Precise dosing control method and device based on image recognition

Abstract

The invention relates to an accurate dosing control method and device based on image recognition, and relates to the field of printing and dyeing wastewater treatment.The method comprises the steps that a wastewater area image sequence is collected for wastewater dye migration recognition, and a wastewater dye migration parameter field is obtained; according to the multiple wastewater parameters, the dosing amount is configured, dosing positions are randomly configured for dosing fitness calculation, and the diffusion speed and migration speed of dosing are analyzed for fitness calculation; and adjusting and optimizing the dosing position according to the dosing fitness to obtain an optimal dosing position, and performing dosing control in combination with the dosing amount. According to the method, the technical problem that the wastewater treatment efficiency and the wastewater treatment quality are affected due to non-uniform chemical diffusion caused by the fact that the chemical adding position cannot be accurately adjusted according to the wastewater pollution state and the flowing characteristics in a traditional method can be solved; the scientificity and the accuracy of chemical adding position setting can be improved, accurate feeding and efficient diffusion of chemicals are achieved, and the purification efficiency and the treatment effect of printing and dyeing wastewater are remarkably improved.

Description

Technical Field

[0001] The invention relates to the field of printing and dyeing wastewater treatment, and in particular to a precise dosing control method and device based on image recognition. Background Art

[0002] Printing and dyeing industrial wastewater has always been one of the difficulties in the field of environmental protection due to its complex composition and high concentration of pollutants, especially dyes, chemical additives, suspended solids, etc. Traditional wastewater treatment methods include physical, chemical and biological treatment methods, among which the dosing method is widely used in wastewater purification. The basic principle of the dosing method is to add a certain amount of reagents (such as flocculants, coagulants or oxidants) to the wastewater to promote the sedimentation or reaction of pollutants, thereby achieving the purpose of purifying water quality.

[0003] The pollution state of printing and dyeing wastewater usually varies with the different production processes. The pollutant concentration may fluctuate in certain periods of time, and the flow rate and direction will also change with the flow of wastewater. Traditional dosing methods usually use fixed-position dosing, that is, the agent is added to the wastewater from one or several preset dosing points. This method does not take into account the flow characteristics of the wastewater and the spatial distribution of pollutants, resulting in the inability to adjust the agent's placement position according to the wastewater pollution state and flow characteristics, making it impossible for the agent to effectively cover the pollution source, thus affecting the treatment effect. Summary of the invention

[0004] The present invention aims to solve the technical problem that in traditional printing and dyeing wastewater treatment methods, the dosing position cannot be accurately adjusted according to the wastewater pollution state and flow characteristics, resulting in uneven diffusion of the agent, affecting the wastewater treatment efficiency and treatment quality. The present invention provides a precise dosing control method and device based on image recognition to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a precise dosing control method based on image recognition, comprising: dividing printing and dyeing wastewater into regions to obtain multiple wastewater regions, collecting wastewater parameters to obtain multiple wastewater parameters; collecting wastewater region image sequences of the multiple wastewater regions, identifying wastewater dye migration, and obtaining a wastewater dye migration parameter field; configuring dosing amounts according to the multiple wastewater parameters, and randomly configuring dosing positions, and calculating dosing fitness of the dosing positions according to the multiple wastewater parameters and the wastewater dye migration parameter field, wherein the diffusion speed of the dosing is analyzed according to the multiple wastewater parameters, and the migration speed of the dosing is analyzed according to the wastewater dye migration parameter field, and the dosing fitness is calculated; according to the dosing fitness, the configuration of the dosing position is adjusted and optimized to obtain the optimal dosing position, and dosing control is performed in combination with the dosing amount.

[0006] Optionally, the precise dosing control method based on image recognition also includes: dividing the printing and dyeing wastewater into regions to obtain multiple wastewater regions of the same size; collecting wastewater parameters of the printing and dyeing wastewater in the multiple wastewater regions to obtain multiple wastewater parameters, wherein each wastewater parameter includes multiple characteristic parameters in the printing and dyeing wastewater.

[0007] Optionally, the precise dosing control method based on image recognition also includes: collecting wastewater area image sequences of the multiple wastewater areas; using printing and dyeing wastewater dye migration monitoring data in historical time to train a wastewater dye migration identifier; extracting wastewater area images according to the first step length and the second step length in the multiple wastewater area image sequences, and pooling the wastewater area images extracted according to the first step length to obtain multiple first wastewater area image sets and multiple second wastewater area image sets, wherein the second step length is greater than the first step length; combining the multiple first wastewater area image sets and the multiple second wastewater area image sets, respectively, and inputting them into the wastewater dye migration identifier to obtain multiple wastewater dye migration parameters, and constructing a wastewater dye migration parameter field, wherein each wastewater dye migration parameter includes a migration direction and a migration speed.

[0008] Optionally, the precise dosing control method based on image recognition also includes: collecting a set of sample wastewater area image sequences based on printing and dyeing wastewater dye migration monitoring data in historical time, extracting sample wastewater area images according to the first step length and the second step length, and performing pooling processing on the sample wastewater area images extracted according to the first step length to obtain multiple sample first wastewater area image sets and multiple sample second wastewater area image sets; marking the migration direction and migration speed of the wastewater dye in each sample wastewater area image sequence to obtain a sample wastewater dye migration parameter set; using a slowfast network to construct a wastewater dye migration identifier; using the multiple sample first wastewater area image sets and the multiple sample second wastewater area image sets as input features, and using the sample wastewater dye migration parameter set as output features, supervised training, verification and testing of the wastewater dye migration identifier, and completing the training after meeting the requirements.

[0009] Optionally, the precise dosing control method based on image recognition also includes: calculating the total pollutant concentration according to the multiple wastewater parameters, and calculating and configuring the dosing amount; obtaining the dosing position space of the printing and dyeing wastewater, and randomly configuring the dosing position in the dosing position space; predicting the dosing diffusion speed according to the multiple wastewater parameters to obtain multiple dosing diffusion speeds; obtaining multiple regional distances between the multiple wastewater areas and the dosing position; according to the multiple dosing diffusion speeds, connecting the dosing position with the line of the center point of each wastewater area as multiple diffusion directions, and constructing multiple diffusion speed vectors; constructing multiple migration speed vectors according to multiple migration directions and multiple migration speeds in the wastewater dye migration parameter field, and calculating multiple regional speed vectors of the drug in the multiple wastewater areas; according to the multiple regional distances, combined with the multiple regional speed vectors, calculating multiple regional times for the dosing diffusion to migrate to the multiple wastewater areas; according to the multiple regional times, calculating the dosing fitness of the dosing position.

[0010] Optionally, the precise dosing control method based on image recognition further includes: extracting and obtaining multiple pollutant concentrations of the multiple wastewater areas according to the multiple wastewater parameters; and calculating and obtaining the dosing fitness of the dosing position according to the multiple pollutant concentrations and multiple area times, as shown in the following formula: ; Among them, YRFIT is the adaptability of dosing, M is the number of multiple wastewater areas, is the pollutant concentration in the ith wastewater area, is the pollutant concentration in the jth wastewater area, is the regional time of the i-th wastewater area.

[0011] Optionally, the precise drug dosing control method based on image recognition also includes: adjusting the drug dosing position, calculating the drug dosing fitness of the adjusted drug dosing position, and performing iterative configuration adjustment and optimization of the drug dosing position until convergence; outputting the optimal drug dosing position with the largest drug dosing fitness, and performing drug dosing control in combination with the drug dosing amount.

[0012] In a second aspect, the present invention provides an accurate dosing control device based on image recognition, comprising: a wastewater area division module, used to divide the printing and dyeing wastewater into areas, obtain multiple wastewater areas, collect wastewater parameters, and obtain multiple wastewater parameters; a wastewater dye migration identification module, used to collect wastewater area image sequences of the multiple wastewater areas, perform wastewater dye migration identification, and obtain a wastewater dye migration parameter field; a dosing fitness calculation module, used to configure the dosing amount according to the multiple wastewater parameters, and randomly configure the dosing position, and perform dosing fitness calculation of the dosing position according to the multiple wastewater parameters and the wastewater dye migration parameter field, wherein the diffusion speed of the dosing is analyzed according to the multiple wastewater parameters, and the migration speed of the dosing is analyzed according to the wastewater dye migration parameter field, and the dosing fitness calculation is performed; a dosing position optimization module, used to perform configuration adjustment and optimization of the dosing position according to the dosing fitness, obtain the optimal dosing position, and perform dosing control in combination with the dosing amount.

[0013] The beneficial effects of the present invention are as follows: by dividing the printing and dyeing wastewater into regions, a plurality of wastewater regions are obtained, wastewater parameters are collected, and a plurality of wastewater parameters are obtained; then, a wastewater region image sequence of the plurality of wastewater regions is collected, wastewater dye migration identification is performed, and a wastewater dye migration parameter field is obtained; then, according to the plurality of wastewater parameters, a dosing amount is configured, and a dosing position is randomly configured, and according to the plurality of wastewater parameters and the wastewater dye migration parameter field, a dosing fitness calculation of the dosing position is performed, wherein, according to the plurality of wastewater parameters, the diffusion speed of the dosing is analyzed, according to the wastewater dye migration parameter field, the migration speed of the dosing is analyzed, and the dosing fitness calculation is performed; finally, according to the dosing fitness, the configuration adjustment and optimization of the dosing position is performed, the optimal dosing position is obtained, and dosing control is performed in combination with the dosing amount; the scientificity and accuracy of the dosing position setting can be improved, the accurate delivery and efficient diffusion of the agent can be realized, and it can be ensured that the area with high pollution concentration is fully treated, thereby significantly improving the purification efficiency and treatment effect of the printing and dyeing wastewater, and effectively reducing the waste of agent resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of the process flow of the precise drug dosing control method based on image recognition provided by the present invention; Figure 2 This is a schematic diagram of the structure of the precise drug dosing control device based on image recognition provided by the present invention.

[0015] In the accompanying drawings, the components represented by the reference numerals are described as follows: Wastewater area division module 01, wastewater dye migration identification module 02, dosing fitness calculation module 03, dosing position optimization module 04. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0019] Embodiment 1, as Figure 1 As shown, the embodiment of the present invention provides a precise dosing control method based on image recognition, which specifically includes the following steps: S100: Divide the printing and dyeing wastewater into regions to obtain multiple wastewater regions, collect wastewater parameters, and obtain multiple wastewater parameters.

[0020] Furthermore, step S100 of the present invention further includes: S110: Divide the printing and dyeing wastewater into regions to obtain a plurality of wastewater regions of the same size; S120: Collect wastewater parameters of the printing and dyeing wastewater in the plurality of wastewater regions to obtain a plurality of wastewater parameters, wherein each wastewater parameter includes a plurality of characteristic parameters in the printing and dyeing wastewater.

[0021] Specifically, in order to effectively treat printing and dyeing wastewater, it is necessary to divide the wastewater into regions so as to more accurately control the dosing and purification treatment of each region. First, the printing and dyeing wastewater is divided into regions to obtain multiple wastewater regions of the same size. The size of the region needs to be determined based on the total volume, flow characteristics and pollutant concentration of the wastewater to ensure that each region can be treated evenly during dosing control.

[0022] Next, the wastewater parameters of the printing and dyeing wastewater in the multiple wastewater areas are collected, which can be obtained through various detection equipment (such as water quality analyzers, sensors, sampling equipment, etc.). Each wastewater parameter includes multiple characteristic parameters in the printing and dyeing wastewater, such as multiple pollutant concentrations, wastewater pH value, etc. The main pollutants include dyes, COD (chemical oxygen demand), BOD (biological oxygen demand), suspended solids, ammonia nitrogen, heavy metals, etc., and multiple wastewater parameters of multiple wastewater areas are obtained. By dividing the printing and dyeing wastewater into regions and collecting wastewater parameters, key data support can be provided for accurate dosing control.

[0023] S200: Collect wastewater area image sequences of the multiple wastewater areas, perform wastewater dye migration identification, and obtain a wastewater dye migration parameter field.

[0024] Furthermore, step S200 of the present invention further includes: S210: Collect wastewater area image sequences of the multiple wastewater areas.

[0025] Specifically, a high-resolution industrial camera or camera is used in combination with a suitable light source device (such as an LED light source) to capture images of multiple wastewater areas at predetermined shooting time nodes (such as shooting every 0.5 seconds). In order to clearly capture the water flow, pollutants and dye migration process, a high-speed camera or a multi-camera array is required for continuous image acquisition. A high-frame rate camera can capture the details of dynamic changes in the wastewater, especially in areas with strong fluidity, and obtain a wastewater area image sequence of the multiple wastewater areas.

[0026] S220: Use the printing and dyeing wastewater dye migration monitoring data in historical time to train the wastewater dye migration identifier.

[0027] Further, step S220 of the present invention further includes: S221: Based on the monitoring data of dye migration in printing and dyeing wastewater in historical time, a set of sample wastewater area image sequences is collected, sample wastewater area images are extracted according to the first step length and the second step length, and the sample wastewater area images extracted according to the first step length are pooled to obtain multiple sample first wastewater area image sets and multiple sample second wastewater area image sets; S222: The migration direction and migration speed of the wastewater dye in each sample wastewater area image sequence are marked to obtain a sample wastewater dye migration parameter set; S223: A wastewater dye migration identifier is constructed using a slowfast network; S224: The multiple sample first wastewater area image sets and the multiple sample second wastewater area image sets are used as input features, and the sample wastewater dye migration parameter set is used as an output feature to supervise the training, verification and testing of the wastewater dye migration identifier. After the requirements are met, the training is completed.

[0028] Specifically, based on the dye migration monitoring data of printing and dyeing wastewater in historical time (such as within the past month), a set of image sequences of sample wastewater areas is collected, and each image sequence corresponds to the dye migration characteristics of a certain wastewater area within a certain time range; then, the first step length and the second step length are configured, wherein the second step length is greater than the first step length. The first step length corresponds to a shorter time interval, which is usually used to capture rapidly changing water flow and dye migration information. Through a shorter time interval (such as 1 second, 2 seconds, etc.), a frame of image is extracted at regular intervals. The purpose of this process is to capture the rapidly changing pollutant distribution characteristics in the wastewater, especially the fast-flowing dye migration information; the second step length corresponds to a longer time interval, which is usually used to capture the global dynamic changes of the wastewater area, such as the overall trend of water flow or the long-term diffusion of dyes. The second step length can be 8 times or 16 times the first step length (for example, if the first step length is 1 second, the second step length can be 8 seconds or 16 seconds). A frame of image is extracted at longer time intervals, aiming to analyze the long-term flow trend of the water body and the overall diffusion pattern of pollutants. Then, sample wastewater area images are extracted from the sample wastewater area image sequence according to the first step length and the second step length, and the sample wastewater area images extracted according to the first step length are pooled. The pooling process is used to reduce the dimension of the image data and extract more representative features. The maximum pooling or average pooling method is usually used for pooling. The maximum value or average value in a certain area is selected to represent the features of the area, thereby reducing the length of the time series and the details of the image. For example, extracting the average value within each pooling window helps to reduce the impact of noise and capture smoother dynamic changes, thereby obtaining multiple sample first wastewater area image sets and multiple sample second wastewater area image sets.

[0029] Next, the migration direction and migration speed of the wastewater dye in each sample wastewater area image sequence are marked. The migration direction refers to the movement trend or direction of the wastewater dye in the water body during the flow. In each wastewater area image sequence, the migration direction of the dye reflects the direction of the water flow, the diffusion trend of the dye and the distribution pattern of the pollutants; the migration speed refers to the movement speed of the wastewater dye within a certain period of time, that is, the displacement rate of the dye in the water flow; obtain the sample wastewater dye migration parameter set; for example, use image segmentation technology (such as color-based segmentation, threshold segmentation, K-means clustering and other methods) to extract the dye area in the image. Since the dye has specific color or spectral characteristics, it can be used through the image segmentation method. The image processing method accurately extracts the area where the dye is located; then, for each pair of continuous images, the optical flow method or block matching method is used to estimate the displacement of the dye area in adjacent time frames. The optical flow method is an estimation method based on local image gradient changes. By analyzing the motion vector of each pixel in the image, the migration direction of the dye area in the image is obtained. The migration direction is usually expressed as an angle and can be determined by calculating the movement trend of the center of mass or boundary points of the dye area; then for each sample wastewater area image sequence, the direction of dye migration in each time step is recorded. The migration direction can be expressed as an angle (such as an offset angle based on the north), or the movement trend of the dye in space can be expressed in the form of a vector. On the other hand, image registration technology (such as cross-correlation method or optical flow method) is used to estimate the displacement of the dye area between adjacent time frames. The optical flow method can estimate the movement speed of each pixel based on the change of the image, thereby calculating the overall displacement of the dye area.

[0030] The SlowFast network is a deep learning model for video data, which is particularly suitable for processing time series data containing fast and slow dynamics. It aims to effectively capture multi-scale temporal features in videos, especially slow actions over long time spans and fast actions over short time spans. It includes a slow path and a fast path, where the slow path processes image data with a lower frame rate and is mainly used to capture dynamic information over long time spans in videos. It usually processes fewer frames per second (such as 8fps), so that the global trend of the video can be captured; the fast path processes image data with a higher frame rate and usually processes more frames per second (such as 64fps). The fast path is used to capture short-term rapid changes in videos, such as rapid movements and details. During processing, the SlowFast network divides the image sequence into two parts, a high-frame rate video stream of the fast path and a low-frame rate video stream of the slow path. The high-frame rate video stream can capture fast dynamics, while the low-frame rate video stream captures the global or slow dynamics of the video.

[0031] Next, based on the slowfast network, a wastewater dye migration identifier is constructed. In the wastewater dye migration identification task, the dynamic changes of wastewater and the flow of dyes have complex time series characteristics. Therefore, the SlowFast network can give full play to its advantages in time series data processing. The goal of the wastewater dye migration identifier is to identify the dye migration features in the image sequence and convert these features into output parameters (migration direction and migration speed); then, the multiple sample first wastewater area image sets and the multiple sample second wastewater area image sets are used as input features, and the sample wastewater dye migration parameter set is used as the output feature to supervise the training, verification and testing of the wastewater dye migration identifier. First, a loss function suitable for regression tasks (such as mean square error MSE) is selected to train the model. The goal is to minimize the error between the predicted migration direction and migration speed and the actual annotation, and use the annotated images The network is trained with data and migration parameters, and the network parameters are optimized through back propagation and gradient descent, so that the network can learn the characteristics of dye migration and accurately predict the migration direction and speed of the dye; then in the training process, the validation set is used to evaluate the performance of the network, and common regression evaluation indicators, such as mean square error (MSE) or mean absolute error (MAE), are used to measure the difference between the predicted value and the true value; after the model training is completed, the test set is used to evaluate the final performance of the model until the output accuracy meets the expected indicators or the loss function is less than the predetermined value, then the training is stopped to obtain a trained wastewater dye migration identifier.

[0032] By constructing a wastewater dye migration identifier based on the SlowFast network, the migration direction and speed of dyes in wastewater can be accurately identified. This model utilizes the temporal information in the image sequence and can effectively capture the long-term and short-term dynamic characteristics in wastewater through multi-scale processing of slow and fast paths.

[0033] S230: extracting wastewater area images in a plurality of wastewater area image sequences according to the first step length and the second step length respectively, and performing pooling processing on the wastewater area images extracted according to the first step length to obtain a plurality of first wastewater area image sets and a plurality of second wastewater area image sets, wherein the second step length is greater than the first step length; S240: combining the plurality of first wastewater area image sets and the plurality of second wastewater area image sets respectively, inputting the combined sets into the wastewater dye migration identifier, obtaining a plurality of wastewater dye migration parameters, and constructing a wastewater dye migration parameter field, wherein each wastewater dye migration parameter includes a migration direction and a migration speed.

[0034] Specifically, according to the first step length and the second step length (if the first step length is 1 second, the second step length can be 8 seconds or 16 seconds), wastewater area images are extracted from multiple wastewater area image sequences respectively, and the wastewater area images extracted according to the first step length are pooled. The pooling operation helps to reduce redundant information in the image and extract more representative features, especially for the rapid dynamic changes in the image (such as the rapid flow of dyes), effective feature compression and extraction are performed to obtain multiple first wastewater area image sets and multiple second wastewater area image sets. Then, the multiple first wastewater area image sets and the multiple second wastewater area image sets are combined and input into the wastewater dye migration identifier as input data, that is, respectively input into the slow path and the fast path of the network. The slow path is used to process image sequences with a long time span and capture long-term change trends; the fast path is used to process image sequences with a short time interval and capture details and rapid changes. At the same time, the short-term dynamic changes (such as the rapid diffusion of local dyes) and long-term dynamic changes (such as the overall flow trend of dyes) in the image sequence are analyzed to obtain multiple wastewater dye migration parameters (including migration direction and migration speed). Finally, according to the dye migration parameters (direction and speed) of each wastewater area, for example, the dye migration parameters in a certain wastewater area include a migration speed of 0.2m / s and a direction of 30°, that is, starting from the east direction (i.e. horizontally to the right, along the positive direction of the X-axis), and rotating 30° clockwise. In this way, based on the wastewater dye migration parameters of multiple wastewater areas, the dye migration parameter field of the entire wastewater area is constructed. The migration parameter field is a multidimensional data structure that contains the migration characteristics of each area and can intuitively present the diffusion pattern of the dye in the entire wastewater treatment system.

[0035] S300: According to the multiple wastewater parameters, configure the dosage and randomly configure the dosing position, and calculate the dosing fitness of the dosing position according to the multiple wastewater parameters and the wastewater dye migration parameter field, wherein the diffusion speed of the dosing is analyzed according to the multiple wastewater parameters, and the migration speed of the dosing is analyzed according to the wastewater dye migration parameter field, and the dosing fitness calculation is performed.

[0036] Furthermore, step S300 of the present invention further includes: S310: According to the multiple wastewater parameters, the total pollutant concentration is calculated and the dosage is calculated and configured; S320: The dosing position space of the printing and dyeing wastewater is obtained, and the dosing position is randomly configured in the dosing position space; S330: According to the multiple wastewater parameters, the dosing diffusion speed is predicted to obtain multiple dosing diffusion speeds; S340: The multiple regional distances between the multiple wastewater areas and the dosing position are obtained; S350: According to the multiple dosing diffusion speeds, the lines connecting the dosing position and the center point of each wastewater area are used as multiple diffusion directions to construct multiple diffusion speed vectors; S360: According to the multiple migration directions and multiple migration speeds in the wastewater dye migration parameter field, multiple migration speed vectors are constructed, and the multiple regional speed vectors of the drug in the multiple wastewater areas are calculated; S370: According to the multiple regional distances, combined with the multiple regional speed vectors, the multiple regional times of the dosing diffusion migration to the multiple wastewater areas are calculated.

[0037] Specifically, first, the total pollutant concentration is calculated based on the multiple wastewater parameters, that is, the pollutant concentration of each wastewater area is weighted and summarized to calculate the total pollutant concentration of the entire wastewater area; then, the required purification agent amount is calculated based on the total pollutant concentration and the total wastewater volume. Then, the dosing position space of the printing and dyeing wastewater is obtained, that is, the predetermined dosing area range in the wastewater pool, and any position in the dosing position space is randomly selected as the dosing position. Then, the dosing diffusion speed is predicted based on the multiple wastewater parameters, where the concentration of the pollutant directly affects the diffusion speed of the agent. The higher the pollutant concentration, the slower the diffusion speed of the agent. The dosing diffusion speed can be predicted through a machine learning model. On the other hand, multiple area distances between the multiple wastewater areas and the dosing position are calculated; then, according to the multiple dosing diffusion speeds, the lines between the dosing position and the center point of each wastewater area are connected as multiple diffusion directions (the direction on the line away from the dosing position is the diffusion direction), and multiple diffusion speed vectors are constructed. The diffusion speed vector represents the speed and direction of the agent diffusing outward from the dosing position. The size of the vector represents the diffusion speed of the agent, and the direction represents the direction of the agent diffusion.

[0038] Then, according to the multiple migration directions and multiple migration speeds in the wastewater dye migration parameter field, multiple migration speed vectors are constructed; and according to the multiple migration speed vectors and multiple diffusion speed vectors, multiple regional speed vectors of the drug in the multiple wastewater areas are calculated. For the given two vectors V1 and V2, the components of their composite vector V composite in the x and y directions are: V composite x=V1x+V2x, V composite y=V1y+V2y; then the size of the composite vector is the sum of the squares of V composite x and composite y under the square root; the direction of the composite vector is the inverse tangent value of the ratio of V composite y to V composite x.

[0039] Further, based on the multiple regional distances and the multiple regional velocity vectors, multiple regional times for the dosing to diffuse and migrate to the multiple wastewater areas are calculated, wherein both diffusion and migration will affect the speed of dosing diffusion, the diffusion speed is greater than the migration speed, the migration speed vector and the diffusion speed vector are synthesized to obtain the regional velocity vector, the time for the dosing to arrive at each area, and the time for the dosing to arrive at the boundary of each area and arrive at the next area are calculated to obtain multiple regional times; at the same time, the regional velocity vector of one area is combined with the regional velocity vector of the next adjacent area to continuously synthesize the velocity vector in the next adjacent area, for example, based on the synthesized velocity vector, the time for the dosing to arrive at the area can be calculated, and this time is calculated by the ratio of distance and speed, that is, the regional distance is divided by the synthesized speed to obtain multiple regional times.

[0040] For example, assuming that in area 1, the diffusion velocity is 1.2 m / s, the migration velocity is 0.2 m / s, and the direction is 30°, that is, starting from the east direction (i.e. horizontally to the right, along the positive direction of the X-axis), rotate 30° clockwise; for the convenience of calculation, each velocity needs to be decomposed into x and y components first, then the diffusion velocity vector is Vdiffusionx=1.2cos(0°)=1.2m / s, Vdiffusiony=1.2sin(0°)=0m / s, and the migration velocity vector is Vmigrationx=0.8cos( 30°)≈0.693m / sec, Vmigration y=0.8sin(30°)=0.4m / sec, then the composite velocity vector is, Vtotal x=1.2+0.693=1.893m / sec, Vtotal y=0+0.4=0.4m / sec; then the composite velocity is ≈1.94m / sec; assuming that the distance D between area 1 and the dosing position is 10m, the arrival time in area 1 is 10 / 1.94, which is approximately equal to 5.15 seconds, that is, the time it takes for the dosing to diffuse to area 1 is about 5.15 seconds.

[0041] In this way, the velocity vector in the next area can be synthesized based on the regional velocity vector of the dosing in area 1 combined with the regional velocity vector in the next area, and the time for the dosing to reach the next area can be calculated by combining the distance between the first area and area 1. In this way, the time for the dosing to reach multiple areas in multiple wastewater areas can be calculated. The shorter the regional time, the faster the dosing reaches multiple wastewater areas, and the higher the efficiency of printing and dyeing wastewater treatment.

[0042] S380: Calculate and obtain the medication adaptability of the medication location according to the multiple area times.

[0043] Further, step S380 of the present invention further includes: S381: extract and obtain multiple pollutant concentrations of the multiple wastewater areas according to the multiple wastewater parameters; S382: calculate and obtain the dosing adaptability of the dosing position according to the multiple pollutant concentrations and multiple area times, as shown in the following formula: ; Among them, YRFIT is the adaptability of dosing, M is the number of multiple wastewater areas, is the pollutant concentration in the ith wastewater area, is the pollutant concentration in the jth wastewater area, is the regional time of the i-th wastewater area.

[0044] Specifically, multiple pollutant concentrations of the multiple wastewater areas are obtained according to the multiple wastewater parameters; then, a dosing fitness calculation formula is constructed, wherein, in the dosing fitness calculation formula, YRFIT is the dosing fitness, the higher the fitness, the better the overall dosing effect, the faster the dosing reaches each wastewater area, and the higher the printing and dyeing wastewater treatment efficiency; M is the number of multiple wastewater areas, is the pollutant concentration of the i-th wastewater area. The proportion of the dosing arrival speed in the wastewater area with a larger pollutant concentration has a greater impact on the dosing fitness. is the pollutant concentration in the jth wastewater area, is the regional time of the ith wastewater area. Then, the dosing fitness calculation formula is used to calculate the dosing fitness of the dosing position according to the multiple pollutant concentrations and the multiple regional times.

[0045] S400: According to the dosing adaptability, the configuration of the dosing position is adjusted and optimized to obtain the optimal dosing position, and the dosing control is performed in combination with the dosing amount.

[0046] Furthermore, step S400 of the present invention further includes: S410: adjusting the dosing position, calculating the dosing fitness of the adjusted dosing position, and performing iterative configuration adjustment optimization of the dosing position until convergence; S420: outputting the optimal dosing position with the maximum dosing fitness, and performing dosing control in combination with the dosing amount.

[0047] Specifically, a second dosing position is randomly selected in the dosing position space, wherein the second dosing position is different from the first dosing position, and a second dosing fitness of the second dosing position is calculated; the iterative configuration adjustment and optimization of the dosing position is continued until a predetermined number of selections is reached, a plurality of dosing positions and a plurality of dosing fitnesses are output, and the dosing position with the largest dosing fitness is selected as the optimal dosing position; finally, at the optimal dosing position, dosing control is performed according to the dosing amount.

[0048] The precise drug dosing control method based on image recognition provided by the embodiment of the present invention has at least the following technical effects: By dividing the printing and dyeing wastewater into regions, a plurality of wastewater regions are obtained, and wastewater parameters are collected to obtain a plurality of wastewater parameters; then, a wastewater region image sequence of the plurality of wastewater regions is collected, wastewater dye migration identification is performed, and a wastewater dye migration parameter field is obtained; then, the dosage is configured according to the plurality of wastewater parameters, and the dosage position is randomly configured, and the dosage fitness of the dosage position is calculated according to the plurality of wastewater parameters and the wastewater dye migration parameter field, wherein the diffusion speed of the dosage is analyzed according to the plurality of wastewater parameters, and the migration speed of the dosage is analyzed according to the wastewater dye migration parameter field, and the dosage fitness is calculated; finally, according to the dosage fitness, the configuration adjustment and optimization of the dosage position is performed to obtain the optimal dosage position, and the dosage control is performed in combination with the dosage; the scientificity and accuracy of the dosage position setting can be improved, the accurate delivery and efficient diffusion of the agent can be realized, and it can be ensured that the area with high pollution concentration is fully treated, thereby significantly improving the purification efficiency and treatment effect of the printing and dyeing wastewater, and effectively reducing the waste of agent resources.

[0049] Embodiment 2, as Figure 2 As shown, based on the same inventive concept as the precise dosing control method based on image recognition provided in the first embodiment, the embodiment of the present invention also provides a precise dosing control device based on image recognition, including: a wastewater area division module 01, used to divide the printing and dyeing wastewater into regions, obtain multiple wastewater regions, collect wastewater parameters, and obtain multiple wastewater parameters; a wastewater dye migration identification module 02, used to collect wastewater region image sequences of the multiple wastewater regions, perform wastewater dye migration identification, and obtain a wastewater dye migration parameter field; a dosing fitness calculation module 03, used to configure the dosing amount according to the multiple wastewater parameters, and randomly configure the dosing position, and perform dosing fitness calculation of the dosing position according to the multiple wastewater parameters and the wastewater dye migration parameter field, wherein the diffusion speed of the dosing is analyzed according to the multiple wastewater parameters, and the migration speed of the dosing is analyzed according to the wastewater dye migration parameter field, and the dosing fitness calculation is performed; a dosing position optimization module 04, used to perform configuration adjustment and optimization of the dosing position according to the dosing fitness, obtain the optimal dosing position, and perform dosing control in combination with the dosing amount.

[0050] Furthermore, the precise dosing control device based on image recognition is also used to: divide the printing and dyeing wastewater into regions to obtain multiple wastewater regions of the same size; collect wastewater parameters of the printing and dyeing wastewater in the multiple wastewater regions to obtain multiple wastewater parameters, wherein each wastewater parameter includes multiple characteristic parameters in the printing and dyeing wastewater.

[0051] Furthermore, the image recognition-based precise dosing control device is also used to: collect wastewater area image sequences of the multiple wastewater areas; use the printing and dyeing wastewater dye migration monitoring data in historical time to train a wastewater dye migration identifier; extract wastewater area images according to the first step length and the second step length in the multiple wastewater area image sequences, and perform pooling processing on the wastewater area images extracted according to the first step length to obtain multiple first wastewater area image sets and multiple second wastewater area image sets, wherein the second step length is greater than the first step length; combine the multiple first wastewater area image sets and the multiple second wastewater area image sets, respectively, and input them into the wastewater dye migration identifier to obtain multiple wastewater dye migration parameters, and construct a wastewater dye migration parameter field, wherein each wastewater dye migration parameter includes a migration direction and a migration speed.

[0052] Furthermore, the image recognition-based precise dosing control device is also used to: collect a set of sample wastewater area image sequences based on printing and dyeing wastewater dye migration monitoring data in historical time, extract sample wastewater area images according to the first step length and the second step length, and perform pooling processing on the sample wastewater area images extracted according to the first step length to obtain multiple sample first wastewater area image sets and multiple sample second wastewater area image sets; mark the migration direction and migration speed of the wastewater dye in each sample wastewater area image sequence to obtain a sample wastewater dye migration parameter set; use a slowfast network to construct a wastewater dye migration identifier; use the multiple sample first wastewater area image sets and the multiple sample second wastewater area image sets as input features, use the sample wastewater dye migration parameter set as output features, and supervise the training, verification and testing of the wastewater dye migration identifier. After meeting the requirements, the training is completed.

[0053] Furthermore, the precise dosing control device based on image recognition is also used to: calculate and obtain the total pollutant concentration according to the multiple wastewater parameters, and calculate and configure the dosing amount; obtain the dosing position space of the printing and dyeing wastewater, and randomly configure the dosing position in the dosing position space; predict the dosing diffusion speed according to the multiple wastewater parameters to obtain multiple dosing diffusion speeds; obtain multiple regional distances between the multiple wastewater areas and the dosing position; according to the multiple dosing diffusion speeds, connect the lines between the dosing position and the center point of each wastewater area as multiple diffusion directions to construct multiple diffusion speed vectors; according to the multiple migration directions and multiple migration speeds in the wastewater dye migration parameter field, construct multiple migration speed vectors, and calculate and obtain multiple regional speed vectors of the drug in the multiple wastewater areas; according to the multiple regional distances, combined with the multiple regional speed vectors, calculate and obtain multiple regional times for the dosing diffusion to migrate to the multiple wastewater areas; according to the multiple regional times, calculate and obtain the dosing fitness of the dosing position.

[0054] Furthermore, the precise dosing control device based on image recognition is also used to: extract and obtain multiple pollutant concentrations of the multiple wastewater areas according to the multiple wastewater parameters; and calculate and obtain the dosing fitness of the dosing position according to the multiple pollutant concentrations and multiple area times, as shown in the following formula: ; Among them, YRFIT is the adaptability of dosing, M is the number of multiple wastewater areas, is the pollutant concentration in the ith wastewater area, is the pollutant concentration in the jth wastewater area, is the regional time of the i-th wastewater area.

[0055] Furthermore, the precise drug dosing control device based on image recognition is also used to: adjust the drug dosing position, calculate the drug dosing fitness of the adjusted drug dosing position, and perform iterative configuration adjustment and optimization of the drug dosing position until convergence; output the optimal drug dosing position with the maximum drug dosing fitness, and perform drug dosing control in combination with the drug dosing amount.

[0056] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0057] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. The precise dosing control method based on image recognition is characterized in that: Methods include: Divide the printing and dyeing wastewater into regions to obtain multiple wastewater regions, collect wastewater parameters, and obtain multiple wastewater parameters; Collecting wastewater area image sequences of the plurality of wastewater areas, performing wastewater dye migration identification, and obtaining a wastewater dye migration parameter field; According to the multiple wastewater parameters, the dosage is configured, and the dosing position is randomly configured, and the dosing fitness of the dosing position is calculated according to the multiple wastewater parameters and the wastewater dye migration parameter field, wherein the diffusion speed of the dosing is analyzed according to the multiple wastewater parameters, and the migration speed of the dosing is analyzed according to the wastewater dye migration parameter field, and the dosing fitness is calculated; According to the dosing adaptability, the configuration adjustment and optimization of the dosing position is performed to obtain the optimal dosing position, and the dosing control is performed in combination with the dosing amount.

2. The precise dosing control method based on image recognition according to claim 1 is characterized in that: The printing and dyeing wastewater is divided into regions to obtain multiple wastewater regions, and wastewater parameters are collected to obtain multiple wastewater parameters, including: Divide the printing and dyeing wastewater into regions to obtain multiple wastewater regions of the same size; Wastewater parameters are collected for the printing and dyeing wastewater in the multiple wastewater areas to obtain multiple wastewater parameters, wherein each wastewater parameter includes multiple characteristic parameters in the printing and dyeing wastewater.

3. The precise dosing control method based on image recognition according to claim 1 is characterized in that: Collecting wastewater area image sequences of the plurality of wastewater areas, performing wastewater dye migration identification, and obtaining a wastewater dye migration parameter field, including: collecting wastewater area image sequences of the plurality of wastewater areas; Use the historical data of dye migration monitoring in printing and dyeing wastewater to train the wastewater dye migration identifier; Extracting wastewater area images according to the first step length and the second step length in the plurality of wastewater area image sequences, respectively, and performing pooling processing on the wastewater area images extracted according to the first step length to obtain a plurality of first wastewater area image sets and a plurality of second wastewater area image sets, wherein the second step length is greater than the first step length; The plurality of first wastewater area image sets and the plurality of second wastewater area image sets are respectively combined and input into the wastewater dye migration identifier to obtain a plurality of wastewater dye migration parameters and construct a wastewater dye migration parameter field, wherein each wastewater dye migration parameter includes a migration direction and a migration speed.

4. The precise dosing control method based on image recognition according to claim 3 is characterized in that: The dye migration monitoring data of printing and dyeing wastewater in historical time is used to train the wastewater dye migration identifier, including: According to the dye migration monitoring data of printing and dyeing wastewater in the historical time, a set of sample wastewater area image sequences is collected, sample wastewater area images are extracted according to the first step length and the second step length, and the sample wastewater area images extracted according to the first step length are pooled to obtain a plurality of sample first wastewater area image sets and a plurality of sample second wastewater area image sets; The migration direction and migration speed of the wastewater dye in each sample wastewater area image sequence are marked to obtain a set of sample wastewater dye migration parameters; Using slowfast network, a wastewater dye migration identifier was constructed; The plurality of sample first wastewater area image sets and the plurality of sample second wastewater area image sets are used as input features, and the sample wastewater dye migration parameter set is used as output features, and the wastewater dye migration identifier is supervisedly trained, verified and tested. After the requirements are met, the training is completed.

5. The precise dosing control method based on image recognition according to claim 1 is characterized in that: According to the multiple wastewater parameters, the dosage is configured, and the dosing position is randomly configured, and according to the multiple wastewater parameters and the wastewater dye migration parameter field, the dosing fitness calculation of the dosing position is performed, including: According to the plurality of wastewater parameters, the total pollutant concentration is calculated and the dosage is calculated and configured; Acquire the dosing position space of the printing and dyeing wastewater, and randomly configure the dosing positions in the dosing position space; According to the multiple wastewater parameters, a dosing diffusion rate prediction is performed to obtain multiple dosing diffusion rates; Obtaining multiple area distances between the multiple wastewater areas and the drug adding locations; According to the multiple dosing diffusion velocities, a line connecting the dosing position and the center point of each wastewater area is used as multiple diffusion directions to construct multiple diffusion velocity vectors; According to the multiple migration directions and multiple migration speeds in the wastewater dye migration parameter field, multiple migration speed vectors are constructed, and multiple regional speed vectors of the dye in the multiple wastewater regions are calculated; According to the multiple regional distances and in combination with the multiple regional velocity vectors, multiple regional times for the dosing to diffuse and migrate to the multiple wastewater regions are calculated; The medication adaptability of the medication adding position is calculated based on the multiple area times.

6. The precise dosing control method based on image recognition according to claim 5 is characterized in that: Calculating and obtaining the dosing adaptability of the dosing position according to the multiple area times includes: Extracting and obtaining a plurality of pollutant concentrations of the plurality of wastewater areas according to the plurality of wastewater parameters; According to the multiple pollutant concentrations and the multiple area times, the dosing adaptability of the dosing position is calculated as follows: ； Among them, YRFIT is the dosing fitness, M is the number of multiple wastewater areas, is the pollutant concentration in the ith wastewater area, is the pollutant concentration in the jth wastewater area, is the regional time of the i-th wastewater area.

7. The precise dosing control method based on image recognition according to claim 1 is characterized in that: According to the dosing adaptability, the configuration adjustment and optimization of the dosing position is performed to obtain the optimal dosing position, and the dosing control is performed in combination with the dosing amount, including: Adjust the dosing position, calculate the dosing fitness of the adjusted dosing position, and perform iterative configuration adjustment and optimization of the dosing position until convergence; The optimal dosing position with the maximum dosing adaptability is output, and the dosing control is performed in combination with the dosing amount.

8. The precise dosing control device based on image recognition is characterized in that: The steps for implementing the precise dosing control method based on image recognition according to any one of claims 1 to 7 include: The wastewater area division module is used to divide the printing and dyeing wastewater into regions, obtain multiple wastewater regions, collect wastewater parameters, and obtain multiple wastewater parameters; A wastewater dye migration identification module is used to collect wastewater area image sequences of the plurality of wastewater areas, perform wastewater dye migration identification, and obtain a wastewater dye migration parameter field; A dosing fitness calculation module is used to configure the dosing amount according to the multiple wastewater parameters, and randomly configure the dosing position, and perform dosing fitness calculation of the dosing position according to the multiple wastewater parameters and the wastewater dye migration parameter field, wherein the diffusion speed of the dosing is analyzed according to the multiple wastewater parameters, and the migration speed of the dosing is analyzed according to the wastewater dye migration parameter field, and the dosing fitness calculation is performed; The dosing position optimization module is used to adjust and optimize the configuration of the dosing position according to the dosing adaptability, obtain the optimal dosing position, and perform dosing control in combination with the dosing amount.

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