An intelligent monitoring system for coal chemical wastewater treatment

Abstract

The present invention relates to the field of intelligent monitoring technology for sewage treatment, and in particular to an intelligent monitoring system for coal chemical sewage treatment, comprising: a water quality acquisition module for collecting water quality data during the coal chemical sewage treatment process; a water quality data processing module for standardizing the water quality data; a treatment identification module for identifying the standardized water quality data based on a physical indicator identification model, a chemical indicator identification model, and a biological indicator identification model; a correction processing module for correcting the treatment identification module based on a non-compliance index of the coal chemical sewage treatment process and a preset non-compliance index; and an optimization correction module for optimizing a preset number of non-compliance events based on the usage time of the coal chemical sewage treatment device and a preset usage time. The present invention improves the reliability and safety of the intelligent monitoring system for sewage treatment.

Description

Technical Field

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

[0002] The wastewater generated in the coal chemical production process is mostly high-salt wastewater, containing a large amount of inorganic salts and organic matter. Direct discharge will cause serious pollution to the water environment, affect the growth and reproduction of aquatic organisms, and thus destroy the balance of the ecosystem. With the increasingly stringent environmental protection regulations, higher standards and requirements are put forward for the wastewater discharge of coal chemical enterprises. Enterprises must use effective means to reduce the concentration of pollutants in the wastewater to achieve 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 intelligent monitoring systems. By installing sensors on sewage treatment equipment, water quality parameters and equipment operating status can be monitored in real time, providing data support for intelligent monitoring.

[0003] Chinese patent publication number CN113885596A discloses an intelligent sewage treatment monitoring system. The system collects real-time operating equipment data, stores it, and analyzes it. It also transmits alarm information, including multi-level high and low water level alarms, passive over-temperature alarms, and active over-temperature alarms. However, this solution lacks intelligent identification of different sewage treatment processes, making it difficult to improve the reliability and safety of the intelligent sewage treatment monitoring system. Summary of the Invention

[0004] To this end, the present invention provides an intelligent monitoring system for coal chemical wastewater treatment, which is used to overcome the problems of low reliability and safety of the intelligent monitoring system for wastewater treatment in the prior art.

[0005] To achieve the above objectives, the present invention provides an intelligent monitoring system for coal chemical wastewater treatment, comprising:

[0006] Water quality collection module, used to collect water quality data during the coal chemical wastewater treatment process;

[0007] A water quality data processing module is used to perform standardization processing on water quality data to obtain standardized water quality data;

[0008] 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 based on the physical indicator identification model, the chemical indicator identification model, and the biological indicator identification model;

[0009] The correction processing module is used to 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 processing identification module according to the comparison result, and calibrate the processing identification module according to the judgment result;

[0010] The optimization correction module is used to compare the usage time T of the coal chemical wastewater treatment device with the preset usage time T0, judge the authenticity of the correction processing module based on the comparison result, and optimize the preset number of non-compliance times P0 based on the judgment result.

[0011] Furthermore, the processing identification module includes:

[0012] 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;

[0013] 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;

[0014] The biological indicator recognition unit is used to construct a biological indicator recognition model and recognize biological indicator data in the standardized water quality data according to the biological indicator recognition model.

[0015] Furthermore, in the physical indicator identification unit, a physical indicator identification model is constructed based on a historical physical indicator database, the historical physical indicator 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 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 parameters of the support vector machine model are 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, and the training compliance of the support vector machine model is judged according to the comparison result, and output is made according to the judgment result, wherein:

[0016] 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;

[0017] When A<A0, it is determined that the support vector machine model training does not meet the standards, 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.

[0018] Furthermore, in the physical indicator identification unit, the physical indicator data in the standardized water quality data is input into the physical indicator identification model for identification to obtain a physical indicator identification result. The compliance of the physical indicator is judged based on the physical indicator identification result, and output is made based on the judgment result, wherein:

[0019] When the identification result shows that the physical indicator data is normal, the physical indicators are determined to be up to standard, and the coal chemical wastewater with up to standard physical indicators is discharged to the chemical treatment process area;

[0020] 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:

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

[0022] 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.

[0023] Furthermore, in the chemical indicator recognition unit, a chemical indicator recognition model is constructed based on a historical chemical indicator database, the historical chemical indicator 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 after parameter optimization to obtain a chemical accuracy rate B, the chemical accuracy rate B is compared with a preset chemical accuracy rate B0, and the training compliance of the convolutional neural network model is judged based on the comparison result, and output is based on the judgment result, wherein:

[0024] When B≥B0, the convolutional neural network model is determined to have met the training standards, and the convolutional neural network model is output as a chemical indicator recognition model;

[0025] When B<B0, it is determined that the training of the convolutional neural network model does not meet the standards, 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.

[0026] Furthermore, in the chemical indicator identification unit, the chemical indicator data in the standardized water quality data is input into the chemical indicator identification model for identification to obtain a chemical indicator identification result. The compliance of the chemical indicator is judged based on the chemical indicator identification result, and output is made based on the judgment result, wherein:

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

[0028] When the identification result is that the chemical indicator data is abnormal, it is determined that the chemical indicator does not meet the standard, the chemical indicator data is compared with the emergency treatment plan database, and the coal chemical wastewater treatment adjustment plan is output according to the comparison result, wherein:

[0029] 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 the coal chemical wastewater treatment adjustment plan;

[0030] 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.

[0031] Furthermore, in the biometric identification unit, a biometric identification model is constructed based on a historical biometric database. The historical biometric 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 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. Based on the comparison result, the training compliance of the linear regression model is judged, and the judgment result is output, wherein:

[0032] When C≥C0, the linear regression model is determined to have met the training criteria, and the linear regression model is output as a biological indicator recognition model;

[0033] When C<C0, it is determined that the linear regression model training does not meet the standard, and a historical bioindicator database update prompt is pushed to the administrator terminal. The administrator updates the historical bioindicator database and constructs a bioindicator recognition model based on the updated historical bioindicator database.

[0034] Furthermore, in the biological indicator identification unit, the biological indicator data in the standardized water quality data is input into the biological indicator identification model for identification to obtain a biological indicator identification result. The compliance of the biological indicator is judged based on the biological indicator identification result, and output is based on the judgment result, wherein:

[0035] 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 a storage device;

[0036] 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 plan database, and an adjustment plan for coal chemical wastewater treatment is output based on the comparison result, wherein:

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

[0038] 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.

[0039] Furthermore, in the correction processing module, the number of times the physical indicator identification unit fails to meet the standard P1, the number of times the chemical indicator identification unit fails to meet the standard P2, and the number of times the biological indicator identification unit fails to meet the standard P3 within the preset period are obtained, and the physical indicator coefficient j, the chemical indicator coefficient q, the biological indicator coefficient u, and the coal chemical wastewater treatment process failure index P, j = e -P1 / (P2+P3) , q=e -P2 / (P1+P3) ,u=e -P3 / (P2+P3) , P=P1×j+P2×q+P3×u, compare the substandard index P of the coal chemical wastewater treatment process with the preset substandard 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:

[0040] When P<P0, it is determined that the processing and identification module has not failed, and no correction is performed on the processing and identification module;

[0041] 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.

[0042] Furthermore, 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 number of non-compliance times P0 is optimized according to the judgment result, wherein:

[0043] When T<T0, the correction processing module is determined to be effective and the preset number of non-compliance times P0 is not optimized:

[0044] When T≥T0, the correction processing module is determined to be distorted, and the distortion coefficient is set to e, the error coefficient is f, and the optimization coefficient is K. The number of preset non-compliance times after optimization 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 existing technology, the beneficial effect of the present invention is that the system collects water quality data in the coal chemical wastewater treatment process in real time through the water quality collection module, ensures the timeliness and integrity of the data, and provides a reliable data source for subsequent data processing and analysis. The system cleans, converts and standardizes the collected water quality data through the water quality data processing module, eliminates differences and inconsistencies between the data, improves data quality, and provides an accurate and consistent data basis for subsequent processing and identification. The system constructs physical indicator identification models, chemical indicator identification models and biological indicator identification models through the processing and identification modules to comprehensively evaluate the water quality status and improve the accuracy and comprehensiveness of identification. When abnormal water quality is found, the system responds quickly to reduce the discharge of pollutants and protect the ecological environment. The system compares the number of non-compliance times with the preset value through the correction processing module, promptly discovers possible faults or misjudgments in the processing and identification module, ensures the accuracy of the identification results, and corrects the processing and identification module according to the detection results to restore its normal function, further improving the stability and reliability of the system. The system dynamically adjusts the preset number of non-compliance times P0 according to the comparison results of the device usage time and the preset value through the optimization correction module to adapt to the water quality characteristics and treatment requirements of different operation stages. By continuously optimizing the preset parameters, the operating efficiency and treatment effect of the sewage treatment system are improved.

[0050] In particular, the physical indicator identification unit uses a support vector machine model to train a historical physical training set, and optimizes the model parameters using a physical validation set to improve the model's accuracy and generalization ability. If the model meets the standards, it is used as a physical indicator identification model; if it does not meet the standards, a database update prompt is pushed to the administrator to optimize the model's training data, thereby improving model performance. The physical indicator data in the standardized water quality data is input into the physical indicator identification model, and the compliance of the physical indicators is judged based on the identification results. For physical indicators that do not meet the standards, the corresponding coal chemical wastewater treatment adjustment plan is output by comparing with the emergency treatment plan database. If there is no matching plan in the database, the database is updated and a new adjustment plan is output. Through accurate physical indicator identification and timely emergency treatment plan output, it helps to ensure that the treatment effect of coal chemical wastewater meets the standards. Through real-time feedback and database update mechanisms, the management level of coal chemical wastewater treatment and the ability to respond to emergencies are improved.

[0051] In particular, the chemical indicator identification unit uses a convolutional neural network model to train the chemical training set, and optimizes the model parameters through the chemical test set to improve the model's recognition accuracy of chemical indicators. If the model meets the standards, it will be used as a chemical indicator identification model; if it does not meet the standards, a database update prompt will be pushed to the administrator to optimize the model's training data, and the chemical indicator data in the standardized water quality data will be input into the chemical indicator identification model. The compliance of the chemical indicators will be judged based on the identification results. For chemical indicators that do not meet the standards, the coal chemical wastewater treatment adjustment plan will be quickly output by comparing with the emergency treatment plan database. If there is no matching plan in the database, the database will be updated and a new adjustment plan will be output. The convolutional neural network model can accurately identify chemical indicators in coal chemical wastewater, improve processing speed, and help to respond to water quality changes in a timely manner. Through rigorous model training and testing, the possibility of misjudgment is reduced and the reliability of treatment results is improved.

[0052] In particular, in the biological indicator identification unit, a linear regression model is used to train the biological training set, and the model parameters are optimized through the biological test set to improve the model's accuracy in identifying biological indicators. The biological indicator data in the standardized water quality data is input into the biological indicator identification model, and the compliance of the biological indicators is judged based on the identification results. For biological indicators that do not meet the standards, the coal chemical wastewater treatment adjustment plan is quickly output by comparing with the emergency treatment plan database. 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 when processing continuous data, which helps to accurately identify biological indicator data. Accurate biological indicator identification and timely emergency treatment help ensure that coal chemical wastewater meets environmental protection standards and regulatory requirements during the treatment process.

[0053] In particular, the correction processing module compares the collected number of non-compliance times P with a preset non-compliance threshold value P0. Based on the comparison result, the module will automatically determine whether the processing identification module has failed. If P < P0, it is considered that the processing effect is good and the processing identification module has not failed; if P ≥ P0, it is considered that the processing effect does not meet the standard and the processing identification module may have failed. Once it is determined that the processing identification module has failed, the module will trigger a series of emergency response measures, including shutting down the coal chemical wastewater treatment device to prevent further problems from occurring, and issuing a fault alarm to the administrator for timely maintenance, reducing production interruption time caused by equipment failure, and improving overall production efficiency.

[0054] In particular, in the optimization and correction module, T0 is set based on the equipment's design life, maintenance cycle, or historical data, and is used to determine whether the equipment has entered a stage requiring special attention. Based on the time comparison results, the module will evaluate the authenticity of the correction processing module. If T < T0, it means that the equipment is still within its normal service life, and the judgment result of the correction processing module is considered to be true and valid; if T ≥ T0, the judgment of the correction processing module may be distorted due to equipment aging or performance degradation. By judging the equipment status and performance issues, maintenance and care can be carried out in a more targeted manner, improving maintenance efficiency and effectiveness. By extending the service life of the equipment and reducing production interruptions due to failures, the company's operating costs can be reduced, ensuring that the sewage treatment equipment always operates in the best condition, enabling the system to respond more flexibly to changes in equipment performance, and improving the adaptability and stability of the entire sewage treatment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the structure of the intelligent monitoring system for coal chemical wastewater treatment in this embodiment;

[0056] Figure 2 This is a structural diagram of the processing and identification module in this embodiment. DETAILED DESCRIPTION

[0057] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

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

[0059] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the 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 cannot be understood as a limitation on the present invention.

[0060] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0061] See also Figure 1 As shown in FIG, it is a structural diagram of the intelligent monitoring system for coal chemical wastewater treatment in this example, and the system includes:

[0062] Water quality collection module, used to collect water quality data during the coal chemical wastewater treatment process;

[0063] A water quality data processing module is used to perform standardization processing on 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 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 based on the physical indicator identification model, the chemical indicator identification model, and the biological indicator identification model. The processing and identification module is connected to the water quality data processing module;

[0065] a correction processing module, for comparing the number of times P of non-compliance with the standard in the coal chemical wastewater treatment process with a preset number of times P0 of non-compliance with the standard, judging the fault condition of the treatment identification module according to the comparison result, and correcting the treatment identification module according to the judgment result; the correction processing module is connected to the treatment identification module;

[0066] The optimization and correction module is used to compare the usage time T of the coal chemical wastewater treatment device with the preset usage time T0, judge the authenticity of the correction processing module based on the comparison result, and optimize the preset number of non-compliance times P0 based on the judgment result. The optimization and correction module is connected to the correction 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 coal chemical sewage treatment process in real time, standardizes the water quality data, obtains standardized water quality data, and constructs physical indicator identification models, chemical indicator identification models and biological indicator identification models. The standardized water quality data is identified according to the physical indicator identification model, chemical indicator identification model and biological indicator identification model. The system also realizes comprehensive, accurate and efficient monitoring and management of the coal chemical sewage treatment process by calibrating the treatment identification module and optimizing the preset number of non-compliance times P0. Among them, the system collects water quality data in the coal chemical sewage treatment process in real time through the water quality collection module to ensure the timeliness and integrity of the data and provide a reliable data source for subsequent data processing and analysis. The system cleans, converts and standardizes the collected water quality data through the water quality data processing module to eliminate differences and inconsistencies between the data. The system improves data quality and provides an accurate and consistent data basis for subsequent processing and identification. The system constructs physical indicator identification models, chemical indicator identification models and biological indicator identification models through the processing and identification module to comprehensively evaluate the water quality status, improve the accuracy and comprehensiveness of identification, and respond quickly when abnormal water quality is found, reduce the emission of pollutants, and protect the ecological environment. The system compares the number of non-compliance times with the preset value through the correction processing module, promptly discovers possible faults or misjudgments of the processing and identification module, ensures the accuracy of the identification results, and corrects the processing and identification module according to the test results to restore its normal function, further improving the stability and reliability of the system. The system dynamically adjusts the preset number of non-compliance times P0 according to the comparison result of the device usage time and the preset value through the optimization correction module to adapt to the water quality characteristics and treatment requirements of 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 coal chemical wastewater treatment process 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 instrument refers to an instrument used to measure and monitor the physical properties of water bodies, environments or industrial processes, such as pH meters, conductivity meters, turbidity meters and thermometers. The chemical indicator detection instrument refers to an instrument used to measure and monitor the chemical composition and pollutants in water bodies or samples, such as chemical oxygen demand online monitors, ammonia nitrogen online monitors, heavy metal detectors, total phosphorus and total nitrogen online monitors. The biological indicator detection instrument refers to an instrument used to evaluate the water quality. , instruments for detecting biomarkers or biological activities in the environment or organisms, such as biotoxicity detectors and microbial detectors. The water quality data refers to various indicators and parameters obtained by water quality monitoring instruments that reflect the quality status of water bodies, including physical indicator data, chemical indicator data and biological indicator data. This embodiment does not limit the specific content of the coal chemical wastewater treatment process. Relevant technical personnel in this field can freely set it according to actual needs, and it is only necessary to meet the needs of treating the wastewater generated in the coal chemical production process. For example, the coal chemical wastewater 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 a 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 data standardization tool. Relevant technical personnel in this field can freely set it according to actual needs, as long as it meets the needs of standardizing water quality data. For example, the data standardization tool can be set to AquaChem, and AquaChem refers to software used for water quality data management and analysis design. The standardization processing refers to operations to normalize, organize and clean data, such as unit unification, data cleaning, data conversion and data encoding.

[0072] See also Figure 2 , which is a schematic diagram of the structure of the processing and identification module of this embodiment, the processing and identification module includes:

[0073] 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;

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

[0076] Specifically, in the physical indicator identification unit, a physical indicator identification model is constructed based on a historical physical indicator database, the historical physical indicator 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 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 parameters of the support vector machine model are 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, and the training compliance of the support vector machine model is judged according to the comparison result, and output is made according to the judgment result, wherein:

[0077] 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;

[0078] When A<A0, it is determined that the support vector machine model training does not meet the standards, 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.

[0079] Specifically, 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 to obtain a physical indicator identification result. The compliance of the physical indicator is judged based on the physical indicator identification result, and output is made based on the judgment result, wherein:

[0080] When the identification result shows that the physical indicator data is normal, the physical indicators are determined to be up to standard, and the coal chemical wastewater with up to standard physical indicators is discharged to the chemical treatment process area;

[0081] 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:

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

[0083] 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.

[0084] Specifically, the historical physical indicator database refers to a database used to store physical indicator data generated during the coal chemical wastewater treatment process, the physical indicator data refers to parameter indicators that describe the physical state of wastewater during the coal chemical wastewater treatment process, the physical indicator recognition model refers to a support vector machine model that meets the preset physical accuracy rate and is trained based on the historical physical indicator database, 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 physical indicator values ​​correctly identified by the support vector machine model to the total number of identifications, and the preset physical accuracy rate refers to a preset value used to judge whether the training of the support vector machine model meets the standards. This embodiment does not limit the specific value of the preset physical accuracy rate, and relevant technical personnel in this field can freely set it according to actual needs. , it is only necessary to meet the requirement of judging whether the training of the support vector machine model meets the standard. For example, the preset physical accuracy A0 can be set to 0.98. The emergency treatment solution database refers to a database that stores emergency treatment solutions for different sewage treatment problems and non-standard situations. The preset physical indicator data refers to the physical indicator standard data that should be achieved after sewage treatment according to relevant laws, regulations and industry standards, including the standard value range of physical indicators, which is used to evaluate whether the coal chemical sewage treatment effect meets the standard. The preset emergency treatment solution refers to an emergency treatment solution pre-established for the preset physical indicator data. This embodiment does not limit the update method of the emergency treatment solution database. Relevant technicians in this field can freely set it according to actual needs. It only needs to meet the needs of coal chemical sewage treatment. For example, the update method can be set to obtain expert advice through big data and update the emergency treatment solution database according to the expert advice. The administrator refers to the user and individual responsible for maintaining, monitoring, configuring and managing the operation status of the system and platform. The chemical treatment process area refers to the area where chemical treatment operations are performed.

[0085] Specifically, in the chemical indicator recognition unit, a chemical indicator recognition model is constructed based on a historical chemical indicator database, the historical chemical indicator 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 after parameter optimization to obtain a chemical accuracy rate B, the chemical accuracy rate B is compared with a preset chemical accuracy rate B0, and the training compliance of the convolutional neural network model is judged based on the comparison result, and output based on the judgment result, wherein:

[0086] When B≥B0, the convolutional neural network model is determined to have met the training standards, and the convolutional neural network model is output as a chemical indicator recognition model;

[0087] When B<B0, it is determined that the training of the convolutional neural network model does not meet the standards, 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.

[0088] Specifically, 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 to obtain a chemical indicator identification result. The compliance of the chemical indicator is judged based on the chemical indicator identification result, and output is based on the judgment result, wherein:

[0089] When the identification result shows that the chemical index data is normal, it is determined that the chemical index meets the standard, and the coal chemical wastewater with the chemical index meeting the standard is discharged to the biological treatment process area;

[0090] When the identification result is that the chemical indicator data is abnormal, it is determined that the chemical indicator does not meet the standard, the chemical indicator data is compared with the emergency treatment plan database, and the coal chemical wastewater treatment adjustment plan is output according to the comparison result, wherein:

[0091] 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 the coal chemical wastewater treatment adjustment plan;

[0092] 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.

[0093] Specifically, the historical chemical indicator database refers to a database for storing chemical indicator data generated during the treatment of coal chemical wastewater, the convolutional neural network model refers to a deep feedforward artificial neural network for data recognition, the chemical accuracy refers to the ratio of the number of chemical indicators correctly identified by the convolutional neural network model to the total number of identifications, the preset chemical accuracy refers to a preset value for judging whether the training of the convolutional neural network model meets the standards, and this embodiment does not limit the specific value of the preset chemical accuracy. Relevant technicians in this field can freely set it according to actual needs, as long as it meets the requirements for judging whether the training of the convolutional neural network model meets the standards. For example, the preset chemical accuracy B0 can be set to 0.95. The chemical indicator data refers to parameter indicators that describe the chemical state of sewage during the treatment of coal chemical wastewater. The preset chemical indicator data refers to the chemical indicator standard data that should be achieved after sewage treatment according to relevant laws, regulations and industry standards, including the standard value range of chemical indicators, which is used to evaluate whether the coal chemical wastewater treatment effect meets the standards. The biological treatment process area refers to the area where biological treatment operations are performed.

[0094] Specifically, in the biometric identification unit, a biometric identification model is constructed based on a historical biometric database. The historical biometric 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 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. Based on the comparison result, the training compliance of the linear regression model is judged, and the judgment result is output, wherein:

[0095] When C≥C0, the linear regression model is determined to have met the training criteria, and the linear regression model is output as a biological indicator recognition model;

[0096] When C<C0, it is determined that the linear regression model training does not meet the standard, and a historical bioindicator database update prompt is pushed to the administrator terminal. The administrator updates the historical bioindicator database and constructs a bioindicator recognition model based on the updated historical bioindicator database.

[0097] Specifically, 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 a biological indicator identification result. The compliance of the biological indicator is judged based on the biological indicator identification result, and output is based on the judgment result, wherein:

[0098] 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 a storage device;

[0099] 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 plan database, and an adjustment plan for coal chemical wastewater treatment is output based on the comparison result, wherein:

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

[0101] 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.

[0102] Specifically, the historical biological indicator database refers to a database used to store biological indicator data generated during the coal chemical wastewater treatment process. The linear regression model refers to a statistical method used to predict and analyze the linear relationship between independent variables and dependent variables. The biological accuracy rate refers to the proportion of the number of biological indicators correctly identified by the linear regression model to the total number of identifications. The preset biological accuracy rate refers to a preset value used to judge whether the training of the linear regression model has met the standard. This embodiment does not limit the specific value of the preset biological accuracy rate. Relevant technicians in this field can freely set it according to actual needs, as long as it meets the requirement of judging whether the training of the linear regression model has met the standard. For example, the preset biological accuracy rate C0 can be set to 0.90. The biological indicator data refers to parameter indicators that describe the biological state of wastewater during the coal chemical wastewater treatment process. The preset biological indicator data refers to the biological indicator standard data that should be achieved after wastewater treatment according to relevant laws, regulations and industry standards, including the standard value range of biological indicators, which is used to evaluate whether the coal chemical wastewater treatment effect meets the standard. The storage device refers to equipment and facilities for storing water resources.

[0103] Specifically, in the correction processing module, the number of non-compliance times P1, P2 and P3 of the physical indicator identification unit within the preset period is 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 substandard index P of the coal chemical wastewater treatment process with the preset substandard 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:

[0104] When P<P0, it is determined that the processing and identification module has not failed, and no correction is performed on the processing and identification module;

[0105] 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.

[0106] Specifically, the preset period refers to a preset time interval for collecting the number of non-compliance times P1 of the physical indicator identification unit, the number of non-compliance times P2 of the chemical indicator identification unit, and the number of non-compliance times P3 of the biological indicator identification unit. If the preset period is set to one month, the number of non-compliance times P1 of the physical indicator identification unit, the number of non-compliance times P2 of the chemical indicator identification unit, and the number of non-compliance times P3 of the biological indicator identification unit within one month are obtained. The number of non-compliance times of the physical indicator identification unit refers to the number of times that the identification result of the physical indicator identification unit is abnormal physical indicator data within the preset period. The number of non-compliance times of the chemical indicator identification unit refers to the number of times that the identification result of the chemical indicator identification unit is abnormal chemical indicator data within the preset period. The number of times the biological indicator identification unit fails to meet the standard refers to the number of times the identification result of the biological indicator identification unit is abnormal biological indicator data within a preset period. The preset failure index refers to a preset coal chemical wastewater treatment process failure index used to reflect the failure of the processing and identification module. This embodiment does not limit the value of the preset failure index. Relevant technicians in this field can freely set it according to actual needs, and it is only necessary to meet the need to reflect the failure of the processing and identification module. For example, the preset failure index P0 can be set to 12. The fault alarm refers to when the coal chemical wastewater treatment system and related equipment fail, are abnormal, and deviate from the normal operating state, the system sends a warning signal to the management personnel through sound, light, electricity and other means.

[0107] Specifically, 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 number of non-compliance times P0 is optimized according to the judgment result, wherein:

[0108] When T<T0, the correction processing module is determined to be effective and the preset number of non-compliance times P0 is not optimized:

[0109] When T≥T0, the correction processing module is determined to be distorted, and the distortion coefficient is set to e, the error coefficient is f, and the optimization coefficient is K. The number of preset non-compliance times after optimization 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 from the start of operation of the device to the current moment. The preset usage time refers to an expected operating time set during the design, procurement and installation phase of the coal chemical wastewater treatment device based on factors such as the technical specifications, service life, and operating conditions of the equipment. This embodiment does not limit the specific value of the preset usage time. Relevant technical personnel in this field can freely set it according to actual needs, and only need to meet the need to judge the authenticity of the correction processing module. For example, the preset usage time T0 can be set to 730 days. The distortion refers to the deformation and loss of data during transmission and processing, which results in differences between the received data and the original data. The distortion coefficient refers to the degree of data distortion used to quantify the description. The error coefficient refers to the accuracy used to evaluate water quality monitoring data, treatment effect evaluation, etc. The positive integer refers to an integer greater than 0, such as 1, 2, 3,...

[0115] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection 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 during the coal chemical wastewater treatment process; 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 based on 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 a preset non-conformity index, 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; An optimization and correction module is used to compare the usage time T of the coal chemical wastewater treatment device with the preset usage time, judge the authenticity of the correction processing module based on the comparison result, and optimize the preset number of non-compliance times based on the judgment result; In the correction processing module, the number of times the physical indicator identification unit fails to meet the standard P1, the number of times the chemical indicator identification unit fails to meet the standard P2, and the number of times the biological indicator identification unit fails to meet the standard P3 within the preset period are obtained, and the physical indicator coefficient j, the chemical indicator coefficient q, the biological indicator coefficient u, and the coal chemical wastewater treatment process failure index P are set. -P1 / （P2+P3） , q=e -P2 / (P1+P3) ,u=e -P3 / (P2+P3) ,P=P1×j+P2×q+P3×u, compare the substandard index P of the coal chemical wastewater treatment process with the preset substandard 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 no correction is performed on the processing and identification module; When P≥P0, it is determined that the treatment identification module has failed, the coal chemical wastewater treatment device is shut down, and a fault alarm is issued to the administrator, who then calibrates the coal chemical wastewater treatment device and the treatment identification module; 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 number of non-compliance times P0 is 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, and the distortion coefficient is set to e, the error coefficient is f, and the optimization coefficient is K. 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.

2. The intelligent monitoring system for coal chemical wastewater treatment according to claim 1 is characterized in that: The processing identification module includes: 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 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 based on a historical physical indicator database, and the historical physical indicator 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 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, and the parameters of the support vector machine model are optimized. The physical test set is input into the support vector machine model after parameter optimization to obtain a physical accuracy rate A, and 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 based on the comparison result, and output is performed based on 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 standards, 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 to obtain a physical indicator identification result. The compliance of the physical indicator is judged based on the physical indicator identification result, and output is based on the judgment result, wherein: When the identification result shows that the physical indicator data is normal, the physical indicators are determined to be up to standard, and the coal chemical wastewater with up to standard physical indicators 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 in the emergency treatment plan database that is consistent with the physical indicator data, the preset emergency treatment plan corresponding to the preset physical indicator data is output as the 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 based on a historical chemical indicator database, the historical chemical indicator database is divided into a 75% chemical training set and a 25% 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, and the training compliance of the convolutional neural network model is judged based on the comparison result, and output is based on the judgment result, wherein: When B≥B0, the convolutional neural network model is determined to have met the training standards, 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 standards, 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 to obtain a chemical indicator identification result. The compliance of the chemical indicator is judged based on the chemical indicator identification result, and output is based on the judgment result, wherein: When the identification result shows that the chemical index data is normal, it is determined that the chemical index meets the standard, and the coal chemical wastewater with the chemical index meeting the standard is discharged to the biological treatment process area; When the identification result is that the chemical indicator data is abnormal, it is determined that the chemical indicator does not meet the standard, the chemical indicator data is compared with the emergency treatment plan database, and the coal chemical wastewater treatment adjustment plan 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 the 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 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 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. Based on the comparison result, the training compliance of the linear regression model is judged, and the judgment result is output, wherein: When C≥C0, the linear regression model is determined to have met the training criteria, 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 bioindicator database update prompt is pushed to the administrator terminal. The administrator updates the historical bioindicator database and constructs a bioindicator recognition model based on the updated historical bioindicator database.

8. The intelligent monitoring system for coal chemical wastewater treatment according to claim 7, 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 a biological indicator identification result. The compliance of the biological indicator is judged based on the biological indicator identification result, and output is based on 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 a 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 plan database, and an adjustment plan for coal chemical wastewater treatment is output based on the comparison result, wherein: When there is preset biological indicator data in the emergency treatment plan database that is consistent with the biological indicator data, the preset emergency treatment plan corresponding to the preset biological indicator data is output as the 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.

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