Intelligent detection system for sewage treatment

Abstract

The invention discloses an intelligent detection system for sewage treatment. The intelligent detection system comprises a sewage multi-parameter sensor array, an edge computing gateway, a cloud analysis platform and a control execution module, the sensor array is connected to the edge computing gateway through LoRa / 5G dual-mode communication, and a data cleaning algorithm is built in the edge computing gateway; and the cloud analysis platform integrates an LSTM prediction model and a reinforcement learning decision engine, and outputs a process optimization instruction to the PLC controller of the control execution module. According to the intelligent detection system for sewage treatment, the spectrum compensation type turbidity sensor and the XRF heavy metal detection module are adopted, linkage control of the emergency neutralization tank is achieved when ions such as Cu < 2 + > and Ni < 2 + > exceed the standard, and the response speed is increased compared with a traditional electrochemical method. And the LoRa / 5G dual-mode communication is dynamically switched according to the signal strength, so that the data throughput is ensured, the energy consumption is reduced, and the method is suitable for wide-area distributed deployment.

Description

Technical Field

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

[0002] Current sewage treatment detection systems mainly rely on single sensors and centralized data processing architectures, which have problems such as insufficient real-time performance, weak multi-source data fusion capabilities, and delayed process optimization. Traditional systems mostly use 4G / Wi-Fi communications, which are susceptible to interference in complex industrial environments, and their high power consumption limits their deployment in remote areas. Although existing sensor arrays can detect basic parameters such as COD and ammonia nitrogen, they are not very sensitive to heavy metal ions (such as Cu 2+ 、Ni 2+ ) lacks real-time monitoring accuracy and lacks dynamic tracking of the sludge settling process. Cloud-based models generally use static regression analysis, which struggles to capture nonlinear changes in water quality, leading to long response delays in aeration-dosing coordinated control strategies. Furthermore, data security relies on traditional encryption technology, making process records susceptible to tampering. Identifying abnormal operating conditions often relies on threshold alarms, resulting in a high false alarm rate. Summary of the Invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides an intelligent detection system for sewage treatment, the purpose of which is to solve the problems raised in the above-mentioned background technology.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] An intelligent detection system for sewage treatment, including a multi-parameter sensor array, an edge computing gateway, a cloud analysis platform and a control execution module;

[0006] The sensor array is connected to an edge computing gateway via LoRa / 5G dual-mode communication. The edge computing gateway has a built-in data cleaning algorithm. The cloud analysis platform integrates an LSTM prediction model and a reinforcement learning decision engine to output process optimization instructions to the PLC controller of the control execution module. The system includes the following steps:

[0007] S1: Data acquisition and preprocessing: Real-time acquisition of sewage quality parameters, equipment operating status, and environmental parameters is achieved through a multi-parameter sensor array, and transmitted to the edge computing gateway using LoRa / 5G dual-mode communication.

[0008] S2: Intelligent feature extraction: Based on the information entropy adaptive principal component analysis method, multi-source data is temporally and spatially aligned and dimensionally reduced to extract characteristic variables that are strongly correlated with effluent water quality;

[0009] S3: Dynamic Modeling and Analysis: In the cloud-based analysis platform, a fuzzy neural network soft-sensing model is used to calculate characteristic variables in real time. Combined with an LSTM prediction model, the model outputs water quality trends for the next 2-6 hours. A reinforcement learning algorithm is then used to generate a coordinated aeration-dosing control strategy.

[0010] S4: Self-calibration and feedback control: The soft sensor model is periodically calibrated based on laboratory test data, and the execution parameters of the PLC controller are adjusted synchronously.

[0011] Furthermore, the multi-parameter sensor array includes:

[0012] Spectral compensation turbidity sensor: adopts scattered light suppression filter and self-cleaning brush structure;

[0013] XRF heavy metal detection module: real-time monitoring of Cu 2+ 、Ni 2+ Cr 6+ Ion concentration, when exceeding the standard, triggers the emergency neutralization pool linkage control;

[0014] Intelligent detection device: automatically records the sludge settling process, calculates the 30-minute settling ratio and identifies abnormal conditions.

[0015] Furthermore, the dynamic modeling and analysis steps include:

[0016] Multi-model fusion decision-making: Random forest classifiers are used to identify abnormal operating conditions, digital twin technology is used for process simulation optimization, and the Q-learning algorithm is used to generate Pareto optimal control instructions;

[0017] Event-driven response mechanism: When an "ammonia nitrogen jump point" is detected on the ORP curve or the dissolved oxygen concentration deviates by more than 10% for three consecutive times, the self-calibration program is automatically started and a maintenance work order is pushed.

[0018] Furthermore, the self-correction and feedback control steps include:

[0019] Closed-loop calibration of laboratory data: COD and BOD5 data measured in the laboratory are input into the cloud analysis platform every day, and the neural network weights are updated through the gradient descent algorithm;

[0020] Dynamic adjustment of process parameters: Taking minimization of reagent consumption as the objective function, the PAC injection rate and aeration fan speed are optimized through particle swarm algorithm.

[0021] Furthermore, it also includes:

[0022] Remote intelligent operation and maintenance module: visualize equipment operation status through the WebGIS platform;

[0023] Data security mechanism: AES-256 encryption is used to transmit water quality data, and key process records are stored in blockchain nodes.

[0024] Furthermore, the LoRa / 5G dual-mode communication module performs mode switching according to real-time signal strength and bandwidth requirements, giving priority to the LoRa mode, and switching to the 5G mode when the 5G signal strength is lower than -85dBm or video surveillance data needs to be transmitted.

[0025] Furthermore, the data cleaning algorithm built into the edge computing gateway adopts a sliding window mechanism, with the window width d set to 5-15 minutes. Abnormal data is identified by calculating the coefficient of variation of the information entropy H(x) within the window, and data removal is triggered when the entropy value is lower than the control limit h_limit.

[0026] Furthermore, the intelligent detection device calculates the sludge volume index SVI = SV30 / (MLSS×10). When SVI>200, it is determined to be filamentous bacteria expansion, triggering an aeration intensity adjustment instruction; when SVI<50, it is determined to be sludge disintegration, triggering a carbon source supplement instruction.

[0027] Furthermore, the "ammonia nitrogen jump point" is when the rising rate of three consecutive sampling points on the ORP curve exceeds 2mV / s, and the pH value simultaneously drops by more than 0.3 units, at which time the coordinated control of enhanced aeration and alkalinity compensation is triggered.

[0028] Furthermore, the blockchain nodes use IPFS distributed storage to store encrypted process records, and the encryption keys are stored in shards using a (t,n) threshold secret sharing algorithm. It takes no less than t=3 nodes to jointly decrypt to restore the original data.

[0029] The beneficial effects of the present invention are:

[0030] Multimodal sensing and intelligent communication: The system uses a spectrum compensation turbidity sensor and XRF heavy metal detection module to achieve Cu 2+ 、Ni 2+ Emergency neutralization cell linkage control when plasma levels exceed standards improves response speed compared to traditional electrochemical methods. LoRa / 5G dual-mode communication dynamically switches based on signal strength, ensuring data throughput while reducing energy consumption, making it suitable for wide-area distributed deployment.

[0031] Edge-cloud collaborative computing architecture: The edge gateway features a built-in sliding window data cleaning algorithm that filters out anomalous data using the coefficient of variation of information entropy, reducing invalid data transmission. The cloud platform integrates an LSTM prediction model with a reinforcement learning decision engine to predict water quality trends over the next 2-6 hours. It also utilizes digital twin technology to optimize aeration volume and PAC injection rates, reducing chemical consumption.

[0032] Dynamic closed-loop calibration and security mechanisms: A gradient descent algorithm based on laboratory test data updates neural network weights, stabilizing the soft-sensing model's error rate. Blockchain technology utilizes IPFS distributed storage and (t,3) threshold secret sharing to ensure that process records cannot be tampered with. Decryption requires the coordination of at least three nodes, reducing the risk of data leakage.

[0033] Intelligent diagnosis of abnormal operating conditions: A random forest classifier identifies the "ammonia-nitrogen jump point" on the ORP curve, triggering enhanced aeration instructions to shorten the ammonia-nitrogen peak suppression time. Real-time calculation of the sludge volume index improves sludge settling efficiency by 35%.

[0034] Economic Benefits and Operational Optimization: A WebGIS platform visualizes equipment status, and a remote operation and maintenance module reduces the frequency of on-site inspections. A particle swarm algorithm optimizes PAC dosage and aeration fan speed, saving coagulant and reducing overall operating costs. An event-driven mechanism triggers self-calibration, reducing unplanned downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is an architecture diagram of the intelligent detection system for sewage treatment of the present invention;

[0036] Figure 2 This is an architecture diagram of the intelligent detection system for sewage treatment of the present invention;

[0037] Figure 3 is a schematic diagram of a sensor array of the present invention;

[0038] Figure 4 This is a schematic diagram of the edge computing gateway of the present invention;

[0039] Figure 5 A schematic diagram of the cloud analysis platform of the present invention;

[0040] Figure 6 This is a schematic diagram of a control execution module of the present invention;

[0041] Figure 7 Schematic diagram of the auxiliary function module of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention are described clearly and completely below with reference to the accompanying drawings.

[0043] like Figures 1 to 7 The figure shows an intelligent sewage treatment detection system consisting of a multi-parameter sensor array, an edge computing gateway, a cloud-based analysis platform, and a control execution module. It also includes a remote intelligent operation and maintenance module and data security mechanisms.

[0044] The multi-parameter sensor array includes:

[0045] Spectral compensation turbidity sensor: uses a scattered light suppression filter and a self-cleaning brush structure to effectively improve the accuracy and stability of turbidity detection.

[0046] XRF heavy metal detection module: capable of real-time monitoring of Cu 2+ 、Ni 2+ Cr 6+ Ion concentration: once it is detected that the ion concentration exceeds the standard, the emergency neutralization tank linkage control will be triggered to ensure the safety of the sewage treatment process.

[0047] Intelligent Detection Device: This device automatically records the sludge settling process, accurately calculates the 30-minute settling ratio, and can keenly identify abnormal conditions. It also calculates the sludge volume index (SVI) = SV30 / (MLSS×10). When SVI>200, it is determined to be filamentous bacterial expansion, triggering aeration intensity adjustment instructions; when SVI<50, it is determined to be sludge disintegration, triggering carbon source replenishment instructions.

[0048] The sensor array is connected to the edge computing gateway via LoRa / 5G dual-mode communication. The edge computing gateway has a built-in data cleaning algorithm. The LoRa / 5G dual-mode communication module switches modes based on real-time signal strength and bandwidth requirements, prioritizing LoRa mode. It switches to 5G mode when the 5G signal strength falls below -85dBm or when video surveillance data needs to be transmitted. The data cleaning algorithm built into the edge computing gateway uses a sliding window mechanism with a window width d set to 5-15 minutes. It identifies anomalous data by calculating the coefficient of variation of the information entropy H(x) within the window. Data is rejected when the entropy falls below the control limit h_limit.

[0049] The cloud-based analytics platform integrates an LSTM prediction model with a reinforcement learning decision engine. It also utilizes multi-model fusion decision-making, employing a random forest classifier to identify abnormal operating conditions, digital twin technology for process simulation optimization, and a Q-learning algorithm to generate Pareto-optimal control instructions. It also features an event-driven response mechanism. When an "ammonia nitrogen jump" on the ORP curve is detected (the rate of increase for three consecutive sampling points on the ORP curve exceeds 2mV / s, and the pH value simultaneously decreases by more than 0.3 units) or when three consecutive deviations in dissolved oxygen concentration exceed 10%, the system automatically initiates a self-calibration process and issues a maintenance work order.

[0050] Control execution module: The cloud analysis platform outputs process optimization instructions to the PLC controller of the control execution module to achieve precise control of the sewage treatment process.

[0051] The remote intelligent operation and maintenance module visualizes the equipment operation status through the WebGIS platform, making it easier for operation and maintenance personnel to understand the equipment status remotely and reduce the frequency of on-site inspections.

[0052] A data security mechanism uses AES-256 encryption to transmit water quality data and stores key process records on blockchain nodes. Blockchain nodes use IPFS distributed storage to store encrypted process records. The encryption key is stored in shards using a (t,n) threshold secret sharing algorithm. Decryption requires at least t = 3 nodes to restore the original data, ensuring data security and immutability.

[0053] The working steps of the intelligent detection system for sewage treatment include:

[0054] S1: Data acquisition and preprocessing

[0055] The multi-parameter sensor array is used to obtain sewage quality parameters, equipment operating status and environmental parameters in real time, and then LoRa / 5G dual-mode communication is used to transmit this data to the edge computing gateway.

[0056] S2: Intelligent Feature Extraction

[0057] Based on the information entropy, the adaptive principal component analysis method is used to perform spatiotemporal alignment and dimensionality reduction on multi-source data, thereby extracting characteristic variables that are strongly correlated with effluent water quality.

[0058] S3: Dynamic Modeling and Analysis

[0059] In the cloud analysis platform, the characteristic variables are calculated in real time through the fuzzy neural network soft measurement model.

[0060] Combined with the LSTM prediction model, it outputs the water quality trend for the next 2-6 hours.

[0061] A reinforcement learning algorithm is used to generate aeration-dosing coordinated control strategies.

[0062] Multi-model fusion decision-making is used, random forest classifier is used to identify abnormal operating conditions, digital twin technology is used for process simulation optimization, and Q-learning algorithm is used to generate Pareto optimal control instructions.

[0063] When an "ammonia nitrogen jump point" is detected on the ORP curve or the dissolved oxygen concentration deviates by more than 10% for three consecutive times, the event-driven response mechanism is triggered, automatically starting the self-calibration program and pushing a maintenance work order.

[0064] S4: Self-correction and feedback control

[0065] The soft sensor model is periodically calibrated based on laboratory test data. Daily laboratory COD and BOD5 data are fed into the cloud-based analysis platform, where the neural network weights are updated using a gradient descent algorithm.

[0066] The execution parameters of the PLC controller are adjusted synchronously. Taking the minimization of reagent consumption as the objective function, the PAC injection rate and aeration fan speed are optimized through the particle swarm algorithm.

[0067] This intelligent sewage treatment detection system uses a random forest classifier to identify the "ammonia nitrogen jump point" on the ORP curve, triggering enhanced aeration instructions to shorten the ammonia nitrogen peak suppression time. The sludge volume index is calculated in real time, significantly improving sludge settling efficiency.

[0068] The WebGIS platform enables visualization of equipment status, and the remote operation and maintenance module reduces the frequency of on-site inspections. The particle swarm algorithm optimizes PAC dosing and aeration fan speed, saving coagulant and reducing overall operating costs. The event-driven mechanism triggers self-calibration to reduce unplanned downtime.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. An intelligent detection system for sewage treatment, characterized in that: It includes pollution multi-parameter sensor array, edge computing gateway, cloud analysis platform and control execution module; The sensor array is connected to an edge computing gateway via LoRa / 5G dual-mode communication. The edge computing gateway has a built-in data cleaning algorithm. The cloud analysis platform integrates an LSTM prediction model and a reinforcement learning decision engine to output process optimization instructions to the PLC controller of the control execution module. The system includes the following steps: S1: Data acquisition and preprocessing: Real-time acquisition of sewage quality parameters, equipment operating status, and environmental parameters is achieved through a multi-parameter sensor array, and transmitted to the edge computing gateway using LoRa / 5G dual-mode communication. S2: Intelligent feature extraction: Based on the information entropy adaptive principal component analysis method, multi-source data is temporally and spatially aligned and dimensionally reduced to extract characteristic variables that are strongly correlated with effluent water quality; S3: Dynamic Modeling and Analysis: In the cloud-based analysis platform, a fuzzy neural network soft-sensing model is used to calculate characteristic variables in real time. Combined with an LSTM prediction model, the model outputs water quality trends for the next 2-6 hours. A reinforcement learning algorithm is then used to generate a coordinated aeration-dosing control strategy. S4: Self-calibration and feedback control: The soft sensor model is periodically calibrated based on laboratory test data, and the execution parameters of the PLC controller are adjusted synchronously.

2. The intelligent detection system for sewage treatment according to claim 1, characterized in that: The multi-parameter sensor array comprises: Spectral compensation turbidity sensor: adopts scattered light suppression filter and self-cleaning brush structure; XRF heavy metal detection module: real-time monitoring of Cu 2+ 、Ni 2+ Cr 6+ Ion concentration, when exceeding the standard, triggers the emergency neutralization pool linkage control; Intelligent detection device: automatically records the sludge settling process, calculates the 30-minute settling ratio and identifies abnormal conditions.

3. The intelligent detection system for sewage treatment according to claim 1, characterized in that: The dynamic modeling and analysis steps include: Multi-model fusion decision-making: Random forest classifiers are used to identify abnormal operating conditions, digital twin technology is used for process simulation optimization, and the Q-learning algorithm is used to generate Pareto optimal control instructions; Event-driven response mechanism: When an "ammonia nitrogen jump point" is detected on the ORP curve or the dissolved oxygen concentration deviates by more than 10% for three consecutive times, the self-calibration program is automatically started and a maintenance work order is issued.

4. The intelligent detection system for sewage treatment according to claim 1, characterized in that: The self-correction and feedback control steps include: Closed-loop calibration of laboratory data: COD and BOD5 data measured in the laboratory are input into the cloud analysis platform every day, and the neural network weights are updated through the gradient descent algorithm; Dynamic adjustment of process parameters: Taking minimization of reagent consumption as the objective function, the PAC injection rate and aeration fan speed are optimized through the particle swarm algorithm.

5. The intelligent detection system for sewage treatment according to claim 1, characterized in that: Also includes: Remote intelligent operation and maintenance module: visualize equipment operation status through the Web GIS platform; Data security mechanism: AES-256 encryption is used to transmit water quality data, and key process records are stored in blockchain nodes.

6. The intelligent detection system for sewage treatment according to claim 1, characterized in that: The LoRa / 5G dual-mode communication module performs mode switching according to real-time signal strength and bandwidth requirements, giving priority to the LoRa mode, and switches to the 5G mode when the 5G signal strength is lower than -85dBm or video surveillance data needs to be transmitted.

7. The intelligent detection system for sewage treatment according to claim 1, characterized in that: The data cleaning algorithm built into the edge computing gateway adopts a sliding window mechanism, with the window width d set to 5-15 minutes. Abnormal data is identified by calculating the coefficient of variation of the information entropy H(x) within the window, and data removal is triggered when the entropy value is lower than the control limit h_limit.

8. The intelligent detection system for sewage treatment according to claim 1, characterized in that: The intelligent detection device calculates the sludge volume index SVI = SV30 / (MLSS×10). When SVI>200, it is determined to be filamentous bacteria expansion, triggering an aeration intensity adjustment instruction; when SVI<50, it is determined to be sludge disintegration, triggering a carbon source supplement instruction.

9. The intelligent detection system for sewage treatment according to claim 1, characterized in that: The "ammonia nitrogen jump point" is when the rising rate of three consecutive sampling points on the ORP curve exceeds 2mV / s, and the pH value simultaneously drops by more than 0.3 units. At this time, the coordinated control of enhanced aeration and alkalinity compensation is triggered.

10. The intelligent detection system for sewage treatment according to claim 1, characterized in that: The blockchain nodes use IPFS distributed storage to store encrypted process records, and the encryption keys are stored in shards using a (t,n) threshold secret sharing algorithm. It takes no less than t=3 nodes to jointly decrypt to restore the original data.

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