Sewage treatment fault diagnosis method and system based on data analysis

Abstract

The invention discloses a sewage treatment fault diagnosis method and system based on data analysis, and the method comprises the steps: continuously optimizing a diagnosis strategy in the input and training process of a large amount of data through introducing a fault diagnosis model, and improving the accurate recognition capability of a fault under a complex working condition; therefore, the system not only has strong learning ability, but also can perform self-optimization through a reward mechanism, thereby reducing the occurrence rate of false report and missing report. An adaptive weight adjustment mechanism is introduced, so that the model can automatically adjust weight distribution and increase response to a new fault when the system has a novel fault or few faults; by dynamically adjusting the weight vector, the diagnosis model of the invention has strong self-learning ability and can continuously adapt to new fault modes and working condition changes, and the weight adjustment mechanism greatly improves the generalization ability and adaptability of the model, and especially has significant advantages in the face of fault scenes with strong uncertainty.

Description

Technical Field

[0001] The present invention relates to the technical field related to sewage treatment, and in particular to a sewage treatment fault diagnosis method and system based on data analysis. Background Art

[0002] The sewage treatment system is an essential part of modern urban and industrial wastewater management. Its core task is to remove pollutants from wastewater through a series of physical, chemical and biological treatment processes so that the treated water meets the discharge or reuse standards. However, the complexity, dynamics and multivariability of the sewage treatment system make it easy for various equipment and links in the system to fail, especially when operating at high load for a long time or when external environmental conditions change dramatically, equipment failures are more frequent.

[0003] Current sewage treatment system fault diagnosis technologies mainly rely on empirical formulas, expert systems or traditional statistical methods. These methods rely on a large amount of historical data or preset rules and cannot dynamically adapt to new or rare faults under different working conditions, resulting in the following deficiencies:

[0004] Poor robustness: Traditional methods often perform preset diagnosis for specific fault types, and are unable to effectively deal with rare or new faults. Especially when operating conditions fluctuate or equipment ages, the accuracy and adaptability of traditional methods are significantly reduced.

[0005] Lack of dynamic adaptability: Existing diagnostic systems are usually designed based on static models and are unable to adjust parameters or model structures in real time to adapt to environmental changes or the emergence of new faults, making it difficult to ensure the accuracy of diagnostic results.

[0006] Insufficient diagnostic accuracy: When dealing with multiple concurrent faults or a small number of faults, the existing methods have low fault diagnosis accuracy due to their limited ability to distinguish different faults. In addition, the existing technology has limited deep learning and feature extraction capabilities for data, which can easily lead to misdiagnosis or missed diagnosis.

[0007] Therefore, the existing sewage treatment fault diagnosis methods face many challenges in terms of the comprehensiveness of fault identification, the ability to deal with a few types of faults, and the accuracy of fault diagnosis. In order to solve these problems, there is an urgent need for a sewage treatment fault diagnosis method that can dynamically adjust the diagnosis strategy according to the real-time working conditions, effectively deal with rare and new faults, and has a high accuracy. Summary of the invention

[0008] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0009] Therefore, in order to solve the above technical problems, the present invention provides the following technical solutions: a sewage treatment fault diagnosis method based on data analysis, comprising the following specific steps:

[0010] Step 1: Collect data in real time through multi-source sensors in the sewage treatment system, as well as historical fault data, to provide a basis for subsequent data analysis and fault diagnosis, ensuring that the system obtains accurate operating status information;

[0011] Step 2: After preprocessing the collected data, feature extraction is performed to extract key features reflecting the sewage treatment conditions, forming feature vectors to describe the state changes and failure modes of the system;

[0012] Step 3: Define different fault categories based on historical data and expert experience, including common faults, rare faults, and new faults; assign labels to each fault, and introduce an adaptive label update mechanism in the fault mode to provide a clear goal for the model, which helps the reinforcement learning model to accurately identify and classify faults;

[0013] Step 4: Use reinforcement learning algorithm to build a fault diagnosis model, perform fault diagnosis on the feature vector based on the reinforcement learning model, identify the fault type and output the diagnosis result.

[0014] As a preferred solution of the sewage treatment fault diagnosis method based on data analysis described in the present invention, in which: in step one, the real-time collected data covers physical and chemical indicators such as flow rate, pH value, chemical oxygen demand, temperature, and equipment operating parameters (such as flow rate, pressure, temperature, etc.).

[0015] As a preferred solution of the sewage treatment fault diagnosis method based on data analysis described in the present invention, in step 2, the core goal of feature extraction is to convert the original data into a feature vector, which contains key information such as operating condition changes and fault symptoms. The feature vector form is as follows: x(t) = [x1(t), x2(t), ..., x m (t)], where each x i (t) represents a characteristic value at a certain moment.

[0016] As a preferred solution of the sewage treatment fault diagnosis method based on data analysis described in the present invention, in step three, the steps of fault category definition and label allocation are as follows:

[0017] Based on historical data and expert experience, define different fault categories (such as blockage, water leakage, pump failure, sensor failure, etc.), and classify these faults into common faults, rare faults and new faults;

[0018] Assign a corresponding label C to each fault categoryi (t), these labels are used as classification targets for the fault diagnosis model;

[0019] Based on real-time data feedback and adaptive learning mechanisms, when new fault types or rare faults occur, the labeling system is adjusted to ensure the update and accuracy of diagnostic targets.

[0020] As a preferred solution of the sewage treatment fault diagnosis method based on data analysis described in the present invention, the multi-source sensor data of the sewage treatment system is collected in real time and a state vector is generated; the model extracts features, inputs the state vector into the reinforcement learning model, and outputs the fault type label C i (t); The label C predicted by the model i (t) Compare with the actual fault situation and calculate the loss function L i (t);

[0021] Loss function L i The calculation formula of (t) is as follows:

[0022] Among them, y i is the true label of the sample; K: the total number of fault categories; p i，k (C i (t)): is the probability that the model predicts the i-th sample as category k;

[0023] This formula measures the accuracy of the model's prediction by calculating the negative sum of the logarithmic values ​​of the true label and the predicted probability; when the predicted probability is close to the true label, the loss value is small; on the contrary, the greater the prediction deviation, the greater the loss value.

[0024] As a preferred solution of the sewage treatment fault diagnosis method based on data analysis described in the present invention, the model is rewarded or punished according to the diagnosis result of the model and the actual situation, and the reinforcement learning model updates the weight according to the feedback signal; specifically, the reward is a positive or negative value, depending on the correctness of the model diagnosis result; when the model correctly diagnoses the fault type, the system gives a positive reward, and the reward is fed back to the model to adjust the loss function L i (t) and weight update w(t); if the model misdiagnoses the fault category, a negative reward is given. At this time, the model reduces the weight of the path and turns to other paths that are more likely to be correct.

[0025] As a preferred solution of the sewage treatment fault diagnosis method based on data analysis described in the present invention, when a new fault mode or a rare fault is detected, the weight parameters of the reinforcement learning model are automatically adjusted to adapt to the new fault situation; specifically, when it is detected that the diagnostic error of the model is large, that is, the loss function L i When (t) increases, the loss function Li (t) The driving model automatically adjusts weights to increase response to new faults.

[0026] As a preferred solution of the sewage treatment fault diagnosis method based on data analysis described in the present invention, it is assumed that the current weight vector is w(t)=[w1(t),w2(t),...,w n (t)], w(t) represents the weight vector at time t, which determines the emphasis of the model on different features. The calculation formula for dynamic adjustment of weights is:

[0027]

[0028] Among them, α: learning rate factor, which determines the sensitivity of the model to new fault responses; J(x(t)): fault response matrix, which represents the response of system state changes under different fault modes (calculated based on real-time data x(t)); β: adjustment parameter, which balances the impact of historical data and new data on the model; λ: scaling parameter, which is used to adjust the nonlinear change amplitude of data; L i (t): The loss function of the i-th sample, which is used to measure the error of the diagnostic model in detecting faults and evaluate the performance of the model in the current fault diagnosis task. By analyzing the loss function, the weight adjustment direction of the model can be determined. If the loss is large, the weight adjustment of the new data is increased. f(t,x i ): Failure mode function, which represents the characteristic response of the system to a new failure mode at time t; this function describes the characteristics of the new failure and includes key features extracted from data analysis; its form can be defined according to specific failure scenarios and sewage treatment processes, thereby enhancing the flexibility of the model in dealing with rare or new failures;

[0029] In the above formula, the integral part of the formula is used to perform cumulative calculations on the adjustment of the model weights within the time window [t, t+Δt]. By integrating the influence of historical data, the weight vector w(t) is dynamically adjusted to gradually adapt the model to new faults. After the new data is input, the model adjusts the fault mode function f(t, x i ) to adapt to different failure scenarios, avoid overreaction to model weight adjustment due to abnormal data at a single moment, and ensure that the model gradually adapts to new failure modes over a period of time;

[0030] By means of the nonlinear scaling function in Eq. The value range of this combined function is between (0, 1, which is used to smooth the changes in the model weight adjustment, ensure that the weight adjustment range is moderate, avoid drastic fluctuations, and enable the reinforcement learning model to adaptively adjust its own parameters to deal with rare or new faults and improve the diagnostic accuracy;

[0031] The value range of the formula for dynamic adjustment of the above weights is: (0,1), and the adjustment direction and amplitude of the weights are effectively controlled to prevent the model from generating unstable oscillations;

[0032] As the system detects new fault data, the loss function L i As (t) increases, the weight adjustment performs a cumulative response through integration, so that the model gradually adapts to new faults.

[0033] As a preferred solution of the sewage treatment fault diagnosis method based on data analysis described in the present invention, the specific steps of fault type identification and diagnostic result output are: in the process of processing sewage data, the feature vector after feature extraction is input into the reinforcement learning model, the model makes a diagnostic decision according to the current state, and outputs the diagnostic result; through the training of the reinforcement learning model, the system automatically identifies the current fault type according to the real-time input data, and outputs the corresponding fault label; the diagnostic result is fed back to the sewage treatment system for automatic adjustment or alarm; when a new type of fault is detected, the system learns through an adaptive mechanism and adjusts future diagnostic strategies.

[0034] The present invention also provides the above-mentioned sewage treatment fault diagnosis system based on data analysis, which includes a sewage treatment monitoring module, a communication module, a data analysis platform, a reinforcement learning module and a decision execution layer. The sewage treatment monitoring module is used to collect the operation data of the sewage treatment system in real time, and send the data to the data analysis platform through the communication module; wherein the sewage treatment monitoring module includes a sensor, a data acquisition unit, and a control unit, and the sensor includes a flow sensor, a PH sensor, a conductivity sensor, and a chemical oxygen demand sensor; the data analysis platform includes a central processing unit, which is used to receive the sensor data sent by the sewage treatment monitoring module through the communication module, and perform data preprocessing, storage and analysis operations; the reinforcement learning module is the core module of this system, which is responsible for training, strengthening and learning the model according to the historical data and real-time data sent by the sewage treatment monitoring module, and applying it to fault diagnosis; the decision execution layer is used to execute corresponding control strategies according to the diagnosis results, such as adjusting treatment process parameters or issuing maintenance alarms.

[0035] Beneficial effects of the present invention:

[0036] 1. The present invention collects data from multi-source sensors in the sewage treatment system, covering multi-dimensional data such as water quality parameters (such as pH value, dissolved oxygen, chemical oxygen demand, etc.) and equipment operation parameters (such as flow, pressure, temperature, etc.), thereby realizing all-round and real-time monitoring of the sewage treatment system; by preprocessing and extracting features of the data, the dimension of the data is significantly reduced, and key features representing changes in the operating conditions of the sewage treatment system, signs of failure, etc. are extracted, thereby effectively improving the training efficiency and diagnostic accuracy of the model.

[0037] 2. The present invention introduces an adaptive label update mechanism, which can dynamically adjust the fault category label based on the feedback of real-time data; when the system detects a new fault mode, it can timely update the classification target of the model to ensure the accuracy and real-time nature of the diagnosis results, especially in the identification of rare faults and new faults. This mechanism breaks through the limitations of traditional static models, enables the diagnostic system to have adaptive capabilities, and improves the intelligence level of the system.

[0038] 3. By introducing the reinforcement learning algorithm, the fault diagnosis model of the present invention can automatically learn the optimal fault diagnosis strategy through interaction with the sewage treatment system environment, thereby avoiding the reliance of traditional diagnosis models on fixed rules. The reinforcement learning model continuously optimizes the diagnosis strategy during the input and training process of a large amount of data, thereby improving the ability to accurately identify faults under complex working conditions. This enables the system to not only have a strong learning ability, but also to self-optimize through a reward mechanism, thereby reducing the incidence of false alarms and missed alarms.

[0039] 4. The present invention introduces an adaptive weight adjustment mechanism to ensure that when a new or rare fault occurs in the system, the model can automatically adjust the weight distribution and increase the response to the new fault; by dynamically adjusting the weight vector, the diagnostic model of the present invention has a strong self-learning ability and can continuously adapt to new fault modes and changes in operating conditions. The weight adjustment mechanism greatly improves the generalization ability and adaptability of the model, especially when facing fault scenarios with strong uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0041] Figure 1 It is a system architecture diagram of the present invention.

[0042] Figure 2 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0046] Example 1

[0047] Reference Figures 1-2 , which is the first embodiment of the present invention, provides a sewage treatment fault diagnosis system based on data analysis, the system includes a sewage treatment monitoring module, a communication module, a data analysis platform, a reinforcement learning module and a decision execution layer, the sewage treatment monitoring module is used to collect the operation data of the sewage treatment system in real time, and send the data to the data analysis platform through the communication module; wherein the sewage treatment monitoring module includes a sensor, a data acquisition unit, and a control unit, and the sensor includes a flow sensor, a PH sensor, a conductivity sensor, a chemical oxygen demand sensor, etc.; the data analysis platform includes a central processing unit, which is used to receive the sensor data sent by the sewage treatment monitoring module through the communication module, and perform data preprocessing, storage and analysis operations; the reinforcement learning module is the core module of the system, which is responsible for training, strengthening and learning the model according to the historical data and real-time data sent by the sewage treatment monitoring module, and applying it to fault diagnosis; the decision execution layer is used to execute corresponding control strategies according to the diagnosis results, such as adjusting the treatment process parameters or issuing maintenance alarms.

[0048] The present invention also provides a sewage treatment fault diagnosis method based on data analysis implemented based on the above system, comprising the following specific steps:

[0049] Step 1: Real-time data collection and historical fault data from multi-source sensors in the sewage treatment system provide a basis for subsequent data analysis and fault diagnosis, ensuring that the system obtains accurate operating status information; real-time collected data covers physical and chemical indicators such as flow, pH value, chemical oxygen demand, temperature, and equipment operating parameters (such as flow, pressure, temperature, etc.), including normal operating conditions and the time, type, severity, and other information of fault occurrence.

[0050] Step 2: Preprocess the collected data, mainly including denoising, outlier processing, data standardization, etc. The data preprocessing step ensures that the data input to the reinforcement learning model is clean and consistent, and avoids the negative impact of noise data on the model;

[0051] The key features reflecting the working conditions of sewage treatment are extracted from the pre-treated data through feature extraction algorithms (such as principal component analysis, time domain / frequency domain analysis, etc.) to form feature vectors to describe the state changes and failure modes of the system. The core goal of feature extraction is to convert the original data into feature vectors, which contain key information such as working condition changes and failure signs. The feature vector form is as follows: x(t) = [x1(t), x2(t), ..., x m (t)], where each x i (t) represents a characteristic value at a certain moment;

[0052] Step 3: Based on historical data and expert experience, define different fault categories (such as blockage, water leakage, pump failure, sensor failure, etc.), and classify these faults into common faults, rare faults, and new faults;

[0053] Assign a corresponding label C to each fault category i (t), these labels are used as classification targets for the fault diagnosis model;

[0054] Based on real-time data feedback and adaptive learning mechanisms, when new fault types or rare faults occur, the labeling system is adjusted to ensure the update and accuracy of diagnostic targets.

[0055] Step 4: Use reinforcement learning algorithm to build a fault diagnosis model, perform fault diagnosis on feature vectors based on the reinforcement learning model, identify fault types and output diagnostic results; the specific steps are: in the process of processing sewage data, the feature vector after feature extraction is input into the reinforcement learning model, the model makes diagnostic decisions based on the current state and outputs diagnostic results; through the training of the reinforcement learning model, the system automatically identifies the current fault type based on the real-time input data and outputs the corresponding fault label; the diagnostic results are fed back to the sewage treatment system for automated adjustment or alarm; when a new type of fault is detected, the system learns through an adaptive mechanism and adjusts future diagnostic strategies.

[0056] Specifically, multi-source sensor data of the sewage treatment system is collected in real time and a state vector is generated. The model extracts features, inputs the state vector into the reinforcement learning model, and outputs the fault type label C i (t); The label C predicted by the model i (t) Compare with the actual fault situation and calculate the loss function L i (t);

[0057] Loss function L i The calculation formula of (t) is as follows:

[0058] Among them, y i is the true label of the sample; K: the total number of fault categories; p i，k (C i (t)): is the probability that the model predicts the i-th sample as category k;

[0059] This formula measures the accuracy of the model prediction by calculating the negative sum of the logarithm of the true label and the predicted probability; when the predicted probability is close to the true label, the loss value is small; on the contrary, the greater the prediction deviation, the greater the loss value;

[0060] According to the model's diagnosis results and actual conditions, the model is rewarded or punished, and the reinforcement learning model updates the weights according to the feedback signal; specifically, the reward is positive or negative, depending on the correctness of the model's diagnosis results; when the model correctly diagnoses the fault type, the system gives a positive reward, which is fed back to the model to adjust the loss function L i (t) and weight update w(t); if the model misdiagnoses the fault category, a negative reward is given. At this time, the model reduces the weight of the path and turns to other more likely correct paths;

[0061] When a new fault mode or rare fault is detected, the weight parameters of the reinforcement learning model are automatically adjusted to adapt to the new fault situation; specifically, when it is detected that the diagnostic error of the model is large, that is, the loss function L i When (t) increases, the loss function L i (t) drives the model to automatically adjust the weights and increase the response to new faults; specifically, assuming that the current weight vector is w(t) = [w1(t), w2(t), ..., w n (t)], w(t) represents the weight vector at time t, which determines the emphasis of the model on different features. The calculation formula for dynamic adjustment of weights is:

[0062]

[0063] The value range of the formula for dynamic adjustment of the weight is: (0,1), and the adjustment direction and amplitude of the weight are effectively controlled to prevent the model from generating unstable oscillations;

[0064] As the system detects new fault data, the loss function L i As (t) increases, the weight adjustment performs a cumulative response through integration, so that the model gradually adapts to new faults.

[0065] Among them, α: learning rate factor, which determines the sensitivity of the model to new fault responses; J(x(t)): fault response matrix, which represents the response of system state changes under different fault modes (calculated based on real-time data x(t)); β: adjustment parameter, which balances the impact of historical data and new data on the model; λ: scaling parameter, which is used to adjust the nonlinear change amplitude of data; L i (t): The loss function of the i-th sample, which is used to measure the error of the diagnostic model in detecting faults and evaluate the performance of the model in the current fault diagnosis task. By analyzing the loss function, the weight adjustment direction of the model can be determined. If the loss is large, the weight adjustment of the new data is increased. f(t,x i ): Failure mode function, which represents the characteristic response of the system to a new failure mode at time t; this function describes the characteristics of the new failure and includes key features extracted from data analysis; its form can be defined according to specific failure scenarios and sewage treatment processes, thereby enhancing the flexibility of the model in dealing with rare or new failures;

[0066] In the above formula, the integral part of the formula is used to perform cumulative calculations on the adjustment of the model weights within the time window [t, t+Δt]. By integrating the influence of historical data, the weight vector w(t) is dynamically adjusted to gradually adapt the model to new faults. After the new data is input, the model adjusts the fault mode function f(t, x i ) to adapt to different failure scenarios, avoid overreaction to model weight adjustment due to abnormal data at a single moment, and ensure that the model gradually adapts to new failure modes over a period of time;

[0067] By means of the nonlinear scaling function in Eq. The value range of this combined function is between (0,1), which is used to smooth the changes in the model weight adjustment, ensure that the weight adjustment range is moderate, avoid drastic fluctuations, and enable the reinforcement learning model to adaptively adjust its own parameters to deal with rare or new faults and improve the diagnostic accuracy.

[0068] Example 2

[0069] This is the second embodiment of the present invention. This embodiment is different from the first embodiment in that in order to verify the technical advantages of the sewage treatment fault diagnosis system based on reinforcement learning proposed in this scheme in an actual operating environment, especially the advantages of the system in diagnosing rare faults and new faults, this embodiment provides experimental comparison data between the present invention and the prior art.

[0070] This embodiment simulates a sewage treatment plant and corresponding equipment, which is monitored by multiple sensors, including water quality parameters such as pH value, dissolved oxygen concentration, chemical oxygen demand (COD), ammonia nitrogen concentration, and equipment operating parameters such as flow, pressure, temperature, etc. The simulation system is equipped with a data acquisition unit and a fault record database to facilitate the training of reinforcement learning models.

[0071] For comparative experiments, a traditional fault diagnosis system was selected as a control, which was based on a fixed decision tree and expert experience. Fault classification included blockage, leakage, pump failure, sensor failure, aeration system failure, etc. Historical data were used to establish training models for the two systems. The traditional model relied on a fixed set of rules, while the reinforcement learning-based model used a deep Q network (DQN) algorithm that could adaptively adjust according to the environment.

[0072] The experimental steps are as follows:

[0073] Data collection: In the initial stage of the experiment, the real-time data stream of the sewage treatment plant was used to collect water quality and equipment parameters in different time periods, and a total of 20 days of simulated continuous data was recorded; through the preprocessing process, abnormal data was cleaned and normalized.

[0074] Feature extraction: Use time domain and frequency domain analysis methods to extract features from the collected data and generate a feature vector x(t) = [x1(t), x2(t), ..., x m (t)], where each eigenvector contains key information of the sewage treatment process, such as flow rate changes, temperature fluctuations, COD concentration trends, etc.

[0075] Model training: The diagnostic model based on reinforcement learning is trained using the DQN algorithm. The initial state of the model is self-adjusted through a large amount of historical data, and the weights are gradually optimized to generate a fault diagnosis strategy suitable for the sewage treatment system. The model performs reinforcement learning through a simulated environment and improves accuracy through thousands of cycles; at the same time, the traditional decision tree model relies on fixed rules established in advance and does not perform adaptive learning.

[0076] System testing: After training, field testing is conducted. The system is exposed to different actual faults, including common faults (such as sensor failure, insufficient aeration) and rare and new faults (such as water pollution caused by excessive aeration, blockage caused by unexpected changes in the sludge layer); during the test, the system receives sensor data in real time and outputs fault diagnosis results based on the model.

[0077] Data recording: When a fault occurs, the response time, accuracy, and false alarm rate of the model diagnosis are recorded, and the performance of the traditional model is also recorded. Through experimental data, the performance of the two systems under different types of faults will be evaluated; the specific situation is shown in the following table:

[0078]

[0079] It can be clearly seen from the experimental data that the fault diagnosis model based on reinforcement learning is significantly better than the traditional fixed rule decision tree model in many key indicators:

[0080] Diagnosis time: The adaptive learning capability of the reinforcement learning model significantly shortens the diagnosis time. In the diagnosis of common faults, the average diagnosis time of the reinforcement learning model is about 4 seconds, which is much lower than the average of 6 seconds of the traditional model. In addition, when faced with rare or new faults (such as excessive aeration and sludge layer blockage), traditional models are usually unable to diagnose due to the lack of predefined rules, while the reinforcement learning model can still respond within a reasonable time (6-8 seconds) by learning from historical data.

[0081] Diagnostic accuracy: In the diagnosis of common faults, the accuracy of the reinforcement learning model is about 10% higher than that of the traditional model, especially in faults such as sensor failure and insufficient aeration, the accuracy of the reinforcement learning model reached 94% and 92% respectively. More significantly, in the face of rare and new faults, the diagnostic accuracy of the traditional model dropped significantly due to insufficient rule sets, such as the diagnostic accuracy of excessive aeration was only 65%; while the reinforcement learning model significantly improved its accuracy to more than 88% through dynamic weight adjustment and adaptive learning; in addition, the traditional model was completely unable to diagnose new faults such as sludge layer blockage, while the reinforcement learning model still had an 85% diagnostic accuracy.

[0082] False alarm rate: In all fault types, the false alarm rate of the reinforcement learning-based model is significantly lower than that of the traditional model. In particular, in the diagnosis of rare faults and new faults, the false alarm rate of the traditional model increased significantly, and the false alarm rate of excessive aeration faults even reached 30%. In contrast, the false alarm rate of the reinforcement learning model remained below 10%, greatly reducing the false alarm alarms of the system.

[0083] The experimental data clearly demonstrates the significant advantages of the sewage treatment fault diagnosis system of the present invention in real-time diagnosis, rare fault handling and adaptability to new faults. Compared with the existing traditional fixed rule model, the system of the present invention can maintain a high diagnostic accuracy rate in the face of various complex working conditions by introducing a reinforcement learning algorithm, and effectively deal with rare and new faults through an adaptive weight adjustment mechanism. This not only improves the operating efficiency of the system, but also significantly reduces the false alarm rate in the fault diagnosis process, thereby enhancing the safety and stability of the sewage treatment plant.

[0084] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A sewage treatment fault diagnosis method based on data analysis, characterized in that: The specific steps include: Step 1: Collect data in real time through multi-source sensors in the sewage treatment system, as well as historical fault data, to provide a basis for subsequent data analysis and fault diagnosis, ensuring that the system obtains accurate operating status information; Step 2: After preprocessing the collected data, feature extraction is performed to extract key features reflecting the sewage treatment conditions, forming feature vectors to describe the state changes and failure modes of the system; Step 3: Define different fault categories based on historical data and expert experience, including common faults, rare faults, and new faults; assign labels to each fault, and introduce an adaptive label update mechanism in the fault mode to provide a clear goal for the model, which helps the reinforcement learning model to accurately identify and classify faults; Step 4: Use reinforcement learning algorithm to build a fault diagnosis model, perform fault diagnosis on the feature vector based on the reinforcement learning model, identify the fault type and output the diagnosis result.

2. The sewage treatment fault diagnosis method based on data analysis according to claim 1, characterized in that: In step one, the data collected in real time include physical and chemical indicators, as well as equipment operating parameters.

3. The sewage treatment fault diagnosis method based on data analysis as claimed in claim 2, characterized in that: In step 2, the core goal of feature extraction is to convert the raw data into feature vectors, including operating condition changes and fault signs. The feature vector format is as follows: x(t) = [x1(t), x2(t), ..., x m (t)], where each x i (t) represents a characteristic value at a certain moment.

4. The sewage treatment fault diagnosis method based on data analysis as claimed in claim 3, characterized in that: In step 3, the steps for fault category definition and label assignment are as follows: Based on historical data and expert experience, define different fault categories and classify these faults into common faults, rare faults and new faults; Assign a corresponding label C to each fault category i (t), used as the classification target of the fault diagnosis model; Based on real-time data feedback and adaptive learning mechanisms, when new fault types or rare faults occur, the labeling system is adjusted to ensure the update and accuracy of diagnostic targets.

5. The sewage treatment fault diagnosis method based on data analysis as claimed in claim 4, characterized in that: Collect multi-source sensor data of the sewage treatment system in real time and generate a state vector; the model extracts features, inputs the state vector into the reinforcement learning model, and outputs the fault type label C i (t); The label C predicted by the model i (t) Compare with the actual fault situation and calculate the loss function L i (t); Loss function L i The calculation formula of (t) is as follows: Among them, y i is the true label of the sample; K: the total number of fault categories; p i，k (C i (t)): is the probability that the model predicts the i-th sample as category k; This formula measures the accuracy of the model's prediction by calculating the negative sum of the logarithmic values ​​of the true label and the predicted probability; when the predicted probability is close to the true label, the loss value is small; on the contrary, the greater the prediction deviation, the greater the loss value.

6. The sewage treatment fault diagnosis method based on data analysis as claimed in claim 5, characterized in that: According to the model's diagnosis results and actual conditions, the model is rewarded or punished, and the reinforcement learning model updates the weights according to the feedback signal; specifically, the reward is positive or negative, depending on the correctness of the model's diagnosis results; when the model correctly diagnoses the fault type, the system gives a positive reward, which is fed back to the model to adjust the loss function L i (t) and weight update w(t); if the model misdiagnoses the fault category, a negative reward is given. At this time, the model reduces the weight of the path and turns to other paths that are more likely to be correct.

7. The sewage treatment fault diagnosis method based on data analysis according to claim 6 is characterized in that: When a new fault mode or rare fault is detected, the weight parameters of the reinforcement learning model are automatically adjusted to adapt to the new fault situation; specifically, when it is detected that the diagnostic error of the model is large, that is, the loss function L i When (t) increases, the loss function L i (t) The driving model automatically adjusts weights to increase response to new faults.

8. The sewage treatment fault diagnosis method based on data analysis according to claim 7, characterized in that: Assume that the current weight vector is w(t)=[w1(t),w2(t),...,w n (t)], w(t) represents the weight vector at time t, which determines the emphasis of the model on different features. The calculation formula for dynamic adjustment of weights is: Among them, α: learning rate factor, which determines the sensitivity of the model to new fault responses; J(x(t)): fault response matrix, which represents the response of system state changes under different fault modes; β: adjustment parameter, which is used to balance the impact of historical data and new data on the model; λ: scaling parameter, which is used to adjust the nonlinear change amplitude of data; L i (t): The loss function of the i-th sample, which is used to measure the error of the diagnostic model in detecting faults and evaluate the performance of the model in the current fault diagnosis task; f(t,x i ): Failure mode function, which represents the characteristic response of the system to the new failure mode at time t; In the above formula, the integral part of the formula is used to perform cumulative calculations on the adjustment of the model weights within the time window [t, t+Δt]. By integrating the influence of historical data, the weight vector w(t) is dynamically adjusted to gradually adapt the model to new faults. After the new data is input, the model adjusts the fault mode function f(t, x i ) to adapt to different failure scenarios, avoid overreaction to model weight adjustment due to abnormal data at a single moment, and ensure that the model gradually adapts to new failure modes over a period of time; By means of the nonlinear scaling function in Eq. The value range of this combined function is between (0,1), which is used to smooth the changes in the model weight adjustment, ensure that the weight adjustment range is moderate, avoid drastic fluctuations, and enable the reinforcement learning model to adaptively adjust its own parameters to deal with rare or new faults and improve the diagnostic accuracy; The value range of the formula for dynamic adjustment of the above weights is: (0,1), and the adjustment direction and amplitude of the weights are effectively controlled to prevent the model from generating unstable oscillations; As the system detects new fault data, the loss function L i As (t) increases, the weight adjustment performs a cumulative response through integration, so that the model gradually adapts to new faults.

9. The sewage treatment fault diagnosis method based on data analysis as claimed in claim 8, characterized in that: The specific steps of fault type identification and diagnosis result output are as follows: in the process of sewage data processing, the feature vector after feature extraction is input into the reinforcement learning model, the model makes a diagnosis decision according to the current state and outputs the diagnosis result; Through the training of reinforcement learning models, the system automatically identifies the current fault type based on real-time input data and outputs the corresponding fault label; the diagnostic results are fed back to the sewage treatment system for automatic adjustment or alarm; when a new type of fault is detected, the system learns through an adaptive mechanism and adjusts future diagnostic strategies.

10. The sewage treatment fault diagnosis system based on data analysis as claimed in claim 9, characterized in that: The system includes a sewage treatment monitoring module, a communication module, a data analysis platform, a reinforcement learning module and a decision-making execution layer. The sewage treatment monitoring module is used to collect the operation data of the sewage treatment system in real time, and send the data to the data analysis platform through the communication module; wherein the sewage treatment monitoring module includes a sensor, a data acquisition unit, and a control unit. The sensor includes a flow sensor, a pH sensor, a conductivity sensor, and a chemical oxygen demand sensor; the data analysis platform includes a central processing unit, which is used to receive the sensor data sent by the sewage treatment monitoring module through the communication module, and perform data preprocessing, storage and analysis operations; the reinforcement learning module is the core module of the system, which is responsible for training, strengthening and learning the model according to the historical data and real-time data sent by the sewage treatment monitoring module, and applying it to fault diagnosis; the decision-making execution layer is used to execute corresponding control strategies according to the diagnosis results, such as adjusting the treatment process parameters or issuing maintenance alarms.

Images