Sewage treatment progress monitoring method and system

Abstract

The invention provides a sewage treatment progress monitoring method and system, and the method comprises the steps: collecting and processing a first water quality parameter value of a key node of a sewage treatment facility in a historical time period, constructing a dynamic model of a sewage treatment process through a deep neural network, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model. An optimized dynamic model is obtained, the optimized dynamic model is used for predicting the sewage treatment effect, and a prediction result and operation data of the sewage treatment facility are obtained; using a fuzzy logic control algorithm to adjust the operation parameter setting value of the sewage treatment system, and using a support vector machine algorithm to classify and identify the fault mode of the sewage treatment system to obtain a fault classification result; based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result, a comprehensive monitoring management system based on the cloud platform is constructed; according to the invention, comprehensive monitoring and fault identification of the sewage treatment process are realized, and efficient, stable and reliable operation of sewage treatment is ensured.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of water treatment technology, and in particular to a method and system for monitoring the progress of sewage treatment. Background Art

[0002] With the acceleration of industrialization and the advancement of urbanization, water pollution is becoming increasingly serious. As an important part of environmental protection, the efficiency and quality of sewage treatment directly affect the sustainable use of water resources and the maintenance of ecological balance. Sewage treatment is widely used in urban sewage treatment plants, industrial wastewater treatment plants and other places. In these application scenarios, real-time monitoring and optimization of the sewage treatment process are crucial.

[0003] At present, the monitoring of sewage treatment progress mainly relies on manual sampling and analysis and online monitoring instruments. Manual sampling and analysis is time-consuming and costly, and cannot provide a continuous data stream, making it difficult to timely detect instantaneous changes or anomalies in the sewage treatment process; online monitoring instruments such as chemical oxygen demand online detectors and ammonia nitrogen online detectors are easily affected by non-target substances when facing complex sewage components, resulting in measurement deviations. High-end online monitoring equipment not only has a high purchase cost, but also has high subsequent operation and maintenance costs, which limits its application in small and medium-sized sewage treatment plants. Summary of the invention

[0004] The embodiment of the present invention provides a method and system for monitoring the progress of sewage treatment, which are used to solve the problems of discontinuous data collection, weak anti-interference ability, inaccurate measurement results and high investment cost in the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a method for monitoring progress of sewage treatment, comprising: Receiving real-time data streams from different sources, the real-time data streams containing structured data and unstructured data; Collect the first water quality parameter values ​​of key nodes of sewage treatment facilities in a historical time period; Performing data processing on the first water quality parameter value, and based on the processed first water quality parameter value, using a deep neural network to construct a dynamic model of the sewage treatment process, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and using the optimized dynamic model to predict the sewage treatment effect, to obtain a prediction result and operation data of the sewage treatment facility; Dynamically adjust the operating parameter setting values ​​of the sewage treatment system of the sewage treatment facility by using a fuzzy logic control algorithm to obtain optimized operating parameter setting values, and classify and identify the failure modes of the sewage treatment system during the sewage treatment process by using a support vector machine algorithm based on the optimized operating parameter setting values ​​to obtain a failure classification result; Based on the prediction results, the operating data, the optimized operating parameter setting values ​​and the fault classification results, a comprehensive monitoring and management system based on a cloud platform is constructed. The comprehensive monitoring and management system integrates expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process.

[0006] Optionally, the fuzzy logic control algorithm is used to dynamically adjust the operating parameter setting values ​​of the sewage treatment system to which the sewage treatment facility belongs to obtain optimized operating parameter setting values, and based on the optimized operating parameter setting values, a support vector machine algorithm is used to classify and identify the failure mode of the sewage treatment system during the sewage treatment process to obtain a failure classification result, including: Based on the prediction result and the operation data, collecting the real-time second water quality parameter value and the operation parameter setting value of the sewage treatment system to which the sewage treatment facility belongs to obtain real-time data; Based on the real-time data, setting input variables of the fuzzy logic control algorithm, the input variables including water quality parameters and operating parameters, the water quality parameters including pH value, dissolved oxygen and chemical oxygen demand, the operating parameters including aeration volume and reagent dosage ratio; Defining fuzzy rules, applying fuzzy logic control algorithms, and performing fuzzy reasoning according to the input variables and the fuzzy rules to obtain optimized operating parameter setting values ​​to optimize the sewage treatment effect; A support vector machine model is trained based on the optimized operating parameter setting value and the second water quality parameter value to identify and classify fault modes and obtain a fault classification result.

[0007] Optionally, the defining of fuzzy rules, applying a fuzzy logic control algorithm, and performing fuzzy reasoning according to the input variables and the fuzzy rules to obtain optimized operating parameter setting values ​​to optimize the sewage treatment effect include: Define a fuzzy set, and use a membership function to convert the second water quality parameter value and the operating parameter setting value in the input variable into the membership of the fuzzy set; Based on the fuzzy sets and membership degrees, defining fuzzy rules; A fuzzy logic control algorithm is applied to perform fuzzy reasoning according to the fuzzy rules and the fuzzy sets to calculate the optimal operating parameter setting values, and a defuzzification method is used to process the optimal operating parameter setting values ​​to obtain optimized operating parameter setting values.

[0008] Optionally, based on the prediction results, the operation data, the optimized operation parameter setting values ​​and the fault classification results, a cloud platform-based comprehensive monitoring and management system is constructed, and the comprehensive monitoring and management system integrates expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process, including: Performing data integration and data cleaning on the prediction results, the operation data, the optimized operation parameter setting values, and the fault classification results to obtain sewage treatment data; Based on the sewage treatment data, the front-end display technology of the cloud platform is used to display the status and forecast trend of sewage treatment in real time to provide a monitoring interface; An integrated expert system, wherein the expert system combines a target expert knowledge base to provide an optimal operation strategy corresponding to the state of the sewage treatment; Generate a user interface to enable the user to set a custom alarm rule according to actual needs and the user-defined alarm rule function, wherein the custom alarm rule includes different thresholds and alarm conditions, and when the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered to achieve real-time response and processing; Based on the sewage treatment data, monitoring interface, expert system and user interface, a comprehensive monitoring and management system based on a cloud platform is constructed to realize automatic monitoring of sewage treatment.

[0009] Optionally, the integrated expert system, the expert system combines the target expert knowledge base to provide an optimal operation strategy corresponding to the state of sewage treatment, including: Formulate a fault diagnosis rule based on historical data, wherein the historical data includes a first water quality parameter value; Based on the fault classification results and the fault diagnosis rules, formulating known optimal operation strategies for different fault types; Based on the fault diagnosis rules and the known optimal operation strategy, an initial expert knowledge base is constructed, an adaptive learning mechanism is introduced, the expert knowledge base is optimized, and a target expert knowledge base is obtained; An optimal operation strategy corresponding to the state of sewage treatment is determined based on the target expert knowledge base.

[0010] Optionally, the user interface is generated so that the user can set a custom alarm rule according to actual needs and the user-defined alarm rule function, and the custom alarm rule includes different thresholds and alarm conditions. When the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered to achieve real-time response and processing, including: Generate a user interface to enable the user to set a custom alarm rule according to actual needs and the user-defined alarm rule function, wherein the custom alarm rule includes different thresholds and alarm conditions; Based on the custom alarm rules, when the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered, and the user is notified in real time through multiple notification channels, and real-time alarm information is generated; Based on the real-time alarm information, a corresponding rapid response mechanism and the optimal operation strategy are selected to achieve real-time response and processing.

[0011] Optionally, the data processing is performed on the first water quality parameter value, and based on the processed first water quality parameter value, a dynamic model of the sewage treatment process is constructed using a deep neural network, and a genetic algorithm is introduced to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and the optimized dynamic model is used to predict the sewage treatment effect to obtain a prediction result and operation data of the sewage treatment facility, including: Performing data normalization processing on the first water quality parameter value, and extracting key information from the normalized data to obtain the processed first water quality parameter value, and based on the processed first water quality parameter value, using a deep neural network to build a dynamic model of the sewage treatment process, and using a genetic algorithm to adjust the weights, biases, the number of layers of the neural network, and the number of neurons in each layer of the dynamic model to optimize the structure and parameters of the dynamic model, and obtain a preliminarily optimized dynamic model; The first water quality parameter value after treatment is used to train the preliminary optimized dynamic model to obtain an optimized dynamic model, and the optimized dynamic model is used to predict the sewage treatment effect to obtain the prediction result and the operation data of the sewage treatment facility.

[0012] In a second aspect, an embodiment of the present invention provides a system for monitoring progress of sewage treatment, comprising: A collection module, used to collect the first water quality parameter values ​​of key nodes of the sewage treatment facility within a historical time period; an optimization module, for performing data processing on the first water quality parameter value, and based on the processed first water quality parameter value, using a deep neural network to construct a dynamic model of the sewage treatment process, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and using the optimized dynamic model to predict the sewage treatment effect, and obtain a prediction result and operation data of the sewage treatment facility; an identification module, for dynamically adjusting the operating parameter setting values ​​of the sewage treatment system of the sewage treatment facility by using a fuzzy logic control algorithm to obtain optimized operating parameter setting values, and classifying and identifying the failure modes of the sewage treatment system during the sewage treatment process by using a support vector machine algorithm based on the optimized operating parameter setting values ​​to obtain a failure classification result; A construction module is used to construct a comprehensive monitoring and management system based on a cloud platform based on the prediction results, the operating data, the optimized operating parameter setting values ​​and the fault classification results. The comprehensive monitoring and management system integrates expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process.

[0013] In a third aspect, an embodiment of the present invention provides a computing device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a method for monitoring the progress of sewage treatment as described in any one of the first aspects.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a method for monitoring the progress of sewage treatment as described in any one of the first aspects.

[0015] In an embodiment of the present invention, the first water quality parameter value of the key node of the sewage treatment facility in the historical time period is collected; the first water quality parameter value is processed, and based on the processed first water quality parameter value, a dynamic model of the sewage treatment process is constructed by using a deep neural network, and a genetic algorithm is introduced to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and the optimized dynamic model is used to predict the sewage treatment effect, and the prediction result and the operation data of the sewage treatment facility are obtained; the fuzzy logic control algorithm is used to dynamically adjust the operating parameter setting value of the sewage treatment system to which the sewage treatment facility belongs, and the optimized operating parameter setting value is obtained. Based on the optimized operating parameter setting value, the fault mode of the sewage treatment system in the sewage treatment process is classified and identified by the support vector machine algorithm to obtain the fault classification result; based on the prediction result, the operating data, the optimized operating parameter setting value and the fault classification result, a comprehensive monitoring and management system based on the cloud platform is constructed, and the comprehensive monitoring and management system integrates the expert system and the user-defined alarm rule function to realize the automatic monitoring of the sewage treatment process. The technical solution provided by the present invention realizes the comprehensive monitoring, optimization and fault identification of the sewage treatment process, improves the intelligence level and management level of the system, and ensures the efficient, stable and reliable operation of sewage treatment. Among them, through the fuzzy logic control algorithm, the operating parameter setting values ​​of the sewage treatment system can be adjusted and optimized in real time to improve the treatment effect and system stability; the fuzzy logic control algorithm can handle uncertainty and inaccurate information to make the control more flexible and accurate; by optimizing the operating parameters, the occurrence of system failures can be reduced and the reliability and safety of the system can be improved; the optimized operating parameter setting values ​​can reduce unnecessary aeration and chemical dosage, reduce energy consumption and chemical costs, and achieve energy conservation and emission reduction; combining fuzzy logic control and defuzzification methods, the intelligent management of the sewage treatment process can be realized, and the automation and management levels can be improved.

[0016] These and other aspects of the present invention will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a method for monitoring the progress of sewage treatment provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a sewage treatment progress monitoring system provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0020] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] Figure 1 A flow chart of a method for monitoring the progress of sewage treatment is provided in an embodiment of the present invention, such as Figure 1 As shown, the method includes: In order to solve the problems of discontinuous data collection, weak anti-interference ability and high cost in the existing sewage treatment progress monitoring, and to improve the technical level and environmental governance efficiency of the sewage treatment industry. Based on this, the present invention provides a sewage treatment progress monitoring method, such as Figure 1 ,include: Step 101: Collecting first water quality parameter values ​​of key nodes of a sewage treatment facility within a historical time period; In this step, key nodes refer to important monitoring locations in the sewage treatment process, such as water inlets, aeration tanks, sedimentation tanks, water outlets, etc. The water quality parameters at these locations can reflect the status of the entire treatment process; the first water quality parameter value refers to the water quality data collected from key nodes within a specific time period, such as biochemical oxygen demand (BOD), suspended solids (SS), pH value, ammonia nitrogen, etc.

[0023] This step determines the key nodes in the sewage treatment facility, such as the water inlet, aeration tank, sedimentation tank, outlet, etc.; then, during the historical time period, water quality parameter values ​​such as COD, BOD, SS, pH value, ammonia nitrogen, etc. are regularly collected from these key nodes. These data will serve as the basis for subsequent modeling.

[0024] Assuming that data from the past year are selected, water quality parameter values ​​are collected from each key node once a day and stored in a database for subsequent processing.

[0025] Step 102: performing data processing on the first water quality parameter value, and based on the processed first water quality parameter value, using a deep neural network to construct a dynamic model of the sewage treatment process, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and using the optimized dynamic model to predict the sewage treatment effect, to obtain a prediction result and operation data of the sewage treatment facility; In this step, deep neural network refers to a multi-layer artificial neural network used to learn and model complex nonlinear relationships and is suitable for processing large amounts of high-dimensional data. Genetic algorithm refers to an optimization algorithm that simulates natural selection and genetic mechanisms and is used to search for optimal solutions. It is often used to optimize the structure and parameters of the model. Dynamic model refers to a mathematical model that describes the changes of a system over time and is used to predict the future state of the system.

[0026] This step processes the collected first water quality parameter values, including data cleaning, normalization, and missing value filling. Data cleaning includes removing outliers and noise, normalization includes converting data of different dimensions into the same dimension, and missing value filling includes filling missing data using interpolation or other methods. A deep neural network model is constructed using the processed first water quality parameter value to describe the dynamic changes of the sewage treatment process. For example, the deep neural network model can use a multi-layer perceptron (MLP) or a convolutional neural network (CNN). A genetic algorithm is introduced to optimize the structure and parameters of the model to obtain an optimized dynamic model. The optimized dynamic model is used to predict the sewage treatment effect to obtain the prediction results and the operation data of the sewage treatment facilities.

[0027] Step 103: dynamically adjusting the operating parameter setting values ​​of the sewage treatment system of the sewage treatment facility by using a fuzzy logic control algorithm to obtain optimized operating parameter setting values, and classifying and identifying the failure modes of the sewage treatment system during the sewage treatment process by using a support vector machine algorithm based on the optimized operating parameter setting values ​​to obtain a failure classification result; In this step, the fuzzy logic control algorithm refers to a control method based on fuzzy set theory and fuzzy logic, which is used to deal with uncertainty and imprecise information. The support vector machine algorithm refers to a supervised learning method used for classification and regression analysis, especially for high-precision classification of small sample data.

[0028] In this step, the fuzzy logic control algorithm can adjust the aeration volume, reagent dosage and other operating parameters according to the preset rules and the current water quality parameter values ​​to optimize the treatment effect. For example, if the prediction result shows that the COD value is too high, the aeration volume or reagent dosage can be increased. The support vector machine algorithm can establish a fault mode classification model by training historical fault data; when the real-time monitoring data is input into the model, it can identify whether there is a fault and its type, such as equipment failure, abnormal process parameters, etc.

[0029] Step 104: Based on the prediction results, the operation data, the optimized operation parameter setting values ​​and the fault classification results, a comprehensive monitoring and management system based on a cloud platform is constructed, wherein the comprehensive monitoring and management system integrates an expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process; In this step, the integrated monitoring and management system refers to a platform that integrates multiple monitoring and management functions, which is used to monitor and manage the sewage treatment process in real time. Expert system refers to a computer system based on knowledge and reasoning, which is used to solve complex problems and provide decision support.

[0030] The comprehensive monitoring and management system built in this step can run on the cloud platform and display various monitoring data and prediction results in real time. The expert system can provide decision support based on the preset knowledge base, and the user-defined alarm rule function allows users to set specific alarm conditions. When the monitoring data exceeds the set threshold, the system will automatically send an alarm to notify relevant personnel.

[0031] The embodiments of the present invention can achieve the following beneficial effects through the above steps: By collecting and processing water quality parameter values ​​in real time, the system can provide continuous and stable monitoring data and detect and handle abnormal situations in a timely manner; The dynamic model optimized by deep neural network and genetic algorithm improves the accuracy and robustness of prediction, which helps to take measures in advance to avoid water quality exceeding the standard; Fuzzy logic control algorithm can dynamically adjust operating parameters according to real-time data, optimize treatment effects, and reduce energy consumption and reagent costs; The support vector machine algorithm can effectively identify fault modes, help troubleshoot faults in a timely manner, and ensure the stable operation of the system; The cloud-based integrated monitoring and management system integrates expert systems and user-defined alarm rule functions, realizes intelligent management and automated monitoring of the sewage treatment process, and improves management level and efficiency.

[0032] In order to realize intelligent management and timely diagnosis of faults in the sewage treatment process and improve the operating efficiency and reliability of the system, the present invention provides a specific embodiment, in which the step 103 uses a fuzzy logic control algorithm to dynamically adjust the operating parameter setting values ​​of the sewage treatment system to which the sewage treatment facility belongs to obtain the optimized operating parameter setting values, and based on the optimized operating parameter setting values, the fault mode of the sewage treatment system in the sewage treatment process is classified and identified by a support vector machine algorithm to obtain a fault classification result, which specifically includes the following steps: Step 301: Based on the prediction result and the operation data, collect the real-time second water quality parameter value and the operation parameter setting value of the sewage treatment system to which the sewage treatment facility belongs to obtain real-time data; This step collects the second water quality parameter values ​​and operating parameter setting values ​​in real time based on the prediction results and operating data to form real-time data. The second water quality parameter values ​​include pH value, dissolved oxygen, chemical oxygen demand, etc., and the operating parameter setting values ​​include aeration volume, chemical dosage ratio, etc.

[0033] Step 302: Based on the real-time data, the input variables of the fuzzy logic control algorithm are set, wherein the input variables include water quality parameters and operation parameters, wherein the water quality parameters include pH value, dissolved oxygen and chemical oxygen demand, and the operation parameters include aeration volume and reagent dosage ratio; This step uses water quality parameters and operating parameters as input variables of the fuzzy logic control algorithm, laying the foundation for running the algorithm in the next step.

[0034] Step 303: define fuzzy rules, apply fuzzy logic control algorithm, and perform fuzzy reasoning according to the input variables and the fuzzy rules to obtain optimized operating parameter setting values ​​to optimize the sewage treatment effect; In this step, fuzzy rules define the relationship between input variables and output variables and are used to guide the fuzzy reasoning process. Fuzzy reasoning refers to the process of deriving optimized operating parameter setting values ​​based on input variables and fuzzy rules.

[0035] This step defines fuzzy rules. For example, when the pH value is low and the aeration volume is high, reduce the aeration volume; when the dissolved oxygen is low and the dosage ratio of the agent is high, increase the dosage ratio of the agent. According to the defined fuzzy rules and input variables, fuzzy reasoning is performed to calculate the optimal operating parameter setting value; defuzzification methods such as the centroid method and the maximum membership method are used to convert the fuzzy reasoning results into specific values ​​to obtain the optimized operating parameter setting value.

[0036] Step 304: training a support vector machine model based on the optimized operating parameter setting value and the second water quality parameter value to identify and classify fault modes and obtain a fault classification result; This step uses the optimized operating parameter setting values ​​and the second water quality parameter values ​​as input to train the support vector machine model; the trained support vector machine model is used to perform fault pattern recognition and classification on real-time data to obtain fault classification results.

[0037] The embodiments of the present invention can achieve the following beneficial effects through the above steps: By collecting and processing key water quality parameters and operating parameters in real time, the sewage treatment process can be monitored in real time to ensure timely adjustment of the treatment effect; Use deep neural networks and genetic algorithms to build dynamic models, and combine fuzzy logic control algorithms to dynamically adjust operating parameters, optimize sewage treatment effects, and improve treatment efficiency; The support vector machine algorithm is used to classify and identify the fault modes in the processing process, and faults are discovered and diagnosed in a timely manner to ensure the stable operation of the system; Integrate expert system and user-defined alarm rule functions to realize automatic monitoring and intelligent management of sewage treatment process, and improve the intelligence level and operation efficiency of the system.

[0038] The present invention provides a specific embodiment, wherein the step 303 defines fuzzy rules, applies a fuzzy logic control algorithm, and performs fuzzy reasoning according to the input variables and the fuzzy rules to obtain optimized operating parameter setting values ​​to optimize the sewage treatment effect, specifically comprising the following steps: Step 311: define a fuzzy set, and use a membership function to convert the second water quality parameter value and the operating parameter setting value in the input variable into the membership of the fuzzy set; In this step, fuzzy sets refer to a mathematical tool used to deal with uncertainty and imprecise information. Each element has a membership value that indicates the degree to which the element belongs to the set. The membership function refers to a function that defines the degree to which an element belongs to the fuzzy set, and usually takes a value ranging from 0 to 1.

[0039] This step defines several fuzzy sets for each input variable, for example, pH value can be defined as three fuzzy sets of low, medium and high, dissolved oxygen can be defined as three fuzzy sets of low, moderate and high, chemical oxygen demand can be defined as three fuzzy sets of low, medium and high, aeration volume can be defined as three fuzzy sets of low, medium and high, and reagent addition ratio can be defined as three fuzzy sets of low, medium and high; a membership function is defined for each fuzzy set, for example, the low fuzzy set of pH value can use the triangular membership function; the second water quality parameter value and the operating parameter setting value collected in real time are substituted into the corresponding membership function, and their membership in each fuzzy set is calculated.

[0040] Step 312: defining fuzzy rules based on the fuzzy sets and membership degrees; When defining fuzzy rules in this step, the relationship between input variables and output variables needs to be considered. For example, Rule 1: if the pH value is low and the dissolved oxygen is low, the aeration rate is high; Rule 2: if the chemical oxygen demand is high and the reagent dosage ratio is low, the reagent dosage ratio is high; Rule 3: if the pH value is moderate and the dissolved oxygen is moderate, the aeration rate is moderate; Rule 4: if the chemical oxygen demand is moderate and the reagent dosage ratio is moderate, the reagent dosage ratio is moderate.

[0041] Step 313: applying a fuzzy logic control algorithm, performing fuzzy reasoning according to the fuzzy rules and the fuzzy sets, calculating the optimal operating parameter setting value, and processing the optimal operating parameter setting value using a defuzzification method to obtain an optimized operating parameter setting value; In this step, the defuzzification method refers to converting the results of fuzzy reasoning into specific numerical values ​​for practical application.

[0042] This step performs fuzzy reasoning based on the defined fuzzy rules and the membership of the input variables. For example, if the current pH value is 5.5, the dissolved oxygen is 1.8 mg / L, the chemical oxygen demand is 600 mg / L, and the agent addition ratio is 0.5%, then according to Rule 1 and Rule 2, it can be concluded that the aeration volume should be high and the agent addition ratio should be high; the results of fuzzy reasoning are converted into specific values. Commonly used defuzzification methods include the centroid method, the maximum membership method, etc. For example, the centroid method is used to calculate the optimal value of the aeration volume.

[0043] More specifically, the embodiment of the present invention further provides a formula for calculating the optimal operating parameter setting value, and the specific calculation formula is as follows: ; in, At the current time Optimized operating parameter setting values; is the number of input variables; It is Input variables at time The weight of It is The water quality parameter value at the current time point The value of It is The operating parameter setting value at the current time point The value of is a fuzzy inference function used to calculate the The contribution of each input variable to the optimal operating parameter setting value; is a time window function; The formula comprehensively considers the fuzzy reasoning function, time window function, and adaptive weight adjustment function to better reflect the actual situation and improve the accuracy and robustness of the optimization of the operating parameter setting values.

[0044] Among them, the calculation formula of the fuzzy inference function is as follows: ; in, is the fuzzy inference function; It is The water quality parameter value at the current time point The degree of membership; It is The operating parameter setting value at the current time point The degree of membership; It is a time window function, which is used to consider the trend of historical data and the weight of current data; The fuzzy reasoning function can flexibly adjust the output value according to different input variables and fuzzy rules to adapt to the optimization needs under different working conditions; at the same time, the function can handle uncertainty and nonlinear problems, improving the robustness and adaptability of the system.

[0045] The calculation formula of the time window function is as follows: ; in, is a time window function; is the impact factor of historical data, and its value range is , used to balance the weights of historical data and current data; is the decay factor, which is used to control the decay rate of the influence of historical data; is the current time point, which is used to calculate the impact of the data at the current time point on the optimization results; It is a reference time point used to calculate the impact of historical data on the current optimization results; It is an adaptive weight adjustment function, which is used to dynamically adjust the weight according to the fluctuation of the current data; This time window function takes into account the impact of historical data on the current optimization results. The impact of historical data gradually weakens over time, which helps to maintain the dynamic adaptability of the model. At the same time, it emphasizes the importance of current data, which helps the model to quickly respond to current changes and improve real-time performance.

[0046] Among them, the calculation formula of the adaptive weight adjustment function is as follows: ; in, is the fluctuation sensitivity factor, which is used to control the sensitivity of the weight to data fluctuations; It is Historical average values ​​of water quality parameters; It is The water quality parameter value at the current time point The value of The adaptive weight adjustment function dynamically adjusts the weight according to the deviation between the current data and the historical average. When the data fluctuates greatly, the weight will be reduced to avoid deviations in the optimization results due to abnormal data. This allows the model to be dynamically adjusted according to actual conditions, thereby improving the robustness and accuracy of the optimization.

[0047] The embodiments of the present invention can achieve the following beneficial effects through the above steps: Through fuzzy logic control algorithm, the operating parameter setting values ​​of the sewage treatment system are adjusted and optimized in real time to improve the treatment effect and system stability; Fuzzy logic control algorithms can handle uncertainty and imprecise information, making control more flexible and precise; By optimizing operating parameters, the occurrence of system failures can be reduced and the reliability and safety of the system can be improved; The optimized operating parameter setting values ​​can reduce unnecessary aeration volume and reagent dosage, reduce energy consumption and reagent costs, and achieve energy conservation and emission reduction; Combining fuzzy logic control and defuzzification methods, intelligent management of the sewage treatment process can be achieved, and the automation and management levels of the system can be improved.

[0048] In order to realize comprehensive automatic monitoring and management of the sewage treatment process and ensure the efficient and stable operation of the sewage treatment process, based on this, the present invention provides a specific embodiment, in which the step 104 builds a comprehensive monitoring and management system based on the cloud platform based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result. The comprehensive monitoring and management system integrates the expert system and the user-defined alarm rule function to realize automatic monitoring of the sewage treatment process, and specifically includes the following steps: Step 401: performing data integration and data cleaning on the prediction results, the operation data, the optimized operation parameter setting values ​​and the fault classification results to obtain sewage treatment data; This step combines the prediction results, operation data, optimized operation parameter setting values, and fault classification results into a unified data set to obtain sewage treatment data. For example, database or data warehouse technology can be used to store these data in a table; the integrated data is cleaned, including removing duplicate data, correcting erroneous data, and filling missing values. For example, SQL queries and data cleaning tools can be used to complete these tasks. Suppose the pH value of a record is negative, which is obviously wrong and needs to be corrected or deleted.

[0049] Step 402: Based on the sewage treatment data, the front-end display technology of the cloud platform is used to display the status and predicted trend of sewage treatment in real time to provide a monitoring interface; In this step, the front-end display technology refers to the technology used to display data in real time on the user interface.

[0050] This step uses the front-end display technology of the cloud platform, combined with the API provided by the cloud platform, to display real-time data to provide a user-friendly monitoring interface that displays the current water quality parameter values, optimized operating parameter setting values, prediction results, and fault classification results. For example, you can use chart libraries such as ECharts or D3.js to draw real-time water quality parameter curves and prediction trend graphs. For example, the monitoring interface can have a dashboard that displays real-time data such as pH, dissolved oxygen, and chemical oxygen demand, and another area that displays a predicted trend graph.

[0051] Step 403: integrating an expert system, wherein the expert system combines with a target expert knowledge base to provide an optimal operation strategy corresponding to the state of the sewage treatment; In this step, the target expert knowledge base refers to a database containing expert experience and knowledge, which is used to guide the decision-making of the expert system.

[0052] This step integrates a rule-based expert system that can provide the best operating strategy based on the current water quality parameters and operating parameters. For example, if the pH value is too low, the system recommends increasing the dosage of the reagent; the system combines a database containing expert experience and knowledge to guide the decision-making of the expert system. For example, the knowledge base can contain treatment methods and optimization strategies for various failure modes.

[0053] Step 404: Generate a user interface to enable the user to set a custom alarm rule according to actual needs and the user-defined alarm rule function, wherein the custom alarm rule includes different thresholds and alarm conditions, and when the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered to achieve real-time response and processing; This step generates a user-friendly interface that allows users to set custom alarm rules. For example, users can set a pH threshold of 6-8, and if the pH value exceeds this range, the system will trigger an alarm; users can set different thresholds and alarm conditions. For example, users can set an alarm to be triggered when the dissolved oxygen is lower than 2 mg / L, or when the aeration volume exceeds 80m³ / h. These rules can be configured through the interface and saved to the database.

[0054] Step 405: Based on the sewage treatment data, monitoring interface, expert system and user interface, a comprehensive monitoring and management system based on a cloud platform is constructed to realize automatic monitoring of sewage treatment; In this step, the integrated monitoring and management system refers to a platform that integrates multiple monitoring and management functions to monitor and manage the sewage treatment process in real time.

[0055] This step integrates all the above functions into a cloud-based system. The system can monitor the status of sewage treatment in real time, provide forecast trends, optimize operations based on the recommendations of the expert system, and trigger alarms based on the alarm rules set by the user. For example, the system can be deployed on a cloud platform, using cloud servers and database services to store and process data; the system can automatically collect and process real-time data, operate according to preset rules and strategies, and automatically trigger alarms when abnormalities occur, ensuring efficient and stable operation of the sewage treatment process.

[0056] The embodiments of the present invention can achieve the following beneficial effects through the above steps: By displaying the status and forecast trend of sewage treatment in real time, users can understand the operation of the system at any time and detect and handle abnormalities in time; The expert system combines the target expert knowledge base to provide the best operation strategy, helping users optimize operating parameters and improve processing effects and system stability; Users can set custom alarm rules according to actual needs, and automatically trigger alarms when data exceeds the preset range to achieve rapid response and processing; The comprehensive monitoring and management system integrates multiple functions, realizes intelligent management and automated monitoring of the sewage treatment process, and improves the automation and management level of the system; Through data integration and cleaning, we ensure the accuracy and consistency of data, provide reliable data support for decision-making, and improve the reliability and efficiency of the system.

[0057] In order to make full use of expert experience and knowledge, guide decision-making and operation in the sewage treatment process, and ensure the stable operation and optimal performance of the system, the present invention provides a specific embodiment, wherein the step 403 integrates an expert system, and the expert system combines the target expert knowledge base to provide an optimal operation strategy corresponding to the state of the sewage treatment, specifically including the following steps: Step 411: formulating a fault diagnosis rule based on historical data, wherein the historical data includes a first water quality parameter value; In this step, historical data refers to the water quality parameter values ​​and other relevant data collected during past time periods for analysis and modeling.

[0058] This step collects water quality parameter values ​​over a period of time, including pH, dissolved oxygen, chemical oxygen demand, etc., as well as other related data, such as operating parameter setting values, fault records, etc.; analyzes historical data to find out the characteristics and patterns related to faults. For example, if the pH value is continuously below 6 and the dissolved oxygen is below 2 mg / L, it may indicate that the aeration system is faulty. These patterns can be converted into fault diagnosis rules. For example, if the pH value is <6 and the dissolved oxygen is <2 mg / L, it is judged to be an aeration system fault.

[0059] Step 412: formulating known optimal operation strategies for different fault types based on the fault classification results and the fault diagnosis rules; This step combines the fault diagnosis rules and fault classification results to develop the optimal operation strategy for each fault type. For example, for an aeration system failure, the optimal operation strategy may be to increase the aeration volume to 80 m³ / h and check the aeration equipment; for a reagent dosing system failure, the optimal operation strategy may be to increase the reagent dosing ratio to 0.8% and check the reagent dosing equipment.

[0060] Step 413: Based on the fault diagnosis rules and the known optimal operation strategy, an initial expert knowledge base is constructed, an adaptive learning mechanism is introduced, the expert knowledge base is optimized, and a target expert knowledge base is obtained; In this step, the adaptive learning mechanism refers to the mechanism for optimizing the expert knowledge base through continuous learning and updating.

[0061] This step integrates the fault diagnosis rules and optimal operation strategies into a database to form an initial expert knowledge base. For example, the knowledge base can contain the following entries: the fault type is aeration system fault, the diagnosis rules are pH value <6 and dissolved oxygen <2 mg / L; increase the aeration volume to 80 m³ / h and check the aeration equipment. By continuously learning new fault data and operation results, the expert knowledge base is optimized. For example, after each fault occurs, the system records the actual operation results and effects. If a certain operation strategy is not effective, the system can automatically adjust or add new rules. This adaptive learning mechanism can gradually improve the accuracy and practicality of the knowledge base.

[0062] Target expert knowledge base: The expert knowledge base optimized by the adaptive learning mechanism has higher accuracy and practicality. For example, the optimized knowledge base may contain more detailed fault diagnosis rules and more effective operation strategies.

[0063] Step 414: determining an optimal operation strategy corresponding to the state of sewage treatment based on the target expert knowledge base; This step uses the optimized target expert knowledge base to match the corresponding fault type and optimal operation strategy according to the current water quality parameter values ​​and operating parameter setting values; when the system detects a certain fault type, it provides the corresponding optimal operation strategy according to the rules in the target expert knowledge base. For example, if the current pH value is 5.5 and the dissolved oxygen is 1.8 mg / L, the system determines that the aeration system is faulty. According to the optimal operation strategy in the knowledge base, it is recommended to increase the aeration volume to 80 m³ / h and check the aeration equipment.

[0064] The embodiments of the present invention can achieve the following beneficial effects through the above steps: The fault diagnosis rules formulated through historical data can more accurately identify and classify fault types and improve the accuracy of fault diagnosis; The optimal operation strategy formulated by combining expert experience and historical data can provide targeted solutions and improve the processing effect and stability of the system; Introducing an adaptive learning mechanism to continuously optimize the expert knowledge base, enabling the system to adapt to new fault types and operational requirements, and improving the system's intelligence level; Based on the target expert knowledge base, the system can provide the best operation strategy in real time, respond to and handle faults in a timely manner, and ensure the efficient and stable operation of the sewage treatment process; Through data-driven methods, fault diagnosis and operation strategies are optimized, system reliability and efficiency are improved, human intervention is reduced, and operating costs are lowered.

[0065] In order to enable users to discover and handle abnormal situations in a timely manner and ensure efficient and stable operation of the sewage treatment process, based on this, the present invention provides a specific embodiment, in which the step 404 generates a user interface to enable users to set custom alarm rules according to actual needs and the user-defined alarm rule function, and the custom alarm rules include different thresholds and alarm conditions. When the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered to achieve real-time response and processing, which specifically includes the following steps: Step 421: Generate a user interface to enable the user to set a custom alarm rule according to actual needs and the user-defined alarm rule function, wherein the custom alarm rule includes different thresholds and alarm conditions; In this step, the threshold refers to the set upper and lower limits. When the data exceeds this range, an alarm is triggered. The alarm condition refers to the specific conditions that trigger the alarm, such as when a water quality parameter value exceeds the threshold.

[0066] This step generates a user-friendly interface, including the ability to set custom alarm rules. The interface is clear, intuitive, and user-friendly, and users can set different thresholds and alarm conditions on the interface. For example, users can set the pH threshold to 6-8, and the system will trigger an alarm if the pH value exceeds this range. Users can also set the dissolved oxygen threshold to 2-8 mg / L, and the system will also trigger an alarm if the dissolved oxygen is lower than 2 mg / L or higher than 8 mg / L; the alarm rules set by users can be saved in the database so that the system can call them in real time.

[0067] Step 422: based on the custom alarm rule, when the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered, and the user is notified in real time through multiple notification channels, and real-time alarm information is generated; In this step, the real-time alarm information refers to the alarm information generated by the system when the alarm conditions are met, including the alarm time, alarm reason, etc.

[0068] This step monitors the second water quality parameter value and the optimized operating parameter setting value in real time, such as pH value, dissolved oxygen, chemical oxygen demand, aeration volume, and reagent dosage ratio; when the real-time data exceeds the threshold set by the user, the system automatically triggers an alarm. For example, if the pH value is 5.5, which is lower than the set threshold of 6, the system will trigger an alarm; the user is notified in real time through multiple notification channels, such as SMS, email, and APP push. For example, the system can send a text message with the content of "Attention, pH value is 5.5, which is lower than the set threshold of 6, please check and handle it"; the system generates real-time alarm information, including alarm time, alarm reason, current water quality parameter value, etc. For example, the alarm information may include the alarm time 2024-11-21 15:30, the alarm reason pH value is lower than the threshold, and the current pH value is 5.5.

[0069] Step 423: Based on the real-time alarm information, select a corresponding rapid response mechanism and the optimal operation strategy to achieve real-time response and processing; In this step, the rapid response mechanism refers to pre-defined emergency measures to deal with alarms, such as automatically adjusting operating parameters, notifying maintenance personnel, etc.

[0070] After receiving the real-time alarm information, this step analyzes the alarm cause and current status. For example, if the alarm cause is that the pH value is lower than the threshold, find the corresponding rapid response mechanism and optimal operation strategy; select the appropriate rapid response mechanism based on the alarm information. For example, if the pH value is lower than the threshold, automatically adjust the agent dosage ratio and increase the agent dosage; combine the target expert knowledge base to apply the optimal operation strategy. For example, according to the rules in the expert knowledge base, increase the agent dosage ratio to 0.8% and check the agent dosage equipment; execute the selected operation strategy, such as adjusting the agent dosage ratio, and record the operation results. At the same time, notify the maintenance personnel for further inspection and processing.

[0071] The embodiments of the present invention can achieve the following beneficial effects through the above steps: By setting custom alarm rules through the user interface, the system can monitor water quality parameters and operating parameters in real time and detect abnormal conditions in a timely manner; Notify users in real time through multiple notification channels to ensure that users can get the alarm information immediately and take timely measures; Based on real-time alarm information, the system can select appropriate rapid response mechanisms and optimal operation strategies to achieve rapid response and processing and reduce the impact of failures; Combined with the target expert knowledge base, the system can provide the best operational suggestions, improve the intelligent management level of the system, reduce human intervention, and improve processing efficiency; Through data-driven methods, we optimize alarm rules and operation strategies, improve system reliability and efficiency, and reduce operating costs.

[0072] The present invention provides a specific embodiment, wherein the step 102 performs data processing on the first water quality parameter value, and based on the processed first water quality parameter value, uses a deep neural network to construct a dynamic model of the sewage treatment process, and introduces a genetic algorithm to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and uses the optimized dynamic model to predict the sewage treatment effect, and obtains the prediction result and the operation data of the sewage treatment facility, which specifically includes the following steps: Step 201: performing data normalization processing on the first water quality parameter value, and extracting key information from the normalized data to obtain the processed first water quality parameter value, and based on the processed first water quality parameter value, using a deep neural network to construct a dynamic model of the sewage treatment process, and using a genetic algorithm to adjust the weight, bias, number of layers of the neural network and the number of neurons in each layer of the dynamic model to optimize the structure and parameters of the dynamic model, and obtain a preliminarily optimized dynamic model; In this step, weight refers to the parameter connecting two neurons in the neural network, indicating the importance of the input signal. Bias refers to an additional parameter for each neuron in the neural network, which is used to adjust the position of the activation function.

[0073] In this step, the first water quality parameter value is normalized and converted to a range of 0 to 1. For example, the minimum-maximum normalization method can be used; features valuable for model training and prediction are extracted from the normalized data to obtain the processed first water quality parameter value; for example, principal component analysis (PCA) can be used to extract the main features, or features highly correlated with the target variable can be selected. Based on the processed first water quality parameter value, a deep neural network is used to construct a dynamic model of the sewage treatment process, such as a multi-layer perceptron (MLP). Assuming that the model structure is an input layer, two hidden layers and an output layer, the number of nodes in the input layer is the number of extracted features, and the number of nodes in the output layer is the predicted water quality parameter; the genetic algorithm is used to optimize the model weights, biases, number of layers and number of neurons in each layer. The genetic algorithm searches for the optimal solution through operations such as crossover and mutation. For example, the initial population size can be set to 100, the crossover probability to 0.8, the mutation probability to 0.1, and the number of iterations to 100; the dynamic model optimized by the genetic algorithm has better prediction performance. For example, an optimized model may have fewer layers and a more reasonable number of neurons, while weights and biases are also optimized.

[0074] Step 202: using the treated first water quality parameter value to train the initially optimized dynamic model to obtain an optimized dynamic model, using the optimized dynamic model to predict the sewage treatment effect, and obtaining the prediction result and the operation data of the sewage treatment facility; This step uses the first water quality parameter value after treatment to train the preliminary optimized dynamic model. During the training process, the gradient descent method (such as Adam optimizer) is used to adjust the model parameters until the loss function converges; for example, the mean square error (MSE) can be used as the loss function; the trained dynamic model has higher prediction accuracy and robustness. For example, the trained model can more accurately predict the future water quality parameter values; the dynamic model in optimization is used to predict the sewage treatment effect, and the prediction results and the operation data of the sewage treatment facility are obtained. For example, the water quality parameter values ​​such as pH value, dissolved oxygen, chemical oxygen demand, etc. at a certain moment in the future can be predicted, as well as the current aeration volume, agent dosage and other operation data.

[0075] The embodiments of the present invention can achieve the following beneficial effects through the above steps: By normalizing the data of the first water quality parameter value, the influence of different dimensions is eliminated, the consistency and comparability of the data are improved, which is beneficial to the training and prediction of the model; Extract key information from normalized data, reduce redundant data, and improve model training efficiency and prediction accuracy; Using deep neural networks to build dynamic models can learn and model complex nonlinear relationships and improve the model's predictive capabilities; The structure and parameters of the model are optimized through genetic algorithms, which improves the prediction accuracy and robustness of the model and reduces the risk of overfitting. By using the dynamic model in optimization to predict the sewage treatment effect, future water quality parameter values ​​and operation data can be provided in real time, helping managers to adjust the operation parameters in time to ensure the sewage treatment effect.

[0076] Figure 2 A schematic diagram of a sewage treatment progress monitoring system is provided in accordance with an embodiment of the present invention. Figure 2 As shown, the system includes: A collection module 21, used to collect first water quality parameter values ​​of key nodes of the sewage treatment facility within a historical time period; The optimization module 22 is used to perform data processing on the first water quality parameter value, and based on the processed first water quality parameter value, use a deep neural network to build a dynamic model of the sewage treatment process, and introduce a genetic algorithm to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and use the optimized dynamic model to predict the sewage treatment effect, and obtain the prediction result and the operation data of the sewage treatment facility; The identification module 23 is used to dynamically adjust the operating parameter setting values ​​of the sewage treatment system of the sewage treatment facility by using a fuzzy logic control algorithm to obtain optimized operating parameter setting values, and classify and identify the failure modes of the sewage treatment system in the sewage treatment process by using a support vector machine algorithm based on the optimized operating parameter setting values ​​to obtain a failure classification result; Construction module 24 is used to construct a comprehensive monitoring and management system based on the cloud platform based on the prediction results, the operating data, the optimized operating parameter setting values ​​and the fault classification results. The comprehensive monitoring and management system integrates expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process.

[0077] Figure 2 The sewage treatment progress monitoring system can be implemented Figure 1 The implementation principle and technical effect of the method for monitoring the progress of sewage treatment described in the embodiment shown are not described in detail. The specific way in which each module and unit performs operations in the monitoring system for the progress of sewage treatment in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0078] In one possible design, Figure 2 A sewage treatment progress monitoring system of the embodiment shown can be implemented as a computing device, such as Figure 3As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0079] The processing component 32 is used to: collect the first water quality parameter values ​​of the key nodes of the sewage treatment facility within the historical time period; perform data processing on the first water quality parameter values, and based on the processed first water quality parameter values, use a deep neural network to build a dynamic model of the sewage treatment process, and introduce a genetic algorithm to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and use the optimized dynamic model to predict the sewage treatment effect to obtain the prediction results and the operation data of the sewage treatment facility; use a fuzzy logic control algorithm to dynamically adjust the operating parameter setting values ​​of the sewage treatment system to which the sewage treatment facility belongs to obtain the optimized operating parameter setting values, and based on the optimized operating parameter setting values, use a support vector machine algorithm to classify and identify the failure modes of the sewage treatment system during the sewage treatment process to obtain a failure classification result; based on the prediction results, the operation data, the optimized operating parameter setting values ​​and the failure classification result, build a comprehensive monitoring and management system based on a cloud platform, and the comprehensive monitoring and management system integrates expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process.

[0080] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0081] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0082] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0083] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0084] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0085] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0086] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 A method for monitoring the progress of sewage treatment in the illustrated embodiment.

[0087] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0088] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0089] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the progress of sewage treatment, characterized in that: include: Collect the first water quality parameter values ​​of key nodes of sewage treatment facilities in a historical time period; Performing data processing on the first water quality parameter value, and based on the processed first water quality parameter value, using a deep neural network to construct a dynamic model of the sewage treatment process, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and using the optimized dynamic model to predict the sewage treatment effect, to obtain a prediction result and operation data of the sewage treatment facility; Dynamically adjust the operating parameter setting values ​​of the sewage treatment system of the sewage treatment facility by using a fuzzy logic control algorithm to obtain optimized operating parameter setting values, and classify and identify the failure modes of the sewage treatment system during the sewage treatment process by using a support vector machine algorithm based on the optimized operating parameter setting values ​​to obtain a failure classification result; Based on the prediction results, the operating data, the optimized operating parameter setting values ​​and the fault classification results, a comprehensive monitoring and management system based on a cloud platform is constructed. The comprehensive monitoring and management system integrates expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process.

2. The method according to claim 1, characterized in that: The fuzzy logic control algorithm is used to dynamically adjust the operating parameter setting values ​​of the sewage treatment system to which the sewage treatment facility belongs, to obtain optimized operating parameter setting values, and based on the optimized operating parameter setting values, the support vector machine algorithm is used to classify and identify the failure mode of the sewage treatment system during the sewage treatment process, to obtain a failure classification result, including: Based on the prediction result and the operation data, collecting the real-time second water quality parameter value and the operation parameter setting value of the sewage treatment system to which the sewage treatment facility belongs to obtain real-time data; Based on the real-time data, setting input variables of the fuzzy logic control algorithm, the input variables including water quality parameters and operating parameters, the water quality parameters including pH value, dissolved oxygen and chemical oxygen demand, the operating parameters including aeration volume and reagent dosage ratio; Defining fuzzy rules, applying fuzzy logic control algorithms, and performing fuzzy reasoning according to the input variables and the fuzzy rules to obtain optimized operating parameter setting values ​​to optimize the sewage treatment effect; A support vector machine model is trained based on the optimized operating parameter setting value and the second water quality parameter value to identify and classify fault modes and obtain a fault classification result.

3. The method according to claim 2, characterized in that The defining of fuzzy rules, applying fuzzy logic control algorithms, and performing fuzzy reasoning according to the input variables and the fuzzy rules to obtain optimized operating parameter setting values ​​to optimize the sewage treatment effect include: Define a fuzzy set, and use a membership function to convert the second water quality parameter value and the operating parameter setting value in the input variable into the membership of the fuzzy set; Based on the fuzzy sets and membership degrees, defining fuzzy rules; A fuzzy logic control algorithm is applied to perform fuzzy reasoning according to the fuzzy rules and the fuzzy sets to calculate the optimal operating parameter setting values, and a defuzzification method is used to process the optimal operating parameter setting values ​​to obtain optimized operating parameter setting values.

4. The method according to claim 2, characterized in that: Based on the prediction results, the operation data, the optimized operation parameter setting values ​​and the fault classification results, a comprehensive monitoring and management system based on a cloud platform is constructed, and the comprehensive monitoring and management system integrates expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process, including: Performing data integration and data cleaning on the prediction results, the operation data, the optimized operation parameter setting values, and the fault classification results to obtain sewage treatment data; Based on the sewage treatment data, the front-end display technology of the cloud platform is used to display the status and forecast trend of sewage treatment in real time to provide a monitoring interface; An integrated expert system, wherein the expert system combines a target expert knowledge base to provide an optimal operation strategy corresponding to the state of the sewage treatment; Generate a user interface to enable the user to set a custom alarm rule according to actual needs and the user-defined alarm rule function, wherein the custom alarm rule includes different thresholds and alarm conditions, and when the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered to achieve real-time response and processing; Based on the sewage treatment data, monitoring interface, expert system and user interface, a comprehensive monitoring and management system based on a cloud platform is constructed to realize automatic monitoring of sewage treatment.

5. The method according to claim 4, characterized in that The integrated expert system, which combines the target expert knowledge base to provide an optimal operation strategy corresponding to the state of sewage treatment, includes: Formulate a fault diagnosis rule based on historical data, wherein the historical data includes a first water quality parameter value; Based on the fault classification results and the fault diagnosis rules, formulating known optimal operation strategies for different fault types; Based on the fault diagnosis rules and the known optimal operation strategy, an initial expert knowledge base is constructed, an adaptive learning mechanism is introduced, the expert knowledge base is optimized, and a target expert knowledge base is obtained; An optimal operation strategy corresponding to the state of sewage treatment is determined based on the target expert knowledge base.

6. The method according to claim 4, characterized in that The user interface is generated so that the user can set a custom alarm rule according to actual needs and the user-defined alarm rule function. The custom alarm rule includes different thresholds and alarm conditions. When the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered to achieve real-time response and processing, including: Generate a user interface to enable the user to set a custom alarm rule according to actual needs and the user-defined alarm rule function, wherein the custom alarm rule includes different thresholds and alarm conditions; Based on the custom alarm rules, when the second water quality parameter value in the real-time data or the optimized operating parameter setting value exceeds the corresponding preset range, an alarm is triggered, and the user is notified in real time through multiple notification channels, and real-time alarm information is generated; Based on the real-time alarm information, a corresponding rapid response mechanism and the optimal operation strategy are selected to achieve real-time response and processing.

7. The method according to claim 1, characterized in that The data processing is performed on the first water quality parameter value, and based on the processed first water quality parameter value, a dynamic model of the sewage treatment process is constructed using a deep neural network, and a genetic algorithm is introduced to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and the optimized dynamic model is used to predict the sewage treatment effect to obtain a prediction result and operation data of the sewage treatment facility, including: Performing data normalization processing on the first water quality parameter value, and extracting key information from the normalized data to obtain the processed first water quality parameter value, and based on the processed first water quality parameter value, using a deep neural network to build a dynamic model of the sewage treatment process, and using a genetic algorithm to adjust the weights, biases, the number of layers of the neural network, and the number of neurons in each layer of the dynamic model to optimize the structure and parameters of the dynamic model, and obtain a preliminarily optimized dynamic model; The first water quality parameter value after treatment is used to train the preliminary optimized dynamic model to obtain an optimized dynamic model, and the optimized dynamic model is used to predict the sewage treatment effect to obtain the prediction result and the operation data of the sewage treatment facility.

8. A sewage treatment progress monitoring system, characterized in that: include: A collection module, used to collect the first water quality parameter values ​​of key nodes of the sewage treatment facility within a historical time period; an optimization module, for performing data processing on the first water quality parameter value, and based on the processed first water quality parameter value, using a deep neural network to construct a dynamic model of the sewage treatment process, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model to obtain an optimized dynamic model, and using the optimized dynamic model to predict the sewage treatment effect, and obtain a prediction result and operation data of the sewage treatment facility; an identification module, for dynamically adjusting the operating parameter setting values ​​of the sewage treatment system of the sewage treatment facility by using a fuzzy logic control algorithm to obtain optimized operating parameter setting values, and classifying and identifying the failure modes of the sewage treatment system during the sewage treatment process by using a support vector machine algorithm based on the optimized operating parameter setting values ​​to obtain a failure classification result; A construction module is used to construct a comprehensive monitoring and management system based on a cloud platform based on the prediction results, the operating data, the optimized operating parameter setting values ​​and the fault classification results. The comprehensive monitoring and management system integrates expert system and user-defined alarm rule functions to realize automatic monitoring of the sewage treatment process.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for monitoring the progress of sewage treatment as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a method for monitoring the progress of sewage treatment as described in any one of claims 1 to 7 is implemented.

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