentioned geographical data belonging to natural impact data, the natural impact data also include meteorological data and hydrological data. For example, meteorological data include historical meteorological data and forecast meteorological data. The specific contents include the daily maximum, minimum and average temperatures, precipitation, evaporation, wind speed, relative humidity, radiation, etc. at meteorological battle points to meet the needs of water quality testing.

[0062] Hydrological data is obtained by accessing the government’s water information interface, including flow and water level data.

[0063] Human impact data include pollution source data, topographic data, and socio-economic data. The pollution source data refer to the industrial enterprises in the catchment area of ​​the river basin, the pollution source conditions of large-scale livestock and poultry breeding, and sewage treatment plants. The pollution source data of industrial enterprises include the enterprise’s water intake, wastewater discharge, discharge destination, major pollutant generation, and discharge, etc. The pollution source conditions of sewage treatment plants include treatment scale, drainage destination, discharge, and online monitoring data of major pollutants; livestock and poultry farm data include sewage generation and discharge conditions, etc.

[0064] The tunnel topographic data in this application refers to the riverbed topography DEM data in the Earth 2000 coordinate system and the measured river width data.

[0065] Socio-economic data include GDP, industrial structure data, population data, etc. of the river basin.

[0066] In order to better describe the details of the river data in this application, it is shown in the following Table 1:

[0067] Table 1

[0068]

[0069]

[0070] S2: Preprocess the acquired river data; in this embodiment, the preprocessing includes data missing filling processing, data spatialization link processing and data spatiotemporal consistency processing. For data missing filling processing, for example, if the 1-hour cumulative rainfall data in the meteorological data is complete, but the 3-hour, 6-hour, 12-hour and 24-hour cumulative rainfall data are null values, the missing data can be filled by adding the 1-hour cumulative rainfall data; for another example, if the water quality parameters in the water quality data are missing in a certain time period, the outlier detection algorithm program is used to fill the missing water quality parameters in the short period of time; in this embodiment, the specific steps of data filling used in this application are disclosed as follows:

[0071] Data filling:

[0072]

[0073]

[0074]

[0075] The spatial linking process of data is to establish a spatial logical chain between the monitoring data and each assessment section site in the river basin, and to establish a unique identification code. For example, for site 1, use R1 code and assign longitude and latitude coordinates to the identification code. Site 2 is coded with R2, and so on.

[0076] Data spatiotemporal consistency processing is to process river data with time and space attributes in a temporal and spatial consistency manner, such as rainfall data, pollution source data, etc., and water quality data in a temporal and spatial consistency manner.

[0077] S3: Perform cluster analysis on the water quality change characteristics of the pre-processed river data in the assessment section through the preset cluster analysis method, and analyze the dominant factors of the water quality change characteristics through the correlation analysis method; S3 includes:

[0078] S3-1: Use K-means cluster analysis to cluster the river data of each assessment section in the river basin to analyze the water quality change characteristics;

[0079] S3-2: Based on the preprocessed river data, the correlation analysis method is used to analyze the correlation between water quality and the surrounding industries and rainfall in the river basin to obtain the dominant factors of water quality change characteristics.

[0080] In this embodiment, the analysis process of the K-means cluster analysis method in S3-1 is:

[0081] Step 1: Select K initial centroids. The initial centroids can be selected randomly, and each centroid is a class.

[0082] Step 2: For each remaining sample point, calculate the Euclidean distance from each centroid, and assign it to the cluster where the centroid with the smallest distance between them is located, and calculate the centroid of each new cluster;

[0083] Step 3: After all sample points are divided, recalculate the location of the centroid of each cluster according to the division situation, and then iteratively calculate the distance from each sample point to the centroid of each cluster, and re-divide all sample points;

[0084] Step 4: Repeat steps 2 and 3 until the centroid no longer changes or the maximum number of iterations is reached.

[0085] Therefore, when analyzing the characteristics of water quality changes, the present application uses the K-means clustering analysis method to cluster similar water quality data into the same cluster and divide dissimilar water quality data into different clusters, thereby analyzing the characteristic properties of different water quality data and the relationship between them.

[0086] In specific use, the K-means cluster analysis method is used to cluster the river data of the assessment section based on the time scale to identify the main types and pollution characteristics of water pollution in each assessment section. After cluster analysis, several groups of classified data can be obtained, and then these classified data are subjected to feature analysis. In this embodiment, the feature analysis includes: 1. Identifying the main water quality characteristics, main pollution factors, etc. of each pollution type based on the overall distribution of factors in each pollution type; 2. The time changes and time distribution characteristics of the pollution type; 3. The high incidence seasons of various pollution types, and the time correlation with the dry season / flood season / normal water season, etc.; 4. The frequency of occurrence of various pollution types, and determining the dominant pollution type of the section (the pollution type with the highest occurrence frequency).

[0087] The water pollution types in the main water quality change characteristics identified by cluster analysis are analyzed for dominant factors using the S3-2 correlation analysis method. The correlation analysis method in this application adopts Pearson correlation analysis. Pearson correlation analysis relies on the correlation coefficient to make judgments. Specifically, when the absolute value of the correlation coefficient is above 0.8, it is considered that there is a strong correlation between the two variables; when it is between 0.6 and 0.8, the two variables are considered to be strongly correlated; when it is between 0.4 and 0.6, the two variables are considered to be moderately correlated; when it is 0.2-0.4, the two variables are considered to be weakly correlated; when it is below 0.2, the two variables can be considered to be extremely weakly correlated or have no correlation.

[0088] In specific applications, it may include: 1. In the analysis of the impact of rainfall on water quality changes in each assessment section, the rainfall data of each assessment section is respectively subjected to Pearson correlation analysis with the water quality time series data. The higher the correlation coefficient, the greater the impact of rainfall on water quality; 2. According to the distribution of industry, population, agriculture, livestock and poultry breeding in the catchment area of ​​each assessment section, as well as the wastewater discharge, the Pearson correlation analysis is conducted with the frequency of water quality exceeding the standard in each assessment section of the basin to analyze the impact of industry, population, agriculture, livestock and poultry breeding on water quality exceeding the standard. The higher the correlation coefficient, the greater the impact on water quality.

[0089] S4: Construct a one-dimensional hydrological and water quality model, calculate the contribution of each assessment section to the water quality pollution of its downstream assessment section through the one-dimensional hydrological and water quality model, and determine the spatial location of the pollution source; S4 includes:

[0090] S4-1: Extract the side inflow position and pollution source position in the preprocessed river data, divide the target river basin into several sections, where the side inflow position and pollution source position are used as the section head, and the flow velocity and flow rate of each river basin are constant to generate a one-dimensional water quality model; the schematic diagram of the one-dimensional water quality model constructed in this embodiment is as follows Figure 3 As shown, where Q i represents the sewage flow into the river from section i, C i represents the pollutant concentration of sewage flowing into the river from section i, Q1, i represents the river flow from upstream to section i, C1, i represents the pollutant concentration of the river water flowing upstream to section i, Q2, i represents the river flow from section i to downstream, C2, i represents the pollutant concentration of the river water flowing downward from section i, and u represents the river flow velocity;

[0091] According to the principle of conservation of mass, we have:

[0092] C2, i Q2, i =C1, i Q1, i +C i Q i

[0093] S4-2: Based on the one-dimensional water quality model, calculate the water pollution concentration from the upstream water to the downstream assessment section; the calculation formula is:

[0094]

[0095] Among them, X i-1 is the length of the river basin in the section, u i-1 is the average flow velocity of the river basin in the section, K is the pollutant attenuation coefficient, C 1,i represents the pollutant concentration of water flowing from the upstream assessment section to the downstream assessment section i, C 2,i-1 It represents the pollutant concentration of the river water flowing downstream from the section above section i. In this case, the pollution concentration of the water quality of the downstream water from the upstream is calculated based on the flow rate of the river and the pollutant attenuation coefficient of 0.2;

[0096] S4-3: Obtain the pollutant concentration distribution map of each assessment section based on the calculation results and determine the spatial location of the pollution source.

[0097] S5: The pollution importance of each upstream water assessment section of the national assessment section is calculated through the constructed random forest algorithm, and the characteristics of the assessment section with the highest pollution importance to the national assessment section are extracted; among them, S5 includes:

[0098] S5-1: Extract the water quality change characteristics X of each assessment section after correlation analysis j , generate the feature set M of each assessment section;

[0099] S5-2: Call the random forest algorithm to calculate the water quality change characteristics X in the characteristic set of each assessment section j The Gini index is calculated as follows:

[0100]

[0101] Among them, K means there are k categories, p mk Indicates the proportion of category k in node m;

[0102] S5-3: Calculate water quality change characteristics X j The importance of node m is calculated as:

[0103]

[0104] Among them, GI l Indicates water quality change characteristics X j The Gini index before branching at node m, GI r Indicates water quality change characteristics X j Gini index after branching at node m;

[0105] S5-4: Water quality change characteristics X j The node that appears in decision tree i is in the feature set M, and the water quality change feature X is calculated. j The importance of the i-th decision tree is calculated as follows:

[0106]

[0107] If there are n decision trees in total, then the water quality change feature X j The importance calculation formula is:

[0108]

[0109] Characteristics of water quality changes j The importance of is normalized, specifically:

[0110]

[0111] Among them, the denominator Represents the sum of the water quality variation characteristics of the entire data set;

[0112] S5-5: Sort the water quality change characteristics in each assessment section in descending order according to the calculated importance;

[0113] S5-6: preset a rejection ratio, perform a rejection operation with a preset rejection ratio according to the importance of the water quality change characteristics, and generate a new feature set;

[0114] S5-7: preset the number of features to be retained, and repeat the operations S5-5 to S5-6 for the new feature set until a final feature set with the preset number of features to be retained remains;

[0115] S5-8: Select the feature set with the lowest Gini index in the final feature set of each assessment section as the assessment section feature set with the highest importance in affecting the water quality of the national assessment section.

[0116] Therefore, this application uses a one-dimensional water quality model to obtain the upstream water pollution contribution of each assessment section site, determines the spatial location of pollution sources that affect water quality in the river basin, and then uses the random forest algorithm to calculate the pollution importance of each upstream water assessment section of the national assessment section, providing the Environmental Bureau with effective suggestions for water management.

[0117] like Figure 2 As shown, in another embodiment of this embodiment, a river basin water pollution source tracing device is also included, which is applied to the above-mentioned river basin water pollution source tracing method, including:

[0118] Data acquisition module: used to acquire river data of each assessment section according to the assessment sections of the divided river basins; the assessment sections include national assessment sections;

[0119] Data preprocessing module: used to preprocess the acquired river data;

[0120] Water quality change characteristic analysis module: used to perform water quality change characteristic cluster analysis on the pre-processed river data in the assessment section according to the preset cluster analysis method to obtain water quality change characteristics;

[0121] Influencing factor analysis module: used to analyze the dominant factors of water quality change characteristics through correlation analysis;

[0122] Pollution contribution calculation module: used to calculate the contribution of each assessment section to the water quality pollution of its downstream assessment section through the constructed one-dimensional hydrological and water quality model, and determine the spatial location of the pollution source;

[0123] Pollution importance calculation module: used to calculate the pollution importance of each upstream water assessment section of the national examination section through the constructed random forest algorithm, and extract the characteristics of the assessment section with the highest pollution importance to the national examination section.

[0124] Also included is an electronic device such as Figure 4 As shown, the electronic device 400 includes one or more processors 401 and a memory 402 .

[0125] The processor 401 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0126] The memory 402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 401 may run the program instructions to implement a river basin water pollution tracing method and / or other desired functions of any embodiment of the present invention described above. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage medium.

[0127] In one example, the electronic device 400 may further include: an input device 403 and an output device 404, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). The input device 403 may include, for example, a keyboard, a mouse, etc. The output device 404 may output various information to the outside, including early warning information, braking force, etc. The output device 404 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0128] Of course, to simplify, Figure 4 Only some of the components related to the present invention in the electronic device 400 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application conditions, the electronic device 400 may also include any other appropriate components.

[0129] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of a method for tracing the source of water pollution in a river basin provided by any embodiment of the present invention.

[0130] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0131] In addition, an embodiment of the present invention may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of a method for tracing the source of water pollution in a river basin provided by any embodiment of the present invention.

[0132] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0133] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field know all the common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for tracing the source of water pollution in a river basin, characterized by: include: S1: Divide the assessment sections of the river basin and obtain river data of each assessment section; the assessment sections include national assessment sections; S2: preprocess the acquired river data; S3: Perform cluster analysis on the water quality change characteristics of the pre-processed river data in the assessment section through the preset cluster analysis method, and analyze the dominant factors of the water quality change characteristics through the correlation analysis method; S4: Construct a one-dimensional hydrological and water quality model, calculate the contribution of each assessment section to the water quality pollution of its downstream assessment section through the one-dimensional hydrological and water quality model, and determine the spatial location of the pollution source; S5: The pollution importance of each upstream water assessment section of the national assessment section is calculated through the constructed random forest algorithm, and the characteristics of the assessment section with the highest pollution importance to the national assessment section are extracted.

2. A method for tracing the source of water pollution in a river basin according to claim 1, characterized in that: The S4 includes: S4-1: Extract the side inflow location and pollution source location from the preprocessed river data, divide the target river basin into several sections, where the side inflow location and pollution source location are used as the section head, and the flow velocity and flow rate of each river basin are constant to generate a one-dimensional water quality model; S4-2: Based on the one-dimensional water quality model, calculate the water pollution concentration from the upstream water to the downstream assessment section; the calculation formula is: Among them, X i-1 is the length of the river basin in the section, u i-1 is the average flow velocity of the river basin in the section, K is the pollutant attenuation coefficient, C 1，i represents the pollutant concentration of water flowing from the upstream assessment section to the downstream assessment section i, C 2，i-1 It represents the pollutant concentration of the river water flowing downstream from the previous section of section i; S4-3: Obtain the pollutant concentration distribution map of each assessment section based on the calculation results and determine the spatial location of the pollution source.

3. A method for tracing the source of water pollution in a river basin according to claim 2, characterized in that: The S5 includes: S5-1: Extract the water quality change characteristics X of each assessment section after correlation analysis j , generate the feature set M of each assessment section; S5-2: Call the random forest algorithm to calculate the water quality change characteristics X in the characteristic set of each assessment section j The Gini index is calculated as follows: Among them, K means there are k categories, p mk Indicates the proportion of category k in node m; S5-3: Calculate water quality change characteristics X j The importance of node m is calculated as follows: Among them, GI l Indicates water quality change characteristics X j The Gini index before the branch at node m, GI r Indicates water quality change characteristics X j Gini index after branching at node m; S5-4: Water quality change characteristics X j The node that appears in decision tree i is in the feature set M, and the water quality change feature X is calculated. j The importance of the i-th decision tree is calculated as follows: If there are n decision trees in total, then the water quality change feature X j The importance calculation formula is: Characteristics of water quality changes j The importance of is normalized, specifically: Among them, the denominator Represents the sum of the water quality variation characteristics of the entire data set.

4. A method for tracing the source of water pollution in a river basin according to claim 3, characterized in that: The S5 further includes: S5-5: Sort the water quality change characteristics in each assessment section in descending order according to the calculated importance; S5-6: preset a rejection ratio, perform a rejection operation with a preset rejection ratio according to the importance of the water quality change characteristics, and generate a new feature set; S5-7: preset the number of features to be retained, and repeat the operations S5-5 to S5-6 for the new feature set until a final feature set with the preset number of features to be retained remains; S5-8: Select the feature set with the lowest Gini index in the final feature set of each assessment section as the assessment section feature set with the highest importance in affecting the water quality of the national assessment section.

5. A method for tracing the source of water pollution in a river basin according to claim 4, characterized in that: The river data in S1 include natural impact data and human impact data on water quality, wherein the natural impact data include geographical data, meteorological data and hydrological data, and the human impact data include pollution source data, underground terrain data and socio-economic data.

6. A method for tracing the source of water pollution in a river basin according to claim 5, characterized in that: The preprocessing in S2 includes data missing filling processing, data spatialization link processing and data spatiotemporal consistency processing.

7. A method for tracing the source of water pollution in a river basin according to claim 6, characterized in that: The S3 includes: S3-1: Use K-means cluster analysis to cluster the river data of each assessment section in the river basin to analyze the water quality change characteristics; S3-2: Based on the preprocessed river data, the correlation analysis method is used to analyze the correlation between water quality and the surrounding industries and rainfall in the river basin to obtain the dominant factors of water quality change characteristics.

8. A river basin water pollution source tracing device, applied to a river basin water pollution source tracing method as described in any one of claims 1 to 7 above, characterized in that: include: Data acquisition module: used to obtain river data of each assessment section according to the assessment sections of the divided river basin; The assessment sections include national assessment sections; Data preprocessing module: used to preprocess the acquired river data; Water quality change characteristic analysis module: used to perform water quality change characteristic cluster analysis on the pre-processed river data in the assessment section according to the preset cluster analysis method to obtain water quality change characteristics; Influencing factor analysis module: used to analyze the dominant factors of water quality change characteristics through correlation analysis; Pollution contribution calculation module: used to calculate the contribution of each assessment section to the water quality pollution of its downstream assessment section through the constructed one-dimensional hydrological and water quality model, and determine the spatial location of the pollution source; Pollution importance calculation module: used to calculate the pollution importance of each upstream water assessment section of the national examination section through the constructed random forest algorithm, and extract the characteristics of the assessment section with the highest pollution importance to the national examination section.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores programs or instructions, and the processor executes a method for tracing the source of water pollution in a river basin as described in any one of claims 1 to 7 by calling the programs or instructions stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and the programs or instructions enable a computer to execute a method for tracing the source of water pollution in a river basin as described in any one of claims 1 to 7.

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