An alarm processing system for the effluent outlet of an industrial wastewater treatment plant

Abstract

This invention discloses an alarm and handling system for the effluent outlet of an industrial wastewater treatment plant, belonging to the field of industrial wastewater treatment technology. It includes: an online water quality monitoring device integrating multiple sensors with an adaptive calibration algorithm; a PLC control box with a built-in embedded computer supporting the execution of mathematical models and machine learning algorithms to predict water quality change trends; edge computing technology combined with an FPGA acceleration card; and a decentralized data recording mechanism built using blockchain technology. When water quality exceeds standards, an intelligent decision-making module automatically selects an emergency plan based on a preset rule base and dynamic adjustment strategies, and continuously improves the response strategy using a Bayesian optimization algorithm to achieve emergency handling. This invention can promptly alert on-duty personnel to abnormal water discharge and can automatically and promptly address drainage issues, preventing the continued discharge of wastewater exceeding standards.

Description

Technical Field

[0001] This invention belongs to the field of industrial wastewater treatment technology, and in particular, an alarm processing system for the outlet of an industrial wastewater treatment plant. Background Technology

[0002] Urban wastewater treatment plants are generally classified into three types—large, medium, and small—based on their investment scale and wastewater treatment capacity. Large plants have a daily treatment capacity of over 200,000 tons, medium plants have a daily capacity of 50,000 to 200,000 tons, and small plants have a daily capacity of less than 50,000 tons. In recent years, with vigorous development efforts, a series of policy documents concerning the construction of wastewater treatment facilities have been issued, effectively promoting the development of wastewater treatment plants.

[0003] Currently, small-scale wastewater treatment plants are mainly located in small towns or industrial parks. Our focus here is on these plants within industrial parks, characterized by their small scale, large number, scattered distribution, relatively simple equipment, and unstable effluent quality. On-site staff are typically full-time non-professionals or part-time workers, who cannot promptly detect abnormalities in the daily operation of the wastewater treatment plant. Furthermore, the on-site inspection frequency is generally only 1-2 hours. When the wastewater treatment system malfunctions, the online monitoring equipment at the effluent outlet may show substandard results (it's not always visually possible to determine whether the wastewater meets discharge standards). Since the wastewater treatment equipment is constantly operating, undetected wastewater exceeding standards will continue to be discharged into the municipal sewage network, impacting subsequent water bodies and polluting the surrounding environment.

[0004] Traditional systems typically rely on single sensors to measure water quality parameters. These sensors are susceptible to factors such as temperature, humidity, and vibration, leading to significant deviations in measurement results. A single sensor cannot comprehensively reflect water quality changes under complex environments, especially in volatile industrial environments where the limitations of a single sensor are particularly pronounced. Inaccurate measurements can cause the system to misjudge water quality conditions, leading to incorrect emergency measures or even overlooking excessive discharges, resulting in environmental pollution. Frequent manual calibration and adjustments increase operating costs and reduce system reliability. For example, at a chemical plant's wastewater treatment station, the pH sensor's readings were too high due to temperature fluctuations. The system failed to detect excessive acidic wastewater discharge in time, causing pollution of surrounding rivers, ecological damage, and substantial fines and reputational losses for the company. Summary of the Invention

[0005] The purpose of this invention is to provide an alarm processing system for the outlet of an industrial wastewater treatment plant to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an alarm processing system for the effluent outlet of an industrial wastewater treatment plant, comprising:

[0007] The online water quality monitoring device integrates multiple sensor fusion settings and has an adaptive calibration algorithm. By comparing the reading differences between different sensors in real time, it automatically corrects measurement deviations caused by environmental factors, ensuring data accuracy.

[0008] The PLC control box has a built-in embedded computer that supports the execution of mathematical models and machine learning algorithms to predict water quality change trends.

[0009] By leveraging edge computing technology combined with FPGA acceleration cards, large amounts of real-time data streams can be processed quickly locally, reducing latency, and transmitted to the cloud or remote server in encrypted form through built-in security protocols to ensure information security.

[0010] Blockchain technology is used to build a decentralized data recording mechanism, where all records are timestamped and hashed to form an immutable data link.

[0011] When water quality exceeds the standard, the intelligent decision-making module automatically selects an emergency plan based on a preset rule base and dynamic adjustment strategy, and continuously improves the response strategy using a Bayesian optimization algorithm to achieve emergency handling.

[0012] In this preferred embodiment, the system constructs a remote monitoring platform based on an Internet of Things (IoT) architecture. This platform also integrates an image recognition module to analyze abnormal situations appearing in the video stream and provides visual guidance to on-site staff through AR glasses.

[0013] The remote monitoring platform adopts a microservice architecture, with each functional module deployed independently and combined through an API gateway, supporting on-demand expansion of new features. The target detection in the image recognition module uses the YOLOv5 algorithm, with the following formula:

[0014]

[0015] Where, λ coord and λ noobj These are the weights of the coordinate loss and the non-object confidence loss, respectively; S is the number of grids; B is the number of bounding boxes per grid. and Indicator functions representing the existence and non-existence of an object, respectively; x i ,y i These are the center coordinates of the predicted bounding box; These are the center coordinates of the actual bounding box; C i and These are the confidence scores for prediction and the actual result, respectively.

[0016] In this preferred embodiment, the system includes an intelligent analysis unit. This unit trains a water quality prediction model using a distributed deep learning framework. The model is deployed on edge cloud nodes and connected to PLC control boxes in various locations via a 5G low-latency network, ensuring that each location receives the latest model updates and service support. Simultaneously, federated learning technology is used to protect user privacy, preventing sensitive data from being directly uploaded to the central server. The FedAvg algorithm is used for the model.

[0017] Among them, w t This represents the values ​​of the global model parameters after the t-th iteration, where K is the set of clients participating in the learning process, and n... k It is the number of samples for client k. These are the local model parameters of client k after the t-th iteration.

[0018] In a preferred embodiment of this solution, the system also includes an adaptive sound and light alarm module based on acoustic fingerprint recognition. This module establishes a database based on the sound characteristics of different areas. When an anomaly occurs, the module will automatically match the corresponding alarm mode according to the current ambient sound scene to ensure the effective transmission of alarm information.

[0019] In this preferred embodiment, the system includes a mobile application for receiving alarm information and managing devices, and incorporates a blockchain-based points incentive strategy. The application also includes a built-in knowledge graph-based intelligent assistant.

[0020] In this preferred embodiment, the PLC control box is equipped with an uninterruptible power supply and a solar-assisted power supply module. The redundant power supply module of the system is designed with hot-swappable capability, allowing replacement of faulty components without interrupting system operation. The operating formula of the MPPT controller in the solar-assisted power supply module is as follows:

[0021] Among them, V mppt It is the maximum power point voltage, V pv It is the open-circuit voltage of the photovoltaic array, P. max It is the power at the maximum power point, I pv It is the maximum current of the photovoltaic array.

[0022] In this preferred embodiment, the wastewater treatment station has an intermediate water tank. The submersible drainage pump in the intermediate water tank adopts magnetic levitation bearing technology and liquid cooling, combined with the dual protection measures of float level switch and ultrasonic level gauge, and a laser rangefinder is introduced as a third layer of protection.

[0023] In this preferred embodiment, the online water quality monitoring device has a self-cleaning function, using high-pressure water flow or airflow to periodically clean the sensor surface, and the device shell is made of nano-coating material.

[0024] In this preferred embodiment, the system includes a soft starter and a variable frequency drive (VFD), the VFD is equipped with an energy feedback device, and the VFD has a built-in preset fault prediction algorithm to identify potential faults in advance and take preventive measures.

[0025] In a preferred embodiment of this solution, the system also includes: modular design and plug-and-play architecture, zero-trust architecture, and digital twin technology.

[0026] Compared with the prior art, the technical effects and advantages of the present invention are as follows:

[0027] This alarm processing system for the effluent outlet of an industrial wastewater treatment plant solves the error problems that may exist with single sensors through multi-sensor fusion and adaptive calibration algorithms, significantly improving the accuracy and reliability of water quality parameter measurements. Automatic calibration reduces reliance on professional personnel, lowering maintenance costs and operational complexity. TinyML can efficiently run complex models in resource-constrained environments, enabling real-time prediction of water quality change trends and ensuring rapid response to anomalies. Edge computing allows data processing to be completed locally, reducing transmission time and cloud processing latency, thus improving the system's responsiveness.

[0028] This solution utilizes an FPGA acceleration card to efficiently process large volumes of real-time data streams, ensuring preliminary analysis is completed within milliseconds. This significantly improves the system's processing power and response speed. Built-in security protocols (such as TLS) encrypt data transmission, ensuring information security and preventing data leakage or tampering. Blockchain technology constructs a decentralized data recording mechanism where all records are timestamped and hashed, forming an immutable data link. This enhances data security and transparency; all historical records cannot be altered, facilitating subsequent traceability and auditing, and improving the system's credibility and compliance.

[0029] This solution utilizes an uninterruptible power supply (UPS), a solar-assisted power supply system, and hot-swappable redundant power modules. The system can replace faulty components without shutting down the system, reducing downtime and maintenance costs. Furthermore, automatic fault detection and early warning functions proactively prevent potential problems, further reducing the risk of unexpected downtime.

[0030] In summary, this solution can promptly alert on-duty personnel to abnormal water quality discharge and automatically handle drainage issues to prevent the continued discharge of wastewater exceeding standards. Attached Figure Description

[0031] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 This is a general flowchart of an alarm processing system for the outlet of an industrial wastewater treatment plant according to the present invention.

[0033] Figure 2 This is a detailed flowchart of an alarm processing system for the outlet of an industrial wastewater treatment plant according to the present invention. Detailed Implementation

[0034] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0035] Unless otherwise defined, the directions mentioned herein, such as up, down, left, right, front, back, inside, and outside, are based on the directions shown in the figures of this invention, and are explained here together.

[0036] This embodiment provides, for example Figure 1 and Figure 2 An alarm processing system for the effluent outlet of an industrial wastewater treatment plant, as shown, includes:

[0037] This online water quality monitoring device integrates multiple sensors with an adaptive calibration algorithm. By comparing readings from different sensors in real time, it automatically corrects measurement deviations caused by environmental factors, ensuring data accuracy. Multiple sensors (such as pH, COD, BOD, and ammonia nitrogen) are installed in the device. Sensor readings are transmitted to a PLC control box via RS485 or Modbus protocol. The PLC's built-in algorithm compares the readings from different sensors in real time and automatically adjusts for deviations. For example, when the pH sensor shows an anomaly, the system will correct based on data from other sensors (such as a conductivity sensor) to ensure the accuracy of the final output data. Through multi-sensor fusion and adaptive calibration algorithms, the error problems that may exist with single sensors are solved, significantly improving the accuracy and reliability of water quality parameter measurements. Automatic calibration reduces reliance on professional personnel, lowering maintenance costs and operational complexity.

[0038] The PLC control box incorporates a high-performance embedded computer with a dedicated operating system and real-time computing environment. This supports the execution of mathematical models and machine learning algorithms. Specifically, it employs a lightweight neural network (such as TinyML) to efficiently operate in resource-constrained environments to predict water quality trends. The high-performance embedded computer, running a dedicated operating system (such as Linux) and a real-time computing environment (such as RT-Linux), integrates the TinyML framework (such as TensorFlowLite for Microcontrollers), pre-trains a water quality prediction model, and deploys it to the PLC. Water quality parameters are collected in real time and input into the TinyML model for prediction. For example, when a continuous decrease in pH is detected, the TinyML model predicts that a pH level may exceed the standard within the next few hours and issues an early warning. TinyML can efficiently run complex models in resource-constrained environments, achieving real-time prediction of water quality trends and ensuring rapid response to anomalies. Edge computing enables local data processing, reducing transmission time and cloud processing latency, thus improving system responsiveness.

[0039] By leveraging edge computing technology combined with FPGA accelerator cards, massive real-time data streams can be processed rapidly locally, reducing latency. Data is then encrypted and transmitted to the cloud or remote server via built-in security protocols (such as TLS) to ensure information security. Alternatively, an FPGA accelerator card (such as the Xilinx Zynq series) can be integrated into the PLC control box. The FPGA is responsible for rapidly processing massive real-time data streams, reducing latency, and the data is transmitted to the cloud server via a 5G module after being encrypted with TLS. For example, the FPGA processes high-frequency sampling data from multiple sensors, ensuring preliminary analysis is completed within milliseconds, and then the results are uploaded to the cloud for in-depth analysis.

[0040] A decentralized data recording mechanism using blockchain technology is established. This mechanism not only records operation logs and water quality monitoring data but also stores equipment maintenance history, fault reports, and other information. All records are timestamped and hashed to form an immutable data link. Blockchain node software (such as Hyperledger Fabric) is installed in the PLC control box. Each operation log and water quality monitoring data is timestamped and hashed, and data blocks are linked sequentially to form an immutable data link. When a pump starts, the event is recorded as a block, and all subsequent operations are linked to this block, ensuring that historical records cannot be tampered with.

[0041] When water quality exceeds standards, the intelligent decision-making module automatically selects the optimal emergency response plan based on a preset rule base and dynamic adjustment strategies. This includes, but is not limited to, adjusting treatment process parameters, activating backup treatment units, or notifying relevant personnel. It also continuously improves the response strategy using a Bayesian optimization algorithm to achieve precise and efficient emergency handling. The intelligent decision-making module has a built-in rule base (such as the IF-THEN rule set) and dynamic adjustment strategies. When water quality exceeds standards, the module selects the optimal emergency response plan based on the current situation (such as closing the inlet valve or activating the backup treatment unit). The Bayesian optimization algorithm continuously updates the rule base to improve response accuracy. If COD exceeds the standard, the system immediately closes the inlet valve, activates the backup treatment unit, and notifies on-duty personnel.

[0042] In this embodiment, the system constructs a remote monitoring platform based on the Internet of Things (IoT) architecture. In addition to conventional functions, the platform also integrates an AI-driven image recognition module, which can analyze abnormal situations (such as equipment leakage and foreign object intrusion) in the video stream and provide visual guidance to on-site staff through AR glasses.

[0043] The remote monitoring platform adopts a microservice architecture, with each functional module deployed independently and combined through an API gateway. This supports on-demand expansion of new features, such as integrating new maintenance methods like drone and robot inspections, significantly improving the system's flexibility and adaptability. The remote monitoring platform server installs image recognition software (such as OpenCV combined with YOLOv5), and on-site cameras capture video streams, which are transmitted to the platform via a 5G network. The platform analyzes anomalies in the video stream (such as equipment leaks or foreign object intrusion) and sends guidance information to personnel via AR glasses. When a liquid leak is detected in a pipeline, the platform generates an AR guidance map, instructing maintenance personnel on how to seal the leak. The remote monitoring platform uses Docker to containerize each functional module (such as video analysis, alarm notification, and data analysis), and uses a Kubernetes cluster to manage the containers, supporting on-demand expansion of new features. The API gateway uniformly manages external requests, ensuring flexible combination of different modules. When adding drone inspection functionality, only a new Docker container needs to be deployed and integrated into the existing system through the API gateway.

[0044] The target detection in the image recognition module uses the YOLOv5 algorithm, whose formula is:

[0045]

[0046] Where, λ coord and λ noobj These are the weights of the coordinate loss and the non-object confidence loss, respectively; S is the number of grids; B is the number of bounding boxes per grid. and Indicator functions representing the existence and non-existence of an object, respectively; x i ,y i These are the center coordinates of the predicted bounding box; These are the center coordinates of the actual bounding box; C i and These are the confidence scores for prediction and the actual result, respectively.

[0047] In this embodiment, the system includes an intelligent analysis unit. This unit uses a distributed deep learning framework to train a water quality prediction model. It not only uses large-scale historical data as training samples but also incorporates a real-time feedback mechanism. Each time the model makes a prediction, the actual result is re-inputted into the training set, enabling the model to continuously optimize itself. The model is deployed on edge cloud nodes and connected to PLC control boxes in various locations via a 5G low-latency network, ensuring that each location receives the latest model updates and service support. Simultaneously, federated learning technology is used to protect user privacy, preventing sensitive data from being directly uploaded to the central server. The FedAvg algorithm is used for the model.

[0048] Among them, w t This represents the values ​​of the global model parameters after the t-th iteration, where K is the set of clients participating in the learning process, and n... k It is the number of samples for client k. These are the local model parameters of client k after the t-th iteration.

[0049] Specifically, the edge cloud node servers are equipped with a distributed deep learning framework (such as TensorFlow Federated). Each wastewater treatment site periodically uploads local water quality data to the edge cloud nodes. The edge cloud nodes aggregate the data and train a water quality prediction model, while incorporating a real-time feedback mechanism. For example, at 2:00 AM every day, each site uploads the previous day's water quality data, and the edge cloud nodes use this data to update the water quality prediction model. The edge cloud node servers are equipped with federated learning client software (such as TFF-TensorFlow Federated). Each wastewater treatment site only uploads model updates, not the original data, to protect user privacy. A 5G low-latency network connects to PLC control boxes in various locations, ensuring that each site receives the latest model updates and service support. For example, the model update for site A only includes weight changes and does not involve specific water quality data. After being uploaded to the edge cloud node, it is merged with updates from other sites to form a global model.

[0050] In this embodiment, the system also includes an adaptive sound and light alarm module based on acoustic fingerprint recognition. This module establishes a database based on the sound characteristics of different areas. When an anomaly occurs, the module will automatically match the corresponding alarm mode according to the current ambient sound scene to ensure the effective transmission of alarm information. It also has a two-way voice communication device and an emotion analysis engine, which can not only realize human-computer dialogue, but also understand the emotional state of the on-duty personnel and adjust the communication method in a timely manner to improve the user experience. In addition, the system supports real-time translation of multiple languages, which facilitates international team collaboration.

[0051] Specifically, the audible and visual alarm device is equipped with a microphone array to collect ambient sound in real time. It has a built-in acoustic fingerprint database and builds models based on the sound characteristics of different areas. When an anomaly occurs, the system matches the most appropriate alarm mode to ensure effective communication of the alarm information. For example, when a sudden increase in noise is detected in the pump room, the system determines it is a mechanical fault, issues a high-pitched alarm, and flashes a red light. The alarm system is also equipped with a two-way voice communication module (such as an IP phone or walkie-talkie interface) and a sentiment analysis engine (such as using Natural Language Processing (NLP) technology) to analyze the emotional state of on-duty personnel in real time and adjust the communication method accordingly to improve the user experience. For example, when on-duty personnel speak urgently, the system infers that they are in an emergency and quickly switches to a priority handling mode to provide more direct assistance.

[0052] In this embodiment, a mobile application is installed in the system for receiving alarm information and managing devices. A blockchain-based points incentive strategy is also introduced, where users earn points for completing specific tasks (such as regular checks and report submissions). These points can be redeemed for value-added services or product discounts, enhancing user engagement. The application includes a built-in knowledge graph-based intelligent assistant that can not only answer common questions but also recommend personalized training courses and technical documents based on user behavior patterns, acting as a personal mentor. The application incorporates a knowledge graph database (such as Neo4j) to store common questions and technical documents. The intelligent assistant recommends personalized training courses and technical documents based on user behavior patterns. When a user frequently searches for solutions to a certain type of fault, the intelligent assistant recommends relevant training courses to help the user acquire more skills.

[0053] In this embodiment, the PLC control box is equipped with an uninterruptible power supply (UPS) and a solar-assisted power supply module. The solar panel integrates a maximum power point tracking (MPPT) controller to ensure maximum power output under varying lighting conditions. The system's redundant power modules are designed with hot-swappable capabilities, allowing for the replacement of faulty components without interrupting system operation, significantly improving system availability and reliability. Simultaneously, the system features automatic fault detection and early warning functions to prevent potential problems from occurring. The operating formula of the MPPT controller in the solar-assisted power supply module is as follows:

[0054] Among them, V mppt It is the maximum power point voltage, V pv It is the open-circuit voltage of the photovoltaic array, P. max It is the power at the maximum power point, I pv It is the maximum current of the photovoltaic array.

[0055] Specifically, the UPS is installed inside the PLC control box to provide short-term power backup. The solar panels are installed outdoors and equipped with an MPPT controller to optimize energy conversion efficiency. The MPPT controller adjusts the photovoltaic panel's operating voltage in real time to ensure maximum power output. During the day when there is sufficient sunlight, the solar panels power the system; at night or on cloudy days, the UPS takes over to ensure stable system operation. The redundant power module design allows for the replacement of faulty components without shutting down the system, and automatic fault detection and early warning functions prevent potential problems in advance. When the main power module detects an anomaly, it automatically switches to the backup power module and triggers an alarm to notify maintenance personnel to replace the faulty component promptly.

[0056] In this embodiment, the wastewater treatment station has an intermediate water tank. The submersible drainage pump in the intermediate water tank adopts magnetic levitation bearing technology and liquid cooling, which effectively reduces mechanical wear and heat accumulation and extends service life. Combined with the dual protection measures of float level switch and ultrasonic level gauge, a laser rangefinder is introduced as a third layer of protection to achieve more precise level control. Furthermore, the Kalman filter algorithm is used to fuse data from the three sources, further improving the accuracy of measurement. When the liquid level approaches the set upper limit, the system integrates the data from the three sensors to ensure precise control of the drainage pump to start and avoid overflow accidents.

[0057] In this embodiment, the online water quality monitoring device has a self-cleaning function, using high-pressure water or airflow to periodically clean the sensor surface and maintain sensor sensitivity. Furthermore, the device casing is made of a nano-coating material, providing excellent corrosion resistance and suitability for various harsh environments. The device supports wireless upgrades, but unlike traditional OTA methods, it uses differential OTA technology, transmitting only the differences between the updated and newer versions. This significantly reduces data transmission volume, accelerates upgrade speed, and lowers bandwidth usage.

[0058] In this embodiment, the system incorporates a soft starter and a variable frequency drive (VFD). The VFD is equipped with an energy feedback device, which recovers the generated electrical energy when the motor decelerates and stores it in a supercapacitor. This energy is then released to other loads when needed, forming a closed-loop energy management system. The VFD has a built-in fault prediction algorithm that monitors parameters such as current and voltage in real time to identify potential faults and take preventative measures, such as reducing speed or stopping the machine for maintenance, thereby improving the stability and safety of the entire system. When the motor current abnormally increases, the VFD predicts the potential fault and takes preventative measures (such as reducing speed or stopping the machine for maintenance) to prevent the fault from escalating. When the current fluctuation frequency exceeds the normal range, the system issues a warning and recommends preventative maintenance to avoid sudden faults affecting production.

[0059] In this embodiment, the system also includes modular design and plug-and-play architecture, zero-trust architecture, and digital twin technology;

[0060] Specifically, the system features a modular design and a plug-and-play architecture: The system employs a modular design, with components interconnected via standardized interfaces, supporting hot-swapping. This allows for the replacement or addition of new devices without shutting down the entire system, simplifying installation, maintenance, and upgrades. This design not only improves system flexibility and scalability but also reduces downtime and enhances system reliability. When a sensor needs to be replaced, the entire system can be continued simply by disconnecting the corresponding interface and inserting the new sensor.

[0061] Specifically, the zero-trust architecture: To ensure network security, the system integrates a zero-trust architecture, abandoning the traditional internal and external network boundary protection model. It performs strict authentication and permission checks on every access request. This architecture continuously monitors and analyzes network traffic, detecting abnormal behavior in real time, effectively preventing unauthorized access and potential security threats, and ensuring the security of data transmission and storage. When a remote user attempts to log in to the system, they must pass two-factor authentication to ensure that only authorized personnel can access sensitive data.

[0062] Specifically, the system incorporates digital twin technology: creating a virtual model for each physical device and synchronizing its operational status in real time on the cloud. These virtual models can simulate and predict device performance changes, identify potential faults early, optimize maintenance plans, and test new control strategies in a virtual environment, reducing trial-and-error costs. Furthermore, digital twin technology supports remote monitoring and management, improving operational efficiency and decision-making accuracy. For example, when a water pump is predicted to reach its maintenance cycle, the system can notify maintenance personnel in advance to prepare the necessary parts and optimize the maintenance plan.

[0063] It should be noted that, in this document, relational terms such as "one" and "two" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, the phrase "comprising an element defined as..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0064] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An alarm processing system for the effluent outlet of an industrial wastewater treatment plant, characterized in that, include: The online water quality monitoring device integrates multiple sensor fusion settings and has an adaptive calibration algorithm. By comparing the reading differences between different sensors in real time, it automatically corrects measurement deviations caused by environmental factors, ensuring data accuracy. The PLC control box has a built-in embedded computer that supports the execution of mathematical models and machine learning algorithms to predict water quality change trends. By leveraging edge computing technology combined with FPGA acceleration cards, large amounts of real-time data streams can be processed quickly locally, reducing latency, and transmitted to the cloud or remote server in encrypted form through built-in security protocols to ensure information security. Blockchain technology is used to build a decentralized data recording mechanism, where all records are timestamped and hashed to form an immutable data link. When water quality exceeds the standard, the intelligent decision-making module automatically selects an emergency plan based on a preset rule base and dynamic adjustment strategy, and continuously improves the response strategy using a Bayesian optimization algorithm to achieve emergency handling.

2. The alarm processing system for the outlet of an industrial wastewater treatment plant according to claim 1, characterized in that: The system constructs a remote monitoring platform based on the Internet of Things architecture. This platform also integrates an image recognition module to analyze abnormal situations in the video stream and provides visual guidance to on-site staff through AR glasses. The remote monitoring platform adopts a microservice architecture, with each functional module deployed independently and combined through an API gateway. It supports on-demand expansion of new features. The target detection in the image recognition module uses the YOLOv5 algorithm, with the following formula: Where, λ coord and λ noobj These are the weights of the coordinate loss and the non-object confidence loss, respectively; S is the number of grids; B is the number of bounding boxes per grid. and Indicator functions representing the existence and non-existence of an object, respectively; x i ,y i These are the center coordinates of the predicted bounding box; These are the center coordinates of the actual bounding box; C i and These are the confidence scores for prediction and the actual result, respectively.

3. The alarm processing system for the outlet of an industrial wastewater treatment plant according to claim 2, characterized in that: The system includes an intelligent analysis unit that trains a water quality prediction model using a distributed deep learning framework. This model is deployed on edge cloud nodes and connected to PLC control boxes in various locations via a 5G low-latency network, ensuring that each location receives the latest model updates and service support. Simultaneously, federated learning technology is used to protect user privacy, preventing sensitive data from being directly uploaded to the central server. The model utilizes the FedAvg algorithm. Among them, w t This represents the values ​​of the global model parameters after the t-th iteration, where K is the set of clients participating in the learning process, and n... k It is the number of samples for client k. These are the local model parameters of client k after the t-th iteration.

4. The alarm processing system for the outlet of an industrial wastewater treatment plant according to claim 3, characterized in that: The system also includes an adaptive sound and light alarm module based on acoustic fingerprint recognition. This module establishes a database based on the sound characteristics of different areas. When an anomaly occurs, the module will automatically match the corresponding alarm mode according to the current ambient sound scene to ensure the effective transmission of alarm information.

5. An alarm processing system for the outlet of an industrial wastewater treatment plant according to claim 4, characterized in that: The system includes a mobile application for receiving alarm information and managing devices, and incorporates a blockchain-based points incentive strategy. The application also includes a built-in knowledge graph-based intelligent assistant.

6. The alarm processing system for the outlet of an industrial wastewater treatment plant according to claim 5, characterized in that: The PLC control box is equipped with an uninterruptible power supply and a solar-assisted power supply module. The redundant power supply module of the system is designed with hot-swappable capability, allowing replacement of faulty components without interrupting system operation. The operating formula of the MPPT controller in the solar-assisted power supply module is as follows: Among them, V mppt It is the maximum power point voltage, V pv It is the open-circuit voltage of the photovoltaic array, P. max It is the power at the maximum power point, I pv It is the maximum current of the photovoltaic array.

7. An alarm processing system for the effluent outlet of an industrial wastewater treatment plant according to claim 6, characterized in that: The wastewater treatment station has an intermediate water tank. The submersible drainage pump in the intermediate water tank adopts magnetic levitation bearing technology and liquid cooling. It is combined with the dual protection measures of float level switch and ultrasonic level gauge, and a laser rangefinder is introduced as a third layer of protection.

8. An alarm processing system for the effluent outlet of an industrial wastewater treatment plant according to claim 7, characterized in that: The online water quality monitoring equipment has a self-cleaning function, which uses high-pressure water flow or airflow to periodically clean the sensor surface, and the equipment shell is made of nano-coating material.

9. An alarm processing system for the outlet of an industrial wastewater treatment plant according to claim 8, characterized in that: The system includes a soft starter and a variable frequency drive (VFD). The VFD is equipped with an energy feedback device and has a built-in fault prediction algorithm to identify potential faults in advance and take preventive measures.

10. An alarm processing system for the effluent outlet of an industrial wastewater treatment plant according to claim 9, characterized in that: The system also includes: modular design and plug-and-play architecture, zero-trust architecture, and digital twin technology.

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