Intelligent Regulation Method and Device of Microwave Precipitation System Based on Sensor Group

Abstract

The present invention discloses an intelligent control method and device for a microwave precipitation system based on a sensor group, belonging to the field of precipitation systems. The intelligent control method includes steps of obtaining the sludge concentration in a hydrocyclone through an on-line sludge concentration monitor, controlling the initial addition amount of each filtration tank, an image sensor obtaining image data, a turbidity sensor obtaining turbidity acquisition data to generate a single-tank purification capacity parameter, generating a single-tank preparation correction amount based on the single-tank purification capacity parameter, and controlling the preparation addition amount of each filtration tank based on the initial addition amount and the single-tank preparation correction amount of each filtration tank. The intelligent control method for the microwave precipitation system based on the sensor group disclosed by the present invention can accurately control the preparation addition amount according to the image and turbidity data of the filtration tank, thereby improving the purification effect and saving the operation cost of the system.

Description

Technical Field

[0001] The present invention relates to the field of precipitation systems, and in particular to an intelligent control method for a microwave precipitation system based on a sensor group and a full-automatic intelligent control device for a full-automatic intelligent control method. Background Art

[0002] Existing microwave precipitation systems are difficult to reasonably control the injection amounts of coagulants, polymer flocculants, and microsands according to the degree of raw water pollution. This is because microwave precipitation systems often consist of multiple tanks such as coagulation tanks, injection tanks, thermalization tanks, and sedimentation tanks, and it is difficult to capture the changes in the quality of raw water. This will cause system debuggers to often set the injection amounts of coagulants, polymer flocculants, microsands, etc. based on experience. Therefore, it is inclined to ensure the precipitation effect by injecting excessive preparations, and rely on manual maintenance to adapt to the dynamic changes in the quality of raw water.

[0003] To solve the above problems, Chinese Patent Document CN216366774U discloses a sedimentation tank sludge control system based on image recognition. The system includes a sedimentation tank, an external sludge synchronization device for the sedimentation tank, an image recognition and analysis device, a sludge discharge device, and a sludge treatment device. The external sludge synchronization device for the sedimentation tank synchronizes the water and sludge precipitation states in the sedimentation tank outside the tank respectively. The image recognition and analysis device uses a high-definition camera to continuously capture the images of sludge precipitation and transmits them to a computer. Through continuous accumulation of the database and algorithms, the sludge images at different liquid levels are converted into sludge concentrations and the simulation calculation results are output. The output results guide the sludge discharge device to discharge sludge and return sludge, and guide the sludge treatment device to perform intelligent matching of the production scheduling operation of sludge treatment. The above patent converts the sludge images at different liquid levels into sludge concentrations to guide the production scheduling operation, but simply calculating the sludge concentration obviously cannot meet the requirements for dynamic monitoring of multiple tanks such as coagulation tanks, injection tanks, thermalization tanks, and sedimentation tanks in the microwave precipitation system. Summary of the Invention

[0004] In order to overcome the defects of the prior art, the technical problem to be solved by the present invention is to provide an intelligent control method for a microwave precipitation system based on a sensor group, which can accurately control the preparation addition amount according to the image and turbidity data of the filtration tank, thereby improving the purification effect and saving the operation cost of the system.

[0005] To achieve this purpose, the present invention adopts the following technical solutions:

[0006] An intelligent control method for a microwave precipitation system based on a sensor group provided by the present invention includes:

[0007] Obtaining the sludge concentration in the hydrocyclone through an on-line sludge concentration monitor;

[0008] The precipitation system controller performs an initial classification of the raw water based on the sludge concentration, and the execution unit of the precipitation system controller controls the initial addition amount of each filtration tank according to the initial classification of the preset scheme;

[0009] The image sensor acquires image data of at least one filtration tank, and the turbidity sensor acquires turbidity collection data at the water outlet of at least one filtration tank. The first intelligent calculation unit of the precipitation system controller generates a single-tank purification capacity parameter based on the image data and the turbidity collection data of at least one of the filtration tanks;

[0010] The first intelligent calculation unit transmits the single-tank purification capacity parameters of each filtration tank to the decision-making unit of the precipitation system controller, and the decision-making unit generates a single-tank preparation correction amount based on the single-tank purification capacity parameters;

[0011] The execution unit controls the preparation addition amount of each filtration tank according to the initial addition amount and the single-tank preparation correction amount of each filtration tank.

[0012] A further technical solution of the present invention is that each of the filtration tanks is respectively a coagulation tank, an injection tank, a heat treatment tank, and a sedimentation tank. The image sensor includes a first image sensor, a second image sensor, and a third image sensor. The first image sensor is used to acquire image data of the coagulation tank, the second image sensor is used to acquire image data of the injection tank, and the third image sensor is used to acquire image data of the sedimentation tank. The turbidity sensor includes a first turbidity sensor, a second turbidity sensor, and a third turbidity sensor. The first turbidity sensor is used to acquire turbidity collection data of the first passage between the coagulation tank and the injection tank, the second turbidity sensor is used to acquire turbidity collection data of the second passage between the injection tank and the heat treatment tank, and the third turbidity sensor is used to acquire turbidity collection data at the water outlet of the sedimentation tank.

[0013] A further technical solution of the present invention is that, for the coagulation tank, the acquisition unit generates the average equivalent diameter of flocs and the average fractal dimension based on the image data of the first image sensor, and the acquisition unit generates the effluent turbidity data based on the turbidity acquisition data of the first turbidity sensor. The first intelligent calculation unit generates the single-tank purification capacity parameter of the coagulation tank based on the average equivalent diameter of flocs, the average fractal dimension, and the effluent turbidity data; for the injection tank, the acquisition unit generates the average equivalent diameter of flocs and the average fractal dimension based on the image data of the second image sensor, and the acquisition unit generates the effluent turbidity data based on the turbidity acquisition data of the second turbidity sensor. The first intelligent calculation unit generates the single-tank purification capacity parameter of the injection tank based on the average equivalent diameter of flocs, the average fractal dimension, and the effluent turbidity data; for the sedimentation tank, the acquisition unit generates the average equivalent diameter of flocs, the average fractal dimension, and the bottom sedimentation area based on the image data of the third image sensor, and the acquisition unit generates the effluent turbidity data based on the turbidity acquisition data of the third turbidity sensor. The first intelligent calculation unit generates the single-tank purification capacity parameter of the sedimentation tank based on the average equivalent diameter of flocs, the average fractal dimension, the bottom sedimentation area, and the effluent turbidity data.

[0014] A further technical solution of the present invention is that the second intelligent calculation unit of the sedimentation system controller calculates the comprehensive purification capacity parameter Y based on the single-tank purification capacity parameter Y1 of the coagulation tank, the single-tank purification capacity parameter Y2 of the injection tank, and the single-tank purification capacity parameter Y3 of the sedimentation tank;

[0015] The second intelligent calculation unit obtains the sampled mean data X of the SS removal rate through the human-computer interaction unit of the sedimentation system controller, and calculates the correlation coefficient between the comprehensive purification capacity parameter Y and the sampled mean data X. The second intelligent calculation unit calculates the real-time SS removal rate based on the correlation coefficient.

[0016] A further technical solution of the present invention is that when the decision-making unit calculates the single-tank preparation correction amount in a query manner, the decision-making unit obtains the single-tank standard interval and the ideal value from the standard database and determines whether the single-tank purification capacity parameter of a certain filtration tank is within the single-tank standard interval. When the single-tank purification capacity parameter is abnormal, the decision-making unit performs secondary classification on the abnormal degree of a single filtration tank according to the standard deviation of the abnormal value, and generates an ideal value deviation based on the difference between the single-tank purification capacity parameter and the ideal value. The decision-making unit generates the single-tank preparation correction amount and the secondary interval based on the ideal value of the secondary classification; when the single-tank purification capacity parameter is not abnormal, the decision-making unit directly generates the single-tank preparation correction amount based on the ideal value of the single-tank standard interval.

[0017] A further technical solution of the present invention is that the precipitation system controller includes a scheme database for the original classification of raw water. The scheme database contains the original classification and initial addition amounts of multiple preset schemes. When the single-tank purification capacity parameter is abnormal and secondary classification is required, the scheme database records the secondary interval corresponding to the secondary classification of the single-tank purification capacity parameter, and associates the original classification and the secondary classification to form a new scheme.

[0018] A further technical solution of the present invention is that when the decision-making unit calculates the single-tank preparation correction amount in the form of a learning algorithm, the decision-making unit uses the image data, the turbidity acquisition data, and the single-tank purification capacity as the input layer of the LSTM neural network model, and uses a set of single-tank preparation correction amounts as the output layer of the LSTM neural network model, and continuously trains the LSTM neural network model based on the historical data and updated data of the standard database, so that the decision-making unit outputs a set of single-tank preparation correction amounts.

[0019] A further technical solution of the present invention is that for the injection tank, the single-tank preparation correction amount includes the correction dosage of the polymer and the correction dosage of the fine sand. The acquisition unit generates the average equivalent diameter of the flocs and the average fractal dimension value based on the image data of the second image sensor. The acquisition unit generates the effluent turbidity data based on the turbidity acquisition data of the second turbidity sensor. The first intelligent calculation unit generates the single-tank purification capacity parameter of the injection tank based on the average equivalent diameter of the flocs, the average fractal dimension value, and the effluent turbidity data;

[0020] The decision-making unit uses the average equivalent diameter of the flocs, the average fractal dimension value, the effluent turbidity data, and the single-tank purification capacity as the input layer of the LSTM neural network model, and uses the correction dosage of the polymer and the correction dosage of the fine sand as the output layer of the LSTM neural network model.

[0021] The present invention also provides an intelligent regulation device for a microwave precipitation system based on a sensor group, which is used for the intelligent regulation method of the microwave precipitation system based on a sensor group as described above, and includes:

[0022] An on-line sludge concentration monitor for obtaining the sludge concentration in the hydrocyclone;

[0023] A precipitation system controller for calculating and controlling the preparation addition amounts of each filtration tank;

[0024] An image sensor for obtaining image data of at least one of the filtration tanks;

[0025] A turbidity sensor for obtaining turbidity acquisition data at the outlet of at least one of the filtration tanks.

[0026] The beneficial effects of the present invention are as follows:

[0027] The intelligent control method of the microwave precipitation system based on the sensor group provided by the present invention can not only set the initial addition amount of each filtration tank according to the terminal sludge concentration, but also dynamically correct the preparation addition amount of each filtration tank by real-time detecting the image data and turbidity acquisition data through the image sensor and turbidity sensor. This not only realizes the purpose of accurately controlling the preparation addition amount and saving the operation cost of the system, but also fundamentally solves the problem that the microwave precipitation system cannot respond in time to the dynamic change of the raw water quality. Description of the Drawings

[0028] Figure 1 is a flowchart of the full-automatic intelligent control method provided in the specific implementation manner of the present application;

[0029] Figure 2 is a functional block diagram of the precipitation system controller provided in the specific implementation manner of the present application;

[0030] Figure 3 is a structural schematic diagram of the microwave precipitation system provided in the specific implementation manner of the present application.

[0031] In the figure: 1, on-line sludge concentration monitor; 2, precipitation system controller; 21, acquisition unit; 22, execution unit; 23, first intelligent computing unit; 24, second intelligent computing unit; 25, decision-making unit; 26, man-machine interaction unit; 51, coagulation tank; 52, injection tank; 53, thermalization tank; 54, precipitation tank; 55, hydrocyclone; 56, first passage; 57, second passage; 31, first image sensor; 32, second image sensor; 33, third image sensor; 41, first turbidity sensor; 42, second turbidity sensor; 43, third turbidity sensor. Specific Embodiments

[0032] The technical solution of the present invention will be further described below in conjunction with the drawings and through specific embodiments.

[0033] As Figures 1 to 3 shown, the intelligent control method of the microwave precipitation system based on the sensor group provided in this embodiment includes the following steps:

[0034] S00 Step: Obtain the sludge concentration inside the hydrocyclone 55 through the on-line sludge concentration monitor 1. Specifically, set the detection probe of the on-line sludge concentration monitor 1 at the water inlet of the hydrocyclone 55. When the sludge concentration at the water inlet of the hydrocyclone 55 is low, it indicates that there are fewer pollutants in the raw water. At this time, the initial addition amounts of various preparations in each filtration tank should be reduced; when the sludge concentration at the water inlet of the hydrocyclone 55 is high, the initial addition amounts of various preparations should be increased. It should be noted that the sludge concentration at the hydrocyclone 55 itself is usually high. Therefore, the original classification in the following text can usually only be a rough classification. The on-line sludge concentration monitor 1 obtains the sludge concentration after the hydrocyclone 55 has been operating stably after startup. In addition, the microwave precipitation system first operates according to the factory-set parameters, and then executes the above-mentioned intelligent control method for the microwave precipitation system after it operates stably.

[0035] S10 Step: The precipitation system controller 2 performs an original classification on the raw water according to the sludge concentration. For example, the original classification is divided into a light pollution level, a medium pollution level, and a heavy pollution level. The execution unit 22 of the precipitation system controller 2 controls the initial addition amounts of each filtration tank according to the original classification of the preset scheme. The light pollution level, the medium pollution level, and the heavy pollution level respectively correspond to three different preset schemes, that is, they correspond to different initial addition amounts. According to the following text, each filtration tank includes a coagulation tank 51, an injection tank 52, a thermalization tank 53, and a sedimentation tank 54. Among them, only the coagulation tank 51 and the injection tank 52 require preparations. The coagulation tank 51 needs to add a coagulant, and the injection tank 52 needs to add a polymer and fine sand. The above-mentioned initial addition amounts include the addition amounts of the coagulant, the polymer, and the fine sand.

[0036] S20 Step: The image sensor obtains image data of at least one filtration tank. The image sensor is usually set on the side wall of the corresponding filtration tank and can record the suspended matter in the filtration tank in real time. The turbidity sensor obtains turbidity collection data at the water outlet of at least one filtration tank. The turbidity sensor is also called a water quality sensor and is used for measuring the degree of water turbidity. The first intelligent calculation unit 23 of the precipitation system controller 2 generates a single-tank purification capacity parameter according to the image data and the turbidity collection data of at least one filtration tank. Since the image data mainly obtains the agglomeration and flocculation conditions of SS (Suspended Solids) pollutants, and the turbidity collection data mainly obtains the particle size conditions of the flow-through turbidity sensor, the monitoring level of SS pollutants can be improved by using the detection values of both at the same time. The more agglomerations in the water and the fewer particles, the better the water quality.

[0037] Step S30: The first intelligent computing unit 23 transmits the single-tank purification capacity parameters of each filtration tank to the decision-making unit 25 of the sedimentation system controller 2. The decision-making unit 25 generates a single-tank preparation correction amount based on the single-tank purification capacity parameters. The single-tank preparation correction amount can effectively avoid the defect of relatively rough original grading. The single-tank preparation correction amount can be calculated based on the single-tank purification capacity parameters. For specific details, refer to the two calculation methods provided below.

[0038] Step S40: The execution unit 22 controls the preparation addition amounts of the respective filtration tanks according to the initial addition amounts and the single-tank preparation correction amounts of the respective filtration tanks. Thus, based on detecting the initial addition amounts of the respective filtration tanks by setting the end sludge concentration, the full-automatic intelligent control method provided in this embodiment can also dynamically correct the preparation addition amounts of the respective filtration tanks by using the image data and turbidity acquisition data detected in real time by the image sensor and the turbidity sensor. This not only achieves the purpose of accurately controlling the preparation addition amount and saving the operation cost of the system, but also fundamentally solves the problem that the microwave sedimentation system cannot respond in time to the dynamic change of the raw water quality.

[0039] In a further embodiment, in order to obtain the image data and turbidity acquisition data of some or all of the filtration tanks, each filtration tank is specifically a coagulation tank 51, an injection tank 52, a thermalization tank 53, and a sedimentation tank 54. The coagulation tank 51, the injection tank 52, the thermalization tank 53, and the sedimentation tank 54 form a common microwave sedimentation system. Among them, the coagulation tank 51 is used to aggregate colloidal particles and minute suspended solids in water by adding chemical agents. The injection tank 52 is used to decompose and transform harmful substances such as suspended solids and organic matters in the sewage. The thermalization tank 53 is used to maintain a constant temperature in the biochemical tank to improve the activity and treatment efficiency of microorganisms. The sedimentation tank 54 is used to remove suspended solids in water, thereby purifying the water quality. Usually, a coagulant needs to be added to the coagulation tank 51, and a polymer and fine sand need to be added to the injection tank 52. Therefore, only the first image sensor 31, the second image sensor 32, the first turbidity sensor 41, and the second turbidity sensor 42 can be set, and the purpose of calculating the single-tank preparation correction amount can also be achieved. However, the third image sensor and the third turbidity sensor 43 can detect the purification effect of the entire microwave sedimentation system. Therefore, the detection data of the third image sensor and the third turbidity sensor 43 can be used as a more accurate correction factor, and it is also beneficial to calculate the real-time SS removal rate in the following text. Therefore, the image sensors include a first image sensor 31, a second image sensor 32, and a third image sensor 33. The first image sensor 31 is used to obtain the image data of the coagulation tank 51. The second image sensor 32 is used to obtain the image data of the injection tank 52. The third image sensor 33 is used to obtain the image data of the sedimentation tank 54. The turbidity sensors include a first turbidity sensor 41, a second turbidity sensor 42, and a third turbidity sensor 43. The first turbidity sensor 41 is used to obtain the turbidity acquisition data of the first passage 56 between the coagulation tank 51 and the injection tank 52. The second turbidity sensor 42 is used to obtain the turbidity acquisition data of the second passage 57 between the injection tank 52 and the thermalization tank 53. The third turbidity sensor 43 is used to obtain the turbidity acquisition data at the water outlet of the sedimentation tank 54. Therefore, detecting the image data and turbidity acquisition data at the above six locations can not only calculate the single-tank preparation correction amount, but also detect the purification effect of the entire microwave sedimentation system. When the first intelligent calculation unit 23 adopts a neural network model, it can also be known that the detection data of the third image sensor 33 and the third image sensor 33 are used as the input layer data for calculating the single-tank preparation correction amounts corresponding to the coagulation tank 51 and the injection tank 52, thereby improving the accuracy of the single-tank preparation correction amount.

[0040] For the coagulation tank 51, the acquisition unit 21 generates the average equivalent diameter of flocs and the average fractal dimension based on the image data of the first image sensor 31. The larger the average fractal dimension, the higher the complexity of the flocs. The average equivalent diameter of flocs and the average fractal dimension can be calculated based on the floc distribution in a single picture. Each set of image data usually contains multiple pictures, and the average values of the average equivalent diameter of flocs and the fractal dimension are obtained by taking the average of the average equivalent diameter of flocs and the fractal dimension. The acquisition unit 21 generates the effluent turbidity data based on the turbidity acquisition data of the first turbidity sensor 41. The turbidity acquisition data becomes the effluent turbidity data after analog-to-digital conversion. The first intelligent calculation unit 23 generates the single-tank purification capacity parameter of the coagulation tank 51 based on the above-generated average equivalent diameter of flocs, average fractal dimension, and effluent turbidity data. The single-tank purification capacity parameter only needs to relatively measure the purification capacity of the coagulation tank 51. Since the average equivalent diameter of flocs and the average fractal dimension are positively correlated with the purification capacity of the coagulation tank 51, and the effluent turbidity data is negatively correlated with the purification capacity, the linear relationship between the single-tank purification capacity parameter, the average equivalent diameter of flocs, the average fractal dimension, and the effluent turbidity data can be simulated by the least squares method to obtain the single-tank purification capacity parameter. Of course, considering the large error of the linear relationship, the neural network model and the curve model can also be used to calculate the single-tank purification capacity parameter, so as to measure the relative purification effect of the coagulation tank 51. Similarly, for the injection tank 52, the acquisition unit 21 generates the average equivalent diameter of flocs and the average fractal dimension based on the image data of the second image sensor 32. The acquisition unit 21 generates the effluent turbidity data based on the turbidity acquisition data of the second turbidity sensor 42. The first intelligent calculation unit 23 generates the single-tank purification capacity parameter of the injection tank 52 based on the above-generated average equivalent diameter of flocs, average fractal dimension, and effluent turbidity data. The calculation method of the single-tank purification capacity parameter of the injection tank 52 is similar. The single-tank purification capacity parameter here is used to measure the relative purification effect of the injection tank 52. For the sedimentation tank 54, the acquisition unit 21 generates the average equivalent diameter of flocs, the average fractal dimension, and the bottom sedimentation area based on the image data of the third image sensor 33. First, the bottom sedimentation area needs to be removed from the image data, and then the average equivalent diameter of flocs and the average fractal dimension are calculated with reference to the above method. The acquisition unit 21 generates the effluent turbidity data based on the turbidity acquisition data of the third turbidity sensor 43. The first intelligent calculation unit 23 generates the single-tank purification capacity parameter of the sedimentation tank 54 based on the above-generated average equivalent diameter of flocs, average fractal dimension, bottom sedimentation area, and effluent turbidity data. The single-tank purification capacity parameter here is used to measure the relative purification effect of the sedimentation tank 54.

[0041] Traditionally, for the SS removal rate, it is usually necessary to collect the clarified water and conduct multiple groups of comparative experiments with the raw water, and then obtain the SS removal rate through statistical analysis, that is, it is necessary to conduct manual comparative experiments to draw a conclusion. This is obviously not applicable to the automatic control of the microwave precipitation system. In a further implementation manner, the second intelligent calculation unit 24 of the precipitation system controller 2 calculates the comprehensive purification ability parameter Y based on the single-tank purification ability parameter Y1 of the coagulation tank 51, the single-tank purification ability parameter Y2 of the injection tank 52, and the single-tank purification ability parameter Y3 of the precipitation tank 54. The second intelligent calculation unit 24 obtains the sampling mean data X of the SS removal rate through the human-computer interaction unit 26 of the precipitation system controller 2. That is, the SS removal rate is usually positively correlated with the comprehensive purification ability parameter Y. When the sampling mean data X of the corresponding SS removal rate is manually input within a period of time, the correlation coefficient between the two can be calculated based on the comprehensive purification ability parameter Y and the sampling mean data X , and the value can also be simulated and calculated using the least squares method, so that the second intelligent calculation unit 24 can calculate the real-time SS removal rate based on the correlation coefficient . When the calculated real-time SS removal rate is abnormal, the precipitation system controller 2 can give an early warning through the human-computer interaction unit 26, thus greatly improving the intelligent level of the microwave precipitation system

[0042] Due to the use of the real-time calculation method, the movement fluctuation of the microwave precipitation system will be relatively large. This is mainly because the SS fluctuation in the raw water itself is relatively large, which will cause the microwave precipitation system to often need to adjust the dosage of the preparation or the rotation speed of the stirrer. This is obviously not conducive to the stable operation of the microwave precipitation system. In a further implementation manner, when the decision-making unit 25 calculates the single-tank preparation correction amount in a query manner, the decision-making unit 25 obtains the single-tank standard interval and the ideal value from the standard database and judges whether the single-tank purification ability parameter of a certain filtration tank is within the single-tank standard interval. The calculated value of the single-tank purification ability parameter of a certain filtration tank fluctuates continuously within a period of time. When the single-tank purification ability parameter is abnormal, when the single-tank purification ability parameter is abnormal, the decision-making unit 25 performs secondary classification on the abnormal degree of a single filtration tank based on the standard deviation of the abnormal value, and generates an ideal value deviation based on the gap between the single-tank purification ability parameter and the ideal value, and then finds the secondary interval using the degree of the ideal value deviation. The secondary interval also has a corresponding ideal value. For example: the ideal value of a certain single-tank standard interval is Z, and its single-tank standard interval is Z 1. Suppose: the deviation from the ideal value is Z + 2.4, Z + 2.4 - 1 = Z + 1.4, which indicates that the secondary interval of the secondary classification is the adjacent interval above the single-tank standard interval. At this time, the ideal value of this adjacent interval is used as the ideal value of the secondary classification, and the corresponding adjacent interval is the secondary interval. When the deviation from the ideal value is Z + 3.4, Z + 3.4 - 1 = Z + 2.4, which indicates that the secondary interval of the secondary classification is the second interval above the single-tank standard interval. At this time, the ideal value of the second interval is used as the ideal value of the secondary classification, and so on. The decision-making unit 25 generates the single-tank preparation correction amount and the secondary interval according to the ideal value of the secondary classification. When the single-tank purification capacity parameter is normal, the decision-making unit directly generates the single-tank preparation correction amount according to the ideal value of the single-tank standard interval. The microwave precipitation system operates with the ideal value instead of directly calculating based on the detected value of the single-tank purification capacity parameter. This is mainly because the detected average value of the single-tank purification capacity parameter is usually a real-time fluctuating curve. In this way, the microwave precipitation system needs to frequently adjust the preparation addition amount of each filtration tank, which is obviously not conducive to the stable operation of the microwave precipitation system. Therefore, the above method is adopted so that the equipment for the preparation addition amount is equivalent to having several fixed working modes, and each mode corresponds to an ideal value. Only when deviating from the single-tank standard interval corresponding to the ideal value, a new mode is switched, which greatly improves the stability of the equipment for the preparation addition amount. In addition, for the injection tank 52, the single-tank preparation correction amount includes two types: the polymer preparation correction amount and the fine sand preparation correction amount. At this time, one of the factors of the polymer preparation correction amount or the fine sand preparation correction amount can be selected according to the pollution situation of the raw water to anchor the ideal value, so as to facilitate the calculation of the single-tank preparation correction amount in a query manner, thus simplifying the calculation.

[0043] In a further embodiment, the precipitation system controller 2 includes a scheme database for the primary classification of the raw water. The scheme database contains the primary classification and the initial addition amount of multiple preset schemes. When the single-tank purification capacity parameter is abnormal and secondary classification is required, the scheme database records the secondary interval corresponding to the secondary classification of the single-tank purification capacity parameter, and associates the primary classification and the secondary classification to form a new scheme. Since the raw water in a certain area has a certain inherent characteristic in the long run, its single-tank purification capacity parameter is relatively stable after being monitored for a period of time. In this way, the corresponding new scheme is relatively determined, and the precipitation system controller 2 can directly call the new scheme to operate the microwave precipitation system to improve the decision-making efficiency.

[0044] Embodiment 2

[0045] The intelligent control method of the microwave precipitation system based on the sensor group provided in Embodiment 2 includes the following steps:

[0046] S00 Step: Obtain the sludge concentration in the hydrocyclone 55 through the on-line sludge concentration monitor 1. Specifically, set the detection probe of the on-line sludge concentration monitor 1 at the water inlet of the hydrocyclone 55. When the sludge concentration at the water inlet of the hydrocyclone 55 is low, it indicates that there are fewer pollutants in the raw water. At this time, the initial addition amounts of various preparations in each filtration tank should be reduced; when the sludge concentration at the water inlet of the hydrocyclone 55 is high, the initial addition amounts of various preparations should be increased. It should be noted that the sludge concentration at the hydrocyclone 55 itself is usually high. Therefore, the original classification in the following text can only be a rough classification. The on-line sludge concentration monitor 1 obtains the sludge concentration after the hydrocyclone 55 operates stably after startup. In addition, the microwave precipitation system first operates according to the factory-set parameters and then executes the above-mentioned fully automatic intelligent control method after stable operation.

[0047] S10 Step: The precipitation system controller 2 performs an original classification on the raw water according to the sludge concentration. For example, the original classification is divided into a light pollution level, a medium pollution level, and a heavy pollution level. The execution unit 22 of the precipitation system controller 2 controls the initial addition amounts of each filtration tank according to the original classification of the preset scheme. The light pollution level, the medium pollution level, and the heavy pollution level respectively correspond to three different preset schemes, that is, they correspond to different initial addition amounts. According to the following text, each filtration tank includes a coagulation tank 51, an injection tank 52, a thermalization tank 53, and a sedimentation tank 54. Among them, only the coagulation tank 51 and the injection tank 52 require preparations. The coagulation tank 51 needs to add a coagulant, and the injection tank 52 needs to add a polymer and fine sand. The above-mentioned initial addition amounts include the addition amounts of the coagulant, the polymer, and the fine sand.

[0048] S20 Step: The image sensor obtains the image data of at least one filtration tank, usually set on the side wall of the corresponding filtration tank, and can record the suspended matter in the filtration tank in real time. The turbidity sensor obtains the turbidity acquisition data at the water outlet of at least one filtration tank. The turbidity sensor is also called a water quality sensor and is used for measuring the turbidity of water. The first intelligent calculation unit 23 of the precipitation system controller 2 generates a single-tank purification capacity parameter according to the image data and the turbidity acquisition data of at least one filtration tank. Since the image data mainly obtains the agglomeration and flocculation of SS (Suspended Solids) pollutants, and the turbidity acquisition data mainly obtains the particle size situation of the flow-through turbidity sensor, the monitoring level of SS pollutants can be improved by using the detection values of both at the same time. The more agglomerations in the water and the fewer particles, the better the water quality.

[0049] Step S30: The first intelligent computing unit 23 transmits the single-tank purification capacity parameters of each filtration tank to the decision-making unit 25 of the sedimentation system controller 2. The decision-making unit 25 generates a single-tank preparation correction amount based on the single-tank purification capacity parameters. The single-tank preparation correction amount can effectively avoid the defect of relatively rough original grading. The single-tank preparation correction amount can be calculated based on the single-tank purification capacity parameters. For specific details, please refer to the two calculation methods provided below.

[0050] Step S40: The execution unit 22 controls the preparation addition amounts of the respective filtration tanks according to the initial addition amounts and the single-tank preparation correction amounts of the respective filtration tanks. Thus, based on detecting the initial addition amounts of the respective filtration tanks by setting the end sludge concentration, the full-automatic intelligent control method provided in this embodiment can also dynamically correct the preparation addition amounts of the respective filtration tanks by using the image sensor and the turbidity sensor to detect the image data and the turbidity acquisition data in real time. This not only achieves the purpose of accurately controlling the preparation addition amount and saving the operation cost of the system, but also fundamentally solves the problem that the microwave sedimentation system cannot respond in time to the dynamic change of the raw water quality.

[0051] The difference between the second embodiment and the first embodiment lies in:

[0052] When the decision-making unit 25 calculates the single-tank preparation correction amount in the form of a learning algorithm: the decision-making unit 25 uses the image data, turbidity acquisition data, and single-tank purification capacity as the input layer of the LSTM neural network model, uses a set of single-tank preparation correction amounts as the output layer of the LSTM neural network model, and continuously trains the LSTM neural network model based on the historical data and updated data in the standard database, so that the decision-making unit 25 outputs a set of single-tank preparation correction amounts and facilitates the LSTM neural network model to optimize the accuracy of the single-tank preparation correction amount. It should be noted that when calculating the tank correction preparation amount in this way, the single-tank standard interval and ideal value have been considered in the LSTM neural network model, that is, it can be considered that it is directly associated with the ideal values corresponding to several modes of the fixed operation of the equipment for the preparation addition amount. This method is more accurate after long-term training. In a further embodiment, for the injection tank 52, the single-tank preparation correction amount includes the polymer correction preparation amount and the fine sand correction preparation amount. The acquisition unit 21 generates the average equivalent diameter of the flocs and the average fractal dimension value based on the image data of the second image sensor 32. The acquisition unit 21 generates the effluent turbidity data based on the turbidity acquisition data of the second turbidity sensor 42. The first intelligent calculation unit 23 generates the single-tank purification capacity parameter of the injection tank 52 based on the average equivalent diameter of the flocs, the average fractal dimension value, and the effluent turbidity data. The decision-making unit 25 uses the average equivalent diameter of the flocs, the average fractal dimension value, the effluent turbidity data, and the single-tank purification capacity as the input layer of the LSTM neural network model, and uses the polymer correction preparation amount and the fine sand correction preparation amount as the output layer of the LSTM neural network model. Using the characteristics of the fuzzy algorithm of the LSTM neural network model, two variables can be calculated simultaneously and several modes corresponding to the fixed operation of the equipment for various preparation addition amounts can be taken into account, and the calculation is more accurate.

[0053] Embodiment III

[0054] As Figures 2 to 3 shown, an intelligent control device for a microwave precipitation system based on a sensor group provided in Embodiment III is used for the intelligent control method of the microwave precipitation system based on the sensor group. The intelligent control device for the microwave precipitation system based on the sensor group includes an on-line sludge concentration monitor 1, an image sensor, a turbidity sensor, and a precipitation system controller 2. Among them, the on-line sludge concentration monitor 1 is used to obtain the sludge concentration in the hydrocyclone 55. The precipitation system controller 2 is used to calculate and control the preparation addition amounts of each filtration tank. The image sensor is used to obtain the image data of at least one filtration tank. The turbidity sensor is used to obtain the turbidity acquisition data at the water outlet of at least one filtration tank. The on-line sludge concentration monitor 1, the image sensor, and the turbidity sensor are all electrically connected to the precipitation system controller 2, so as to transmit the sludge concentration, image data, and turbidity acquisition data to the precipitation system controller 2, so as to control the preparation addition amounts of each filtration tank.

[0055] The precipitation system controller 2 includes a collection unit 21, an execution unit 22, a first intelligent computing unit 23, a second intelligent computing unit 24, and a decision-making unit 25. The collection unit 21 is used to obtain image data and turbidity collection data. After the collection unit 21 collects the image data and turbidity collection data, it outputs them to the decision-making unit 25, and then indirectly outputs them to the first intelligent computing unit 23 through the decision-making unit 25 for calculation. That is, the decision-making unit 25 is the central unit of other units, and is electrically connected to other units and can perform data transmission. The first intelligent computing unit 23 is used to generate the single-tank purification capacity parameter. The first intelligent computing unit 23 transports the single-tank purification capacity parameters of each filter tank to the decision-making unit 25. The decision-making unit 25 is used to generate the single-tank preparation correction amount and generate a decision signal based on the single-tank preparation correction amount and transport it to the execution unit 22 for execution. The execution unit 22 is used to control the preparation addition amount of each filter tank. The decision-making unit 25 transports the single-tank purification capacity parameters of each filter tank to the second intelligent computing unit 24, and the second intelligent computing unit 24 is used to calculate the correlation coefficient and the real-time SS removal rate.

[0056] The microwave precipitation system includes a coagulation tank 51, an injection tank 52, a thermalization tank 53, a precipitation tank 54, and a hydrocyclone 55. The image sensors include a first image sensor 31, a second image sensor 32, and a third image sensor 33. The first image sensor 31 is used to obtain the image data of the coagulation tank 51. The second image sensor 32 is used to obtain the image data of the injection tank 52. The third image sensor 33 is used to obtain the image data of the precipitation tank 54. The turbidity sensors include a first turbidity sensor 41, a second turbidity sensor 42, and a third turbidity sensor 43. The first turbidity sensor 41 is used to obtain the turbidity collection data of the first channel 56 between the coagulation tank 51 and the injection tank 52. The second turbidity sensor 42 is used to obtain the turbidity collection data of the second channel 57 between the injection tank 52 and the thermalization tank 53. The third turbidity sensor 43 is used to obtain the turbidity collection data at the water outlet of the precipitation tank 54.

[0057] The present invention is described through preferred embodiments. Those skilled in the art know that without departing from the spirit and scope of the present invention, various changes or equivalent replacements can be made to these features and embodiments. The present invention is not limited by the specific embodiments disclosed herein, and other embodiments falling within the scope of the claims of this application belong to the protection scope of the present invention.

Claims

1. A method for intelligent control of a microwave precipitation system based on a sensor group, characterized in that: include: The sludge concentration in the hydrocyclone is obtained through an online sludge concentration monitor; The sedimentation system controller performs an initial classification of the raw water according to the sludge concentration, and the execution unit of the sedimentation system controller controls the initial addition amount of each filter tank according to the initial classification of the preset scheme; The image sensor acquires image data of at least one filter tank, the turbidity sensor acquires turbidity data at the water outlet of at least one filter tank, and the first intelligent calculation unit of the sedimentation system controller generates a single tank purification capacity parameter according to the image data and the turbidity data of at least one filter tank; The first intelligent calculation unit transmits the single tank purification capacity parameters of each filter tank to the decision unit of the sedimentation system controller, and the decision unit generates a single tank preparation correction amount according to the single tank purification capacity parameters; The execution unit controls the amount of preparation added to each filter tank according to the initial amount of preparation added to each filter tank and the single tank preparation correction amount; Each of the filtering tanks is a coagulation tank, an injection tank, a thermal tank and a sedimentation tank; The image sensor comprises a first image sensor, a second image sensor and a third image sensor, wherein the first image sensor is used to obtain image data of the coagulation tank, the second image sensor is used to obtain image data of the injection tank, and the third image sensor is used to obtain image data of the sedimentation tank; The turbidity sensor includes a first turbidity sensor, a second turbidity sensor and a third turbidity sensor, wherein the first turbidity sensor is used to obtain turbidity data of the first channel between the coagulation tank and the injection tank, the second turbidity sensor is used to obtain turbidity data of the second channel between the injection tank and the thermalization tank, and the third turbidity sensor is used to obtain turbidity data at the water outlet of the sedimentation tank; For the coagulation tank, the acquisition unit generates an average equivalent diameter of alum flocs and a mean value of fractal dimensions based on the image data of the first image sensor, the acquisition unit generates effluent turbidity data based on the turbidity acquisition data of the first turbidity sensor, and the first intelligent calculation unit generates a single tank purification capacity parameter of the coagulation tank based on the average equivalent diameter of alum flocs, the mean value of fractal dimensions and the effluent turbidity data; For the injection tank, the acquisition unit generates an average equivalent diameter of alum flowers and a mean value of fractal dimensions based on the image data of the second image sensor, the acquisition unit generates outlet turbidity data based on the turbidity acquisition data of the second turbidity sensor, and the first intelligent calculation unit generates a single tank purification capacity parameter of the injection tank based on the average equivalent diameter of alum flowers, the mean value of fractal dimensions and the outlet turbidity data; For the sedimentation tank, the acquisition unit generates an average equivalent diameter of alum flowers, a mean value of fractal dimensions, and a bottom sedimentation area according to the image data of the third image sensor, the acquisition unit generates outlet turbidity data according to the turbidity acquisition data of the third turbidity sensor, and the first intelligent calculation unit generates a single tank purification capacity parameter of the sedimentation tank according to the average equivalent diameter of alum flowers, the mean value of fractal dimensions, the bottom sedimentation area, and the outlet turbidity data; The second intelligent calculation unit of the sedimentation system controller calculates the comprehensive purification capacity parameter Y according to the single tank purification capacity parameter Y1 of the coagulation tank, the single tank purification capacity parameter Y2 of the injection tank and the single tank purification capacity parameter Y3 of the sedimentation tank; The second intelligent calculation unit obtains the sampling mean data X of the SS removal rate through the human-computer interaction unit of the sedimentation system controller, and calculates the correlation coefficient β between the comprehensive purification capacity parameter Y and the sampling mean data X. The second intelligent calculation unit calculates the real-time SS removal rate based on the correlation coefficient β.

2. The intelligent control method of microwave precipitation system based on sensor group according to claim 1 is characterized in that: When the decision unit calculates the correction amount of a single tank preparation in a query mode: The decision unit obtains the single-tank standard interval and ideal value from the standard database and determines whether the single-tank purification capacity parameter of a certain filter tank is within the single-tank standard interval. When the single-tank purification capacity parameter is abnormal, the decision unit performs secondary classification of the abnormality of the individual filter tank according to the standard deviation of the abnormal value, and generates an ideal value deviation according to the gap between the single-tank purification capacity parameter and the ideal value. The decision unit generates a single-tank preparation correction amount and a secondary interval according to the ideal value of the secondary classification; when the single-tank purification capacity parameter is not abnormal, the decision unit directly generates a single-tank preparation correction amount according to the ideal value of the single-tank standard interval.

3. The intelligent control method of microwave precipitation system based on sensor group according to claim 2 is characterized in that: The sedimentation system controller includes a scheme database for original classification of raw water, wherein the scheme database contains original classifications and initial addition amounts of multiple preset schemes. When the single-tank purification capacity parameter is abnormal and requires secondary classification, the scheme database records the secondary interval corresponding to the secondary classification of the single-tank purification capacity parameter, and associates the original classification and the secondary classification to form a new scheme.

4. The intelligent control method of microwave precipitation system based on sensor group according to claim 1 is characterized in that: When the decision-making unit calculates the correction amount of a single tank preparation in a learning algorithm: The decision unit uses the image data, the turbidity collection data and the single-tank purification capacity as the input layer of the LSTM neural network model, uses a set of single-tank preparation correction amounts as the output layer of the LSTM neural network model, and continuously trains the LSTM neural network model based on the historical data and update data of the standard database, so that the decision unit outputs a set of single-tank preparation correction amounts.

5. The intelligent control method of microwave precipitation system based on sensor group according to claim 4 is characterized in that: For the injection tank, the single tank preparation correction amount includes a high molecular polymer correction amount and a micro sand correction amount. The acquisition unit generates an average equivalent diameter of alum flowers and a mean value of fractal dimension according to the image data of the second image sensor. The acquisition unit generates outlet turbidity data according to the turbidity acquisition data of the second turbidity sensor. The first intelligent calculation unit generates a single tank purification capacity parameter of the injection tank according to the average equivalent diameter of alum flowers, the mean value of fractal dimension and the outlet turbidity data. The decision unit uses the average equivalent diameter of the alum flowers, the mean value of the fractal dimension, the effluent turbidity data and the single tank purification capacity as the input layer of the LSTM neural network model, and uses the amount of high molecular polymer correction preparation and the amount of micro sand correction preparation as the output layer of the LSTM neural network model.

6. An intelligent control device for a microwave precipitation system based on a sensor group, used in the intelligent control method for a microwave precipitation system based on a sensor group according to any one of claims 1 to 5, characterized in that: include: Online sludge concentration monitor, used to obtain the sludge concentration in the hydrocyclone; Sedimentation system controller, used to calculate and control the amount of preparation added to each filter tank; An image sensor, used to obtain image data of at least one of the filter tanks; The turbidity sensor is used to obtain turbidity data at the water outlet of at least one of the filter tanks.

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