Medicament adding equipment for purification treatment of river and lake water bodies and medicament adding control method of medicament adding equipment

Abstract

The invention relates to the technical field of water treatment equipment, in particular to chemical dosing equipment for river and lake water purification treatment and a chemical dosing control method of the chemical dosing equipment. Based on a response mechanism of incoming water quality and purifying agent feeding amount, the invention develops agent feeding equipment for high-efficiency purification treatment of large-flux micro-polluted river and lake water quality, and the agent feeding equipment is mainly composed of a water quality monitoring module, a water purifying agent feeding module, a water purifying agent storage module, a water diversion module and an integrated control module; when the water quality of the target water body needs to be purified, the water diversion module is started for water diversion, and the integrated control module automatically starts the water purifying agent adding module after analyzing and judging the current water quality condition of the target water body through the water quality monitoring module by utilizing built-in control logic; the problems of low automation degree, relatively large occupied area and relatively high manual operation and maintenance cost of existing medicament adding equipment are solved, precise medicament adding is realized, and the water quality purification effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment equipment, and particularly to a chemical dosing device for purifying river and lake water bodies and its dosing control method. Background Art

[0002] Currently, in the field of river and lake water quality purification, most equipment still operates with water distribution systems, monitoring systems, and dosing systems as independent units, lacking a highly integrated solution. This decentralized operation mode not only increases the complexity of the work process but also results in a relatively low degree of automation and low efficiency in the water purification process. For example, in the traditional water purification process, when the water quality monitoring system detects abnormal water quality, the operator needs to manually start the water pump to pump the water to be treated into the treatment area, and then manually read the water quality data and input it into the dosing system for chemical dosing. This series of operations is cumbersome and error-prone. Especially when dealing with large water areas, manual intervention not only increases the labor intensity but also reduces the real-time response ability of the entire purification process. In addition, the supporting facilities between the existing dosing equipment and the chemical storage system have not been fully optimized. The chemical storage room is often separated from the dosing equipment and lacks intelligent management, which not only affects the operation efficiency of the equipment but also makes the operability of the river and lake water purification equipment less than satisfactory. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a chemical dosing device for purifying river and lake water bodies and its dosing control method.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect of the present invention, there is provided a chemical dosing device for purifying river and lake water bodies, characterized in that the chemical dosing device comprises:

[0006] A water quality monitoring module, which is used for real-time monitoring of the pollution indexes of the target purified water body sample and pumping the target purified water body sample into the water quality monitoring equipment;

[0007] A water purification agent storage module, which includes a chemical storage tank and a discharging pump. The chemical storage tank is used for storing water purification chemicals of different dosage forms and varieties, and the discharging pump is used for pumping the chemical raw materials into the chemical storage tank;

[0008] A water purification agent dosing module, which includes a dosing metering pump, and the dosing metering pump is used for dosing the chemicals stored in the water purification agent storage module in front of the water intake pump according to the calculated initial chemical dosage;

[0009] A water intake module, which is responsible for fully stirring and mixing the dosed chemicals with the target purified water body;

[0010] An integrated control module, which is used to control the sampling pump to sample and calculate the chemical dosage, and at the same time control the opening and closing of the water intake pump and the opening and closing of the chemical dosing pump.

[0011] The second aspect of the present invention provides a chemical dosing control method for a chemical dosing device for purifying river and lake water bodies, which is applied to the chemical dosing device for purifying river and lake water bodies, and is characterized in that it specifically includes the following steps:

[0012] S102: The integrated control module controls the sampling pump to start, pumps an appropriate amount of target water body to be purified into the water quality monitoring module for detection, maps and expresses the real-time multi-dimensional water quality index data according to the water use standard regulation system, obtains the real-time water quality index evaluation radar chart of the target purified water body sample, and feeds back the real-time water quality index evaluation radar chart to the integrated control module;

[0013] S104: Obtain the established purification efficacy of the chemical varieties stored in the water purification agent storage module, quantify and curve interpolate the chemical dosage required to meet the water quality purification requirements for the real-time water quality index evaluation radar chart based on the established purification efficacy, and obtain an initial chemical dosage curve to output the initial chemical dosage;

[0014] S106: During the process of calculating and outputting the initial chemical dosage, the integrated control module synchronously controls the operation of the water intake pump and the chemical dosing metering pump of the water intake module, and controls the chemical dosing metering pump to add the chemicals stored in the water purification agent storage module in front of the water intake pump according to the initial chemical dosage curve;

[0015] S108: When the chemical is added in front of the water intake pump, extract the spectral reflection gray-level co-occurrence matrix of the drug variety and the target purified water body sample, use the spectral reflection gray-level co-occurrence matrix to perform regression calculation and analyze the real-time water turbidity, so as to control the rotation of the water intake pump blade. Driven by the rotation of the water intake pump blade, the target purified water body sample and the chemical dosing agent are fully mixed, and after the chemical is applied, the target water body is discharged into the river and lake through flocculation and sedimentation;

[0016] S110: Analyze the change in the water quality state between the real-time water quality index evaluation radar chart from the previous time series point to the next time series point based on the preset water quality monitoring strategy of the water quality monitoring device, calculate the optimized amplitude of the water quality state according to the state change, and adjust the chemical dosage of the water purification agent dosing module based on the optimized amplitude of the water quality state.

[0017] More specifically, the step S102 specifically includes the following steps:

[0018] Pump an appropriate amount of target water body to be purified into the water quality monitoring module to perform real-time monitoring on the target purified water body sample, so as to obtain the real-time multi-dimensional water quality index data of the target purified water body sample;

[0019] A water use standard regulation system is obtained based on a big data network, critical thresholds of exceeding the standard regulations for different water quality dimensions are extracted through the water use standard regulation system, and a radar weighted model for estimating different water quality dimensions is constructed according to the critical threshold constraints of exceeding the standard regulations;

[0020] The edge weights of each real-time multidimensional water quality index data and adjacent data points are calculated by using a radar weighted model to generate a real-time multidimensional water quality index adjacency graph, and a Bayesian inference algorithm is introduced to infer the node connection strength of the real-time multidimensional water quality index adjacency graph to obtain an adjacency graph probability distribution;

[0021] Obtain water purification requirements, extract expected water quality assessment indicators based on water purification requirements, and establish a radar indicator mapping space for expected water quality assessment indicators based on the water use standard regulation system;

[0022] Based on the adjacency graph probability distribution, the real-time multidimensional water quality index data is mapped and embedded into the radar index mapping space, and the mapping point matrix pattern of the radar index mapping space is obtained at this time, and the current mapping probability distribution of the radar index mapping space is determined according to the mapping point matrix pattern, and the KL divergence between the adjacency graph probability distribution and the current mapping probability is calculated;

[0023] The minimum KL divergence is preset based on the real-time multidimensional water quality index adjacency graph. If the KL divergence is higher than the minimum KL divergence, the mapping point position of the radar index mapping space is continuously optimized until it is lower than the minimum KL divergence, and the real-time water quality index evaluation radar map of the target purified water sample is obtained.

[0024] More specifically, the step S104 includes the following steps:

[0025] Obtain the pharmaceutical varieties and pharmaceutical formula components of the pharmaceutical varieties stored in the water purification agent dosing module, and retrieve the purification efficacy knowledge graph of the pharmaceutical varieties for different preset water quality index contents in the big data network based on the pharmaceutical formula components;

[0026] According to the real-time multi-dimensional water quality index data, the real-time water quality index content of the target purified water body sample is obtained, the real-time water quality index content is identified through the purification efficacy knowledge graph, and the purification efficacy of the agent variety for the target purified water body sample is output, which is defined as the established purification efficacy;

[0027] Based on a preset medication constraint threshold for a given purification efficacy, a projection constraint set is constructed according to the medication constraint threshold, an expected water quality index evaluation radar chart of a target purified water sample is constructed according to the expected water quality evaluation index, and a series of graph points are constructed according to the expected water quality index evaluation radar chart, which are defined as target graph points;

[0028] Construct a series of points of the real-time water quality index evaluation radar chart, define it as the current point, calculate the gradient vector of the target point on the current point, update the gradient step of the current point along the gradient vector, and obtain a new point;

[0029] Projecting the new graph points onto the projection constraint set, repeating the above gradient vector iteration steps until all current graph points are updated, and finally generating the initial dosing quantization coefficients;

[0030] An interpolation control point array of the pharmaceutical formula components is constructed, and the tangent vector of the interpolation control point array is calculated based on the initial dosage quantization coefficient. The real-time water quality index evaluation radar chart is interpolated on the interpolation control point array according to the tangent vector to obtain an initial dosage curve.

[0031] More specifically, the step S108 includes the following steps:

[0032] Obtaining a dosing and purification log of a drug dosing device, and extracting historical water body spectral image data of spectral measurement after the drug dosing device performs spectral measurement on water body samples with different preset water quality index contents by executing an initial dosing amount curve;

[0033] The cosine similarity method is introduced to calculate the cosine similarity between the real-time water quality index content of the target purified water sample and the content of different preset water quality indexes, and only the historical water spectral image data corresponding to the preset water quality index content with the maximum cosine similarity is extracted and marked as the target water spectral image data;

[0034] A gray-level co-occurrence matrix algorithm is introduced to calculate the gray-level values of neighboring pixel pairs contained in the gray-scaled spectral image data of the target water body, to generate a spectral reflectance gray-level co-occurrence matrix, and a multivariate linear regression equation describing the spectral reflectance is constructed based on the spectral reflectance gray-level co-occurrence matrix;

[0035] Based on the big data network, the pharmaceutical dosage forms and water quality mixing characteristics knowledge graph of pharmaceutical varieties are obtained, and the pharmaceutical dosage forms and the real-time water quality index content are jointly identified through the water quality mixing characteristics knowledge graph, and the standard water characteristics of the mixed pharmaceutical varieties of the target purified water sample are output;

[0036] The turbidity residual of the reagent variety for the target purified water sample is calculated based on the standard water characteristics, and the fully mixed water turbidity is set according to the expected water quality assessment index, and the multivariate linear regression equation is solved by minimizing the turbidity residual to obtain the real-time water turbidity of the target purified water sample mixed with the reagent variety;

[0037] If the real-time water turbidity is lower than the fully mixed water turbidity, the blade speed of the water diversion pump is controlled to increase; if it is higher than the fully mixed water turbidity, the blade speed of the water diversion pump is controlled to decrease. The target purified water sample and the dosing agent are fully mixed under the rotation of the water diversion pump blades. After the agent is applied, the target water is discharged into rivers and lakes through flocculation and sedimentation.

[0038] More specifically, the step S110 includes the following steps:

[0039] Obtaining a preset water quality monitoring strategy for a water quality monitoring device, and extracting a dosing and purification monitoring timing of the water quality monitoring device for a target purified water sample through the preset water quality monitoring strategy;

[0040] Taking the real-time water quality index evaluation radar chart of the target purified water sample as the time series starting point, the real-time water quality index evaluation radar chart at the next time series chart point starting from the time series starting point is obtained in the dosing purification monitoring time series, and marking the real-time water quality index evaluation radar chart at the next time series chart point as the time series update radar chart;

[0041] Aligning the time-series update radar map with the real-time water quality index assessment radar map, calculating the non-overlapping radar blocks of the time-series update radar map and the real-time water quality index assessment radar map, obtaining the area value of the non-overlapping radar blocks, and determining the state value function of the time-series update radar map to the real-time water quality index assessment radar map based on the area value;

[0042] The Bellman algorithm is introduced. Based on the dosing purification monitoring time sequence, the Bellman algorithm is used to calculate the state of the time-series update radar chart to the real-time water quality index evaluation radar chart, so as to construct the Bellman state deduction equation for the real-time water quality state of the previous time-series chart point to the real-time water quality state of the next time-series chart point.

[0043] Based on the expected water quality index evaluation radar chart, the state value function threshold is preset, and the state value function is continuously updated and iterated according to the Bellman state deduction equation. If the current state value function is less than the state value function threshold, the update and iteration process is stopped to obtain the optimized amplitude of the water quality state;

[0044] The initial dosage curve is adjusted and optimized according to the optimized amplitude of the water quality state to adjust the dosage of the water purifier dosing module.

[0045] A third aspect of the present invention provides a dosing control system for a drug dosing device for purifying river and lake waters, the dosing control system comprising a memory and a processor, the memory storing a dosing control method program for a drug dosing device for purifying river and lake waters, and when the dosing control method program is executed by the processor, any one of the steps of the dosing control method is implemented.

[0046] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer detection program, which, when executed by at least one processor, implements the dosing control method of any one of the dosing devices for purifying and treating river and lake water bodies.

[0047] The present invention solves the technical defects existing in the background art, and the beneficial technical effects of the present invention are as follows:

[0048] Based on the response mechanism between the incoming water quality and the dosage of the purifying agent, the present invention develops a dosing device for efficiently purifying and treating the water quality of large-flux slightly polluted river and lake water bodies, which mainly consists of a water quality monitoring module, a purifying agent dosing module, a purifying agent storage module, a water diversion module, and an integrated control module; when purifying the water quality of the target water body, the water diversion system is turned on to divert water, and the integrated control system uses the built-in control logic. After analyzing and judging the current water quality situation by the water quality monitoring module, the purifying agent dosing system is automatically turned on, solving the problems of low automation degree, large floor area, and high labor operation and maintenance cost of the existing dosing devices, realizing accurate dosing, and improving the water quality purification effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain the drawings of other embodiments without creative efforts.

[0050] Figure 1 Shows the overall structural schematic diagram of the dosing device for purifying and treating river and lake water bodies;

[0051] Figure 2 Shows the A-A structural schematic diagram of the dosing device for purifying and treating river and lake water bodies.

[0052] The description of the reference numerals is as follows:

[0053] 1011, medicine storage tank; 1012, discharging pump; 1013, dosing metering pump. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] In order to be able to more clearly understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0055] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0056] The first aspect of the present invention provides a chemical dosing device for purifying river and lake water bodies, characterized in that the chemical dosing device comprises:

[0057] A water quality monitoring module, which is used for monitoring the pollution indexes of the target purified water body sample in real time and pumping the target purified water body sample into the water quality monitoring equipment;

[0058] A water purification agent storage module, which includes a medicine storage tank and a discharging pump. The medicine storage tank is used for storing water purification agents of different dosage forms and varieties, and the discharging pump is used for pumping the medicine raw materials into the medicine storage tank;

[0059] A water purification agent dosing module, which includes a dosing metering pump, and the dosing metering pump is used for dosing the medicine stored in the water purification agent storage module in front of the water diversion pump according to the calculated initial medicine dosage;

[0060] A water diversion module, which is responsible for fully stirring and mixing the dosed medicine and the target purified water body;

[0061] An integrated control module, which is used for controlling the sampling pump to sample and calculate the medicine dosage, and at the same time controlling the opening and closing of the water diversion pump and the dosing pump.

[0062] It should be noted that the chemical dosing device has a wide range of treatment scales and various forms of composition. The device is equipped with a two-end control system for the local end and the mobile device end, and can realize the one-key start operation of different control ends to cope with different adaptation scenarios.

[0063] It should be noted that when the target water body needs to be purified, the integrated control module controls the sampling pump to start. At this time, the sampling pump collects an appropriate amount of the target water body to be purified and pumps it into the water quality monitoring module for detection. The detection equipment detects the water quality index status of the target water body, and then feeds back the water quality index status to the integrated control module. At this time, the integrated control module calculates the initial medicine dosage according to the feedback result of the water quality index status. During the calculation process of the initial medicine dosage, the integrated control module synchronously controls the operation of the water diversion pump and the dosing metering pump of the water diversion module. The dosing metering pump doses the medicine in front of the water diversion pump according to the initial medicine dosage. Driven by the rotation of the water diversion pump blades, the target water body and the medicine are fully and evenly mixed in the water diversion pump. Finally, after the medicine is applied, the target water body is discharged into the river and lake through flocculation and sedimentation, achieving the purification treatment effect of the river and lake water body with accurate dosing.

[0064] The second aspect of the present invention provides a dosing control method for a chemical dosing device for purifying river and lake water bodies, which is applied to the chemical dosing device for purifying river and lake water bodies, and specifically includes the following steps:

[0065] S102: The integrated control module controls the sampling pump to start collecting an appropriate amount of target water body to be purified and pumps it into the water quality monitoring module for detection. Express the real-time multi-dimensional water quality index data according to the mapping of the water use standard regulation system, obtain the real-time water quality index evaluation radar chart of the target purified water body sample, and feedback the real-time water quality index evaluation radar chart to the integrated control module;

[0066] S104: Obtain the established purification efficacy of the chemical varieties stored in the water purifying agent storage module, quantify and perform curve interpolation on the dosing amount required to meet the water quality purification requirements for the real-time water quality index evaluation radar chart based on the established purification efficacy, obtain the initial dosing amount curve, and output the initial chemical dosing amount;

[0067] S106: During the process of calculating and outputting the initial chemical dosing amount, the integrated control module synchronously controls the operation of the water intake pump of the water intake module and the dosing metering pump, and controls the dosing metering pump to add the chemicals stored in the water purifying agent storage module in front of the water intake pump according to the initial dosing amount curve;

[0068] S108: When the chemical is added in front of the water intake pump, extract the spectral reflection gray-level co-occurrence matrix of the drug variety and the target purified water body sample, use the spectral reflection gray-level co-occurrence matrix to perform regression calculation and analyze the real-time water turbidity, control the rotation of the water intake pump blade, and drive the full mixing of the target purified water body sample and the dosing chemical under the rotation of the water intake pump blade. After the chemical is applied, the target water body undergoes flocculation and precipitation and is discharged into the river and lake;

[0069] S110: Analyze the change in the water quality state between the real-time water quality index evaluation radar chart from the previous time series point to the next time series point based on the preset water quality monitoring strategy of the water quality monitoring device, calculate the optimized amplitude of the water quality state according to the state change, and adjust the dosing amount of the water purifying agent dosing module based on the optimized amplitude of the water quality state.

[0070] More specifically, the step S102 specifically includes the following steps:

[0071] Collect an appropriate amount of target water body to be purified and pump it into the water quality monitoring module to perform real-time monitoring on the target purified water body sample to obtain the real-time multi-dimensional water quality index data of the target purified water body sample;

[0072] Obtain the water use standard regulation system based on the big data network, extract the critical threshold of exceeding the standard for different water quality dimensions through the water use standard regulation system, and construct a radar weighting model for estimating different water quality dimensions according to the constraint of the critical threshold of exceeding the standard;

[0073] Calculate the edge weights between the real-time multi-dimensional water quality index data and adjacent data points through the radar weighting model, generate a real-time multi-dimensional water quality index adjacency graph, and introduce the Bayesian inference algorithm to estimate the node connection strength of the real-time multi-dimensional water quality index adjacency graph to obtain the adjacency graph probability distribution;

[0074] Obtain the water quality purification requirement, extract the expected water quality evaluation index based on the water quality purification requirement, and establish a radar index mapping space for the expected water quality evaluation index based on the water use standard regulation system;

[0075] Map and embed the real-time multi-dimensional water quality index data into the radar index mapping space based on the adjacency graph probability distribution. At this time, obtain the mapping point matrix pattern of the radar index mapping space, determine the current mapping probability distribution of the radar index mapping space according to the mapping point matrix pattern, and calculate the KL divergence between the adjacency graph probability distribution and the current mapping probability;

[0076] Preset the minimum KL divergence based on the real-time multi-dimensional water quality index adjacency graph. If the KL divergence is higher than the minimum KL divergence, continuously optimize the mapping point position of the radar index mapping space until it is lower than the minimum KL divergence, and obtain the real-time water quality index evaluation radar chart of the target purified water body sample.

[0077] It should be noted that the real-time multi-dimensional water quality index data includes turbidity, total phosphorus, ammonia nitrogen, pH value, and permanganate index. The water quality monitoring equipment can conduct all-round index detection on the water quality of the water body to be purified. For example, it can monitor the degree of sediment mixing or nitrate content in the water body. It can be seen that these monitoring data are often multi-dimensional. It is difficult for the water quality monitoring equipment to efficiently and accurately evaluate these data according to the water use standards and convert them into a visual global evaluation report for output, which easily leads to large errors in the calculation of the chemical dosage for subsequent water body purification. In response to this, this method uses the critical threshold of exceeding the standard for different water quality dimensions stipulated by the water use standard regulation system to perform radar weighted calculation on the obtained real-time multi-dimensional water quality index data. The weight reflects the similarity or distance between multi-dimensional water quality data points. Through the data point relationship of the adjacency graph, it is possible to clearly evaluate the overall water quality of the water quality index data obtained from different dimensions according to the water use standard regulation system, providing a reliable evaluation basis for subsequent visual dimensionality reduction fitting. Among them, the probability distribution of the adjacency graph represents the connection strength and association possibility between these water quality index data points in different dimensions. Converting the weight distance into a probability enables even distant multi-dimensional data points to still have a certain probability of being considered through similarity, increasing the accuracy and robustness of the evaluation results. Then, a radar index mapping space for the expected water quality evaluation index is established according to the water purification requirements, which is used as a framework for data point fitting visualization. Then, the real-time multi-dimensional water quality index data is mapped and embedded into the radar index mapping space, so that the data in different dimensional spaces is compressed into a visual low-dimensional space. In order to ensure that the data points in the radar index mapping space are displayed as much as possible to maintain the structural relationship in the adjacency graph of multi-dimensional water quality data, this method optimizes the mapping point position of the radar index mapping space by minimizing the KL divergence between the probability distributions of the two, thereby ensuring the visualization accuracy of the evaluation results and making the evaluation of the water quality monitoring indicators before dosing more reliable and accurate. Through this method, the water quality index data obtained from different dimensional monitoring can be dimensionally reduced and mapped into a visual radar chart evaluated according to the water use standards, thereby providing a highly credible control basis for subsequent chemical purification dosing and improving the accuracy of water quality purification and dosing.

[0078] More specifically, the step S104 specifically includes the following steps:

[0079] Obtain the chemical varieties stored in the chemical dosing module and the chemical formula components of the chemical varieties, and retrieve the purification efficacy knowledge graph of the chemical varieties for different preset water quality index contents in the big data network;

[0080] Obtain the real-time water quality index content of the target purified water body sample according to the real-time multi-dimensional water quality index data, identify the real-time water quality index content through the purification efficacy knowledge graph, and output the purification efficacy of the chemical variety for the target purified water body sample, which is defined as the established purification efficacy;

[0081] Based on a preset medication constraint threshold for a given purification efficacy, a projection constraint set is constructed according to the medication constraint threshold, an expected water quality index evaluation radar chart of a target purified water sample is constructed according to the expected water quality evaluation index, and a series of graph points are constructed according to the expected water quality index evaluation radar chart, which are defined as target graph points;

[0082] Construct a series of points of the real-time water quality index evaluation radar chart, define it as the current point, calculate the gradient vector of the target point on the current point, update the gradient step of the current point along the gradient vector, and obtain a new point;

[0083] Projecting the new graph points onto the projection constraint set, repeating the above gradient vector iteration steps until all current graph points are updated, and finally generating the initial dosing quantization coefficients;

[0084] An interpolation control point array of the pharmaceutical formula components is constructed, and the tangent vector of the interpolation control point array is calculated based on the initial dosage quantization coefficient. The real-time water quality index evaluation radar chart is interpolated on the interpolation control point array according to the tangent vector to obtain an initial dosage curve.

[0085] It should be noted that the water purifier storage module reserves a variety of agents for purifying sewage, and controls the water purifier dosing module to add agents according to the degree of water pollution, so as to cope with the purification of different water pollution conditions. However, the existing water purifier storage module and water purifier dosing module have low accuracy in dosing agents under different water pollution conditions, and it is difficult to dosing drugs according to the efficacy of the drugs. It is easy to have wrong dosing decisions, excessive or too little dosing, making it difficult to accurately and reasonably purify the pollution in the water. Therefore, it is necessary to accurately calculate the dosage of the drug according to the efficacy of the drug for the real-time water pollution situation. Therefore, this method obtains the established purification efficacy of the drug variety for the target purified water sample by identifying it. Since the ultimate purpose of drug dosing purification is to make the water quality meet an available standard requirement, it is necessary to measure the process of drug dosing and set an expected water quality index evaluation radar chart to meet the water purification requirements as the drug dosing target, and then use the established purification efficacy of the drug as a constraint to move a series of current points of the real-time water quality index evaluation radar chart towards the expected water quality index evaluation radar. Figure 1The gradient of the series of target graph points is updated, and the updated new graph points are projected onto the projection constraint set of the drug efficacy, so that the drug purification target can purify the water sample under the drug efficacy constraint to meet the requirements, and the dosage of the drug can be accurately quantified. Finally, an accurate and reliable initial drug dosage quantification coefficient can be further output. In order to further improve the dosing control accuracy of different drugs for the water purification agent storage module and the water purification agent dosing module, this method also interpolates and controls the initial drug dosage quantification coefficient according to the drug formula components to generate an initial drug dosage curve, which makes the module control more linear, increases the decision-making smoothness and accuracy of the water purification agent storage module and the water purification agent dosing module for the dosing of different drugs, realizes a high-precision dosing effect, and improves the dosing efficiency.

[0086] More specifically, the step S108 specifically includes the following steps:

[0087] Obtain the dosing purification log of the drug dosing device, and extract the historical water body spectral image data of the water body sample after mixing with different preset water quality index contents by the drug dosing device according to the initial drug dosage curve through the dosing purification log;

[0088] Introduce the cosine similarity method to calculate the cosine similarity between the real-time water quality index content of the target purified water sample and different preset water quality index contents, and only extract the historical water body spectral image data corresponding to the preset water quality index content with the maximum cosine similarity, which is marked as the target water body spectral image data;

[0089] Introduce the gray-level co-occurrence matrix algorithm to calculate the gray-level values of adjacent pixel pairs included after graying the target water body spectral image data, generate a spectral reflection gray-level co-occurrence matrix, and construct a multiple linear regression equation describing the spectral reflectance based on the spectral reflection gray-level co-occurrence matrix;

[0090] Based on the big data network, obtain the drug dosage form of the drug variety and the knowledge graph of water quality mixing characteristics, and jointly identify the drug dosage form and the real-time water quality index content through the knowledge graph of water quality mixing characteristics, and output the standard water body characteristics of the mixed drug variety of the target purified water sample;

[0091] Calculate the turbidity residual of the drug variety for the target purified water sample based on the standard water body characteristics, and set the turbidity of the fully mixed water body according to the expected water quality evaluation index. Solve the multiple linear regression equation by minimizing the turbidity residual to obtain the real-time water body turbidity of the mixed drug variety of the target purified water sample;

[0092] If the real-time water turbidity is lower than that of the fully mixed water body, the blade speed of the water intake pump is controlled to increase; if it is higher than that of the fully mixed water body, the blade speed of the water intake pump is controlled to decrease. Driven by the rotation of the blades of the water intake pump, the target purified water sample and the dosing agent are fully mixed. After the agent is applied, the target water body undergoes flocculation precipitation and is discharged into rivers and lakes.

[0093] It should be noted that the water purification agent storage module can store agents in different dosage forms, such as powdery agents, liquid agents, or solid agents. In order to enable these agents to exert their maximum effects, the blades of the water intake pump should be controlled to fully mix them with the target purified water sample. However, improper control of the rotation of the blades of the water intake pump will affect the mixing quality of agents in different dosage forms with the polluted water body, thereby reducing the effect of the agent in purifying the target water body. Therefore, it is crucial to reasonably control the rotation of the blades of the water intake pump. Since the water body characteristics will change when these agent dosage forms are mixed in water bodies with different water quality index contents, and these water body characteristic differences show different turbidities, the turbidity of the water body characteristics after the agent is mixed with the target purified water sample can be used to judge whether they are fully mixed, so as to achieve precise rotation control of the blades of the water intake pump. For this purpose, this method obtains the spectral image data of the target purified water sample extracted from the previous water quality spectrum detection of water with different water quality index contents, and then extracts the spectral reflection features contained in the image data, that is, the spectral reflection gray-level co-occurrence matrix. This spectral reflection gray-level co-occurrence matrix reflects the reflectance characteristics of different water suspensions (such as sediment, organic matter, algae, etc.) in the target purified water sample at different wavelengths, which is an important basis for determining its turbidity. Then, it is converted into a multiple linear regression equation describing the spectral reflectance. Then, based on the drug dosage form and the real-time water quality index content, the standard water body characteristics after their mixing are identified. By minimizing the turbidity residual of this standard water body characteristic to solve the multiple linear regression equation, the real-time water turbidity of the target purified water sample mixed with the drug variety can be calculated quickly and accurately. Finally, based on this real-time water turbidity, it is determined whether they are fully mixed. If the real-time water turbidity is lower than that of the fully mixed water body, it means that the mixing is not sufficient and the agent is not completely dissolved in the target water body, so the blade speed of the water intake pump is controlled to increase; if it is higher than that of the fully mixed water body, it means that the mixing has reached a fully dissolved state, and at this time, the blade speed of the water intake pump can be controlled to decrease.

[0094] It should be noted that through this method, spectral reflection analysis and calculation can be carried out based on the characteristics of the agent mixed in the water body using the spectral image, so as to quickly determine whether the mixing of the agent in the target water body is sufficient. Based on this, the reasonable and precise rotation of the blades of the water intake pump is controlled, skillfully completing the full mixing of the agent and the water body, improving the mixing degree of the agent and the target water to be purified, ensuring the maximum exertion of the drug effect, and optimizing the water quality purification benefit.

[0095] More specifically, the step S110 includes the following steps:

[0096] Obtaining a preset water quality monitoring strategy for a water quality monitoring device, and extracting a dosing and purification monitoring timing of the water quality monitoring device for a target purified water sample through the preset water quality monitoring strategy;

[0097] Taking the real-time water quality index evaluation radar chart of the target purified water sample as the time series starting point, the real-time water quality index evaluation radar chart at the next time series chart point starting from the time series starting point is obtained in the dosing purification monitoring time series, and marking the real-time water quality index evaluation radar chart at the next time series chart point as the time series update radar chart;

[0098] Aligning the time-series update radar map with the real-time water quality index assessment radar map, calculating the non-overlapping radar blocks of the time-series update radar map and the real-time water quality index assessment radar map, obtaining the area value of the non-overlapping radar blocks, and determining the state value function of the time-series update radar map to the real-time water quality index assessment radar map based on the area value;

[0099] The Bellman algorithm is introduced. Based on the dosing purification monitoring time sequence, the Bellman algorithm is used to calculate the state of the time-series update radar chart to the real-time water quality index evaluation radar chart, so as to construct the Bellman state deduction equation for the real-time water quality state of the previous time-series chart point to the real-time water quality state of the next time-series chart point.

[0100] Based on the expected water quality index evaluation radar chart, the state value function threshold is preset, and the state value function is continuously updated and iterated according to the Bellman state deduction equation. If the current state value function is less than the state value function threshold, the update and iteration process is stopped to obtain the optimized amplitude of the water quality state;

[0101] The initial dosage curve is adjusted and optimized according to the optimized amplitude of the water quality state to adjust the dosage of the water purifier dosing module.

[0102] It should be noted that the monitoring of the water quality monitoring module is real-time, which means that the water quality status monitored at different time sequences of chemical dosing purification will change. For example, after purification with a certain dose of chemicals, the water quality gradually becomes highly visible, clear, and the harmfulness decreases. It is possible to appropriately adjust the dosing amount of the chemicals to optimize the purification benefit in response to subsequent changes in the water quality status, thereby improving the stability and effectiveness of water quality purification in time sequences. Therefore, this method analyzes the temporal evolution of the water quality status through the real-time water quality index evaluation radar chart monitored under the premise of the monitoring time sequence of dosing purification, that is, by comparing the real-time water quality index evaluation radar chart at a certain time sequence point with the real-time water quality index evaluation radar chart at the next time sequence point to optimize the water quality index. The area value of the non-overlapping radar chart blocks is the difference manifestation of the water quality change after purification, which is the main basis for subsequent chemical dose adjustment. Therefore, a state value function is calculated based on this area value to reveal the change amplitude of the state evolution of the water quality after purification between adjacent time sequences. Then, the Bellman algorithm is used to calculate the state of the radar chart transformed from the time sequence update radar chart to the real-time water quality index evaluation radar chart to obtain a Bellman state deduction equation for the state evolution of the water quality after purification. The state value function is updated and iterated through this equation. Each time of iteration, the Bellman equation will use the current state value function to update the value of each state, thereby improving the estimation of the state value and making the state value function gradually approach the true optimal value, that is, maximizing the value of the cumulative discounted reward. The expected return of the water quality status at each time sequence after purification is calculated using the Bellman equation, and thus an optimized amplitude of the water quality status after purification can be accurately output. Furthermore, the dosing amount of the chemicals can be reasonably optimized based on this optimized amplitude. Through this method, the result can be transmitted to the chemical dosing module in real time according to the water quality change of the water body to be treated, and then the dosing amount of the chemicals can be calculated and adjusted in real time to perform accurate chemical dosing, improving the stability and accuracy of real-time water quality purification.

[0103] In the third aspect of the present invention, a dosing control system for a chemical dosing device for purifying river and lake water bodies is provided. The dosing control system includes a memory and a processor. A dosing control method program for a chemical dosing device for purifying river and lake water bodies is stored in the memory. When the dosing control method program is executed by the processor, the steps of any of the dosing control methods are implemented.

[0104] In the fourth aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer detection program. When the computer detection program is executed by at least one processor, a dosing control method for a chemical dosing device for purifying river and lake water bodies as described in any of the above is implemented.

[0105] The above is only a specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A chemical dosing device for purifying river and lake water bodies, characterized in that, The described chemical dosing equipment includes: A water quality monitoring module, which is used to monitor the pollution indicators of the target purified water body sample in real time and pump the target purified water body sample into the water quality monitoring equipment; A water purification agent storage module, which includes a chemical storage tank and a discharging pump. The chemical storage tank is used to store water purification agents of different dosage forms and varieties, and the discharging pump is used to pump the chemical raw materials into the chemical storage tank; A water purification agent dosing module, which includes a dosing metering pump. The dosing metering pump is used to dose the agent stored in the water purification agent storage module before the water intake pump according to the calculated initial agent dosage; A water diversion module, which is responsible for fully stirring and mixing the dosed agent and the target purified water body; An integrated control module, which is used to control the sampling pump to sample and calculate the dosing amount, and at the same time control the opening and closing of the water intake pump and the dosing pump.

2. A dosing control method for a chemical dosing device used for purifying and treating river and lake water bodies, which is applied to the chemical dosing device for purifying and treating river and lake water bodies described in claim 1, characterized in that, Specifically, it includes the following steps: S102: The integrated control module controls the sampling pump to start, collects an appropriate amount of the target water body to be purified, pumps it into the water quality monitoring module for detection, maps and expresses the real-time multi-dimensional water quality index data according to the water use standard regulation system, obtains the real-time water quality index evaluation radar chart of the target purified water body sample, and feeds back the real-time water quality index evaluation radar chart to the integrated control module; S104: Obtain the established purification efficacy of the chemical varieties stored in the water purification agent storage module, quantify and perform curve interpolation on the dosing amount required to meet the water quality purification requirements for the real-time water quality index evaluation radar chart based on the established purification efficacy, obtain the initial dosing amount curve, and output the initial agent dosage; S106: During the process of calculating and outputting the initial agent dosage, the integrated control module synchronously controls the operation of the water intake pump and the dosing metering pump of the water diversion module, and controls the dosing metering pump to dose the agent stored in the water purification agent storage module before the water intake pump according to the initial dosing amount curve; S108: When the agent is dosed before the water intake pump, extract the spectral reflection gray-level co-occurrence matrix of the drug variety mixed with the target purified water body sample, use the spectral reflection gray-level co-occurrence matrix to perform regression calculation and analyze the real-time water turbidity, so as to control the rotation of the water intake pump blades. Driven by the rotation of the water intake pump blades, the target purified water body sample and the dosed agent are fully mixed, and after the agent is applied, the target water body undergoes flocculation and precipitation and is discharged into rivers and lakes; S110: Analyze the water quality state change between the real-time water quality index evaluation radar chart from the previous time series point to the next time series point based on the preset water quality monitoring strategy of the water quality monitoring equipment, calculate the optimized amplitude of the water quality state according to the state change, and adjust the dosing amount of the water purification agent dosing module based on the optimized amplitude of the water quality state.

3. The dosing control method of the chemical dosing device for river and lake water purification treatment according to claim 2, wherein, The step S102 specifically includes the following steps: Collect an appropriate amount of the target water body to be purified, pump it into the water quality monitoring module to perform real-time monitoring on the target purified water body sample, so as to obtain the real-time multi-dimensional water quality index data of the target purified water body sample; Obtain the water use standard regulation system based on the big data network, extract the critical threshold of the standard exceeding regulations for different water quality dimensions through the water use standard regulation system, and construct a radar weighting model for estimating different water quality dimensions according to the constraints of the standard exceeding regulations critical threshold; The edge weights of each real-time multidimensional water quality index data and adjacent data points are calculated by using a radar weighted model to generate a real-time multidimensional water quality index adjacency graph, and a Bayesian inference algorithm is introduced to infer the node connection strength of the real-time multidimensional water quality index adjacency graph to obtain an adjacency graph probability distribution; Obtain water purification requirements, extract expected water quality assessment indicators based on water purification requirements, and establish a radar indicator mapping space for expected water quality assessment indicators based on the water use standard regulation system; Based on the adjacency graph probability distribution, the real-time multidimensional water quality index data is mapped and embedded into the radar index mapping space, and the mapping point matrix pattern of the radar index mapping space is obtained at this time, and the current mapping probability distribution of the radar index mapping space is determined according to the mapping point matrix pattern, and the KL divergence between the adjacency graph probability distribution and the current mapping probability is calculated; The minimum KL divergence is preset based on the real-time multidimensional water quality index adjacency graph. If the KL divergence is higher than the minimum KL divergence, the mapping point position of the radar index mapping space is continuously optimized until it is lower than the minimum KL divergence, and the real-time water quality index evaluation radar map of the target purified water sample is obtained.

4. The dosing control method of the chemical dosing device for purifying river and lake water bodies according to claim 2, characterized in that, The step S104 specifically includes the following steps: Obtain the pharmaceutical varieties and pharmaceutical formula components of the pharmaceutical varieties stored in the water purification agent dosing module, and retrieve the purification efficacy knowledge graph of the pharmaceutical varieties for different preset water quality index contents in the big data network based on the pharmaceutical formula components; According to the real-time multi-dimensional water quality index data, the real-time water quality index content of the target purified water body sample is obtained, the real-time water quality index content is identified through the purification efficacy knowledge graph, and the purification efficacy of the agent variety for the target purified water body sample is output, which is defined as the established purification efficacy; Based on a preset medication constraint threshold for a given purification efficacy, a projection constraint set is constructed according to the medication constraint threshold, an expected water quality index evaluation radar chart of a target purified water sample is constructed according to the expected water quality evaluation index, and a series of graph points are constructed according to the expected water quality index evaluation radar chart, which are defined as target graph points; Construct a series of points of the real-time water quality index evaluation radar chart, define it as the current point, calculate the gradient vector of the target point on the current point, update the gradient step of the current point along the gradient vector, and obtain a new point; Projecting the new graph points onto the projection constraint set, repeating the above gradient vector iteration steps until all current graph points are updated, and finally generating the initial dosing quantization coefficients; An interpolation control point array of the pharmaceutical formula components is constructed, and the tangent vector of the interpolation control point array is calculated based on the initial dosage quantization coefficient. The real-time water quality index evaluation radar chart is interpolated on the interpolation control point array according to the tangent vector to obtain an initial dosage curve.

5. The dosing control method of the chemical dosing equipment for purifying river and lake water bodies according to claim 2, characterized in that, The step S108 specifically includes the following steps: Obtaining a dosing and purification log of a drug dosing device, and extracting historical water body spectral image data of spectral measurement after the drug dosing device performs spectral measurement on water body samples with different preset water quality index contents by executing an initial dosing amount curve; The cosine similarity method is introduced to calculate the cosine similarity between the real-time water quality index content of the target purified water sample and the content of different preset water quality indexes, and only the historical water spectral image data corresponding to the preset water quality index content with the maximum cosine similarity is extracted and marked as the target water spectral image data; A gray-level co-occurrence matrix algorithm is introduced to calculate the gray-level values of neighboring pixel pairs contained in the gray-scaled spectral image data of the target water body, to generate a spectral reflectance gray-level co-occurrence matrix, and a multivariate linear regression equation describing the spectral reflectance is constructed based on the spectral reflectance gray-level co-occurrence matrix; Based on the big data network, the pharmaceutical dosage forms and water quality mixing characteristics knowledge graph of pharmaceutical varieties are obtained, and the pharmaceutical dosage forms and the real-time water quality index content are jointly identified through the water quality mixing characteristics knowledge graph, and the standard water characteristics of the mixed pharmaceutical varieties of the target purified water sample are output; The turbidity residual of the reagent variety for the target purified water sample is calculated based on the standard water characteristics, and the fully mixed water turbidity is set according to the expected water quality assessment index, and the multivariate linear regression equation is solved by minimizing the turbidity residual to obtain the real-time water turbidity of the target purified water sample mixed with the reagent variety; If the real-time water turbidity is lower than the fully mixed water turbidity, the blade speed of the water diversion pump is controlled to increase; if it is higher than the fully mixed water turbidity, the blade speed of the water diversion pump is controlled to decrease. The target purified water sample and the dosing agent are fully mixed under the rotation of the water diversion pump blades. After the agent is applied, the target water is discharged into rivers and lakes through flocculation and sedimentation.

6. The dosing control method of the chemical dosing device for purifying river and lake water bodies according to claim 2, characterized in that, The step S110 specifically includes the following steps: Obtaining a preset water quality monitoring strategy for a water quality monitoring device, and extracting a dosing and purification monitoring timing of the water quality monitoring device for a target purified water sample through the preset water quality monitoring strategy; Taking the real-time water quality index evaluation radar chart of the target purified water sample as the time series starting point, the real-time water quality index evaluation radar chart at the next time series chart point starting from the time series starting point is obtained in the dosing purification monitoring time series, and marking the real-time water quality index evaluation radar chart at the next time series chart point as the time series update radar chart; Aligning the time-series update radar map with the real-time water quality index assessment radar map, calculating the non-overlapping radar blocks of the time-series update radar map and the real-time water quality index assessment radar map, obtaining the area value of the non-overlapping radar blocks, and determining the state value function of the time-series update radar map to the real-time water quality index assessment radar map based on the area value; The Bellman algorithm is introduced. Based on the dosing purification monitoring time sequence, the Bellman algorithm is used to calculate the state of the time-series update radar chart to the real-time water quality index evaluation radar chart, so as to construct the Bellman state deduction equation for the real-time water quality state of the previous time-series chart point to the real-time water quality state of the next time-series chart point. Based on the expected water quality index evaluation radar chart, the state value function threshold is preset, and the state value function is continuously updated and iterated according to the Bellman state deduction equation. If the current state value function is less than the state value function threshold, the update and iteration process is stopped to obtain the optimized amplitude of the water quality state; The initial dosage curve is adjusted and optimized according to the optimized amplitude of the water quality state to adjust the dosage of the water purifier dosing module.

7. A dosing control system for a chemical dosing device for purifying and treating river and lake water bodies, characterized in that, The dosing control system includes a memory and a processor. A dosing control method program for a chemical dosing device for purifying and treating river and lake water bodies is stored in the memory. When the dosing control method program is executed by the processor, the dosing control method steps described in any one of claims 2-6 are implemented.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer detection program. When the computer detection program is executed by at least one processor, a dosing control method for a chemical dosing device for purifying and treating river and lake water bodies described in any one of claims 2-6 is implemented.

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