A data management system and method for wastewater monitoring

Abstract

The present invention discloses a data management system and method for wastewater monitoring, which relates to the technical field of data management and includes: taking a panoramic image of the wastewater discharge pool, obtaining historical sampling data, and obtaining all characteristic sampling data; determining the total number of wastewater monitoring devices, dividing the pool area into several characteristic areas according to the pool area corresponding to the wastewater discharge pool and the water body characteristic ratio corresponding to each characteristic sampling data, setting the number of cycles for cycling, obtaining the target area in the pool area and the corresponding target points; determining the target sampling quantity, obtaining the water body characteristic degree of each target point, and marking the target points according to the distance between the target points, and sampling at the marked target points. By intelligently and reasonably selecting the sampling point positions according to the actual characteristics of the water body, the data sampled by the present invention is more representative, providing strong data support for subsequent wastewater treatment work.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and specifically to a data management system and method for wastewater monitoring. Background Art

[0002] During the operation of a factory, various types of wastewater are continuously generated. These wastewaters must go through multiple treatment steps before being discharged to meet the discharge standards. Before discharge, sampling work is crucial as it is a key link to ensure that the discharged water body meets the standards. Generally, wastewaters are stored in wastewater discharge ponds such as regulating ponds, disinfection ponds, and sedimentation ponds, and then sampled. However, in the current sampling situation, there are some problems that cannot be ignored: The current main sampling method relies on manual sampling, which has problems such as improper selection of sampling points, low efficiency, and large sampling subjectivity, resulting in the lack of representativeness of the sampled data. Therefore, the sampling point positions should be intelligently and reasonably selected according to the actual characteristics of the water body to make the sampled data more representative, which can not only comprehensively understand whether the wastewater treatment process is reasonable, but also accumulate valuable data resources for subsequent wastewater treatment work and provide strong data support for it. Summary of the Invention

[0003] The purpose of the present invention is to provide a data management system and method for wastewater monitoring to solve the problems raised in the prior art.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] A data management method for wastewater monitoring includes the following steps:

[0006] Step S100: Take a panoramic image of the wastewater discharge pond, establish a plane coordinate system of the wastewater discharge pond, obtain historical sampling data, and according to the positions of the sampling points corresponding to each sampling data in the plane coordinate system and the water body state parameters during sampling, obtain the degree of water body characteristics corresponding to each sampling data, and convert it into a water body characteristic ratio. Then, according to the sampling results corresponding to each sampling data, obtain all characteristic sampling data;

[0007] In this solution, a drone can be deployed directly above the wastewater discharge pond to take a panoramic image. The panoramic image should cover the entire wastewater discharge pond, and the plane coordinate system can be obtained according to the length and width of the wastewater discharge pond;

[0008] Step S200: Determine the total number of wastewater monitoring devices, and according to the pond area corresponding to the wastewater discharge pond and the water body characteristic ratio corresponding to each characteristic sampling data, divide the pond area into several characteristic areas;

[0009] Step S300: Set the number of loops according to the water body feature ratios corresponding to the respective feature sampling data, perform loops to obtain the target areas in the pool area and the target points corresponding to the respective target areas, and deploy each wastewater monitoring device at the position of the target point;

[0010] Step S400: Determine the target sampling quantity, analyze the monitoring data of the wastewater monitoring devices to obtain the water body feature degrees of the respective target points, and mark the target points according to the distances between the target points, and perform sampling at the marked target points.

[0011] Further, Step S100 includes:

[0012] Step S110: Randomly select several sampling points from the plane coordinate system, and perform sampling at each sampling point as the sampling data of a certain batch. Then randomly select several sampling points and perform sampling, and so on, to obtain several batches. Group all the sampling data of the same batch into a sampling set to obtain several sampling sets; the sampling data includes the sampling point, sampling time, water body state parameters, and sampling result at the time of sampling, and the sampling result includes water quality parameters; in the plane coordinate system, obtain the boundary area of the wastewater discharge pool and the area of the components with a height greater than the height threshold, and both are used as obstacle areas;

[0013] Step S120: Obtain the sampling point P0 corresponding to a certain sampling data in a certain sampling set. Take the range with a radius of r centered on the sampling point P0 as the sampling detection range. Divide the radius r evenly into N parts, and then divide the sampling detection range into N sub-detection ranges, and sort them in ascending order of area; obtain the area S of the obstacle area within the nth sub-detection range n , and obtain the weight of the nth sub-detection range: ; Obtain the water body state parameter V corresponding to a certain sampling data and the weights of all sub-detection ranges to obtain the water body feature degree of a certain sampling data: , where k c is the water body feature coefficient;

[0014] Step S130: Obtain all the water body feature degrees corresponding to a certain sampling set, and obtain the sampling data D corresponding to the maximum water body feature degree max , and divide the water body feature degrees of the remaining sampling data except the sampling data D max by the maximum water body feature degree to obtain several water body feature ratios; obtain the water quality parameter A corresponding to the sampling data D max , and obtain the variances of the water quality parameters corresponding to the remaining sampling data and the water quality parameter A. Take the sampling data with a variance less than the variance threshold as the feature sampling data; then, according to each sampling set, obtain all the feature sampling data.

[0015] In this solution, the degree of water body characteristics is the degree of water mixing. The obstacle areas include two types. One is components such as columns and walls in the pool, and the other is the surrounding walls. The water body state parameter is the water flow velocity, and the water quality parameter is the pH value, etc. Due to the viscous effect of the wall surface, a boundary layer will be formed around the wall when the water flows, so the closer the sampling point is to the wall, the more uneven the water flow is in the surrounding area. Therefore, the closer to the wall and the more walls there are, the smaller the mixing degree here. And because there is also a large correlation between the flow velocity and the mixing degree, when the flow velocity is large, the mixing of the surrounding substances is more uniform. So the larger the flow velocity V, the greater the mixing degree. When the flow velocity is large, the various acid-base substances in the pool are quickly mixed rather than accumulated. Therefore, at the positions with a large flow velocity and fewer obstacle areas, the water quality parameters are closer to the actual equilibrium state of the water body and are more representative. And the data with a large difference from the pH value when the flow velocity is large, because its authenticity cannot be estimated, so this part of the data is not representative and cannot be used as characteristic sampling data;

[0016] Further, step S200 includes:

[0017] Step S210: Mark the positions of the sampling points corresponding to each characteristic sampling data on the plane coordinate system; obtain the sampling point b closest to the sampling point a, and take the distance between the sampling point a and the sampling point b as L ab , and take the corresponding water body characteristic ratios as C a and C b . If C a < C b , starting from the sampling point a, in the direction pointing to the sampling point b, with a magnitude of K × |C b - C a | × L ab , obtain the sampling vector of the sampling point a, where K is the vector coefficient and K > 0; if C a > C b , starting from the sampling point a, in the opposite direction of the sampling point b, with a magnitude of K × |C b - C a | × L ab , obtain the sampling vector of the sampling point a; if C a = C b , then there is no sampling vector for the sampling point a; and then obtain the sampling vectors corresponding to all sampling points and add them up to obtain the positioning vector;

[0018] Step S220: Randomly obtain several coordinate points in the pool area, calculate the average value to obtain the central facility point, take the central facility point as the starting point of the positioning vector, and take the end point of the positioning vector as the characteristic point; take the total number of wastewater monitoring devices as M, obtain the total area area of the pool area, and obtain the evenly divided area S ^= area / M; Randomly obtain a coordinate point from the edge of the pool area, connect it with the feature point to obtain the feature line segment L1, rotate the feature line segment L1 clockwise with the feature point as the center until the area enclosed by the rotated feature line segment L2, the feature line segment L1, and the edge line of the pool area is equal to the equalized area S ^ When this happens, stop the rotation, and take the enclosed area as the feature area. By analogy, obtain M feature areas with an area of S each ^ each.

[0019] Further, step S300 includes:

[0020] Step S310: Set the loop count T = 1. Randomly obtain several coordinate points within a certain feature area, calculate the average value to obtain the center area coordinates of a certain feature area. According to the distance between a certain sampling point q in a certain feature area and the center area coordinates, obtain the distance weight value W corresponding to a certain sampling point q as q = e -D where e is the natural exponent and D is the distance between a certain sampling point q and the center area coordinates; According to the distance weight value and the water body feature ratio corresponding to each sampling data within a certain feature area, obtain the characteristic water body feature ratio corresponding to a certain feature area as , K Y is the water body feature degree coefficient, Q is the total number of sampling data in a certain feature area, and C q is the water body feature ratio corresponding to a certain sampling data; If there is no corresponding sampling data within a certain feature area, take the water body feature ratio corresponding to the sampling point closest to the center area coordinates as the characteristic water body feature ratio corresponding to a certain feature area; According to the characteristic water body feature ratios of all feature areas, obtain the average ratio C ^ , and divide each characteristic water body feature ratio by C ^ to obtain the area adjustment ratio corresponding to each feature area;

[0021] Step S320: Multiply each area adjustment ratio by the equalized area S ^ , to obtain the iterative area of each feature area. According to steps S200 to S300, adjust the area of each feature area to the iterative area, reset the center area coordinates of each, calculate the characteristic water body feature ratio of each adjusted feature area, obtain the area adjustment ratio again, and add 1 to the value of the loop count T; Set the maximum value of the loop count T to T max , when the loop count T reaches T max , stop the adjustment to obtain the final each feature area as each target area, and take the center area coordinates of the last loop as the target points to obtain M target points.

[0022] The target points are determined only based on historical sampling data. Its main purpose is to determine the positions where the sampling points should be during subsequent sampling, as well as to determine the locations of each monitoring device. The sampling point positions during specific sampling are selected from the target points. The advantage of doing this is that by using the method of the above steps, the placement positions of the monitoring devices are fixed, and it is not necessary to re-place the monitoring devices every time sampling is carried out, which not only saves manpower but also is more convenient. Moreover, the placement positions of the monitoring devices obtained through the above method are more reasonable.

[0023] Further, step S400 includes:

[0024] Step S410: Obtain the wastewater monitoring device at a certain target point, record the water body state parameters at each moment within the previous S seconds starting from the current moment, and obtain the parameter average value. According to step S100, obtain the current water body characteristic degree of a certain target point; determine that the target sampling quantity is G, where 1 ≤ G ≤ M, and M is the total number of target points. Obtain the coordinates of each target point. According to step S200, obtain the target vectors between target point a and each of the other target points, a total of M - 1 target vectors. Then add up the various target vectors to obtain the total target vector VT corresponding to target point a a , and use the feature point as the starting point of the total target vector VT a to obtain the end point of the total target vector VT a as the characteristic end point corresponding to target point a, and then obtain the characteristic end points corresponding to each target point;

[0025] Step S420: Set the loop count B = 1. Respectively obtain the minimum radii required for each characteristic end point to be tangent to the pool area. Mark the target points corresponding to the minimum values among the minimum radii. If there are multiple minimum values among the minimum radii that are the same, mark the target point with the smallest water body characteristic degree; then eliminate the marked target points, re-select each characteristic end point, and increase the value of the loop count B by 1 until the value of the loop count B is G and then end. Thus, obtain G marked target points and perform sampling at the marked target points.

[0026] When the sampling quantity is small, what is needed are characteristic points with relatively low mixing degree. This is because characteristic points with low mixing degree can more comprehensively reflect the actual situation of the wastewater. Characteristic points with low mixing degree mean that the external influence on this point is small, and its water quality characteristics are more representative. Therefore, when the sampling quantity is limited, selecting characteristic points with low mixing degree for sampling can more accurately reflect the water quality of the wastewater, which helps to formulate more effective treatment and management measures.

[0027] A data management system for wastewater monitoring, including a module for obtaining characteristic sampling data, a module for dividing characteristic regions, a module for obtaining target regions, and a module for sampling at target points;

[0028] The module for obtaining characteristic sampling data: used to take panoramic images of the wastewater discharge pool, establish a planar coordinate system of the wastewater discharge pool, obtain historical sampling data, and according to the positions of the sampling points corresponding to each sampling data in the planar coordinate system, as well as the water body state parameters at the time of sampling, obtain the degree of water body characteristics corresponding to each sampling data, and convert it into a water body characteristic ratio. Furthermore, according to the sampling results corresponding to each sampling data, all characteristic sampling data are obtained;

[0029] The module for dividing characteristic regions: used to determine the total number of wastewater monitoring devices, and according to the pool area corresponding to the wastewater discharge pool, as well as the water body characteristic ratio corresponding to each characteristic sampling data, divide the pool area into several characteristic regions;

[0030] The module for obtaining target regions: used to set the number of loops according to the water body characteristic ratio corresponding to each characteristic sampling data, perform loops to obtain the target regions in the pool area, and the target points corresponding to each target region, and deploy each wastewater monitoring device at the position of the target points;

[0031] The module for sampling at target points: used to determine the target sampling quantity, analyze the monitoring data of the wastewater monitoring devices to obtain the degree of water body characteristics of each target point, and mark the target points according to the distances between the target points, and perform sampling at the marked target points.

[0032] Furthermore, the module for obtaining characteristic sampling data includes a unit for obtaining obstacle regions, a unit for calculating the degree of water body characteristics, and a unit for obtaining characteristic sampling data;

[0033] The unit for obtaining obstacle regions: used to randomly select several sampling points from the planar coordinate system, and perform sampling at each sampling point to obtain several sampling sets; draw obstacle regions in the planar coordinate system;

[0034] The unit for calculating the degree of water body characteristics: used to obtain the sampling points corresponding to a certain sampling data in a certain sampling set, obtain the sampling detection range, divide it into several sub-detection ranges, and obtain the weight of each sub-detection range; and according to the water body state parameters corresponding to a certain sampling data, obtain the degree of water body characteristics of a certain sampling data;

[0035] The unit for obtaining characteristic sampling data: used to obtain all the degrees of water body characteristics in a certain sampling set, and then obtain the water body characteristic ratios of each sampling data; furthermore, according to each sampling set, obtain all the characteristic sampling data.

[0036] Furthermore, the module for obtaining target regions includes a unit for obtaining the area adjustment ratio and a unit for obtaining target regions;

[0037] An area adjustment ratio unit: used to randomly obtain several coordinate points within a certain feature area, obtain the central area coordinates of a certain feature area, and obtain the feature mixing ratio corresponding to a certain feature area; according to the feature mixing ratios of all feature areas, obtain the area adjustment ratios corresponding to each feature area.

[0038] A target area unit: used to obtain the iterative areas of each feature area, perform a loop, obtain the final feature areas as each target area, and use the central area coordinates of the last loop as target points.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention provides a data management system and method for wastewater monitoring, including: taking a panoramic image of a wastewater discharge pool, obtaining historical sampling data, and obtaining all feature sampling data; determining the total number of wastewater monitoring devices, dividing the pool area into several feature areas according to the pool area corresponding to the wastewater discharge pool and the water body feature ratio corresponding to each feature sampling data, setting the number of loops for cycling, obtaining the target areas in the pool area and the corresponding target points; determining the target sampling quantity, obtaining the water body feature degree of each target point, and marking the target points according to the distance between the target points, and sampling at the marked target points. By intelligently and reasonably selecting the sampling point positions according to the actual characteristics of the water body, the sampling data obtained by the present invention is more representative, providing strong data support for subsequent wastewater treatment work. Description of the Drawings

[0040] Figure 1 It is a schematic flowchart of a data management method for wastewater monitoring according to the present invention;

[0041] Figure 2 It is a structural diagram of a data management system for wastewater monitoring according to the present invention. Detailed Embodiments

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0043] Embodiment: As Figure 1 shown, the present invention provides a technical solution for a data management method for wastewater monitoring, including the following steps:

[0044] Step S100: Take a panoramic image of the wastewater discharge pool, establish a planar coordinate system for the wastewater discharge pool, obtain historical sampling data, and based on the positions of the sampling points corresponding to each sampling data in the planar coordinate system and the water body state parameters at the time of sampling, obtain the degree of water body characteristics corresponding to each sampling data, convert it into a water body characteristic ratio, and then obtain all characteristic sampling data based on the sampling results corresponding to each sampling data.

[0045] In this solution, a drone can be deployed directly above the wastewater discharge pool to take a panoramic image. The panoramic image should capture the entire wastewater discharge pool, and a planar coordinate system can be obtained based on the length and width of the wastewater discharge pool.

[0046] Step S110: Randomly select several sampling points from the planar coordinate system, and perform sampling at each sampling point as the sampling data for a certain batch. Then randomly select several sampling points and perform sampling, and so on, to obtain several batches. Group all the sampling data of the same batch into a sampling set to obtain several sampling sets; the sampling data includes the sampling point, sampling time, water body state parameters, and sampling result at the time of sampling, and the sampling result includes water quality parameters; in the planar coordinate system, obtain the boundary area of the wastewater discharge pool and the area of the components with a height greater than the height threshold, and both are used as obstacle areas.

[0047] Step S120: Obtain the sampling point P0 corresponding to a certain sampling data in a certain sampling set. Take the range with a radius of r centered on the sampling point P0 as the sampling detection range. Divide the radius r evenly into N parts, and then divide the sampling detection range into N sub-detection ranges, and sort them in ascending order of area; obtain the area S of the obstacle area within the nth sub-detection range n , and obtain the weight of the nth sub-detection range: ; Obtain the water body state parameter V corresponding to a certain sampling data and the weights of all sub-detection ranges, and obtain the degree of water body characteristics of a certain sampling data: , where k c is the water body characteristic coefficient.

[0048] Step S130: Obtain all the degrees of water body characteristics corresponding to a certain sampling set, and obtain the sampling data D corresponding to the maximum degree of water body characteristics max , and divide the degrees of water body characteristics of the remaining sampling data except the sampling data D max by the maximum degree of water body characteristics to obtain several water body characteristic ratios; obtain the water quality parameter A corresponding to the sampling data D max , and obtain the variances of the water quality parameters corresponding to the remaining sampling data and the water quality parameter A. Take the sampling data with a variance less than the variance threshold as the characteristic sampling data; then obtain all the characteristic sampling data based on each sampling set.

[0049] In this solution, the degree of water body characteristics is the degree of water mixing. The obstacle areas include two types. One is components such as columns and walls in the pool, and the other is the surrounding walls. The water body state parameter is the water flow velocity, and the water quality parameter is the pH value, etc. Due to the viscous effect of the wall surface, a boundary layer will be formed around the wall when the water flows, so the closer the sampling point is to the wall, the more uneven the water flow mixing with the surrounding area. Therefore, the closer to the wall and the more walls there are, the smaller the mixing degree at this place. And because there is also a large correlation between the flow velocity and the mixing degree, when the flow velocity is larger, the mixing of the surrounding substances is more uniform. So the larger the flow velocity V, the greater the mixing degree. When the flow velocity is larger, the various acid-base substances in the pool are quickly mixed instead of accumulating. Therefore, at the position with a larger flow velocity and fewer obstacle areas, the water quality parameter is closer to the actual equilibrium state of the water body and is more representative. And the data with a large difference from the pH value when the flow velocity is large, because its authenticity cannot be estimated, so this part of the data is not representative and cannot be used as characteristic sampling data.

[0050] Step S200: Determine the total number of wastewater monitoring devices, and divide the pool area into several characteristic areas according to the pool area corresponding to the wastewater discharge pool and the water body characteristic ratio corresponding to each characteristic sampling data.

[0051] Step S210: Mark the positions of the sampling points corresponding to each characteristic sampling data on the plane coordinate system; obtain the sampling point b closest to the sampling point a, and take the distance between the sampling point a and the sampling point b as L ab and take the corresponding water body characteristic ratios as C a and C b . If C a <C b , starting from the sampling point a, in the direction pointing to the sampling point b, with a magnitude of K×|C b -C a |×L ab , obtain the sampling vector of the sampling point a, where K is the vector coefficient and K>0; if C a >C b , starting from the sampling point a, in the opposite direction of the sampling point b, with a magnitude of K×|C b -C a |×L ab , obtain the sampling vector of the sampling point a; if C a =C b , then the sampling point a has no sampling vector; thus obtain the sampling vectors corresponding to all sampling points and add them up to obtain the positioning vector.

[0052] Step S220: Randomly obtain several coordinate points in the pool area, calculate the average value to obtain the central facility point, use the central facility point as the starting point of the positioning vector, and use the end point of the positioning vector as the feature point; take the total number of wastewater monitoring devices as M, obtain the total area area of the pool area, and get the evenly divided area S ^ = area / M; Randomly obtain a coordinate point from the edge of the pool area, and connect it with the feature point to obtain the feature line segment L1. Rotate the feature line segment L1 clockwise with the feature point as the center of the circle until the area enclosed by the rotated feature line segment L2, the feature line segment L1, and the edge line of the pool area is equal to the evenly divided area S ^ When it stops rotating, and take the enclosed area as the feature area. By analogy, obtain M feature areas with an area of S each ^ each.

[0053] Step S300: According to the water body feature ratios corresponding to each feature sampling data, set the number of loop iterations to perform a loop, obtain the target area in the pool area and the target points corresponding to each target area, and deploy each wastewater monitoring device at the target point positions

[0054] Step S310: Set the number of loop iterations T = 1. Randomly obtain several coordinate points in a certain feature area, calculate the average value to obtain the central area coordinates of a certain feature area. According to the distance between a certain sampling point q in a certain feature area and the central area coordinates, obtain the distance weight value W corresponding to a certain sampling point q q = e -D , where e is the natural exponent and D is the distance between a certain sampling point q and the central area coordinates; According to the distance weight value and the water body feature ratio corresponding to each sampling data in a certain feature area, obtain the characteristic water body feature ratio corresponding to a certain feature area as , K Y is the water body feature degree coefficient, Q is the total number of sampling data in a certain feature area, C q is the water body feature ratio corresponding to a certain sampling data; If there is no corresponding sampling data in a certain feature area, take the water body feature ratio corresponding to the sampling point closest to the central area coordinates as the characteristic water body feature ratio corresponding to a certain feature area; According to the characteristic water body feature ratios of all feature areas, obtain the average ratio C ^ , and divide each characteristic water body feature ratio by C ^ to obtain the area adjustment ratio corresponding to each feature area

[0055] Step S320: Multiply each area adjustment ratio by the evenly divided area S ^, the iterative area of each feature region is obtained. According to steps S200 to S300, the area of each feature region is adjusted to the iterative area, and the coordinates of each central region are reset. The characteristic water body characteristic ratio of each adjusted feature region is calculated, and the area adjustment ratio is obtained again. The value of the loop count T is incremented by 1; the maximum value of the loop count T is set to T max , when the loop count T reaches T max , stop the adjustment to obtain the final feature regions as the target regions, and use the coordinates of each central region in the last loop as the target points to obtain M target points.

[0056] The target points are determined only based on historical sampling data. Its main purpose is to determine the positions where the sampling points should be in future sampling and to determine the locations of each monitoring device; while the sampling point positions during specific sampling are selected from the target points. The advantage of doing this is that by using the method of the above steps, the placement positions of the monitoring devices are fixed, and it is not necessary to re-place the monitoring devices every time sampling is carried out, which not only saves manpower but also is more convenient; and the placement positions of the monitoring devices obtained by the above method are more reasonable.

[0057] Step S400: Determine the target sampling quantity. By analyzing the monitoring data of the wastewater monitoring devices, obtain the water body characteristic degree of each target point, and mark the target points according to the distances between the target points, and perform sampling at the marked target points.

[0058] Step S410: Obtain the wastewater monitoring device at a certain target point, and record the water body state parameters at each moment within the previous S seconds starting from the current moment, and obtain the parameter average value. According to step S100, obtain the current water body characteristic degree of a certain target point; determine the target sampling quantity as G, where 1 ≤ G ≤ M and M is the total number of target points. Obtain the coordinates of each target point. According to step S200, obtain the target vectors of target point a and each of the other target points, a total of M - 1 target vectors, and then add up the target vectors to obtain the total target vector VT corresponding to target point a a , and use the characteristic point as the starting point of the total target vector VT a to obtain the end point of the total target vector VT a as the characteristic end point corresponding to target point a, and thus obtain the characteristic end point corresponding to each target point;

[0059] Step S420: Set the number of loops B = 1. Respectively obtain the minimum radii required for each feature end point to be tangent to the pool area, and mark the target point corresponding to the minimum value among the minimum radii. If there are multiple minimum values that are the same among the minimum radii, mark the target point with the smallest water body feature degree; then remove the marked target points, reselect each feature end point, and increment the value of the loop count B by 1 until the value of the loop count B is G and then end. Thus, G marked target points are obtained, and sampling is performed at the marked target points.

[0060] When the sampling quantity is small, what is primarily needed are the feature points with relatively low mixing degrees. This is because the feature points with lower mixing degrees can more comprehensively reflect the actual situation of the wastewater. Feature points with lower mixing degrees mean that the external influence on this point is smaller, and its water quality characteristics are more representative. Therefore, when the sampling quantity is limited, selecting the feature points with lower mixing degrees for sampling can more accurately reflect the water quality of the wastewater, which helps to formulate more effective treatment and management measures.

[0061] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed by the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A data management method for wastewater monitoring, characterized in that, Including the following steps: Step S100: Take a panoramic image of the wastewater discharge pool, establish a plane coordinate system for the wastewater discharge pool, obtain historical sampling data, and based on the positions of the sampling points corresponding to each sampling data in the plane coordinate system and the water body state parameters at the time of sampling, obtain the water body characteristic degree corresponding to each sampling data, convert it into a water body characteristic ratio, and then obtain all characteristic sampling data based on the sampling results corresponding to each sampling data; Step S200: Determine the total number of wastewater monitoring devices, and based on the pool area corresponding to the wastewater discharge pool and the water body characteristic ratio corresponding to each characteristic sampling data, divide the pool area into several characteristic areas; Step S300: Set the number of loops according to the water body characteristic ratio corresponding to each characteristic sampling data to perform a loop, obtain the target areas in the pool area and the target points corresponding to each target area, and deploy each wastewater monitoring device at the target point positions; Step S400: Determine the target sampling quantity, analyze the monitoring data of the wastewater monitoring devices to obtain the water body characteristic degree of each target point, and mark the target points according to the distances between the target points, and perform sampling at the marked target points; Step S100 includes: Step S110: Randomly select several sampling points from the plane coordinate system, and perform sampling at each sampling point as the sampling data of a certain batch. Then randomly select several sampling points and perform sampling, and so on, to obtain several batches. Group all the sampling data of the same batch into a sampling set to obtain several sampling sets; the sampling data includes the sampling point, sampling time, water body state parameters, and sampling result at the time of sampling, and the sampling result includes water quality parameters; in the plane coordinate system, obtain the boundary area of the wastewater discharge pool and the area of the components with a height greater than the height threshold, and both are used as obstacle areas; Step S120: Obtain a sampling point P0 corresponding to a certain sampling data in a certain sampling set. Take the range with P0 as the center and a radius of r as the sampling detection range. Divide the radius r evenly into N parts, and then divide the sampling detection range into N sub-detection ranges, and sort them in ascending order of area. Obtain the area S of the obstacle area within the nth sub-detection range n , and obtain the weight of the nth sub-detection range: ; Obtain the water body state parameter V corresponding to a certain sampling data and the weights of all sub-detection ranges, and obtain the water body characteristic degree of the certain sampling data: , where k c is the water body characteristic coefficient; Step S130: Obtain all the water body feature degrees corresponding to the certain sampling set, and obtain the sampling data D corresponding to the maximum water body feature degree max , and divide the water body feature degrees of the remaining sampling data except the sampling data D max by the maximum water body feature degree to obtain a number of water body feature ratios; Obtain the water quality parameter A corresponding to the sampling data D max , obtain the water quality parameters corresponding to the remaining sampling data, and the variance from the water quality parameter A, and use the sampling data with a variance less than the variance threshold as the characteristic sampling data; Furthermore, according to each sampling set, obtain all the characteristic sampling data.

2. The data management method for wastewater monitoring according to claim 1, characterized in that, Step S200 includes: Step S210: Mark the positions of the sampling points corresponding to each characteristic sampling data on the plane coordinate system; obtain the sampling point b that is closest to the sampling point a, and take the distance between the sampling point a and the sampling point b as L ab , and take the corresponding water body characteristic ratios as C a and C b . If C a < C b , starting from the sampling point a, in the direction pointing to the sampling point b, with a magnitude of K × |C b - C a | × L ab , obtain the sampling vector of the sampling point a, where K is the vector coefficient and K > 0; if C a > C b , starting from the sampling point a, in the opposite direction of the sampling point b, with a magnitude of K × |C b - C a | × L ab , obtain the sampling vector of the sampling point a; if C a = C b , then there is no sampling vector for the sampling point a; further obtain the sampling vectors corresponding to all sampling points and add them up to obtain the positioning vector; Step S220: Randomly obtain several coordinate points in the pool area, calculate the average value to obtain the central facility point, use the central facility point as the starting point of the positioning vector, and use the end point of the positioning vector as the feature point; take the total number of wastewater monitoring devices as M, obtain the total area area of the pool area, and get the evenly divided area S ^ = area / M; randomly obtain a coordinate point from the edge of the pool area, and connect it with the feature point to obtain the feature line segment L1. Rotate the feature line segment L1 clockwise with the feature point as the center of the circle until the area enclosed by the rotated feature line segment L2, the feature line segment L1, and the edge line of the pool area is equal to the evenly divided area S ^ When it stops rotating, and take the enclosed area as the feature area, and so on, to obtain M feature areas with an area of S each ^ respectively.

3. A data management method for wastewater monitoring according to claim 2, characterized in that, Step S300 includes: Step S310: Set the number of loops \(T = 1\). Randomly obtain several coordinate points within a certain feature region, calculate the average value to obtain the central region coordinates of the certain feature region. According to the distance between a certain sampling point \(q\) in the certain feature region and the central region coordinates, obtain the distance weight value \(W\) corresponding to the certain sampling point \(q\) as \(W=e^{-D}\), where \(e\) is the natural exponent and \(D\) is the distance between the certain sampling point \(q\) and the central region coordinates. According to the distance weight value and the water body feature ratio corresponding to each sampling data within the certain feature region, obtain the characteristic water body feature ratio corresponding to the certain feature region as \(R=\frac{\sum_{i = 1}^{Q}W_{i}C_{i}}{K}\), where \(K\) is the water body feature degree coefficient, \(Q\) is the total number of sampling data in the certain feature region, and \(C_{i}\) is the water body feature ratio corresponding to a certain sampling data. If there is no corresponding sampling data within the certain feature region, use the water body feature ratio corresponding to the sampling point closest to the central region coordinates as the characteristic water body feature ratio corresponding to the certain feature region. According to the characteristic water body feature ratios of all feature regions, obtain the average ratio \(\overline{C}\), and divide each characteristic water body feature ratio by \(\overline{C}\) to obtain the area adjustment ratio corresponding to each feature region. q =e -D , where \(e\) is the natural exponent and \(D\) is the distance between a certain sampling point \(q\) and the central region coordinates; according to the distance weight value and the water body feature ratio corresponding to each sampling data within the certain feature region, obtain the characteristic water body feature ratio corresponding to the certain feature region as , K Y is the water body feature degree coefficient, \(Q\) is the total number of sampling data in the certain feature region, C q is the water body feature ratio corresponding to a certain sampling data; if there is no corresponding sampling data within the certain feature region, use the water body feature ratio corresponding to the sampling point closest to the central region coordinates as the characteristic water body feature ratio corresponding to the certain feature region. According to the characteristic water body feature ratios of all feature regions, obtain the average ratio C ^ , and divide each characteristic water body feature ratio by C ^ to obtain the area adjustment ratio corresponding to each feature region; Step S320: Multiply each area adjustment ratio by the evenly divided area S ^ , to obtain the iterative area of each feature region. Adjust the area of each feature region to the iterative area according to Steps S200 to S300, reset the coordinates of each central region, calculate the characteristic water body characteristic ratio of each adjusted feature region, obtain the area adjustment ratio again, and increment the value of the loop count T by 1; set the maximum value of the loop count T to T max , when the loop count T reaches T max , stop the adjustment, obtain the final feature regions as the target regions, and use the coordinates of each central region in the last loop as the target points to obtain M target points.

4. A data management method for wastewater monitoring according to claim 3, characterized in that, Step S400 includes: Step S410: Obtain the wastewater monitoring equipment at a certain target point, record the water body state parameters at each moment within the previous S seconds starting from the current moment, and obtain the parameter average value. According to step S100, obtain the current water body characteristic degree of a certain target point; determine that the target sampling quantity is G, where 1 ≤ G ≤ M and M is the total number of target points. Obtain the coordinates of each target point. According to step S200, obtain the target vectors between target point a and each of the other target points, a total of M - 1 target vectors. Then add up the various target vectors to obtain the total target vector VT corresponding to target point a a , and use the feature point as the starting point of the total target vector VT a to obtain the total target vector VT a of the end point, which is used as the characteristic end point corresponding to target point a, and then obtain the characteristic end point corresponding to each target point; Step S420: Set the number of loops B = 1, respectively obtain the minimum radii required for each characteristic end point to be tangent to the pool area, and mark the target points corresponding to the minimum value among the minimum radii. If there are multiple minimum values that are the same, mark the target point with the minimum water body characteristic degree; then remove the marked target points, re-select each characteristic end point, and increase the value of the loop number B by 1 until the value of the loop number B is G and then end. Thus, G marked target points are obtained, and sampling is performed at the marked target points.

5. A data management system for implementing a data management method for wastewater monitoring according to any one of claims 1-4, characterized in that, The system includes a module for obtaining characteristic sampling data, a module for dividing characteristic areas, a module for obtaining target areas, and a module for sampling target points; Module for obtaining characteristic sampling data: Used to take a panoramic image of the wastewater discharge pool, establish a plane coordinate system for the wastewater discharge pool, obtain historical sampling data, and based on the positions of the sampling points corresponding to each sampling data in the plane coordinate system and the water body state parameters at the time of sampling, obtain the water body characteristic degree corresponding to each sampling data, convert it into a water body characteristic ratio, and then obtain all characteristic sampling data based on the sampling results corresponding to each sampling data; Feature Region Division Module: It is used to determine the total number of wastewater monitoring devices, divide the pool area into several feature regions according to the pool area corresponding to the wastewater discharge pool and the water body feature ratio corresponding to each feature sampling data; Target Region Obtaining Module: It is used to set the number of loops according to the water body feature ratios corresponding to each feature sampling data, loop to obtain the target regions in the pool area and the target points corresponding to each target region, and deploy each wastewater monitoring device at the position of the target point; Target Point Sampling Module: It is used to determine the target sampling quantity, analyze the monitoring data of the wastewater monitoring device to obtain the water body feature degree of each target point, mark the target points according to the distances between the target points, and sample at the marked target points; The Feature Sampling Data Obtaining Module includes an Obstacle Region Obtaining Unit, a Water Body Feature Degree Calculation Unit, and a Feature Sampling Data Obtaining Unit; Obstacle Region Obtaining Unit: It is used to randomly select several sampling points from the plane coordinate system, sample at each sampling point to obtain several sampling sets; draw an obstacle region in the plane coordinate system; Water Body Feature Degree Calculation Unit: It is used to obtain the sampling point corresponding to a certain sampling data in a certain sampling set, obtain the sampling detection range, divide it into several sub-detection ranges, and obtain the weight value of each sub-detection range; and obtain the water body feature degree of a certain sampling data according to the water body state parameter corresponding to a certain sampling data; Feature Sampling Data Obtaining Unit: It is used to obtain all the water body feature degrees in a certain sampling set, and then obtain the water body feature ratios of each sampling data; and then obtain all the feature sampling data according to each sampling set.

6. A data management system according to claim 5, characterized in that, The Target Region Obtaining Module includes an Area Adjustment Ratio Obtaining Unit and a Target Region Obtaining Unit; Area Adjustment Ratio Obtaining Unit: It is used to randomly obtain several coordinate points in a certain feature region, obtain the central region coordinates of a certain feature region, and obtain the feature mixing ratio corresponding to a certain feature region; obtain the area adjustment ratio corresponding to each feature region according to the feature mixing ratios of all feature regions; Target Region Obtaining Unit: It is used to obtain the iterative areas of each feature region, loop to obtain the final feature regions as each target region, and use the central region coordinates of the last loop as the target points.

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