Intelligent medical wastewater treatment and monitoring integrated system

Abstract

The invention discloses an intelligent medical wastewater treatment and monitoring integrated system. According to the method, the support vector machine (SVM) algorithm is integrated, the Gaussian kernel function is used for model training, the optimization problem is solved, and L2 regularization is introduced, so that the model complexity and the training error are effectively balanced, and the accuracy of anomaly detection is improved. The SVM algorithm is excellent in performance when high-dimensional data are processed, abnormal modes in the data can be accurately recognized, and powerful support is provided for early warning of potential problems. According to the early warning mechanism, the system can timely find and treat abnormal conditions in the medical wastewater treatment process, pollution accidents are prevented, and the environment and public health safety are guaranteed. Meanwhile, the data analysis module helps a decision maker to formulate more scientific and reasonable treatment strategies and emergency measures, and the intelligent level and management efficiency of the whole intelligent medical wastewater treatment and monitoring integrated system are further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical wastewater treatment, and specifically relates to an integrated intelligent medical wastewater treatment and monitoring system. Background Art

[0002] The intelligent medical wastewater treatment system is an efficient wastewater treatment device integrating modern sensing technology, automatic control technology, and environmental protection treatment processes. This system is specifically designed to efficiently treat harmful substances such as bacteria, viruses, and chemical pollutants in medical wastewater to ensure that the discharged water quality meets national environmental protection standards. The system adopts an intelligent monitoring and control unit that can real-time monitor various indicators in the wastewater and automatically adjust the treatment process, including disinfection, sedimentation, filtration, adsorption, and other links. The intelligent medical wastewater treatment system not only reduces the harm of medical wastewater to the environment, but also improves the treatment efficiency, reduces the labor operation cost, and provides strong technical support for the environmental protection work of medical institutions. With the rapid development of the medical industry, the treatment and monitoring of medical wastewater have become important links to ensure environmental and public health safety. Medical wastewater contains various harmful substances. If not properly treated, it will pose a serious threat to the environment and human health. Therefore, effectively treating and monitoring medical wastewater and timely detecting and handling abnormal situations have become technical problems that need to be solved urgently.

[0003] However, the existing medical wastewater treatment and monitoring systems have many deficiencies in data processing and analysis. First, medical wastewater data is diverse and complex, and there are large differences in the dimensions between different characteristics, making it difficult to directly compare and analyze the data. Second, traditional methods have limited capabilities in anomaly detection and early warning, and it is difficult to accurately identify and handle potential pollution risks. Summary of the Invention

[0004] The purpose of the present invention is to provide an integrated intelligent medical wastewater treatment and monitoring system to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: An integrated intelligent medical wastewater treatment and monitoring system, the system includes: a wastewater pretreatment module, a biological treatment module, a deep treatment module, a sludge treatment module, a monitoring sensor network, a data acquisition and transmission module, a data analysis module, an emergency treatment module, and a remote monitoring and maintenance module; The data analysis module internally is provided with a data preprocessing sub-module, a data analysis sub-module, a data storage sub-module, and a control strategy sub-module.

[0006] In a preferred embodiment, the wastewater pretreatment module includes facilities such as a grid well and an adjustment tank. The grid well removes large solid substances in the wastewater through physical interception. The adjustment tank is used to store and balance the wastewater flow rate to ensure the stable operation of subsequent treatment units; The wastewater pretreatment module further includes a grit chamber and a primary sedimentation tank facility.

[0007] In a preferred embodiment, an activated sludge process treatment module and a biofilm process treatment module are provided inside the biological treatment module.

[0008] In a preferred embodiment, the advanced treatment module includes a coagulation and sedimentation treatment unit, a filtration treatment unit, and a disinfection treatment unit. The coagulation and sedimentation unit adds a coagulant to cause the fine suspended solids and colloidal substances in the wastewater to coagulate into larger particles, which then settle in the sedimentation tank, thereby achieving solid-liquid separation. The filtration unit uses filter media to intercept the fine particles and suspended solids in the wastewater to further purify the water quality. The disinfection unit uses methods such as chlorine disinfection, ozone disinfection, or ultraviolet disinfection to kill the pathogenic microorganisms in the wastewater and ensure that the effluent meets the health and safety requirements.

[0009] In a preferred embodiment, the sludge treatment module includes a sludge thickening treatment unit, a digestion treatment unit, and a dewatering treatment unit. The sludge thickening unit reduces the moisture content of the sludge and the sludge volume through gravity thickening or mechanical thickening. The digestion unit decomposes the organic matter in the sludge through the metabolic action of anaerobic or aerobic microorganisms to produce biogas and achieve the stabilization of the sludge. The dewatering unit uses equipment such as centrifuges and filter presses to further reduce the moisture content of the sludge, facilitating the transportation and disposal of the sludge.

[0010] In a preferred embodiment, the monitoring sensor network includes a dissolved oxygen sensor, a pH sensor, a temperature sensor, and a flow sensor, which are respectively used to monitor the key parameters of dissolved oxygen, pH value, temperature, and flow rate in the wastewater; The data acquisition and transmission module includes data acquisition equipment, data transmission equipment, and data processing software. The data acquisition equipment is connected to various sensors through interfaces to collect data in real time during the wastewater treatment process. The data transmission equipment then transmits the collected data to the central control and data analysis module through a wired or wireless network. The data processing software performs preliminary processing, format conversion, and storage of the data to ensure the integrity and real-time nature of the data.

[0011] In a preferred embodiment, the kernel function formula of the Gaussian kernel of the data analysis sub-module is: ; where x and x' are data points, and σ is the kernel function parameter; During the training process, the following optimization problem needs to be solved: ; subject to where \(y_i\) is the label of the \(i\)-th sample (\(+1\) or \(-1\)), and \(x_i\) is the feature vector of the \(i\)-th sample; Using L2 regularization, that is, the optimization formula for adding the L2 norm of the weight vector to the optimization objective is: ; where \(w\) is the weight vector used to define the decision boundary.

[0012] \(b\) is the bias term used to define the decision boundary. [[ID=:12]]

[0013] \(C\) is the regularization parameter that controls the balance between model complexity and training error.

[0014] \(y_i\) is the label of the \(i\)-th sample (\(+1\) or \(-1\)).

[0015] \(x_i\) is the feature vector of the \(i\)-th sample.

[0016] \(n\) is the number of samples in the training set.

[0017] In a preferred embodiment, the data storage sub-module specifically comprises: A data receiving interface for receiving data from the sensor network and the preprocessing module; A data cleaning and formatting unit for cleaning, de-duplicating, and formatting the raw data to ensure data quality; A data storage unit for storing structured and unstructured data using a relational database or a non-relational database; A data backup and recovery module for regularly backing up the data and recovering it when needed; a data access interface for providing a standardized API interface to facilitate other modules or systems to access and query the data; The control strategy sub-module includes: A strategy formulation unit for formulating targeted control strategies based on machine learning algorithms, statistical analysis results, and expert knowledge; A strategy execution unit for sending the formulated strategies to the wastewater treatment equipment through the control system interface to achieve automated control; A real-time monitoring unit for continuously monitoring the system status and effects after the strategy execution to ensure the effectiveness of the strategy; A feedback adjustment unit for dynamically adjusting and optimizing the control strategy according to the real-time monitoring results and feedback information.

[0018] In a preferred embodiment, the emergency treatment module includes an emergency storage facility, emergency treatment equipment, and an emergency control strategy. The emergency storage facility is used to temporarily store the excessive wastewater generated in case of emergencies, preventing environmental pollution caused by direct discharge of wastewater. The emergency treatment equipment includes a backup power supply, emergency disinfection equipment, etc., ensuring that basic treatment functions can still be maintained in case of main equipment failure.

[0019] In a preferred embodiment, the remote monitoring and maintenance module includes remote monitoring software and remote communication equipment. The remote monitoring software is connected to the central control and data analysis module through the Internet, and can display the system operation status, processed data, and alarm information in real time, enabling management personnel to remotely understand the system situation. The remote communication equipment ensures the stability and security of data transmission, and supports the sending and receiving of remote control instructions.

[0020] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows: 1. In the present invention, through the data preprocessing sub-module, the minimum-maximum normalization method is used to effectively normalize the medical wastewater data, scaling all feature values to the range of 0 to 1. This step not only eliminates the influence of different feature dimensions, but also improves the consistency and comparability of the data, laying a solid foundation for subsequent statistical analysis, machine learning algorithm applications, and pattern recognition. The normalized data is more conducive to model training and analysis, reducing the complexity of the algorithm during the processing process, thus significantly improving the efficiency and quality of data processing. In addition, through normalization, the sensitivity of the model to outliers is reduced, enhancing the robustness and generalization ability of the model, enabling the system to maintain stable processing performance and accuracy when facing medical wastewater data from different sources and with different characteristics.

[0021] 2. In the present invention, the support vector machine (SVM) algorithm is integrated, and the Gaussian kernel function is used for model training. By solving the optimization problem and introducing L2 regularization, the balance between model complexity and training error is effectively achieved, improving the accuracy of anomaly detection. The SVM algorithm performs excellently in processing high-dimensional data and can accurately identify abnormal patterns in the data, providing strong support for early warning of potential problems. This early warning mechanism enables the system to promptly detect and handle abnormal situations during the medical wastewater treatment process, preventing pollution accidents and ensuring environmental and public health safety. At the same time, the statistical analysis function and pattern recognition ability of the data analysis module also provide in-depth data insights for the system, helping decision-makers formulate more scientific and reasonable treatment strategies and emergency measures, further enhancing the intelligent level and management efficiency of the entire integrated intelligent medical wastewater treatment and monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is the overall system block diagram of the present invention; Figure 2 This is the system block diagram of the data analysis module in the present invention. Detailed implementation manners

[0023] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0024] Embodiment: Refer to Figure 1-2 , an integrated system for intelligent medical wastewater treatment and monitoring, the system includes: a wastewater pretreatment module, a biological treatment module, a deep treatment module, a sludge treatment module, a monitoring sensor network, a data acquisition and transmission module, a data analysis module, an emergency treatment module, and a remote monitoring and maintenance module; Inside the data analysis module, there are a data preprocessing sub-module, a data analysis sub-module, a data storage sub-module, and a control strategy sub-module; The wastewater pretreatment module is the front-end part of the medical wastewater treatment system, mainly responsible for removing large solid substances and suspended solids in the wastewater to prepare for subsequent treatment processes. This module includes facilities such as a grille well and an adjustment tank. The grille well removes large solid substances in the wastewater, such as paper and plastic, through physical interception to prevent these substances from clogging or damaging subsequent treatment equipment. The adjustment tank is used to store and balance the wastewater flow rate to ensure the stable operation of subsequent treatment units. In addition, the pretreatment module may also include facilities such as a grit chamber and a primary sedimentation tank to further remove suspended solids and relatively heavy particulate matter in the wastewater. Through the treatment of the pretreatment module, the biodegradability of the wastewater is improved, creating more favorable conditions for the biological treatment module.

[0025] The biological treatment module is the core part of the medical wastewater treatment system, using the metabolic action of microorganisms to degrade organic pollutants in the wastewater. This module usually adopts biological treatment technologies such as the activated sludge method and the biofilm method. In the activated sludge method, the wastewater is mixed with activated sludge and undergoes aerobic biological treatment in an aeration tank. Microorganisms convert organic pollutants into inorganic substances through processes such as adsorption and oxidative decomposition to achieve the purification purpose. The biofilm method uses the biofilm attached to the surface of the packing for wastewater treatment. The microbial community structure on the biofilm is complex and has strong degradation ability. The effective operation of the biological treatment module depends on suitable environmental conditions such as dissolved oxygen, pH value, and temperature, so corresponding monitoring and regulation equipment needs to be equipped. After the treatment of the biological treatment module, the organic pollutants in the wastewater are greatly removed and the water quality is significantly improved.

[0026] The advanced treatment module further purifies the biologically treated wastewater to meet higher discharge standards or reuse requirements. This module usually includes treatment units such as coagulation sedimentation, filtration, and disinfection. In the coagulation sedimentation unit, coagulants are added to cause the tiny suspended solids and colloidal substances in the wastewater to coagulate into larger particles, which then settle in the sedimentation tank, thus achieving solid-liquid separation. The filtration unit uses filter media to intercept the fine particles and suspended solids in the wastewater to further purify the water quality. The disinfection unit usually adopts methods such as chlorine disinfection, ozone disinfection, or ultraviolet disinfection to kill the pathogenic microorganisms in the wastewater and ensure that the effluent meets the health and safety requirements. The effective operation of the advanced treatment module can significantly improve the effluent quality and reduce the environmental pollution risk of the wastewater.

[0027] The sludge treatment module is responsible for treating the sludge generated during the medical wastewater treatment process to achieve sludge reduction, stabilization, and harmlessness. This module usually includes treatment units such as sludge thickening, digestion, and dewatering. The sludge thickening unit reduces the moisture content of the sludge and decreases the sludge volume through gravity thickening or mechanical thickening. The digestion unit uses the metabolic action of anaerobic or aerobic microorganisms to decompose the organic matter in the sludge, producing biogas and achieving sludge stabilization. The dewatering unit uses equipment such as centrifuges and filter presses to further reduce the moisture content of the sludge, facilitating the transportation and disposal of the sludge. After being treated by the sludge treatment module, the volume and harmfulness of the sludge are effectively controlled, which is conducive to the resource utilization and harmless disposal of the sludge.

[0028] The monitoring sensor network is an important part of the intelligent integrated medical wastewater treatment and monitoring system, responsible for collecting various data during the wastewater treatment process in real time. This network includes various sensors, such as dissolved oxygen sensors, pH sensors, temperature sensors, flow sensors, etc., which are respectively used to monitor key parameters such as dissolved oxygen, pH value, temperature, and flow rate in the wastewater. These sensors are connected to the data acquisition and transmission module by wired or wireless means to achieve real-time data transmission and storage. The effective operation of the monitoring sensor network can provide comprehensive and accurate wastewater treatment data, providing strong support for the automatic control, data analysis, and decision-making of the system; The data acquisition and transmission module is the data hub of the intelligent integrated medical wastewater treatment and monitoring system, responsible for collecting, processing, and transmitting the data from the monitoring sensor network. This module includes data acquisition equipment, data transmission equipment, and data processing software, etc. The data acquisition equipment is connected to various sensors through interfaces to collect data during the wastewater treatment process in real time. The data transmission equipment then transmits the collected data to the central control and data analysis module through wired or wireless networks. The data processing software preliminarily processes, converts the format, and stores the data to ensure the integrity and real-time nature of the data. The effective operation of the data acquisition and transmission module can ensure the smooth flow of data between various parts of the system and provide a data foundation for the intelligent management of the system.

[0029] The data preprocessing submodule uses the minimum-maximum normalization method to perform data normalization. The specific steps are as follows: S1. Calculate the maximum and minimum values: For each feature, find the maximum and minimum values in the medical wastewater dataset; S2. Apply the transformation formula: For each feature value x in the medical wastewater dataset, use the following formula to transform it: in, is the transformed value; S3. Transform the medical wastewater dataset: Apply the above transformation to each feature of the entire dataset; after min-max normalization, all features will be scaled to the range of 0 to 1; The data analysis submodule uses the support vector machine (SVM) algorithm to identify abnormal patterns in the data and warn of potential problems. It specifically includes: statistical analysis module, machine learning algorithm module and pattern recognition module; The kernel function formula of the Gaussian kernel used in SVM model training is: ; Where x and x' are data points, and σ is the kernel function parameter; During the training process, the following optimization problems need to be solved: ; subject to Where y_i is the label of the i-th sample (+1 or -1), x_i is the feature vector of the i-th sample; Using L2 regularization, that is, adding the L2 norm of the weight vector to the optimization target, the optimization formula is: ; Where w is the weight vector that defines the decision boundary.

[0030] b is the bias term, which is used to define the decision boundary.

[0031] C is a regularization parameter that controls the balance between model complexity and training error.

[0032] y_i is the label of the i-th sample (+1 or -1).

[0033] x_i is the feature vector of the i-th sample.

[0034] n is the number of samples in the training set The data storage sub-module is a core component of the data analysis module, responsible for efficiently and securely storing and managing various types of data collected from the medical wastewater treatment system. This sub-module adopts a distributed database architecture to ensure high availability and scalability of the data. Its specific components include: a data reception interface for receiving data from the sensor network and the preprocessing module; a data cleaning and formatting unit that cleans, deduplicates, and formats the raw data to ensure data quality; a data storage unit that stores structured and unstructured data using a relational database or a non-relational database; a data backup and recovery mechanism that regularly backs up the data and performs recovery when needed; and a data access interface that provides standardized API interfaces for other modules or systems to access and query the data. In addition, the data storage sub-module also integrates data encryption and permission control functions to ensure data security and privacy; The control strategy sub-module is responsible for formulating and implementing corresponding control strategies based on the data analysis results to optimize the medical wastewater treatment process and ensure the stable operation of the system. This sub-module includes the following key parts: a strategy formulation unit that formulates targeted control strategies, such as adjusting treatment parameters and initiating emergency measures, based on machine learning algorithms, statistical analysis results, and expert knowledge; a strategy execution unit that issues the formulated strategies to the wastewater treatment equipment through the control system interface to achieve automated control; a real-time monitoring unit that continuously monitors the system status and effects after the strategies are executed to ensure the effectiveness of the strategies; a feedback adjustment mechanism that dynamically adjusts and optimizes the control strategies based on the real-time monitoring results and feedback information; and an exception handling unit that immediately initiates an emergency plan when an abnormal situation is detected to ensure the safe operation of the system. Through the coordinated work of these components, the control strategy sub-module realizes the intelligent and precise control of the medical wastewater treatment process.

[0035] The emergency treatment module is the safety line of the intelligent medical wastewater treatment and monitoring integrated system, responsible for quickly taking measures when abnormal situations occur to ensure the stable operation of the system and environmental protection. This module includes emergency storage facilities, emergency treatment equipment, and emergency control strategies, etc. The emergency storage facilities are used to temporarily store the excessive wastewater generated in case of emergencies to prevent environmental pollution caused by direct wastewater discharge. The emergency treatment equipment includes backup power supplies, emergency disinfection equipment, etc., to ensure that basic treatment functions can still be maintained when the main equipment fails. The emergency control strategies automatically or manually initiate the emergency treatment process according to the abnormal situations monitored by the system, such as adjusting treatment parameters and starting backup equipment. The effective operation of the emergency treatment module can significantly improve the system's response ability and environmental safety.

[0036] The remote monitoring and maintenance module is a remote management tool for the intelligent medical wastewater treatment and monitoring integrated system, responsible for realizing the remote monitoring, fault diagnosis and maintenance support of the system. This module includes remote monitoring software, remote communication equipment and a maintenance support team. The remote monitoring software is connected to the central control and data analysis module through the Internet, and real-time displays the system operation status, processed data and alarm information, enabling managers to remotely grasp the system situation. The remote communication equipment ensures the stability and security of data transmission, and supports the sending and receiving of remote control instructions. The maintenance support team provides professional technical support, remotely diagnoses system faults, guides on-site personnel for repair operations, or dispatches technical personnel for on-site services when necessary. The effective operation of the remote monitoring and maintenance module can greatly improve the management efficiency of the system, reduce maintenance costs, and ensure the long-term stable operation of the system.

[0037] In the present invention, through the data preprocessing sub-module adopting the min-max normalization method, the medical wastewater data is effectively normalized, and all feature values are scaled to the range of 0 to 1. This step not only eliminates the influence of different feature dimensions, but also improves the consistency and comparability of the data, laying a solid foundation for subsequent statistical analysis, machine learning algorithm application and pattern recognition. The normalized data is more conducive to model training and analysis, reduces the complexity of the algorithm during the processing, and thus significantly improves the efficiency and quality of data processing. In addition, through normalization, the sensitivity of the model to outliers is reduced, enhancing the robustness and generalization ability of the model, enabling the system to maintain stable processing performance and accuracy when facing medical wastewater data from different sources and with different characteristics.

[0038] In the present invention, the support vector machine (SVM) algorithm is integrated, and the Gaussian kernel function is used for model training. By solving the optimization problem and introducing L2 regularization, the model complexity and training error are effectively balanced, and the accuracy of anomaly detection is improved. The SVM algorithm performs excellently in dealing with high-dimensional data, and can accurately identify abnormal patterns in the data, providing strong support for the early warning of potential problems. This early warning mechanism enables the system to timely detect and handle abnormal situations in the medical wastewater treatment process, prevent pollution accidents from occurring, and ensure environmental and public health safety. At the same time, the statistical analysis function and pattern recognition ability of the data analysis module also provide in-depth data insights for the system, helping decision-makers formulate more scientific and reasonable treatment strategies and emergency measures, and further enhancing the intelligent level and management efficiency of the entire intelligent medical wastewater treatment and monitoring integrated system.

[0039] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0040] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An integrated system for intelligent medical wastewater treatment and monitoring, characterized in that: The system includes: a wastewater pretreatment module, a biological treatment module, an advanced treatment module, a sludge treatment module, a monitoring sensor network, a data acquisition and transmission module, a data analysis module, an emergency treatment module, and a remote monitoring and maintenance module; Inside the data analysis module, there are a data preprocessing sub-module, a data analysis sub-module, a data storage sub-module, and a control strategy sub-module.

2. The integrated system for intelligent medical wastewater treatment and monitoring according to claim 1, wherein: The wastewater pretreatment module includes facilities such as a grille well and an equalization tank; the grille well removes large solid substances in the wastewater through physical interception; the equalization tank is used to store and balance the wastewater flow rate to ensure the stable operation of subsequent treatment units; The wastewater pretreatment module also includes a grit chamber and a primary sedimentation tank facility.

3. The integrated system for intelligent medical wastewater treatment and monitoring according to claim 1, characterized in that: Inside the biological treatment module, there are an activated sludge process treatment module and a biofilm process treatment module.

4. An integrated system for intelligent medical wastewater treatment and monitoring according to claim 1, characterized in that: The advanced treatment module includes a coagulation and sedimentation treatment unit, a filtration treatment unit, and a disinfection treatment unit; the coagulation and sedimentation unit adds a coagulant to cause the tiny suspended solids and colloidal substances in the wastewater to coagulate into larger particles, which then settle in the sedimentation tank, thus achieving solid-liquid separation; the filtration unit uses filter media to intercept the fine particles and suspended solids in the wastewater to further purify the water quality; The disinfection unit uses methods such as chlorine disinfection, ozone disinfection, or ultraviolet disinfection to kill the pathogenic microorganisms in the wastewater to ensure that the effluent meets the hygienic and safety requirements.

5. An integrated system for intelligent medical wastewater treatment and monitoring as claimed in claim 1, characterized in that: The sludge treatment module includes a sludge thickening treatment unit, a digestion treatment unit, and a dewatering treatment unit; The sludge thickening unit reduces the moisture content of the sludge and decreases the sludge volume through gravity thickening or mechanical thickening; the digestion unit uses the metabolic action of anaerobic or aerobic microorganisms to decompose the organic substances in the sludge, generating biogas and achieving the stabilization of the sludge; The dewatering unit uses equipment such as centrifuges and filter presses to further reduce the moisture content of the sludge, facilitating the transportation and disposal of the sludge.

6. An integrated system for intelligent medical wastewater treatment and monitoring according to claim 1, characterized in that: The monitoring sensor network includes a dissolved oxygen sensor, a pH sensor, a temperature sensor, and a flow sensor, which are respectively used to monitor the key parameters of dissolved oxygen, pH value, temperature, and flow rate in the wastewater; The data acquisition and transmission module includes data acquisition equipment, data transmission equipment, and data processing software; The data acquisition equipment is connected to various sensors through interfaces to collect data in real time during the wastewater treatment process; The data transmission equipment transmits the collected data to the central control and data analysis module through wired or wireless networks; the data processing software conducts preliminary processing, format conversion, and storage of the data.

7. An integrated system for intelligent medical wastewater treatment and monitoring according to claim 1, characterized in that: The kernel function formula of the Gaussian kernel of the data analysis sub-module is: ； where x and x' are data points, and σ is the kernel function parameter; During the training process, the following optimization problem needs to be solved: ； subject to where y_i is the label of the i-th sample (+1 or -1), and x_i is the feature vector of the i-th sample; Using L2 regularization, that is, adding the L2 norm of the weight vector to the optimization objective, the optimization formula is: ； where w is the weight vector used to define the decision boundary; b is the bias term used to define the decision boundary; C is the regularization parameter that controls the balance between the model complexity and the training error; ​ ​ n is the number of training set samples.

8. The integrated system for intelligent medical wastewater treatment and monitoring according to claim 1, characterized in that: The specific composition of the data storage sub-module includes: A data receiving interface for receiving data from the sensor network and the preprocessing module; A data cleaning and formatting unit for cleaning, deduplicating, and formatting the original data to ensure data quality; A data storage unit that stores structured and unstructured data using a relational database or a non-relational database; A data backup and recovery module that periodically backs up the data and performs recovery when needed; a data access interface that provides a standardized API interface for other modules or systems to access and query the data; The control strategy sub-module includes: A strategy formulation unit that formulates targeted control strategies based on machine learning algorithms, statistical analysis results, and expert knowledge; A strategy execution unit that issues the formulated strategies to the wastewater treatment equipment through the control system interface to achieve automated control; A real-time monitoring unit that continuously monitors the system status and effects after the strategy is executed to ensure the effectiveness of the strategy; A feedback adjustment unit that dynamically adjusts and optimizes the control strategy according to the real-time monitoring results and feedback information.

9. The integrated system for intelligent medical wastewater treatment and monitoring according to claim 1, wherein: The emergency treatment module includes emergency storage facilities, emergency treatment equipment, and emergency control strategies; the emergency storage facilities are used to temporarily store the excessive wastewater generated in case of emergencies to prevent environmental pollution caused by direct wastewater discharge; the emergency treatment equipment includes a backup power supply, emergency disinfection equipment, etc., to ensure that the basic treatment functions can still be maintained when the main equipment fails.

10. The integrated system for intelligent medical wastewater treatment and monitoring according to claim 1, wherein: The remote monitoring and maintenance module includes remote monitoring software and remote communication equipment; the remote monitoring software is connected to the central control and data analysis module through the Internet to display the system operation status, processed data, and alarm information in real time, enabling managers to remotely understand the system situation; the remote communication equipment ensures the stability and security of data transmission.

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