CN112817299A - Industrial wastewater treatment data management cloud platform and control method thereof - Google Patents
Industrial wastewater treatment data management cloud platform and control method thereof Download PDFInfo
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Abstract
The invention belongs to the technical field of industrial wastewater treatment data management, and discloses an industrial wastewater treatment data management cloud platform and a control method thereof, wherein the industrial wastewater treatment data management cloud platform comprises: the system comprises a wastewater information acquisition module, a central control module, a network communication module, a wastewater treatment module, a suspended solid prediction module, a treatment evaluation module, an information statistics module, a data management module, a cloud storage module and an update display module. According to the industrial wastewater treatment data management cloud platform provided by the invention, the suspended solid prediction module is used for establishing a prediction model for predicting TSS in the wastewater by utilizing an MLP neural network and carrying out prediction result simulation based on the model, so that the method can be accurately used for such prediction; simultaneously, can supervise the enterprise through the evaluation module and carry out the blowdown standard with discharged waste water and handle just to discharge, avoid the enterprise's waste water that the waste water monitoring data that makes fake corresponds to be discharged out, and then influence ecological environment's problem.
Description
Technical Field
The invention belongs to the technical field of industrial wastewater treatment data management, and particularly relates to an industrial wastewater treatment data management cloud platform and a control method thereof.
Background
At present, industrial wastewater refers to wastewater, sewage and waste liquid generated in industrial production process, which contains industrial production materials, intermediate products and products lost with water and pollutants generated in the production process. With the rapid development of industry, the variety and quantity of waste water are rapidly increased, the pollution to water bodies is more and more extensive and serious, and the health and the safety of human beings are threatened. Therefore, the treatment of industrial wastewater is more important than the treatment of municipal sewage for environmental protection. The main pollution caused by industrial wastewater is: organic aerobic substance pollution, chemical poison pollution, inorganic solid suspended substance pollution, heavy metal pollution, acid pollution, alkali pollution, plant nutrient substance pollution, heat pollution, pathogen pollution and the like. Many pollutants have color, odor or easily generate foam, so the industrial wastewater often presents an unpleasant appearance, causes large-area pollution of water and directly threatens the life and health of people, and therefore, the control of the industrial wastewater is particularly important. However, the existing industrial wastewater treatment data management cloud platform has inaccurate prediction on the total amount of wastewater suspended solids; meanwhile, in order to avoid purifying the wastewater in most enterprises, the wastewater which is not qualified in purification originally can be subjected to human intervention or intelligent machine intervention, so that the wastewater monitoring data of the discharged wastewater can meet the regulations, the wastewater which is truly polluted by pollution factors and cannot be discharged according to the standards can be discharged, and the ecological environment is polluted.
In summary, the problems and disadvantages of the prior art are: the existing industrial wastewater treatment data management cloud platform cannot accurately predict the total amount of wastewater suspended solids; meanwhile, in order to avoid purifying the wastewater in most enterprises, the wastewater which is not qualified in purification originally can be subjected to human intervention or intelligent machine intervention, so that the wastewater monitoring data of the discharged wastewater can meet the regulations, the wastewater which is truly polluted by pollution factors and cannot be discharged according to the standards can be discharged, and the ecological environment is polluted.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an industrial wastewater treatment data management cloud platform and a control method thereof.
The invention is realized in such a way that the control method of the industrial wastewater treatment data management cloud platform comprises the following steps:
acquiring information data of wastewater components, toxicity and heavy metal content by using wastewater monitoring equipment through a wastewater information acquisition module; the central control module is used for coordinating and controlling the normal work of each module of the industrial wastewater treatment data management cloud platform by using a central processing unit; the network communication module is used for accessing the internet by using a network interface to carry out network communication;
step two, filtering, precipitating and purifying the wastewater by using wastewater treatment equipment through a wastewater treatment module; acquiring target characteristic data by using a prediction program through a suspended solid prediction module, and performing data preprocessing on the target characteristic data; wherein the target characteristic data refers to water quality parameters of a water inlet stage, and comprises water inlet flow, Carbonaceous Biochemical Oxygen Demand (CBOD) and Total Suspended Solids (TSS);
performing PCA data dimensionality reduction on the inflow water flow and the carbonaceous biochemical oxygen demand CBOD subjected to data preprocessing; inputting the data subjected to dimensionality reduction selection into an MLP neural network model, establishing a time sequence model of total suspended solids TSS at a water inlet stage, and evaluating the performance of the data model by using an average absolute error MAE and an average relative error MRE;
inputting the past 7 days recorded value of the total suspended solid TSS into an MLP neural network model, establishing a time sequence prediction model of the total suspended solid TSS in the wastewater, and evaluating the performance of the data model by using an average absolute error MAE and an average relative error MRE; presetting at least one quality evaluation characteristic of the wastewater monitoring data by using an evaluation program through a treatment evaluation module; the quality evaluation characteristics comprise frequency characteristics, stability characteristics, steep peak value characteristics, change frequency characteristics of daily monitoring values, change frequency characteristics of daytime monitoring values, random number characteristics, video monitoring flow characteristics and intrusion characteristics of video monitoring;
acquiring wastewater monitoring data corresponding to enterprise wastewater, performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data, and calculating the characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data to obtain weights of all quality evaluation characteristics of the wastewater monitoring data; obtaining a quality evaluation value of the wastewater monitoring data based on the characteristic value and the weight corresponding to each quality evaluation characteristic of the wastewater monitoring data;
feeding back the quality evaluation value of the wastewater monitoring data to a supervisor so that the supervisor can judge whether the wastewater monitoring data has authenticity or not according to the quality evaluation value of the wastewater monitoring data; the information statistics module is used for carrying out statistics on the wastewater treatment information data by using a statistics program; managing the treatment data of the industrial wastewater by using a data management program through a data management module;
step seven, carrying out cloud storage on the collected wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the statistical information of wastewater treatment by using a cloud server through a cloud storage module; and the updating display module is used for updating and displaying the acquired wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the real-time data of the statistical information of wastewater treatment by using the display.
Further, in step two, before preprocessing the target feature data, an abnormal value identification and removal operation is also required, including:
and (3) carrying out outlier determination on the value exceeding +3 sigma and the value smaller than-3 sigma by using a double-side outlier detection method and removing the outliers, wherein the TSS value is kept between 32mg/L and 530 mg/L.
Further, in step three, performing PCA data dimensionality reduction on the inflow water flow and the carbonaceous biochemical oxygen demand CBOD subjected to data preprocessing comprises:
(1) normalization, namely calculating the mean values of the inflow and CBOD data respectively, and subtracting the mean value from each element in the set; solving a covariance matrix and a corresponding eigenvalue matrix and eigenvector matrix for the matrix with the dimensionality mean removed; arranging the corresponding eigenvectors according to the eigenvalues from big to small, and selecting the eigenvectors corresponding to the first K eigenvalues;
(2) multiplying the original data matrix by the obtained eigenvector matrix to obtain a final matrix after dimensionality reduction; and K represents the dimensionality after dimensionality reduction, K is 5-dimensional, and the original data matrix represents a multidimensional matrix formed by inflow and CBOD.
Further, in step three, the method for calculating the mean absolute error MAE and the mean relative error MRE includes:
wherein the content of the first and second substances,
and
to representtThe predicted value of the time model is obtained,y i (t) Andy(t) To representtThe actual value of the moment.
Further, in the fourth step, the MLP neural network is 5 input and 1 output, the number of nodes in the hidden layer is 16, and the neurons adopt hyperbolic tangent T-shaped transfer functions;
the MLP neural network adopts an iterative neural network learning scheme to update and train a prediction model; the training method comprises the following steps:
for the data set containing 1395 sets of input parameters, 930 sets of input parameters are used for training the MLP neural network model, and the rest 465 sets are used as input parameters to verify the prediction capability of the BP neural network model.
Further, in the fifth step, acquiring wastewater monitoring data corresponding to enterprise wastewater, and performing quality assessment on the wastewater monitoring data to obtain characteristic values corresponding to each quality assessment characteristic of the wastewater monitoring data, includes:
(1) selecting a target month, and acquiring daily corresponding wastewater monitoring data and monitoring days of enterprise wastewater generated during enterprise work within the target month;
(2) respectively performing quality evaluation on the wastewater monitoring data corresponding to each day to obtain characteristic values corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day;
(3) obtaining characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to the target month based on the monitoring days and the characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day;
(4) and determining the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to the target month as the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data.
Further, the quality evaluation is performed on the wastewater monitoring data corresponding to each day, so as to obtain characteristic values corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day, and the method comprises the following steps:
1) acquiring daily monitoring frequency of wastewater monitoring data corresponding to each day;
2) and comparing the daily monitoring frequency of the wastewater monitoring data corresponding to each day with the corresponding preset daily monitoring frequency threshold value to obtain the characteristic value corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to each day.
Another object of the present invention is to provide an industrial wastewater treatment data management cloud platform to which the control method of the industrial wastewater treatment data management cloud platform is applied, the industrial wastewater treatment data management cloud platform including:
the system comprises a wastewater information acquisition module, a central control module, a network communication module, a wastewater treatment module, a suspended solid prediction module, a treatment evaluation module, an information statistics module, a data management module, a cloud storage module and an update display module.
The waste water information acquisition module is connected with the central control module and is used for acquiring information data of waste water components, toxicity and heavy metal content through waste water monitoring equipment;
the central control module is connected with the wastewater information acquisition module, the central control module, the network communication module, the wastewater treatment module, the suspended solid prediction module, the treatment evaluation module, the information statistics module, the data management module, the cloud storage module and the update display module and is used for coordinating and controlling the normal work of each module of the industrial wastewater treatment data management cloud platform through the central processing unit;
the network communication module is connected with the central control module and is used for accessing the Internet through a network interface to carry out network communication;
the wastewater treatment module is connected with the central control module and is used for filtering, precipitating and purifying wastewater through wastewater treatment equipment;
the suspended solid prediction module is connected with the central control module and used for predicting the total amount of suspended solids in the wastewater through a prediction program;
the treatment evaluation module is connected with the central control module and is used for evaluating the wastewater treatment result through an evaluation program;
the information statistics module is connected with the central control module and is used for counting the wastewater treatment information data through a statistics program;
the data management module is connected with the central control module and is used for managing the treatment data of the industrial wastewater through a data management program;
the cloud storage module is connected with the central control module and is used for carrying out cloud storage on the collected wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the statistical information of wastewater treatment through the cloud server;
and the updating display module is connected with the central control module and is used for updating and displaying the acquired wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the real-time data of the statistical information of wastewater treatment through the display.
Another object of the present invention is to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the control method of the industrial wastewater treatment data management cloud platform when the computer program product is executed on an electronic device.
Another object of the present invention is to provide a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to execute the control method of the industrial wastewater treatment data management cloud platform.
By combining all the technical schemes, the invention has the advantages and positive effects that: according to the industrial wastewater treatment data management cloud platform provided by the invention, the suspended solid prediction module is used for establishing a prediction model for predicting TSS in the wastewater by utilizing an MLP neural network and carrying out prediction result simulation based on the model, so that the method can be accurately used for such prediction; except for setting the node number of the input layer and the node number of the output layer of the neural network, the neural network is used for training a sample, an internal mechanism for solving TSS change in sewage is not needed, the method is more convenient and faster than a traditional prediction method based on a complex mathematical model, the inflow water flow and the inflow water CBOD are selected as quantitative input parameters, time sequence construction is carried out on the TSS model, and the robustness of the prediction model is improved; compared with model prediction TSS schemes established by other machine learning, the model prediction TSS scheme has more uniqueness and adaptability, and has the characteristics of high convergence rate and strong network generalization capability; meanwhile, at least one quality evaluation characteristic of the wastewater monitoring data and the corresponding weight of the wastewater monitoring data are preset before the quality of the wastewater monitoring data corresponding to the enterprise wastewater is evaluated through an evaluation module; acquiring wastewater monitoring data corresponding to enterprise wastewater, and performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data; the quality assessment value of the wastewater monitoring data is obtained based on the characteristic value and the weight corresponding to each quality assessment characteristic of the wastewater monitoring data, so that the quality assessment of the wastewater monitoring data of the enterprise wastewater is realized, a supervisor can judge whether the wastewater monitoring data of the enterprise wastewater discharged by the enterprise is real and effective through calculating the quality assessment value of the wastewater monitoring data obtained by the supervisor, the enterprise can be supervised to discharge the discharged wastewater after sewage standard treatment, and the enterprise wastewater corresponding to the waste water monitoring data is prevented from being discharged, so that the ecological environment is influenced.
Drawings
Fig. 1 is a flowchart of a control method of an industrial wastewater treatment data management cloud platform according to an embodiment of the present invention.
FIG. 2 is a block diagram of an industrial wastewater treatment data management cloud platform architecture provided by an embodiment of the present invention;
in the figure: 1. a waste water information acquisition module; 2. a central control module; 3. a network communication module; 4. a wastewater treatment module; 5. a suspended solids prediction module; 6. a treatment evaluation module; 7. an information statistics module; 8. a data management module; 9. a cloud storage module; 10. and updating the display module.
Fig. 3 is a flowchart of a method for predicting the total amount of suspended solids in wastewater by a suspended solids prediction module using a prediction program according to an embodiment of the present invention.
FIG. 4 is a flow chart of a method for evaluating wastewater treatment results by a abatement evaluation module using an evaluation program according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for obtaining wastewater monitoring data corresponding to enterprise wastewater and performing quality assessment on the wastewater monitoring data to obtain a feature value corresponding to each quality assessment feature of the wastewater monitoring data according to an embodiment of the present invention.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the control method of the industrial wastewater treatment data management cloud platform provided by the embodiment of the present invention includes the following steps:
s101, acquiring information data of wastewater components, toxicity and heavy metal content by using wastewater monitoring equipment through a wastewater information acquisition module;
s102, a central control module coordinates and controls normal work of each module of the industrial wastewater treatment data management cloud platform by using a central processing unit;
s103, accessing the Internet through a network communication module by using a network interface to perform network communication; the wastewater treatment module utilizes wastewater treatment equipment to filter, precipitate and purify the wastewater;
s104, predicting the total amount of suspended solids in the wastewater by using a prediction program through a suspended solid prediction module; evaluating the wastewater treatment result by using an evaluation program through a treatment evaluation module;
s105, counting the wastewater treatment information data by using a statistical program through an information counting module; managing the treatment data of the industrial wastewater by using a data management program through a data management module;
s106, carrying out cloud storage on the collected wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the statistical information of wastewater treatment by using a cloud server through a cloud storage module;
and S107, updating and displaying the acquired wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the real-time data of the statistical information of wastewater treatment by using the display through the updating and displaying module.
As shown in fig. 2, the industrial wastewater treatment data management cloud platform provided by the embodiment of the present invention includes: the system comprises a wastewater information acquisition module 1, a central control module 2, a network communication module 3, a wastewater treatment module 4, a suspended solid prediction module 5, a treatment evaluation module 6, an information statistics module 7, a data management module 8, a cloud storage module 9 and an update display module 10.
The waste water information acquisition module 1 is connected with the central control module 2 and is used for acquiring information data of waste water components, toxicity and heavy metal content through waste water monitoring equipment;
the central control module 2 is connected with the wastewater information acquisition module 1, the network communication module 3, the wastewater treatment module 4, the suspended solid prediction module 5, the treatment evaluation module 6, the information statistics module 7, the data management module 8, the cloud storage module 9 and the update display module 10, and is used for coordinating and controlling the normal work of each module of the industrial wastewater treatment data management cloud platform through a central processing unit;
the network communication module 3 is connected with the central control module 2 and is used for accessing the internet through a network interface to carry out network communication;
the wastewater treatment module 4 is connected with the central control module 2 and is used for filtering, precipitating and purifying wastewater through wastewater treatment equipment;
the suspended solid prediction module 5 is connected with the central control module 2 and used for predicting the total amount of suspended solids in the wastewater through a prediction program;
the treatment evaluation module 6 is connected with the central control module 2 and is used for evaluating the wastewater treatment result through an evaluation program;
the information statistics module 7 is connected with the central control module 2 and is used for counting the wastewater treatment information data through a statistics program;
the data management module 8 is connected with the central control module 2 and is used for managing the treatment data of the industrial wastewater through a data management program;
the cloud storage module 9 is connected with the central control module 2 and is used for carrying out cloud storage on the collected wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the statistical information of wastewater treatment through a cloud server;
and the updating display module 10 is connected with the central control module 2 and is used for updating and displaying the acquired wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the real-time data of the statistical information of wastewater treatment through a display.
The invention is further described with reference to specific examples.
Example 1
The control method of the industrial wastewater treatment data management cloud platform provided by the embodiment of the invention is shown in fig. 1, and as a preferred embodiment, as shown in fig. 3, the method for predicting the total amount of suspended solids in wastewater by using a prediction program through a suspended solids prediction module provided by the embodiment of the invention comprises the following steps:
s201, acquiring target characteristic data by a suspended solid prediction module through a prediction program, and performing data preprocessing on the target characteristic data; wherein the target characteristic data refers to water quality parameters of a water inlet stage, and comprises water inlet flow, Carbonaceous Biochemical Oxygen Demand (CBOD) and Total Suspended Solids (TSS);
s202, performing PCA data dimensionality reduction on the inflow water flow and the carbonaceous biochemical oxygen demand CBOD after data pretreatment; inputting the data subjected to dimensionality reduction selection into an MLP neural network model, establishing a time sequence model of total suspended solids TSS at a water inlet stage, and evaluating the performance of the data model by using an average absolute error MAE and an average relative error MRE;
s203, inputting the past 7 days recorded value of the total suspended solid TSS into an MLP neural network model, establishing a time sequence prediction model of the total suspended solid TSS in the wastewater, and evaluating the performance of the data model by using the average absolute error MAE and the average relative error MRE.
Before preprocessing the target characteristic data, the method provided by the embodiment of the invention also needs to perform abnormal value identification and removal operation, and comprises the following steps: and (3) carrying out outlier determination on the value exceeding +3 sigma and the value smaller than-3 sigma by using a double-side outlier detection method and removing the outliers, wherein the TSS value is kept between 32mg/L and 530 mg/L.
The PCA data dimensionality reduction of the inflow water flow and the carbonaceous biochemical oxygen demand CBOD subjected to data preprocessing provided by the embodiment of the invention comprises the following steps:
(1) normalization, namely calculating the mean values of the inflow and CBOD data respectively, and subtracting the mean value from each element in the set; solving a covariance matrix and a corresponding eigenvalue matrix and eigenvector matrix for the matrix with the dimensionality mean removed; arranging the corresponding eigenvectors according to the eigenvalues from big to small, and selecting the eigenvectors corresponding to the first K eigenvalues;
(2) multiplying the original data matrix by the obtained eigenvector matrix to obtain a final matrix after dimensionality reduction; and K represents the dimensionality after dimensionality reduction, K is 5-dimensional, and the original data matrix represents a multidimensional matrix formed by inflow and CBOD.
The method for calculating the average absolute error MAE and the average relative error MRE provided by the embodiment of the invention comprises the following steps:
wherein the content of the first and second substances,
and
to representtThe predicted value of the time model is obtained,y i (t) Andy(t) To representtThe actual value of the moment.
The MLP neural network provided by the embodiment of the invention is 5 input and 1 output, the number of nodes of a hidden layer is 16, and a neuron adopts a hyperbolic tangent T-shaped transfer function; the MLP neural network adopts an iterative neural network learning scheme to update and train a prediction model; the training method comprises the following steps: for the data set containing 1395 sets of input parameters, 930 sets of input parameters are used for training the MLP neural network model, and the rest 465 sets are used as input parameters to verify the prediction capability of the BP neural network model.
Example 2
The control method of the industrial wastewater treatment data management cloud platform provided by the embodiment of the invention is shown in fig. 1, and as a preferred embodiment, as shown in fig. 4, the method for evaluating the wastewater treatment result by using an evaluation program through a treatment evaluation module provided by the embodiment of the invention comprises the following steps:
s301, presetting at least one quality evaluation characteristic of the wastewater monitoring data by using an evaluation program through a treatment evaluation module; the quality evaluation characteristics comprise frequency characteristics, stability characteristics, steep peak value characteristics, change frequency characteristics of daily monitoring values, change frequency characteristics of daytime monitoring values, random number characteristics, video monitoring flow characteristics and intrusion characteristics of video monitoring;
s302, acquiring wastewater monitoring data corresponding to enterprise wastewater, performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data, and calculating the characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data to obtain weights of all quality evaluation characteristics of the wastewater monitoring data;
s303, obtaining a quality evaluation value of the wastewater monitoring data based on the characteristic value and the weight corresponding to each quality evaluation characteristic of the wastewater monitoring data; and feeding back the quality evaluation value of the wastewater monitoring data to a supervisor so that the supervisor can judge whether the wastewater monitoring data is authentic according to the quality evaluation value of the wastewater monitoring data.
As shown in fig. 5, acquiring wastewater monitoring data corresponding to enterprise wastewater and performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to quality evaluation characteristics of the wastewater monitoring data according to an embodiment of the present invention includes:
s401, selecting a target month, and acquiring daily corresponding wastewater monitoring data and monitoring days of enterprise wastewater generated during enterprise work within the target month;
s402, respectively carrying out quality evaluation on the wastewater monitoring data corresponding to each day to obtain characteristic values corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day;
s403, obtaining characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to the target month based on the monitoring days and the characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day;
s404, determining the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to the target month as the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data.
The quality evaluation of the wastewater monitoring data corresponding to each day provided by the embodiment of the invention to obtain the characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day comprises the following steps:
1) acquiring daily monitoring frequency of wastewater monitoring data corresponding to each day;
2) and comparing the daily monitoring frequency of the wastewater monitoring data corresponding to each day with the corresponding preset daily monitoring frequency threshold value to obtain the characteristic value corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to each day.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A control method of an industrial wastewater treatment data management cloud platform is characterized by comprising the following steps:
acquiring information data of wastewater components, toxicity and heavy metal content by using wastewater monitoring equipment through a wastewater information acquisition module; the central control module is used for coordinating and controlling the normal work of each module of the industrial wastewater treatment data management cloud platform by using a central processing unit; the network communication module is used for accessing the internet by using a network interface to carry out network communication;
step two, filtering, precipitating and purifying the wastewater by using wastewater treatment equipment through a wastewater treatment module; acquiring target characteristic data by using a prediction program through a suspended solid prediction module, and performing data preprocessing on the target characteristic data; wherein the target characteristic data refers to water quality parameters of a water inlet stage, and comprises water inlet flow, Carbonaceous Biochemical Oxygen Demand (CBOD) and Total Suspended Solids (TSS);
performing PCA data dimensionality reduction on the inflow water flow and the carbonaceous biochemical oxygen demand CBOD subjected to data preprocessing; inputting the data subjected to dimensionality reduction selection into an MLP neural network model, establishing a time sequence model of total suspended solids TSS at a water inlet stage, and evaluating the performance of the data model by using an average absolute error MAE and an average relative error MRE;
inputting the past 7 days recorded value of the total suspended solid TSS into an MLP neural network model, establishing a time sequence prediction model of the total suspended solid TSS in the wastewater, and evaluating the performance of the data model by using an average absolute error MAE and an average relative error MRE; presetting at least one quality evaluation characteristic of the wastewater monitoring data by using an evaluation program through a treatment evaluation module; the quality evaluation characteristics comprise frequency characteristics, stability characteristics, steep peak value characteristics, change frequency characteristics of daily monitoring values, change frequency characteristics of daytime monitoring values, random number characteristics, video monitoring flow characteristics and intrusion characteristics of video monitoring;
acquiring wastewater monitoring data corresponding to enterprise wastewater, performing quality evaluation on the wastewater monitoring data to obtain characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data, and calculating the characteristic values corresponding to all quality evaluation characteristics of the wastewater monitoring data to obtain weights of all quality evaluation characteristics of the wastewater monitoring data; obtaining a quality evaluation value of the wastewater monitoring data based on the characteristic value and the weight corresponding to each quality evaluation characteristic of the wastewater monitoring data;
feeding back the quality evaluation value of the wastewater monitoring data to a supervisor so that the supervisor can judge whether the wastewater monitoring data has authenticity or not according to the quality evaluation value of the wastewater monitoring data; the information statistics module is used for carrying out statistics on the wastewater treatment information data by using a statistics program; managing the treatment data of the industrial wastewater by using a data management program through a data management module;
step seven, carrying out cloud storage on the collected wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the statistical information of wastewater treatment by using a cloud server through a cloud storage module; and the updating display module is used for updating and displaying the acquired wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the real-time data of the statistical information of wastewater treatment by using the display.
2. The method as claimed in claim 1, wherein in step two, before preprocessing the target feature data, the abnormal value identification and removal operation is further performed, and the method comprises:
and (3) carrying out outlier determination on the value exceeding +3 sigma and the value smaller than-3 sigma by using a double-side outlier detection method and removing the outliers, wherein the TSS value is kept between 32mg/L and 530 mg/L.
3. The method for controlling the industrial wastewater treatment data management cloud platform of claim 1, wherein in step three, the step of performing PCA data dimensionality reduction on the inlet water flow and the carbonaceous biochemical oxygen demand CBOD after data preprocessing comprises:
(1) normalization, namely calculating the mean values of the inflow and CBOD data respectively, and subtracting the mean value from each element in the set; solving a covariance matrix and a corresponding eigenvalue matrix and eigenvector matrix for the matrix with the dimensionality mean removed; arranging the corresponding eigenvectors according to the eigenvalues from big to small, and selecting the eigenvectors corresponding to the first K eigenvalues;
(2) multiplying the original data matrix by the obtained eigenvector matrix to obtain a final matrix after dimensionality reduction; and K represents the dimensionality after dimensionality reduction, K is 5-dimensional, and the original data matrix represents a multidimensional matrix formed by inflow and CBOD.
4. The method for controlling the industrial wastewater treatment data management cloud platform according to claim 1, wherein in step three, the method for calculating the mean absolute error MAE and the mean relative error MRE comprises:
wherein the content of the first and second substances,
and
to representtThe predicted value of the time model is obtained,y i (t) Andy(t) To representtThe actual value of the moment.
5. The method for controlling the industrial wastewater treatment data management cloud platform according to claim 1, wherein in step four, the MLP neural network is 5 input and 1 output, the number of nodes in the hidden layer is 16, and the neurons adopt hyperbolic tangent T-shaped transfer functions;
the MLP neural network adopts an iterative neural network learning scheme to update and train a prediction model; the training method comprises the following steps:
for the data set containing 1395 sets of input parameters, 930 sets of input parameters are used for training the MLP neural network model, and the rest 465 sets are used as input parameters to verify the prediction capability of the BP neural network model.
6. The method for controlling the industrial wastewater treatment data management cloud platform according to claim 1, wherein in step five, the obtaining of wastewater monitoring data corresponding to enterprise wastewater and the quality evaluation of the wastewater monitoring data to obtain characteristic values corresponding to quality evaluation characteristics of the wastewater monitoring data comprises:
(1) selecting a target month, and acquiring daily corresponding wastewater monitoring data and monitoring days of enterprise wastewater generated during enterprise work within the target month;
(2) respectively performing quality evaluation on the wastewater monitoring data corresponding to each day to obtain characteristic values corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day;
(3) obtaining characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to the target month based on the monitoring days and the characteristic values corresponding to the quality evaluation characteristics of the wastewater monitoring data corresponding to each day;
(4) and determining the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to the target month as the characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data.
7. The method for controlling the industrial wastewater treatment data management cloud platform according to claim 6, wherein the step of performing quality evaluation on the wastewater monitoring data corresponding to each day to obtain a characteristic value corresponding to each quality evaluation characteristic of the wastewater monitoring data corresponding to each day comprises the steps of:
1) acquiring daily monitoring frequency of wastewater monitoring data corresponding to each day;
2) and comparing the daily monitoring frequency of the wastewater monitoring data corresponding to each day with the corresponding preset daily monitoring frequency threshold value to obtain the characteristic value corresponding to the monitoring frequency characteristic of the wastewater monitoring data corresponding to each day.
8. An industrial wastewater treatment data management cloud platform to which the control method of the industrial wastewater treatment data management cloud platform according to any one of claims 1 to 7 is applied, the industrial wastewater treatment data management cloud platform comprising:
the system comprises a wastewater information acquisition module, a central control module, a network communication module, a wastewater treatment module, a suspended solid prediction module, a treatment evaluation module, an information statistics module, a data management module, a cloud storage module and an update display module;
the waste water information acquisition module is connected with the central control module and is used for acquiring information data of waste water components, toxicity and heavy metal content through waste water monitoring equipment;
the central control module is connected with the wastewater information acquisition module, the central control module, the network communication module, the wastewater treatment module, the suspended solid prediction module, the treatment evaluation module, the information statistics module, the data management module, the cloud storage module and the update display module and is used for coordinating and controlling the normal work of each module of the industrial wastewater treatment data management cloud platform through the central processing unit;
the network communication module is connected with the central control module and is used for accessing the Internet through a network interface to carry out network communication;
the wastewater treatment module is connected with the central control module and is used for filtering, precipitating and purifying wastewater through wastewater treatment equipment;
the suspended solid prediction module is connected with the central control module and used for predicting the total amount of suspended solids in the wastewater through a prediction program;
the treatment evaluation module is connected with the central control module and is used for evaluating the wastewater treatment result through an evaluation program;
the information statistics module is connected with the central control module and is used for counting the wastewater treatment information data through a statistics program;
the data management module is connected with the central control module and is used for managing the treatment data of the industrial wastewater through a data management program;
the cloud storage module is connected with the central control module and is used for carrying out cloud storage on the collected wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the statistical information of wastewater treatment through the cloud server;
and the updating display module is connected with the central control module and is used for updating and displaying the acquired wastewater information, the wastewater treatment result, the suspended solid prediction result, the treatment evaluation result and the real-time data of the statistical information of wastewater treatment through the display.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the method of controlling an industrial wastewater treatment data management cloud platform of any of claims 1 to 7 when executed on an electronic device.
10. A computer readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the method of controlling an industrial wastewater treatment data management cloud platform of any of claims 1 to 7.
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