Intelligent monitoring system and method for sewage treatment and medium

Abstract

The invention belongs to the technical field of sewage treatment, and particularly relates to an intelligent monitoring system and method for sewage treatment and a medium, production data and operation data are collected from field instruments of a field station, and the production data and the operation data are read and uploaded to a central control station through a PLC control station to be processed and analyzed; the central control station firstly receives production data and operation data transmitted from the PLC control station, performs data report analysis and data trend analysis after preprocessing, and generates a data analysis result; a data exception result is extracted according to the data analysis result, and an early warning result is output; and finally, constructing a digital mirror image of the sewage treatment plant based on a digital twinborn technology, and displaying a data analysis result and an early warning result through the digital mirror image of the sewage treatment plant. The problems of low efficiency and poor benefit of sewage treatment in the prior art can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of sewage treatment, and in particular relates to an intelligent monitoring system, method and medium for sewage treatment. Background Art

[0002] The future development trend of my country's sewage treatment industry is towards scale, efficiency, intelligence and resource utilization.

[0003] Compared with traditional sewage treatment, the intelligent monitoring system for sewage treatment can effectively reduce the use of resources and promote the automation and intelligence of sewage treatment. The automation development of sewage treatment plant's control over process operation is divided into three stages. The first stage is the manual control stage, the second stage is the automatic control stage, and the third stage is the unmanned stage. At present, my country's sewage treatment industry is generally in the first to second stages, and the third stage is relatively small. Specifically, the running program transmits all control signals to the host computer operating system. The control interface of the operating system on the host computer still needs to be manually switched and adjusted, which is essentially no different from on-site operation. The use of manual operation mode relies on experience for competent judgment, control and delivery, which will lead to low treatment efficiency and poor economic benefits. It is necessary to develop a system and solution that can realize intelligent sewage treatment. Summary of the invention

[0004] The technical problem solved by the present invention is to provide a sewage treatment intelligent monitoring system, method and medium to solve the problems of low efficiency and poor benefit of sewage treatment in the prior art.

[0005] The basic solution provided by the present invention: an intelligent monitoring system for sewage treatment, comprising a central control station, a PLC control station and a field station connected in sequence via Ethernet, wherein the field station collects production data and operation data through field instruments distributed throughout various treatment systems of the sewage treatment plant, and the PLC control station reads the production data and operation data and uploads them to the central control station for processing and analysis;

[0006] The central control station includes a data receiving module, a preprocessing module, a data analysis module, an early warning analysis module, a storage module and a display module, wherein:

[0007] The data receiving module is used to receive the production data and operation data transmitted from the PLC control station, and transmit it to the preprocessing module for preprocessing and the storage module for storage;

[0008] The preprocessing module preprocesses the production data and operation data and transmits them to the analysis module for analysis;

[0009] The data analysis module is used to perform data report analysis and data trend analysis based on the pre-processed production data and operation data, and generate data analysis results;

[0010] The early warning analysis module extracts data anomaly results based on data analysis results and outputs early warning results;

[0011] The display module is used to receive data analysis results and early warning results, and build a digital mirror of the sewage treatment plant based on the digital twin technology, and display the data analysis results and early warning results through the digital mirror of the sewage treatment plant.

[0012] Furthermore, in the data analysis module, data report analysis and data trend analysis are performed based on the pre-processed production data and operation data, and the data analysis results generated are specifically:

[0013] Perform statistical analysis on the pre-processed production data and operation data according to parameter types and parameter parameters, generate real-time reports from the pre-processed production data and operation data received in real time from the data receiving module, and generate historical reports from the pre-processed production data and operation data extracted from the storage module;

[0014] Extract parameter types and parameter parameters in real-time reports and historical reports, and generate real-time trend curves and historical trend curves corresponding to the parameter types on the time axis;

[0015] An operation control is generated according to the user operation parameters, and the real-time trend curve graph and the historical trend curve graph are operated according to the operation control.

[0016] Furthermore, the warning analysis module includes an abnormality extraction unit, a warning screening unit, a warning positioning unit and a graded warning unit, wherein:

[0017] The abnormality extraction unit is used to extract abnormal data of real-time reports, abnormal data of historical reports, abnormal data of real-time trend curve graphs and abnormal data of historical trend curve graphs in the data analysis results, and generate abnormality extraction results;

[0018] The warning processing unit is used to generate warning information based on the abnormal extraction results according to the warning type, start time, end time, warning location and processing method;

[0019] The warning screening unit is used to evaluate the importance of the generated warning information, and generate corresponding priority levels from high to low according to the importance evaluation results; and determine whether the importance of the warning information exceeds a preset importance threshold. If not, the warning content of the corresponding warning information is simplified in a title-like manner;

[0020] The warning location unit is used to locate the source of the corresponding warning information according to the priority level, and associate the warning points involved in the source location process to generate a warning association chain;

[0021] The warning processing push unit is used to push the generated warning association chain to the operation and maintenance personnel, who will take the source warning point of the warning association chain as the processing starting point.

[0022] Furthermore, the display module includes a scene model construction unit, an operation control unit, a rendering unit and a visualization unit, wherein:

[0023] The scenario model building unit is used to build a digital image of the sewage treatment plant based on the scenario data of the sewage treatment plant and the operation data and simulation data of each treatment system;

[0024] The operation control unit is used to control the digital image of the sewage treatment plant according to the operation parameters; the operation control unit is also used to control the digital image of the sewage treatment plant according to the early warning analysis results;

[0025] The rendering unit is used for rendering the digital image of the sewage treatment plant controlled by the operation control unit;

[0026] The visualization unit is used to visualize the digital image of the sewage treatment plant rendered by the rendering unit and the data analysis results analyzed by the data analysis module.

[0027] Intelligent monitoring method for sewage treatment, including:

[0028] S1: Obtain production and operation data of each treatment system in the sewage treatment plant from the field station and transmit it to the central control station through the PLC control station;

[0029] S2: The central control station receives production data and operation data and performs pre-processing;

[0030] S3: Perform data report analysis and data trend analysis on the pre-processed production data and operation data to generate data analysis results;

[0031] S4: Extract data anomaly results based on analysis results and output warning results;

[0032] S5: Display the data analysis results and early warning results through the constructed digital image of the sewage treatment plant.

[0033] Further, the S3 includes:

[0034] S3-1: Statistically analyzing the pre-processed production data and operation data according to parameter types and parameter parameters, generating real-time reports from the pre-processed production data and operation data received in real time from the data receiving module, and generating historical reports from the pre-processed production data and operation data extracted from the storage module;

[0035] S3-2: extract parameter types and parameter parameters in real-time reports and historical reports, and generate real-time trend curves and historical trend curves corresponding to the parameter types on the time axis;

[0036] S3-3: Generate operation control according to user operation parameters, and operate the real-time trend curve graph and the historical trend curve graph according to the operation control.

[0037] Further, the S4 includes:

[0038] S4-1: extracting abnormal data of real-time reports, abnormal data of historical reports, abnormal data of real-time trend curves and abnormal data of historical trend curves from the data analysis results, and generating abnormal extraction results;

[0039] S4-2: Generate warning information based on the abnormal extraction results according to the warning type, start time, end time, warning location and processing method;

[0040] S4-3: Evaluate the importance of the generated warning information, and generate corresponding priority levels from high to low according to the importance evaluation results; and determine whether the importance of the warning information exceeds a preset importance threshold. If not, simplify the warning content of the corresponding warning information in a title-like manner;

[0041] S4-4: locate the source of the corresponding warning information according to the priority level, and associate the warning points involved in the source location process to generate a warning association chain;

[0042] S4-5: The generated warning association chain is pushed to the operation and maintenance personnel, who take the source warning point of the warning association chain as the processing starting point.

[0043] Further, the S5 includes:

[0044] S5-1: Establish a digital image of the sewage treatment plant based on the scene data of the sewage treatment plant and the operation data and simulation data of each treatment system;

[0045] S5-2: Control the digital image of the sewage treatment plant according to the operating parameters; and control the digital image of the sewage treatment plant according to the early warning analysis results;

[0046] S5-3: Rendering of digital image of sewage treatment plant after operation control;

[0047] S5-4: Visualize the digital image of the sewage treatment plant and the data analysis results rendered by the rendering unit.

[0048] A computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute the intelligent monitoring method for sewage treatment as described above.

[0049] The principles and advantages of the present invention are as follows: In the present application, the problem of low treatment efficiency in some existing sewage treatment plants is caused, on the one hand, by the data analysis method, and on the other hand, by the low efficiency of equipment early warning analysis and processing. In this regard, the present application improves these two aspects, respectively optimizing the data analysis presentation method and optimizing the early warning analysis logic, so as to improve the efficiency of the sewage treatment plant during production operation, thereby improving production benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a functional block diagram of an embodiment of the present invention;

[0051] Figure 2 is a schematic diagram of the central control station architecture in an embodiment of the present invention;

[0052] Figure 3 It is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following is further described in detail through specific implementation methods:

[0054] The embodiment is basically as shown in the attached Figure 1 As shown: The intelligent monitoring system for sewage treatment includes a central control station, a PLC control station and a field station which are connected in sequence via Ethernet. The field station collects production data and operation data through field instruments distributed throughout the treatment systems of the sewage treatment plant. The PLC control station reads the production data and operation data and uploads them to the central control station for processing and analysis.

[0055] In order to form a complete three-level monitoring network for sewage treatment plants, Figure 2 The figure shows the architecture of the system of the present application, wherein the field station includes several monitoring modules, which are arranged in various treatment systems of the sewage treatment plant in the form of sensors, instruments, cameras, etc., such as the coarse screen system, pretreatment system, biochemical system, membrane pool system, sludge system, storage tank, deodorization system, chlorination tank, aeration system, fan system, etc. of the sewage treatment plant. The collected production data and operation data include influent water volume, effluent water volume, influent indicators (PH, DO, SS, temperature, COD, ammonia nitrogen, TN, TP, turbidity, conductivity), effluent indicators (PH, DO, SS, temperature, COD, ammonia nitrogen, TN, TP, turbidity, conductivity), power consumption, fan current, voltage, pump voltage, current, equipment power supply status, etc.; the collected data is transmitted to the central control station for data acquisition processing, data storage processing, data application and monitoring and early warning. Specifically, the central control station includes a data receiving module, a preprocessing module, a data analysis module, an early warning analysis module, a storage module and a display module, wherein:

[0056] The data receiving module is used to receive the production data and operation data transmitted from the PLC control station, and transmit it to the preprocessing module for preprocessing and the storage module for storage;

[0057] In this embodiment, the storage module includes a real-time data storage module and a historical data storage module. The real-time data storage module is used to receive real-time production data and real-time operation data of the sewage treatment plant transmitted in real time, while the historical data storage module stores historical production data and operation data; wherein, the historical data storage has the following functions: 1. The historical data storage time is configurable, that is, the time interval for scheduled saving can be set, including 1 minute, 10 minutes, 30 minutes and 1 hour, and the user can set the time for each scheduled saving according to needs; 2. The historical data storage module has data import and export functions, so that it is convenient for the sewage treatment system to back up, restore and extract data for comparative analysis and processing; 3. It supports breakpoint resumption, data caching and adjustment functions. When data is missing due to network failure or equipment communication failure, adjustment can be performed to keep the historical data intact; 4. The storage period of the historical data storage module in this application is set to be greater than 1 year.

[0058] The real-time data storage module is implemented in the present application using a temporary memory. After the data stored in the real-time data storage module is extracted for analysis and processing, it is stored in the historical data storage module for storage.

[0059] After the preprocessing module preprocesses the production data and operation data, it is transmitted to the analysis module for analysis; in this application, preprocessing includes data cleaning processing, data missing processing, data format unification processing, data type conversion processing, etc. The interfaces of system equipment used in different sewage treatment plants are different. This application performs unified processing through preprocessing to facilitate subsequent data analysis and processing more smoothly.

[0060] During the analysis process, the application is provided with a data analysis module and an early warning analysis module, wherein the data analysis module is used to perform data report analysis and data trend analysis based on the pre-processed production data and operation data, and generate data analysis results; specifically, the detailed process of data analysis is as follows:

[0061] Step 1: Perform statistical analysis on the pre-processed production data and operation data according to parameter types and parameter parameters, generate real-time reports from the pre-processed production data and operation data received in real time from the data receiving module, and generate historical reports from the pre-processed production data and operation data extracted from the storage module;

[0062] Step 2: extract parameter types and parameter parameters in real-time reports and historical reports, and generate real-time trend curve graphs and historical trend curve graphs corresponding to the parameter types on the time axis;

[0063] Step 3: Generate operation control according to user operation parameters, and operate the real-time trend curve chart and the historical trend curve chart according to the operation control.

[0064] In this embodiment, the data analysis results are presented in the form of reports and trend curves. Specifically, the pre-processed production data and operation data include, on the one hand, data of various indicators of the sewage treatment plant, and on the other hand, some operation data of each treatment system. The various indicators and treatment system types are the parameter types mentioned in step one, and the parameters of each indicator are parameter parameters. Among these data, real-time production data and operation data are instantaneous quantities, which are presented in the form of real-time reports, specifically recorded in the form of tables, while historical reports are obtained by accessing the historical data storage module to obtain detailed data, and generating historical reports through graphical processing, etc. The historical reports of this application include daily reports, monthly reports and free reports.

[0065] As for the generation of trend curve graphs, in this embodiment, the real-time trend curve graph can intuitively reflect the dynamic changes and fluctuations of the data collected by the field station, and the historical trend curve graph is used to observe the change pattern of the data in the later stage, so as to summarize relevant experience. For example, the liquid level difference before and after the coarse / fine grid in the grid channel, the water quality monitoring data of the biochemical pool, the DO of the aeration tank, the liquid level of the secondary pump room suction well, the water quality of the outgoing water, the harmful gases in the plant area and other important data are statistically observed, and the change pattern of these data can be determined.

[0066] In addition to presenting the real-time trend graph and the historical trend graph, the present application also includes an adjustment control unit in the data analysis module, which generates an operation control according to the user operation parameters to realize the operation of the real-time trend graph and the historical trend graph. Specifically, the adjustment control unit includes the following functions:

[0067] 1. By changing the time range of the trend chart and enlarging the curve ratio, you can carefully observe the specific changes in data within a short period of time, which is helpful for engineers to find key data;

[0068] 2. You can choose to display real-time data trends or historical data trends, and both current data and historical data can be compared in detail;

[0069] 3. Meet user needs through functions such as adding pens, deleting pens, starting grid scrolling, changing colors, and data display direction;

[0070] 4. Connect historical data into a process curve to help operators make judgments on future development trends and take effective measures in advance to prepare for a rainy day.

[0071] With the help of the above-mentioned data analysis module, the production data and operation data during the operation of the sewage treatment plant can be generated into corresponding report content and trend curve charts, so that users can check relevant content in time and make decisions in advance.

[0072] The early warning analysis module extracts data anomaly results based on the data analysis results and outputs the early warning results; wherein the early warning analysis module includes an anomaly extraction unit, an early warning screening unit, an early warning positioning unit and a hierarchical early warning unit, specifically:

[0073] The abnormality extraction unit is used to extract abnormal data of real-time reports, abnormal data of historical reports, abnormal data of real-time trend curve graphs and abnormal data of historical trend curve graphs in the data analysis results, and generate abnormality extraction results;

[0074] The warning processing unit is used to generate warning information based on the abnormal extraction results according to the warning type, start time, end time, warning location and processing method;

[0075] The warning screening unit is used to evaluate the importance of the generated warning information, and generate corresponding priority levels from high to low according to the importance evaluation results; and determine whether the importance of the warning information exceeds a preset importance threshold. If not, the warning content of the corresponding warning information is simplified in a title-like manner;

[0076] The warning location unit is used to locate the source of the corresponding warning information according to the priority level, and associate the warning points involved in the source location process to generate a warning association chain;

[0077] The warning processing push unit is used to push the generated warning association chain to the operation and maintenance personnel, who will take the source warning point of the warning association chain as the processing starting point.

[0078] In this embodiment, the purpose of setting the early warning analysis unit is that in the sewage treatment process of the sewage treatment plant, the monitoring amount is very large. When a certain device fails, a lot of alarm prompts may appear. The reason is that the device belongs to the source device. When the source device has a problem, the connected subordinate devices will also have data abnormalities, causing the subordinate devices to also start to have alarm information. Therefore, a large amount of alarm information from the entire equipment operation process fills the screen, making it difficult for the staff to capture effective information and locate the key to the failure in time, and even ignore the loss of important alarm data, making it difficult to continue to analyze the alarm information. To prevent this kind of situation, the solution adopted by this application is:

[0079] First, abnormal data are extracted from the analysis results of the data analysis module, including real-time report abnormal data, historical report abnormal data, real-time trend curve abnormal data and historical trend curve abnormal data. These abnormal data reflect the abnormal production indicators and equipment operation of the sewage treatment plant during the sewage treatment process. Abnormal production indicators are often caused by abnormal equipment operation. For example, when the harmful substances in the water quality after filtering in the filter pool increase significantly, it is caused by filter failure. Therefore, the above abnormal data as a whole reflects the abnormal operation of the equipment.

[0080] When the equipment is operating abnormally, warning information is generated according to the warning type, start time, end time, warning location and processing method. The warning information can facilitate users to view detailed warning details, but when the warning information increases, dense warning information will be presented on the display interface, making it difficult for users to find accurate information. To this end, the warning information is screened. The screening method is to first evaluate the importance of the warning information. The importance of this application is the influence of the warning point on the position of the entire sewage treatment process, the start and stop influence, and the influence on other equipment. These influence levels are evaluated as the importance of the warning information, and the corresponding priority levels are generated from high to low according to the importance. The level, that is, the higher the importance, the higher the priority, and the lower the importance of these warning information. On the other hand, it also reflects that the problem of the equipment is very easy to solve. What's more, after the problem of the high-priority equipment is solved, the problem of the low-priority equipment is automatically solved. For example, there is an instantaneous over-flow warning of a flow meter, and the reason for the instantaneous over-flow of the flow meter is that the previous sewage treatment equipment has a fault and is affected. As long as the previous equipment fault is solved, the instantaneous over-flow warning of the flow meter is solved. Therefore, for such low-priority and low-importance warning information, it is simplified in title style without displaying its details, so that the warning information of multiple days is presented in order.

[0081] When some warning information is generated and needs to be processed and resolved by operation and maintenance personnel, in order to better maintain the stability of the entire production line of the equipment, this application locates the source of the corresponding warning information according to the priority level, specifically, from the warning information with low priority level to the warning device with high priority according to its equipment thread, all warning points involved in the positioning process are associated to generate a warning association chain. When the operation and maintenance personnel are processing, they give priority to the source in the warning association chain, that is, the warning information with the highest priority, and then exclude the remaining warning information. In this way, on the one hand, the processing efficiency of the operation and maintenance personnel can be improved, because when the warning information with the highest priority is processed, the remaining warning points associated with it may naturally return to normal. On the other hand, after the warning information of an associated chain is processed in an orderly manner, the normal operation of the sewage treatment plant can be restored more quickly.

[0082] The display module is used to receive data analysis results and early warning results, and build a digital image of the sewage treatment plant based on the digital twin technology, and display the data analysis results and early warning results through the digital image of the sewage treatment plant; wherein, the display module includes a scene model construction unit, an operation control unit, a rendering unit and a visualization unit, specifically:

[0083] The scenario model building unit is used to build a digital image of the sewage treatment plant based on the scenario data of the sewage treatment plant and the operation data and simulation data of each treatment system;

[0084] The operation control unit is used to control the digital image of the sewage treatment plant according to the operation parameters; the operation control unit is also used to control the digital image of the sewage treatment plant according to the early warning analysis results;

[0085] The rendering unit is used for rendering the digital image of the sewage treatment plant controlled by the operation control unit;

[0086] The visualization unit is used to visualize the digital image of the sewage treatment plant rendered by the rendering unit and the data analysis results analyzed by the data analysis module.

[0087] In this embodiment, the structure of the display module is presented in the form of a digital image of the sewage treatment plant established based on the digital twin technology. Specifically, the scene data of the sewage treatment plant is first obtained, including green plants, buildings, the appearance and location of each treatment system, and then the operation data and simulation data of each treatment system are obtained. The operation data is the data generated by the actual operation process of each treatment system, and real-time data transmission is achieved through sensors or instrument collection. The simulation data is the detailed operation process of the modeling model of each treatment system. The digital image of the sewage treatment plant generated in this way can effectively simulate the actual operation of the actual sewage treatment plant.

[0088] After the digital image of the sewage treatment plant is generated, it is a twin of the actual sewage treatment plant, which can realize intelligent monitoring and management of the sewage treatment plant. At the same time, it can also receive parameters under user operation to control the digital image of the sewage treatment plant, and receive abnormal data transmitted by the early warning analysis module to trigger the corresponding early warning mechanism. For example, when a filtration abnormality occurs in the filter tank of the sewage treatment plant, the abnormality is displayed synchronously in the digital image of the sewage treatment plant. The user can operate the digital image of the sewage treatment plant through zooming operations to check the specific situation. With the automatic processing function, the digital image of the sewage treatment plant can also be managed intelligently, such as pushing early warning information, so as to optimize the operating cost of the sewage treatment plant.

[0089] The reports and graphs in the data analysis results of the present application are displayed through a visualization unit to facilitate user reference.

[0090] like Figure 3 As shown, in another embodiment of the present embodiment, a sewage treatment intelligent monitoring method is also included, which is applied to the above-mentioned sewage treatment intelligent monitoring system, including:

[0091] S1: Obtain production and operation data of each treatment system in the sewage treatment plant from the field station and transmit it to the central control station through the PLC control station;

[0092] S2: The central control station receives production data and operation data and performs pre-processing;

[0093] S3: Perform data report analysis and data trend analysis on the pre-processed production data and operation data to generate data analysis results; S3 includes:

[0094] S3-1: Statistically analyzing the pre-processed production data and operation data according to parameter types and parameter parameters, generating real-time reports from the pre-processed production data and operation data received in real time from the data receiving module, and generating historical reports from the pre-processed production data and operation data extracted from the storage module;

[0095] S3-2: extract parameter types and parameter parameters in real-time reports and historical reports, and generate real-time trend curves and historical trend curves corresponding to the parameter types on the time axis;

[0096] S3-3: Generate an operation control according to the user operation parameters, and operate the real-time trend curve graph and the historical trend curve graph according to the operation control;

[0097] S4: Extract data anomaly results based on analysis results and output warning results; S4 includes:

[0098] S4-1: extracting abnormal data of real-time reports, abnormal data of historical reports, abnormal data of real-time trend curves and abnormal data of historical trend curves from the data analysis results, and generating abnormal extraction results;

[0099] S4-2: Generate warning information based on the abnormal extraction results according to the warning type, start time, end time, warning location and processing method;

[0100] S4-3: Evaluate the importance of the generated warning information, and generate corresponding priority levels from high to low according to the importance evaluation results; and determine whether the importance of the warning information exceeds a preset importance threshold. If not, simplify the warning content of the corresponding warning information in a title-like manner;

[0101] S4-4: locate the source of the corresponding warning information according to the priority level, and associate the warning points involved in the source location process to generate a warning association chain;

[0102] S4-5: Push the generated warning association chain to the operation and maintenance personnel, who will take the source warning point of the warning association chain as the processing starting point;

[0103] S5: Display the data analysis results and early warning results through the constructed digital image of the sewage treatment plant; S5 includes:

[0104] S5-1: Establish a digital image of the sewage treatment plant based on the scene data of the sewage treatment plant and the operation data and simulation data of each treatment system;

[0105] S5-2: Control the digital image of the sewage treatment plant according to the operating parameters; and control the digital image of the sewage treatment plant according to the early warning analysis results;

[0106] S5-3: Rendering of digital image of sewage treatment plant after operation control;

[0107] S5-4: Visualize the digital image of the sewage treatment plant and the data analysis results rendered by the rendering unit.

[0108] The embodiments of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the intelligent monitoring method for sewage treatment provided by any embodiment of the present invention.

[0109] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0110] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the intelligent monitoring method for sewage treatment provided by any embodiment of the present invention.

[0111] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field are aware of all the common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. Intelligent sewage treatment monitoring system, characterized by: It includes a central control station, a PLC control station and a field station which are connected in sequence via Ethernet. The field station collects production data and operation data through field instruments distributed throughout the treatment systems of the sewage treatment plant. The PLC control station reads the production data and operation data and uploads them to the central control station for processing and analysis. The central control station includes a data receiving module, a preprocessing module, a data analysis module, an early warning analysis module, a storage module and a display module, wherein: The data receiving module is used to receive the production data and operation data transmitted from the PLC control station, and transmit it to the preprocessing module for preprocessing and the storage module for storage; The preprocessing module preprocesses the production data and operation data and transmits them to the analysis module for analysis; The data analysis module is used to perform data report analysis and data trend analysis based on the pre-processed production data and operation data, and generate data analysis results; The early warning analysis module extracts data anomaly results based on data analysis results and outputs early warning results; The display module is used to receive data analysis results and early warning results, and build a digital mirror of the sewage treatment plant based on the digital twin technology, and display the data analysis results and early warning results through the digital mirror of the sewage treatment plant.

2. The intelligent monitoring system for sewage treatment according to claim 1 is characterized in that: In the data analysis module, data report analysis and data trend analysis are performed based on the pre-processed production data and operation data, and the data analysis results generated are specifically as follows: Perform statistical analysis on the pre-processed production data and operation data according to parameter types and parameter parameters, generate real-time reports from the pre-processed production data and operation data received in real time from the data receiving module, and generate historical reports from the pre-processed production data and operation data extracted from the storage module; Extract parameter types and parameter parameters in real-time reports and historical reports, and generate real-time trend curves and historical trend curves corresponding to the parameter types on the time axis; An operation control is generated according to the user operation parameters, and the real-time trend curve graph and the historical trend curve graph are operated according to the operation control.

3. The intelligent monitoring system for sewage treatment according to claim 2 is characterized in that: The warning analysis module includes an abnormality extraction unit, a warning screening unit, a warning positioning unit and a graded warning unit, wherein: The abnormality extraction unit is used to extract abnormal data of real-time reports, abnormal data of historical reports, abnormal data of real-time trend curve graphs and abnormal data of historical trend curve graphs in the data analysis results, and generate abnormality extraction results; The warning processing unit is used to generate warning information based on the abnormal extraction results according to the warning type, start time, end time, warning location and processing method; The warning screening unit is used to evaluate the importance of the generated warning information, and generate corresponding priority levels from high to low according to the importance evaluation results; and determine whether the importance of the warning information exceeds a preset importance threshold. If not, the warning content of the corresponding warning information is simplified in a title-like manner; The warning location unit is used to locate the source of the corresponding warning information according to the priority level, and associate the warning points involved in the source location process to generate a warning association chain; The warning processing push unit is used to push the generated warning association chain to the operation and maintenance personnel, who will take the source warning point of the warning association chain as the processing starting point.

4. The intelligent monitoring system for sewage treatment according to claim 3 is characterized in that: The display module includes a scene model construction unit, an operation control unit, a rendering unit and a visualization unit, wherein: The scenario model building unit is used to build a digital image of the sewage treatment plant based on the scenario data of the sewage treatment plant and the operation data and simulation data of each treatment system; The operation control unit is used to control the digital image of the sewage treatment plant according to the operation parameters; the operation control unit is also used to control the digital image of the sewage treatment plant according to the early warning analysis results; The rendering unit is used for rendering the digital image of the sewage treatment plant controlled by the operation control unit; The visualization unit is used to visualize the digital image of the sewage treatment plant rendered by the rendering unit and the data analysis results analyzed by the data analysis module.

5. An intelligent monitoring method for sewage treatment, characterized in that: include: S1: Obtain production and operation data of each treatment system in the sewage treatment plant from the field station and transmit it to the central control station through the PLC control station; S2: The central control station receives production data and operation data and performs pre-processing; S3: Perform data report analysis and data trend analysis on the pre-processed production data and operation data to generate data analysis results; S4: Extract data anomaly results based on analysis results and output warning results; S5: Display the data analysis results and early warning results through the constructed digital image of the sewage treatment plant.

6. The intelligent monitoring method for sewage treatment according to claim 5 is characterized in that: The S3 includes: S3-1: Statistically analyzing the pre-processed production data and operation data according to parameter types and parameter parameters, generating real-time reports from the pre-processed production data and operation data received in real time from the data receiving module, and generating historical reports from the pre-processed production data and operation data extracted from the storage module; S3-2: extract parameter types and parameter parameters in real-time reports and historical reports, and generate real-time trend curve graphs and historical trend curve graphs corresponding to the parameter types on the time axis; S3-3: Generate operation control according to user operation parameters, and operate the real-time trend curve graph and the historical trend curve graph according to the operation control.

7. The intelligent monitoring method for sewage treatment according to claim 6, characterized in that: The S4 includes: S4-1: extracting abnormal data of real-time reports, abnormal data of historical reports, abnormal data of real-time trend curves and abnormal data of historical trend curves from the data analysis results, and generating abnormal extraction results; S4-2: Generate warning information based on the abnormal extraction results according to the warning type, start time, end time, warning location and processing method; S4-3: Evaluate the importance of the generated warning information, and generate corresponding priority levels from high to low according to the importance evaluation results; and determine whether the importance of the warning information exceeds a preset importance threshold. If not, simplify the warning content of the corresponding warning information in a title-like manner; S4-4: locate the source of the corresponding warning information according to the priority level, and associate the warning points involved in the source location process to generate a warning association chain; S4-5: The generated warning association chain is pushed to the operation and maintenance personnel, who take the source warning point of the warning association chain as the processing starting point.

8. The intelligent monitoring method for sewage treatment according to claim 7 is characterized in that: The S5 includes: S5-1: Establish a digital image of the sewage treatment plant based on the scene data of the sewage treatment plant and the operation data and simulation data of each treatment system; S5-2: Control the digital image of the sewage treatment plant according to the operating parameters; and control the digital image of the sewage treatment plant according to the early warning analysis results; S5-3: Rendering of digital image of sewage treatment plant after operation control; S5-4: Visualize the digital image of the sewage treatment plant and the data analysis results rendered by the rendering unit.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and the programs or instructions enable a computer to execute the intelligent monitoring method for sewage treatment as described in any one of claims 5-8.

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