Intelligent monitoring system for sewage treatment in coal chemical industry

Abstract

The invention relates to the technical field of intelligent monitoring of sewage treatment, in particular to an intelligent monitoring system for coal chemical sewage treatment, which comprises a water quality acquisition module used for acquiring water quality data in the coal chemical sewage treatment process; the water quality data processing module is used for standardizing the water quality data; the processing identification module is used for identifying the standardized water quality data according to a physical index identification model, a chemical index identification model and a biological index identification model; the correction treatment module is used for correcting the treatment identification module according to the substandard index of the coal chemical sewage treatment process and a preset substandard index; and the optimization correction module is used for optimizing the preset substandard times according to the use time of the coal chemical sewage treatment device and the preset use time. The reliability and safety of the intelligent monitoring system for sewage treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring of sewage treatment, and particularly to an intelligent monitoring system for coal chemical sewage treatment. Background Technique

[0002] Most of the wastewater generated in the coal chemical production process belongs to high-salt wastewater, containing a large amount of inorganic salts and organic substances. Direct discharge will cause serious pollution to the water environment, affect the growth and reproduction of aquatic organisms, and further damage the balance of the ecosystem. With the increasingly strict environmental protection regulations, higher standards and requirements are put forward for the wastewater discharge of coal chemical enterprises. Enterprises must adopt effective means to reduce the pollutant concentration in the wastewater and achieve up-to-standard discharge or zero discharge. The rapid development of modern sensor technology, data acquisition technology, and network communication technology provides a solid foundation for the construction of an intelligent monitoring system. By installing sensors on sewage treatment equipment, water quality parameters and equipment operation status can be monitored in real time, providing data support for intelligent monitoring.

[0003] Chinese Patent Publication No.: CN113885596A discloses an intelligent monitoring system for sewage treatment. The system collects the working condition data of operating equipment in real time, stores and analyzes the collected data, and can realize the transmission of alarm information, including multi-level high water level alarm, multi-level low water level alarm, over-temperature passive alarm, and over-temperature active alarm, etc. However, this solution cannot intelligently identify different sewage treatment processes, and it is difficult to improve the reliability and safety of the intelligent monitoring system for sewage treatment. Summary of the Invention

[0004] Therefore, the present invention provides an intelligent monitoring system for coal chemical sewage treatment to overcome the problems of low reliability and safety of the intelligent monitoring system for sewage treatment in the prior art.

[0005] To achieve the above object, the present invention provides an intelligent monitoring system for coal chemical sewage treatment, including:

[0006] A water quality acquisition module for acquiring water quality data during the coal chemical sewage treatment process;

[0007] A water quality data processing module for performing standardized processing on the water quality data to obtain standardized water quality data;

[0008] A processing and identification module for constructing a physical index identification model, a chemical index identification model, and a biological index identification model, and identifying the standardized water quality data according to the physical index identification model, the chemical index identification model, and the biological index identification model;

[0009] A calibration processing module is used to compare the non-compliance index P in the coal chemical sewage treatment process with the preset non-compliance index P0, judge the fault condition of the processing and identification module according to the comparison result, and calibrate the processing and identification module according to the judgment result;

[0010] An optimization and calibration module is used to compare the usage time T of the coal chemical sewage treatment device with the preset usage time T0, judge the authenticity of the calibration processing module according to the comparison result, and optimize the preset non-compliance times P0 according to the judgment result.

[0011] Further, the processing and identification module includes:

[0012] A physical index identification unit is used to construct a physical index identification model and identify the physical index data in the standardized water quality data according to the physical index identification model;

[0013] A chemical index identification unit is used to construct a chemical index identification model and identify the chemical index data in the standardized water quality data according to the chemical index identification model;

[0014] A biological index identification unit is used to construct a biological index identification model and identify the biological index data in the standardized water quality data according to the biological index identification model.

[0015] Further, in the physical index identification unit, a physical index identification model is constructed according to the historical physical index database. The historical physical index database is divided into a 70% physical training set, a 20% physical verification set, and a 10% physical test set. The physical training set is input into the support vector machine model to train the support vector machine model. The physical verification set is input into the trained support vector machine model to optimize the parameters of the support vector machine model. The physical test set is input into the support vector machine model with optimized parameters to obtain the physical accuracy rate A. The physical accuracy rate A is compared with the preset physical accuracy rate A0. The training compliance of the support vector machine model is judged according to the comparison result and output according to the judgment result, where:

[0016] When A≥A0, it is determined that the support vector machine model training is qualified, and the support vector machine model is output as the physical index identification model;

[0017] When A<A0, it is determined that the support vector machine model training is unqualified, a historical physical index database update prompt is pushed to the administrator terminal, the administrator updates the historical physical index database, and a physical index identification model is constructed according to the updated historical physical index database.

[0018] Further, in the physical index recognition unit, the physical index data in the standardized water quality data is also input into the physical index recognition model for recognition to obtain the physical index recognition result. The compliance of the physical index is judged according to the physical index recognition result, and the output is made according to the judgment result, where:

[0019] When the recognition result is that the physical index data is normal, it is determined that the physical index meets the standard, and the coal chemical sewage with the physical index meeting the standard is discharged to the chemical treatment process area;

[0020] When the recognition result is that the physical index data is abnormal, it is determined that the physical index does not meet the standard. The physical index data is compared with the emergency treatment plan database, and the coal chemical sewage treatment adjustment plan is output according to the comparison result, where:

[0021] When there is preset physical index data consistent with the physical index data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset physical index data is output as the coal chemical sewage treatment adjustment plan;

[0022] When there is no preset physical index data consistent with the physical index data in the emergency treatment plan database, the emergency treatment plan database is updated, and the coal chemical sewage treatment adjustment plan is output according to the updated emergency treatment plan database.

[0023] Further, in the chemical index recognition unit, a chemical index recognition model is constructed according to the historical chemical index database. The historical chemical index database is divided into a 75% chemical training set and a 25% chemical test set. The chemical training set is input into the convolutional neural network model to train the convolutional neural network model, and the chemical test set is input into the convolutional neural network model with optimized parameters to obtain the chemical accuracy rate B. The chemical accuracy rate B is compared with the preset chemical accuracy rate B0. The training compliance of the convolutional neural network model is judged according to the comparison result, and the output is made according to the judgment result, where:

[0024] When B≥B0, it is determined that the training of the convolutional neural network model meets the standard, and the convolutional neural network model is output as the chemical index recognition model;

[0025] When B<B0, it is determined that the training of the convolutional neural network model does not meet the standard. A historical chemical index database update prompt is pushed to the administrator terminal, and the administrator updates the historical chemical index database, and a chemical index recognition model is constructed according to the updated historical chemical index database.

[0026] Further, in the chemical index recognition unit, the chemical index data in the standardized water quality data is also input into a chemical index recognition model for recognition to obtain a chemical index recognition result. The compliance of the chemical index is judged according to the chemical index recognition result, and the output is made according to the judgment result, where:

[0027] When the recognition result shows that the chemical index data is normal, it is determined that the chemical index meets the standard, and the coal chemical sewage with the chemical index meeting the standard is discharged to the biological treatment process area;

[0028] When the recognition result shows that the chemical index data is abnormal, it is determined that the chemical index does not meet the standard. The chemical index data is compared with the emergency treatment plan database, and a coal chemical sewage treatment adjustment plan is output according to the comparison result, where:

[0029] When there is preset chemical index data consistent with the chemical index data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset chemical index data is output as the coal chemical sewage treatment adjustment plan;

[0030] When there is no preset chemical index data consistent with the chemical index data in the emergency treatment plan database, the emergency treatment plan database is updated, and a coal chemical sewage treatment adjustment plan is output according to the updated emergency treatment plan database.

[0031] Further, in the biological index recognition unit, a biological index recognition model is constructed according to the historical biological index database. The historical biological index database is divided into an 80% biological training set and a 20% biological test set. The biological training set is input into a linear regression model to train the linear regression model, and the biological test set is input into the linearly regression model with optimized parameters to obtain a biological accuracy rate C. The biological accuracy rate C is compared with a preset biological accuracy rate C0. The training compliance of the linear regression model is judged according to the comparison result, and the output is made according to the judgment result, where:

[0032] When C≥C0, it is determined that the training of the linear regression model is qualified, and the linear regression model is output as the biological index recognition model;

[0033] When C<C0, it is determined that the training of the linear regression model is unqualified, and a prompt for updating the historical biological index database is pushed to the administrator terminal. The administrator updates the historical biological index database, and a biological index recognition model is constructed according to the updated historical biological index database.

[0034] Further, in the biological index recognition unit, the biological index data in the standardized water quality data is also input into a biological index recognition model for recognition to obtain a biological index recognition result. The compliance of the biological index is judged according to the biological index recognition result, and the output is made according to the judgment result, where:

[0035] When the recognition result shows that the biological index data is normal, it is determined that the biological index meets the standard, and the coal chemical sewage with the biological index meeting the standard is discharged to the storage device;

[0036] When the recognition result shows that the biological index data is abnormal, it is determined that the biological index does not meet the standard. The biological index data is compared with the emergency treatment plan database, and a coal chemical sewage treatment adjustment plan is output according to the comparison result, where:

[0037] When there is preset biological index data consistent with the biological index data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset biological index data is output as the coal chemical sewage treatment adjustment plan;

[0038] When there is no preset biological index data consistent with the biological index data in the emergency treatment plan database, the emergency treatment plan database is updated, and a coal chemical sewage treatment adjustment plan is output according to the updated emergency treatment plan database.

[0039] Further, in the calibration processing module, the number of non-compliance times P1 of the physical index recognition unit, the number of non-compliance times P2 of the chemical index recognition unit, and the number of non-compliance times P3 of the biological index recognition unit within a preset period are obtained. The physical index coefficient j, the chemical index coefficient q, and the biological index coefficient u are set, and the non-compliance index P of the coal chemical sewage treatment process is set. j = e -P1 / (P2+P3) , q = e -P2 / (P1+P3) , u = e -P3 / (P2+P3) , P = P1×j + P2×q + P3×u. The non-compliance index P of the coal chemical sewage treatment process is compared with the preset non-compliance index P0, and the fault condition of the processing recognition module is judged according to the comparison result, and the processing recognition module is calibrated according to the judgment result, where:

[0040] When P < P0, it is determined that the processing recognition module has no fault and the processing recognition module is not calibrated;

[0041] When P ≥ P0, it is determined that the processing recognition module has a fault, the coal chemical sewage treatment device is shut down, and a fault alarm is sent to the administrator, and the administrator calibrates the coal chemical sewage treatment device and the processing recognition module.

[0042] Further, in the optimization and correction module, the usage time T of the coal chemical sewage treatment device is obtained, the usage time T of the coal chemical sewage treatment device is compared with the preset usage time T0, the authenticity of the correction processing module is judged according to the comparison result, and the preset non-compliance times P0 are optimized according to the judgment result, where:

[0043] When T < T0, it is determined that the correction processing module is true and effective, and the preset non-compliance times P0 are not optimized:

[0044] When T ≥ T0, it is determined that the correction processing module is distorted. The distortion coefficient is set as e, the error coefficient is f, the optimization coefficient is K, and the optimized preset non-compliance times is Pk0, where:

[0045] 0.40 < e < 0.65;

[0046] 0.05 < f < 0.30;

[0047] K = T / T0;

[0048] Pk0 = (e × f) 2 / K, and the value of Pk0 is a positive integer.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows. The system collects the water quality data in the process of coal chemical sewage treatment in real time through the water quality collection module, ensuring the timeliness and integrity of the data, and providing a reliable data source for subsequent data processing and analysis. The system cleans, transforms, and standardizes the collected water quality data through the water quality data processing module, eliminates the differences and inconsistencies between the data, improves the data quality, and provides an accurate and consistent data basis for subsequent processing and identification. The system constructs a physical index identification model, a chemical index identification model, and a biological index identification model through the processing and identification module to comprehensively evaluate the water quality status, improve the accuracy and comprehensiveness of the identification, and quickly respond when water quality anomalies are found, reducing pollutant emissions and protecting the ecological environment. The system compares the non-compliance times with the preset value through the correction processing module, timely discovers possible failures or misjudgments in the processing and identification module, ensures the accuracy of the identification result, and corrects the processing and identification module according to the detection result to restore its normal function, further improving the stability and reliability of the system. The system dynamically adjusts the preset non-compliance times P0 according to the comparison result of the device usage time and the preset value through the optimization and correction module to adapt to the water quality characteristics and treatment requirements in different operation stages, and improves the operation efficiency and treatment effect of the sewage treatment system by continuously optimizing the preset parameters.

[0050] In particular, in the physical index recognition unit, a support vector machine model is used to train the historical physical training set, and the model is optimized for parameters through the physical verification set to improve the accuracy and generalization ability of the model. If the model meets the standard, it is used as the physical index recognition model; if it does not meet the standard, a database update prompt is pushed to the administrator to optimize the training data of the model, thereby improving the model performance. Then, the physical index data in the standardized water quality data is input into the physical index recognition model, and the compliance of the physical index is judged according to the recognition result. For the non-compliant physical index, by comparing with the emergency treatment plan database, the corresponding adjustment plan for coal chemical sewage treatment is output. If there is no matching plan in the database, the database is updated and a new adjustment plan is output. Through accurate physical index recognition and timely output of emergency treatment plans, it helps to ensure that the treatment effect of coal chemical sewage meets the standards, and through the real-time feedback and database update mechanism, the management level of coal chemical sewage treatment and the ability to respond to emergencies are improved.

[0051] In particular, in the chemical index recognition unit, a convolutional neural network model is used to train the chemical training set, and the model is optimized for parameters through the chemical test set to improve the recognition accuracy of the model for chemical indexes. If the model meets the standard, it is used as the chemical index recognition model; if it does not meet the standard, a database update prompt is pushed to the administrator to optimize the training data of the model, and the chemical index data in the standardized water quality data is input into the chemical index recognition model. The compliance of the chemical index is judged according to the recognition result. For the non-compliant chemical index, by comparing with the emergency treatment plan database, the adjustment plan for coal chemical sewage treatment is quickly output. If there is no matching plan in the database, the database is updated and a new adjustment plan is output. The convolutional neural network model can accurately identify the chemical indexes in coal chemical sewage, improve the treatment speed, help to respond to water quality changes in a timely manner, and through strict model training and testing, reduce the possibility of misjudgment and improve the reliability of treatment results.

[0052] In particular, in the biological index recognition unit, a linear regression model is used to train the biological training set, and the model is optimized for parameters through the biological test set to improve the recognition accuracy of the model for biological indexes. The biological index data in the standardized water quality data is input into the biological index recognition model, and the compliance of the biological index is judged according to the recognition result. For the non-compliant biological index, by comparing with the emergency treatment plan database, the adjustment plan for coal chemical sewage treatment is quickly output. If there is no matching plan in the database, the database is updated and a new adjustment plan is output. The linear regression model performs well in processing continuous data, helps to accurately identify biological index data, and accurate biological index recognition and timely emergency treatment help to ensure that coal chemical sewage meets environmental protection standards and regulatory requirements during the treatment process.

[0053] In particular, in the calibration processing module, the collected number of non-compliance times P is compared with a preset non-compliance times threshold P0. According to the comparison result, the module will automatically determine whether the processing and identification module has failed. If P < P0, it is considered that the processing effect is good and the processing and identification module has not failed; if P ≥ P0, it is considered that the processing effect is not up to standard and the processing and identification module may have failed. Once it is determined that the processing and identification module has failed, the module will trigger a series of emergency response measures, including shutting down the coal chemical sewage treatment device to prevent further problems, and sending a fault warning to the administrator for timely repair, reducing the production interruption time caused by equipment failures, and improving the overall production efficiency.

[0054] In particular, in the optimization and calibration module, T0 is set based on the equipment design life, maintenance cycle or historical data, and is used to determine whether the equipment has entered a stage that requires special attention. According to the time comparison result, the module will evaluate the authenticity of the calibration processing module. If T < T0, it means that the equipment is still within the normal service life, and the judgment result of the calibration processing module is considered to be true and valid; if T ≥ T0, it may be due to equipment aging or performance degradation that causes the judgment of the calibration processing module to be distorted. By judging the equipment status and performance problems, maintenance and repair can be carried out more targeted, improving the maintenance efficiency and effect. By extending the equipment service life and reducing production interruptions caused by failures, the operating costs of the enterprise can be reduced, ensuring that the sewage treatment device is always operating in the best state, enabling the system to more flexibly respond to changes in equipment performance, and improving the adaptability and stability of the entire sewage treatment system. Description of the Drawings

[0055] Figure 1 It is a schematic structural diagram of the intelligent monitoring system for coal chemical sewage treatment in this embodiment;

[0056] Figure 2 It is a schematic structural diagram of the processing and identification module in this embodiment. Detailed Embodiments

[0057] In order to make the purpose and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0059] It should be noted that in the description of the present invention, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the direction or positional relationship shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0060] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] Please refer to Figure 1 as shown, which is a schematic structural diagram of the intelligent monitoring system for coal chemical wastewater treatment in this example. The system includes:

[0062] A water quality acquisition module for acquiring water quality data during the coal chemical wastewater treatment process;

[0063] A water quality data processing module for performing standardization processing on the water quality data to obtain standardized water quality data. The water quality data processing module is connected to the water quality acquisition module;

[0064] A processing and identification module for constructing physical index identification models, chemical index identification models, and biological index identification models, and identifying the standardized water quality data according to the physical index identification models, chemical index identification models, and biological index identification models. The processing and identification module is connected to the water quality data processing module;

[0065] A calibration processing module for comparing the number of non-compliance times P during the coal chemical wastewater treatment process with the preset number of non-compliance times P0, judging the fault condition of the processing and identification module according to the comparison result, and calibrating the processing and identification module according to the judgment result. The calibration processing module is connected to the processing and identification module;

[0066] An optimization and calibration module for comparing the usage time T of the coal chemical wastewater treatment device with the preset usage time T0, judging the authenticity of the calibration processing module according to the comparison result, and optimizing the preset number of non-compliance times P0 according to the judgment result. The optimization and calibration module is connected to the calibration processing module.

[0067] Specifically, the system is applied to the field of intelligent monitoring technology for sewage treatment. The system collects water quality data in the process of coal chemical sewage treatment in real time, performs standardized processing on the water quality data to obtain standardized water quality data, and constructs a physical index recognition model, a chemical index recognition model, and a biological index recognition model. The standardized water quality data is recognized according to the physical index recognition model, the chemical index recognition model, and the biological index recognition model. The system also realizes comprehensive, accurate, and efficient monitoring and management of the coal chemical sewage treatment process by calibrating the processing and recognition module and optimizing the preset non-compliance times P0. Among them, the system collects water quality data in the process of coal chemical sewage treatment through the water quality collection module to ensure the timeliness and integrity of the data, providing a reliable data source for subsequent data processing and analysis. The system performs cleaning, conversion, and standardized processing on the collected water quality data through the water quality data processing module to eliminate the differences and inconsistencies between the data and improve the data quality, providing an accurate and consistent data basis for subsequent processing and recognition. The system constructs a physical index recognition model, a chemical index recognition model, and a biological index recognition model through the processing and recognition module to comprehensively evaluate the water quality status, improve the accuracy and comprehensiveness of recognition, and quickly respond when water quality anomalies are found, reducing pollutant emissions and protecting the ecological environment. The system compares the non-compliance times with the preset value through the calibration processing module to timely detect possible failures or misjudgments in the processing and recognition module, ensure the accuracy of the recognition result, and calibrate the processing and recognition module according to the detection result to restore its normal function, further improving the stability and reliability of the system. The system dynamically adjusts the preset non-compliance times P0 according to the comparison result of the device usage time and the preset value through the optimization and calibration module to adapt to the water quality characteristics and treatment requirements in different operation stages. By continuously optimizing the preset parameters, the operation efficiency and treatment effect of the sewage treatment system are improved.

[0068] Specifically, in the water quality collection module, water quality data in the process of coal chemical sewage treatment is collected through water quality monitoring instruments.

[0069] Specifically, the water quality monitoring instrument refers to equipment used to monitor and measure various physical, chemical, and biological indicators in water bodies, including physical indicator monitoring instruments, chemical indicator monitoring instruments, and biological indicator monitoring instruments. The physical indicator monitoring instruments refer to instruments used to measure and monitor the physical properties in water bodies, the environment, or industrial processes, such as pH meters, conductivity meters, turbidity meters, and thermometers. The chemical indicator detection instruments refer to instruments used to measure and monitor the chemical components and pollutants in water bodies or samples, such as on-line chemical oxygen demand monitors, on-line ammonia nitrogen monitors, heavy metal detectors, and on-line total phosphorus and total nitrogen monitors. The biological indicator detection instruments refer to instruments used to evaluate biomarkers or biological activities in water bodies, the environment, or organisms, such as biological toxicity detectors and microorganism detectors. The water quality data refers to various indicators and parameters obtained through water quality monitoring instruments that reflect the water quality status, including physical indicator data, chemical indicator data, and biological indicator data. This embodiment does not limit the specific content of the coal chemical sewage treatment process, and relevant technical personnel in the field can freely set it according to actual needs, as long as it meets the requirement of treating the sewage generated in the coal chemical production process. For example, the coal chemical sewage treatment process can be set as a pretreatment process, a biochemical treatment process, a tertiary treatment process, a desalination treatment process, and a sludge treatment process.

[0070] Specifically, in the water quality data processing module, the water quality data is standardized according to the data standardization tool to obtain standardized water quality data.

[0071] Specifically, the data standardization tool refers to software and systems used to standardize data. This embodiment does not limit the specific type of the data standardization tool, and relevant technical personnel in the field can freely set it according to actual needs, as long as it meets the requirement of standardizing the water quality data. For example, the data standardization tool can be set as AquaChem. The AquaChem refers to software used for water quality data management and analysis design. The standardization process refers to operations such as normalizing, sorting, and cleaning data, such as unit unification, data cleaning, data conversion, and data encoding.

[0072] Please refer to Figure 2 as shown, which is a structural schematic diagram of the processing and recognition module of this embodiment. The processing and recognition module includes:

[0073] A physical indicator recognition unit, used to construct a physical indicator recognition model and recognize the physical indicator data in the standardized water quality data according to the physical indicator recognition model;

[0074] A chemical indicator recognition unit, used to construct a chemical indicator recognition model and recognize the chemical indicator data in the standardized water quality data according to the chemical indicator recognition model. The chemical indicator recognition unit is connected to the physical indicator recognition unit;

[0075] A biological index recognition unit, which is used to construct a biological index recognition model and recognize the biological index data in the standardized water quality data according to the biological index recognition model. The biological index recognition unit is connected to the chemical index recognition unit.

[0076] Specifically, in the physical index recognition unit, a physical index recognition model is constructed according to the historical physical index database. The historical physical index database is divided into a 70% physical training set, a 20% physical verification set, and a 10% physical test set. The physical training set is input into the support vector machine model to train the support vector machine model. The physical verification set is input into the trained support vector machine model to optimize the parameters of the support vector machine model. The physical test set is input into the support vector machine model with optimized parameters to obtain the physical accuracy rate A. The physical accuracy rate A is compared with the preset physical accuracy rate A0. According to the comparison result, the training compliance of the support vector machine model is judged, and the output is made according to the judgment result, where:

[0077] When A≥A0, it is determined that the training of the support vector machine model is qualified, and the support vector machine model is output as the physical index recognition model;

[0078] When A<A0, it is determined that the training of the support vector machine model is unqualified. A historical physical index database update prompt is pushed to the administrator terminal. The administrator updates the historical physical index database, and a physical index recognition model is constructed according to the updated historical physical index database.

[0079] Specifically, in the physical index recognition unit, the physical index data in the standardized water quality data is also input into the physical index recognition model for recognition to obtain a physical index recognition result. According to the physical index recognition result, the compliance of the physical index is judged, and the output is made according to the judgment result, where:

[0080] When the recognition result is that the physical index data is normal, it is determined that the physical index is qualified, and the coal chemical sewage with qualified physical indexes is discharged to the chemical treatment process area;

[0081] When the recognition result is that the physical index data is abnormal, it is determined that the physical index is unqualified. The physical index data is compared with the emergency treatment plan database, and a coal chemical sewage treatment adjustment plan is output according to the comparison result, where:

[0082] When there is preset physical index data consistent with the physical index data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset physical index data is output as the coal chemical sewage treatment adjustment plan;

[0083] When there is no preset physical index data consistent with the physical index data in the emergency treatment plan database, the emergency treatment plan database is updated, and a coal chemical sewage treatment adjustment plan is output according to the updated emergency treatment plan database.

[0084] Specifically, the historical physical index database refers to a database used to store physical index data generated during the coal chemical sewage treatment process. The physical index data refers to parameter indicators that describe the physical state of sewage during the coal chemical sewage treatment process. The physical index recognition model refers to a support vector machine model that is trained based on the historical physical index database and meets the preset physical accuracy rate. The physical training set refers to a data set used to train the support vector machine model. The physical verification set refers to a data set used to evaluate the performance of the support vector machine model during the training process. The physical test set refers to a data set used to evaluate the generalization ability of the support vector machine model after the training is completed. The support vector machine model refers to a machine learning model used for classification and regression analysis. The physical accuracy rate refers to the proportion of the number of correctly recognized physical index values by the support vector machine model to the total number of recognized values. The preset physical accuracy rate refers to a preset value used to judge the training compliance of the support vector machine model. In this embodiment, the specific value of the preset physical accuracy rate is not limited, and relevant technical personnel in the field can freely set it according to actual needs, as long as it meets the requirement of judging the training compliance of the support vector machine model. For example, the preset physical accuracy rate A0 = 0.98 can be set. The emergency treatment plan database refers to a database that stores emergency treatment plans for different sewage treatment problems and non-compliance situations. The preset physical index data refers to the physical index standard data that should be achieved after sewage treatment set according to relevant laws, regulations, and industry standards, including the standard value range of physical indexes, which is used to evaluate whether the coal chemical sewage treatment effect meets the standard. The preset emergency treatment plan refers to an emergency treatment plan formulated in advance for the preset physical index data. In this embodiment, the update method of the emergency treatment plan database is not limited, and relevant technical personnel in the field can freely set it according to actual needs, as long as it meets the needs of coal chemical sewage treatment. For example, the update method can be set to obtain expert suggestions through big data and update the emergency treatment plan database according to the expert suggestions. The administrator refers to a user or individual responsible for maintaining, monitoring, configuring, and managing the operation status of the system and platform. The chemical treatment process area refers to an area where chemical treatment operations are carried out.

[0085] Specifically, in the chemical index recognition unit, a chemical index recognition model is constructed based on the historical chemical index database. The historical chemical index database is divided into a 75% chemical training set and a 25% chemical test set. The chemical training set is input into the convolutional neural network model to train the convolutional neural network model, and the chemical test set is input into the convolutional neural network model with optimized parameters to obtain the chemical accuracy rate B. The chemical accuracy rate B is compared with the preset chemical accuracy rate B0, and the training compliance of the convolutional neural network model is judged according to the comparison result and output according to the judgment result, where:

[0086] When B≥B0, it is determined that the training of the convolutional neural network model is qualified, and the convolutional neural network model is output as the chemical index recognition model;

[0087] When B<B0, it is determined that the training of the convolutional neural network model is unqualified, and a prompt for updating the historical chemical index database is pushed to the administrator terminal. The administrator updates the historical chemical index database, and a chemical index recognition model is constructed based on the updated historical chemical index database.

[0088] Specifically, in the chemical index recognition unit, the chemical index data in the standardized water quality data is also input into the chemical index recognition model for recognition to obtain the chemical index recognition result. The compliance of the chemical index is judged according to the chemical index recognition result and output according to the judgment result, where:

[0089] When the recognition result is that the chemical index data is normal, it is determined that the chemical index is qualified, and the coal chemical sewage with qualified chemical indexes is discharged to the biological treatment process area;

[0090] When the recognition result is that the chemical index data is abnormal, it is determined that the chemical index is unqualified. The chemical index data is compared with the emergency treatment plan database, and a coal chemical sewage treatment adjustment plan is output according to the comparison result, where:

[0091] When there is preset chemical index data consistent with the chemical index data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset chemical index data is output as the coal chemical sewage treatment adjustment plan;

[0092] When there is no preset chemical index data consistent with the chemical index data in the emergency treatment plan database, the emergency treatment plan database is updated, and a coal chemical sewage treatment adjustment plan is output according to the updated emergency treatment plan database.

[0093] Specifically, the historical chemical index database refers to a database used to store chemical index data generated during the coal chemical wastewater treatment process. The convolutional neural network model refers to a deep feedforward artificial neural network for data recognition. The chemical accuracy rate refers to the ratio of the number of chemical indexes correctly recognized by the convolutional neural network model to the total number of recognized indexes. The preset chemical accuracy rate refers to a preset value used to judge the training compliance of the convolutional neural network model. In this embodiment, the specific value of the preset chemical accuracy rate is not limited, and relevant technical personnel in the field can freely set it according to actual needs, as long as it meets the requirement of judging the training compliance of the convolutional neural network model. For example, the preset chemical accuracy rate B0 = 0.95 can be set. The chemical index data refers to the parameter indexes describing the chemical state of the wastewater during the coal chemical wastewater treatment process. The preset chemical index data refers to the standard data of chemical indexes that should be achieved after wastewater treatment set according to relevant laws, regulations and industry standards, including the standard value range of chemical indexes, used to evaluate whether the effect of coal chemical wastewater treatment meets the standard. The biological treatment process area refers to the area where biological treatment operations are carried out.

[0094] Specifically, in the biological index recognition unit, a biological index recognition model is constructed based on the historical biological index database. The historical biological index database is divided into an 80% biological training set and a 20% biological test set. The biological training set is input into the linear regression model to train the linear regression model, and the biological test set is input into the linearly regression model with optimized parameters to obtain the biological accuracy rate C. The biological accuracy rate C is compared with the preset biological accuracy rate C0, and the training compliance of the linear regression model is judged according to the comparison result, and the output is made according to the judgment result, where:

[0095] When C ≥ C0, it is determined that the linear regression model training is qualified, and the linear regression model is output as the biological index recognition model;

[0096] When C < C0, it is determined that the linear regression model training is unqualified, a historical biological index database update prompt is pushed to the administrator terminal, and the administrator updates the historical biological index database, and a biological index recognition model is constructed according to the updated historical biological index database.

[0097] Specifically, in the biological index recognition unit, the biological index data in the standardized water quality data is also input into the biological index recognition model for recognition to obtain the biological index recognition result. The compliance of the biological index is judged according to the biological index recognition result, and the output is made according to the judgment result, where:

[0098] When the recognition result is that the biological index data is normal, it is determined that the biological index is qualified, and the coal chemical wastewater with qualified biological indexes is discharged to the storage device;

[0099] When the recognition result is that the biological index data is abnormal, it is determined that the biological index does not meet the standard. The biological index data is compared with the emergency treatment plan database, and a coal chemical sewage treatment adjustment plan is output according to the comparison result, where:

[0100] When there is preset biological index data consistent with the biological index data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset biological index data is output as the coal chemical sewage treatment adjustment plan;

[0101] When there is no preset biological index data consistent with the biological index data in the emergency treatment plan database, the emergency treatment plan database is updated, and a coal chemical sewage treatment adjustment plan is output according to the updated emergency treatment plan database.

[0102] Specifically, the historical biological index database refers to a database for storing biological index data generated during the coal chemical sewage treatment process. The linear regression model refers to a statistical method for predicting and analyzing the linear relationship between independent variables and dependent variables. The biological accuracy rate refers to the proportion of the number of biological indexes correctly identified by the linear regression model to the total number of identified ones. The preset biological accuracy rate is a preset value used to judge the training compliance of the linear regression model. In this embodiment, the specific value of the preset biological accuracy rate is not limited, and those skilled in the relevant art can freely set it according to actual needs, as long as it meets the requirement of judging the training compliance of the linear regression model. For example, the preset biological accuracy rate C0 = 0.90 can be set. The biological index data refers to the parameter indexes describing the biological state of the sewage during the coal chemical sewage treatment process. The preset biological index data refers to the standard data of the biological indexes that should be achieved after sewage treatment set according to relevant laws, regulations and industry standards, including the standard value range of the biological indexes, and is used to evaluate whether the effect of coal chemical sewage treatment meets the standard. The storage device refers to the equipment and facilities for storing water resources.

[0103] Specifically, in the calibration processing module, the number of non-compliance times P1 of the physical index recognition unit, the number of non-compliance times P2 of the chemical index recognition unit, and the number of non-compliance times P3 of the biological index recognition unit within a preset period are obtained. The physical index coefficient j, the chemical index coefficient q, and the biological index coefficient u are set, and the non-compliance index P of the coal chemical sewage treatment process is set. j = e -P1 / (P2+P3) , q = e -P2 / (P1+P3) , u = e -P3 / (P2+P3) , P = P1×j + P2×q + P3×u. The non-compliance index P of the coal chemical sewage treatment process is compared with the preset non-compliance index P0, and the fault condition of the processing recognition module is judged according to the comparison result, and the processing recognition module is calibrated according to the judgment result, where:

[0104] When P < P0, it is determined that the processing recognition module has no fault, and the processing recognition module is not corrected.

[0105] When P ≥ P0, it is determined that the processing recognition module has a fault, the coal chemical industrial sewage treatment device is shut down, and a fault warning is sent to the administrator. The administrator corrects the coal chemical industrial sewage treatment device and the processing recognition module.

[0106] Specifically, the preset period refers to the time interval preset for collecting the number of non-compliance times P1 of the physical index recognition unit, the number of non-compliance times P2 of the chemical index recognition unit, and the number of non-compliance times P3 of the biological index recognition unit. For example, if the preset period is set to one month, then the number of non-compliance times P1 of the physical index recognition unit, the number of non-compliance times P2 of the chemical index recognition unit, and the number of non-compliance times P3 of the biological index recognition unit within one month are obtained. The number of non-compliance times of the physical index recognition unit refers to the number of times when the recognition result of the physical index recognition unit is abnormal physical index data within the preset period. The number of non-compliance times of the chemical index recognition unit refers to the number of times when the recognition result of the chemical index recognition unit is abnormal chemical index data within the preset period. The number of non-compliance times of the biological index recognition unit refers to the number of times when the recognition result of the biological index recognition unit is abnormal biological index data within the preset period. The preset non-compliance index refers to the non-compliance index of the coal chemical industrial sewage treatment process preset for reflecting the fault situation of the processing recognition module. In this embodiment, the value of the preset non-compliance index is not limited, and those skilled in the relevant art can freely set it according to actual needs, as long as it meets the requirement of reflecting the fault situation of the processing recognition module. For example, the preset non-compliance index P0 = 12 can be set. The fault warning refers to the warning signal sent by the system to the management personnel through sound, light, electricity and other means when the coal chemical industrial sewage treatment system and related equipment have faults, anomalies and deviate from the normal operating state.

[0107] Specifically, in the optimization and correction module, the usage time T of the coal chemical industrial sewage treatment device is obtained, the usage time T of the coal chemical industrial sewage treatment device is compared with the preset usage time T0, the authenticity of the correction processing module is judged according to the comparison result, and the preset non-compliance number P0 is optimized according to the judgment result, where:

[0108] When T < T0, it is determined that the correction processing module is true and effective, and the preset non-compliance number P0 is not optimized.

[0109] When T ≥ T0, it is determined that the correction processing module is distorted. The distortion coefficient is set as e, the error coefficient is set as f, the optimization coefficient is set as K, and the optimized preset non-compliance number is Pk0, where:

[0110] 0.40 < e < 0.65;

[0111] 0.05 < f < 0.30;

[0112] K = T / T0;

[0113] Pk0 = (e × f) 2 / K, and the value of Pk0 is a positive integer.

[0114] Specifically, the usage time of the coal chemical wastewater treatment device refers to the time interval elapsed from the start of operation of the device to the current moment. The preset usage time refers to an expected operation time set during the design, procurement, and installation stages of the coal chemical wastewater treatment device according to factors such as the technical specifications, service life, and operating conditions of the equipment. In this embodiment, the specific value of the preset usage time is not limited, and those skilled in the relevant art can freely set it according to actual needs, as long as it meets the requirement of judging the authenticity of the calibration processing module. For example, the preset usage time T0 = 730 days can be set. The distortion refers to the deformation and loss that occur during the transmission and processing of data, resulting in a difference between the received data and the original data. The distortion coefficient refers to a coefficient used to quantitatively describe the degree of data distortion. The error coefficient refers to a coefficient used to evaluate the accuracy in aspects such as water quality monitoring data and treatment effect evaluation. The positive integer refers to an integer greater than 0, such as 1, 2, 3,....

[0115] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. An intelligent monitoring system for coal chemical wastewater treatment, characterized in that: include: Water quality collection module, used to collect water quality data in the process of coal chemical wastewater treatment; A water quality data processing module is used to perform standardization processing on water quality data to obtain standardized water quality data; A processing and identification module is used to construct a physical indicator identification model, a chemical indicator identification model and a biological indicator identification model, and to identify standardized water quality data according to the physical indicator identification model, the chemical indicator identification model and the biological indicator identification model; A correction processing module is used to compare the non-conformity index of the coal chemical wastewater treatment process with the preset non-conformity index, judge the fault condition of the processing identification module according to the comparison result, and correct the processing identification module according to the judgment result; The optimization correction module is used to compare the use time T of the coal chemical wastewater treatment device with the preset use time, judge the authenticity of the correction processing module according to the comparison result, and optimize the preset number of non-compliance times according to the judgment result.

2. The intelligent monitoring system for coal chemical wastewater treatment according to claim 1 is characterized in that: The processing identification module comprises: A physical indicator recognition unit is used to construct a physical indicator recognition model and recognize physical indicator data in the standardized water quality data according to the physical indicator recognition model; A chemical indicator recognition unit is used to construct a chemical indicator recognition model and recognize chemical indicator data in standardized water quality data according to the chemical indicator recognition model; The biological indicator recognition unit is used to construct a biological indicator recognition model and recognize the biological indicator data in the standardized water quality data according to the biological indicator recognition model.

3. The intelligent monitoring system for coal chemical wastewater treatment according to claim 2 is characterized in that: In the physical indicator identification unit, a physical indicator identification model is constructed according to a historical physical indicator database, the historical physical indicator database is divided into 70% of a physical training set, 20% of a physical verification set and 10% of a physical test set, the physical training set is input into a support vector machine model to train the support vector machine model, the physical verification set is input into the trained support vector machine model, the support vector machine model is parameter optimized, and the physical test set is input into the support vector machine model after parameter optimization to obtain a physical accuracy rate A, the physical accuracy rate A is compared with a preset physical accuracy rate A0, the training compliance of the support vector machine model is judged according to the comparison result, and output is performed according to the judgment result, wherein: When A≥A0, it is determined that the support vector machine model training has reached the standard, and the support vector machine model is output as a physical indicator recognition model; When A<A0, it is determined that the support vector machine model training does not meet the standard, and a historical physical indicator database update prompt is pushed to the administrator terminal. The administrator updates the historical physical indicator database and constructs a physical indicator recognition model based on the updated historical physical indicator database.

4. The intelligent monitoring system for coal chemical wastewater treatment according to claim 3 is characterized in that: In the physical indicator identification unit, the physical indicator data in the standardized water quality data is also input into the physical indicator identification model for identification, and the physical indicator identification result is obtained. The compliance of the physical indicator is judged according to the physical indicator identification result, and output is performed according to the judgment result, wherein: When the identification result shows that the physical index data is normal, the physical index is determined to be up to standard, and the coal chemical wastewater with up to standard physical index is discharged to the chemical treatment process area; When the identification result is that the physical indicator data is abnormal, the physical indicator is determined to be substandard, the physical indicator data is compared with the emergency treatment solution database, and the coal chemical wastewater treatment adjustment plan is output according to the comparison result, wherein: When there is preset physical indicator data consistent with the physical indicator data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset physical indicator data is output as a coal chemical wastewater treatment adjustment plan; When there is no preset physical indicator data consistent with the physical indicator data in the emergency treatment solution database, the emergency treatment solution database is updated, and the coal chemical wastewater treatment adjustment plan is output according to the updated emergency treatment solution database.

5. The intelligent monitoring system for coal chemical wastewater treatment according to claim 2 is characterized in that: In the chemical indicator recognition unit, a chemical indicator recognition model is constructed according to a historical chemical indicator database, the historical chemical indicator database is divided into 75% of a chemical training set and 25% of a chemical test set, the chemical training set is input into a convolutional neural network model to train the convolutional neural network model, and the chemical test set is input into the convolutional neural network model after parameter optimization to obtain a chemical accuracy rate B, the chemical accuracy rate B is compared with a preset chemical accuracy rate B0, the training compliance of the convolutional neural network model is judged according to the comparison result, and output is performed according to the judgment result, wherein: When B≥B0, it is determined that the convolutional neural network model training has reached the standard, and the convolutional neural network model is output as a chemical indicator recognition model; When B<B0, it is determined that the training of the convolutional neural network model does not meet the standard, and a historical chemical indicator database update prompt is pushed to the administrator terminal. The administrator updates the historical chemical indicator database and constructs a chemical indicator recognition model based on the updated historical chemical indicator database.

6. The intelligent monitoring system for coal chemical wastewater treatment according to claim 5 is characterized in that: In the chemical indicator identification unit, the chemical indicator data in the standardized water quality data is also input into the chemical indicator identification model for identification, and the chemical indicator identification result is obtained. The compliance of the chemical indicator is judged according to the chemical indicator identification result, and output is performed according to the judgment result, wherein: When the identification result shows that the chemical index data is normal, the chemical index is determined to be up to standard, and the coal chemical wastewater with up to standard chemical index is discharged to the biological treatment process area; When the identification result is that the chemical indicator data is abnormal, the chemical indicator is determined to be substandard, the chemical indicator data is compared with the emergency treatment solution database, and the coal chemical wastewater treatment adjustment solution is output according to the comparison result, wherein: When there is preset chemical indicator data consistent with the chemical indicator data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset chemical indicator data is output as a coal chemical wastewater treatment adjustment plan; When there is no preset chemical indicator data consistent with the chemical indicator data in the emergency treatment solution database, the emergency treatment solution database is updated, and the coal chemical wastewater treatment adjustment plan is output according to the updated emergency treatment solution database.

7. The intelligent monitoring system for coal chemical wastewater treatment according to claim 2 is characterized in that: In the biometric identification unit, a biometric identification model is constructed based on a historical biometric database, the historical biometric database is divided into 80% of a biological training set and 20% of a biological test set, the biological training set is input into a linear regression model to train the linear regression model, and the biological test set is input into a linear regression model after parameter optimization to obtain a biological accuracy rate C, the biological accuracy rate C is compared with a preset biological accuracy rate C0, the training compliance of the linear regression model is judged based on the comparison result, and output is performed based on the judgment result, wherein: When C≥C0, it is determined that the linear regression model training has reached the standard, and the linear regression model is output as a biological indicator recognition model; When C<C0, it is determined that the linear regression model training does not meet the standard, and a historical bio-indicator database update prompt is pushed to the administrator terminal, and the administrator updates the historical bio-indicator database and constructs a bio-indicator recognition model based on the updated historical bio-indicator database.

8. The intelligent monitoring system for coal chemical wastewater treatment according to claim 7 is characterized in that: In the biological indicator identification unit, the biological indicator data in the standardized water quality data is also input into the biological indicator identification model for identification to obtain the biological indicator identification result, and the compliance of the biological indicator is judged according to the biological indicator identification result, and output is made according to the judgment result, wherein: When the identification result shows that the biological indicator data is normal, the biological indicator is determined to be up to standard, and the coal chemical wastewater that meets the biological indicator standard is discharged to the storage device; When the identification result is that the biological indicator data is abnormal, the biological indicator is determined to be substandard, the biological indicator data is compared with the emergency treatment solution database, and the coal chemical wastewater treatment adjustment plan is output according to the comparison result, wherein: When there is preset biological indicator data consistent with the biological indicator data in the emergency treatment plan database, the preset emergency treatment plan corresponding to the preset biological indicator data is output as a coal chemical wastewater treatment adjustment plan; When there is no preset biological indicator data consistent with the biological indicator data in the emergency treatment solution database, the emergency treatment solution database is updated, and the coal chemical wastewater treatment adjustment plan is output according to the updated emergency treatment solution database.

9. The intelligent monitoring system for coal chemical wastewater treatment according to claim 1 is characterized in that: In the correction processing module, the number of non-compliance times P1, P2 and P3 of the non-compliance times of the physical indicator identification unit within the preset period are obtained, and the physical indicator coefficient j, chemical indicator coefficient q, biological indicator coefficient u, and the non-compliance index P of the coal chemical wastewater treatment process are set. -P1 / (P2+P3) , q = e -P2 / (P1+P3) ,u=e -P3 / (P2+P3) ,P=P1×j+P2×q+P3×u, compare the non-conformity index P of the coal chemical wastewater treatment process with the preset non-conformity index P0, judge the fault condition of the treatment identification module according to the comparison result, and calibrate the treatment identification module according to the judgment result, where: When P<P0, it is determined that the processing and identification module has not failed, and the processing and identification module is not corrected; When P≥P0, it is determined that the processing identification module fails, the coal chemical wastewater treatment device is shut down, and a fault alarm is sent to the administrator, who then calibrates the coal chemical wastewater treatment device and the processing identification module.

10. The intelligent monitoring system for coal chemical wastewater treatment according to claim 1 is characterized in that: In the optimization and correction module, the use time T of the coal chemical wastewater treatment device is obtained, and the use time T of the coal chemical wastewater treatment device is compared with the preset use time T0. The authenticity of the correction processing module is judged according to the comparison result, and the preset non-compliance times P0 are optimized according to the judgment result, wherein: When T<T0, the correction processing module is determined to be effective, and the preset number of non-compliance times P0 is not optimized: When T≥T0, the correction processing module is determined to be distorted, the distortion coefficient is set to e, the error coefficient is set to f, the optimization coefficient is set to K, and the number of preset non-compliance times after optimization is Pk0, where: 0.40＜e＜0.65； 0.05＜f＜0.30； K = T / T0; Pk0=(e×f) 2 / K, and the value of Pk0 is a positive integer.

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