Medical sewage treatment monitoring control system and device

Abstract

This invention relates to the field of data processing technology, and specifically discloses a medical wastewater treatment monitoring and control system and device. The method includes: an acquisition module for acquiring basic information data and historical treatment data to determine the treatment process data; a collection module for real-time collection of water quality data and real-time equipment operation data; an analysis module for determining historical process data and a set of treatment strategies for each treatment process, and constructing a decision tree for each treatment process; a calculation module for calculating the strategy evaluation value of each historical treatment strategy in the set of treatment strategies for each treatment process; and a control module for determining and executing real-time control commands. This system enables dynamic and precise correlation and adaptation between the treatment process, water quality, treatment strategies, and equipment operation, improving the efficiency and stability of medical wastewater treatment, reducing manual intervention and operating costs, ensuring stable water quality compliance while reducing resource waste, and achieving intelligent and refined control of medical wastewater treatment.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a medical wastewater treatment monitoring and control system and device. Background Technology

[0002] Medical wastewater contains various pathogens, toxic and harmful substances, and chemical pollutants, making it difficult to treat and posing a serious threat to environmental and public health safety. Early medical wastewater treatment lacked systematic monitoring and intelligent control, relying mainly on manual operation and experience-based judgment. In the mid-to-late 20th century, although infrastructure such as sedimentation tanks and disinfection tanks were introduced, the treatment process remained fixed, such as uniformly using "bar screen + chlorine disinfection," without considering the type of hospital (e.g., infectious disease hospitals versus general hospitals) or the differences in wastewater characteristics (e.g., the presence of pathogens or heavy metals). This often resulted in overtreatment or low compliance rates. Monitoring relied on periodic manual sampling and testing, with data lags of hours or even days, failing to respond in real-time to water quality fluctuations. Historical treatment data was simply recorded and not used to optimize the process. In the early 21st century, some systems introduced single-parameter sensors such as pH meters, but lacked multi-data linkage analysis. Furthermore, intelligent sensors with multi-parameter synchronous monitoring and data interaction capabilities were not adopted, resulting in insufficient comprehensiveness and timeliness of data collection. Equipment control still relied on manual adjustments, making it difficult to cope with sudden changes in water quality, leading to low overall treatment efficiency and stability.

[0003] Therefore, the present invention proposes a medical wastewater treatment monitoring and control system and device. Summary of the Invention

[0004] This invention provides a medical wastewater treatment monitoring and control system and device. By acquiring basic information data and historical treatment data of the medical wastewater to be treated, the system determines the treatment process data, collects real-time water quality data and real-time equipment operation data, analyzes historical treatment data and process data, constructs a decision tree for each treatment process, calculates the strategy evaluation value of each historical treatment strategy in the set of treatment strategies for each process, and determines and executes real-time control commands. This system enables dynamic and precise correlation and adaptation between the treatment process, water quality, treatment strategies, and equipment operation, quantifies the strategy execution effect, improves the accuracy and economy of control, enhances the efficiency and stability of medical wastewater treatment, reduces manual intervention and operating costs, ensures stable water quality compliance while reducing resource waste, and achieves intelligent and refined control of medical wastewater treatment.

[0005] This invention provides a medical wastewater treatment monitoring and control system, comprising:

[0006] Acquisition module: Acquires basic information data and historical treatment data of the medical wastewater to be treated, and determines the treatment process data of the medical wastewater to be treated based on the basic information data;

[0007] Data Acquisition Module: Real-time acquisition of water quality data of medical wastewater to be treated; based on the real-time water quality data and treatment process data of the medical wastewater to be treated, real-time acquisition of equipment operation data of the medical wastewater to be treated.

[0008] Analysis module: Analyzes historical processing data and processing flow data to determine the historical flow data and processing strategy set for each processing flow in the processing flow data of the medical wastewater to be treated, and constructs a decision tree for each processing flow in the processing flow data;

[0009] Calculation module: Based on the historical process data, processing strategy set, and decision tree of each processing process in the processing flow data, calculate the strategy evaluation value of each historical processing strategy in the processing strategy set of each processing process;

[0010] Control module: Based on the historical process data of all processing processes in the processing process data, the strategy evaluation values ​​of all historical treatment strategies in the processing strategy set of all processing processes, real-time water quality data, and real-time equipment operation data, it determines and executes real-time control commands to realize real-time monitoring and control of the wastewater to be treated.

[0011] Preferably, a medical wastewater treatment monitoring and control system includes an acquisition module comprising:

[0012] Basic information data unit: Acquire basic information data of the medical wastewater to be treated, including the characteristics of the wastewater, the type of hospital, and the discharge destination;

[0013] Processing flow data unit: Based on the basic information data of the medical wastewater to be treated, the processing flow data of the medical wastewater to be treated is determined. The processing flow data includes multiple processing flows and the processing standard data for each processing flow. The processing standard data includes the parameter standards for multiple water quality parameters.

[0014] Historical treatment data unit: Acquires historical treatment data of medical wastewater to be treated. The historical treatment data includes historical treatment sub-data from multiple historical treatments. The historical treatment sub-data includes historical equipment operation data, historical initial water quality data, historical treated water quality data, and historical treatment strategies for each treatment process.

[0015] Preferably, a medical wastewater treatment monitoring and control system includes a data acquisition module, comprising:

[0016] Process Equipment Set Unit: Obtain the set of equipment for treating medical wastewater, and based on the set of equipment, all treatment processes, and the treatment standards for each process, determine the set of process equipment for each treatment process;

[0017] Real-time water quality data unit: Based on the first sensor group, real-time water quality data of the medical wastewater to be treated is collected in real time. The real-time water quality data includes the real-time parameter values ​​of multiple water quality parameters.

[0018] Preferably, a medical wastewater treatment monitoring and control system, including a data acquisition module, further comprises:

[0019] Real-time processing unit: Compare the real-time water quality data of the medical wastewater to be treated with the treatment standard data of each treatment process in the treatment process data to determine the real-time treatment process of the medical wastewater to be treated.

[0020] Real-time equipment operation sub-data unit: Based on the real-time treatment process of the medical wastewater to be treated and the set of process equipment for all treatment processes, real-time equipment operation sub-data of each device in the set of process equipment for the real-time treatment process of the medical wastewater to be treated is collected.

[0021] Real-time equipment operation data unit: Based on the real-time equipment operation data of all equipment in the process equipment set of the real-time treatment process for medical wastewater, the real-time equipment operation data of the medical wastewater to be treated is determined.

[0022] Preferably, a medical wastewater treatment monitoring and control system includes an analysis module comprising:

[0023] Historical process data unit: The historical treatment data of the medical wastewater to be treated is preprocessed. Based on the historical treatment sub-data of all historical treatments in the preprocessed historical treatment data, the historical equipment operation data, historical initial water quality data, historical treated water quality data and historical treatment strategies of each treatment process are extracted to determine the historical process data of each treatment process of the medical wastewater to be treated.

[0024] Historical Equipment Feature Vector Unit: For each treatment process of medical wastewater to be treated, feature extraction is performed on the historical equipment operation data of each equipment in the historical process data of each historical treatment, and the historical equipment feature vector of each equipment in the historical process data of each treatment process is determined.

[0025] Historical initial water quality feature vector and historical treated water quality feature vector unit: feature extraction is performed on the historical initial water quality data and historical treated water quality data of each historical treatment process in the historical process data of each treatment process of the medical wastewater to be treated, and the historical initial water quality feature vector and historical treated water quality feature vector of each historical treatment process in the historical process data of each treatment process are determined.

[0026] Processing strategy set unit: Based on the historical processing strategies of all previous processes in the historical process data of each processing flow, determine the processing strategy set for each processing flow;

[0027] Decision tree unit: Based on the historical initial water quality feature vectors of all historical treatments in the historical process data of each treatment process as input, and based on the historical treatment strategies of all historical treatments in the historical process data of each treatment process as output, a decision tree is constructed for each treatment process in the process data.

[0028] Preferably, a medical wastewater treatment monitoring and control system includes a computing module comprising:

[0029] Path condition unit: Traverse the decision tree of each processing flow to determine all leaf nodes of each historical processing strategy in the processing strategy set of each processing flow and the path condition of each leaf node.

[0030] Initial water quality characteristic range set unit: Based on the path conditions of all leaf nodes of each historical treatment strategy in the treatment strategy set of each treatment process, determine the initial water quality characteristic range set of each historical treatment strategy in the treatment strategy set of each treatment process;

[0031] Historical strategy data unit: Determine whether the historical initial water quality feature vector of each historical treatment in the historical process data of each treatment process satisfies the initial water quality feature range set of each historical treatment strategy in the treatment strategy set of each treatment process. Based on the historical initial water quality feature vectors of all historical treatments that satisfy the historical initial water quality feature vectors, the historical treatment water quality feature vectors, and the historical equipment feature vectors of all equipment, determine the historical strategy data of each historical treatment strategy in the treatment strategy set of each treatment process. The historical strategy data includes historical treatment sub-data of multiple historical treatments.

[0032] Strategy Evaluation Value Unit: Based on the historical initial water quality feature vector, historical treated water quality feature vector, historical equipment feature vector of all equipment, and historical strategy data of all historical treatment strategies in the treatment strategy set of each treatment process, calculate the wastewater treatment effect value, equipment stability value, and cost value of each historical treatment strategy in the historical strategy data of each historical treatment strategy in the treatment strategy set of each treatment process, and calculate the strategy evaluation value of each historical treatment strategy in the treatment strategy set of each treatment process.

[0033] Preferably, a medical wastewater treatment monitoring and control system includes a computing module comprising:

[0034] Process control model unit: Based on the processing standard data, historical process data, and policy evaluation values ​​of all historical processing strategies in the processing strategy set of each processing process in the processing process data, a process control model for each processing process in the processing process data is constructed.

[0035] Real-time control model unit: Based on the real-time treatment process of the medical wastewater to be treated, determine the real-time control model of the medical wastewater to be treated;

[0036] Real-time control command unit: Input the real-time water quality data and real-time equipment operation data of the medical wastewater to be treated into the real-time control model of the medical wastewater to be treated, determine the real-time control command of the medical wastewater to be treated based on the output results of the real-time control model, and execute the real-time control command to realize the real-time monitoring and control of the medical wastewater to be treated.

[0037] The present invention provides a medical wastewater treatment monitoring and control device for implementing a medical wastewater treatment monitoring and control system as described in any one of embodiments 1 to 7.

[0038] The beneficial effects of this invention compared to the prior art are as follows: by acquiring basic information data and historical treatment data of the medical wastewater to be treated, determining the treatment process data of the medical wastewater to be treated, collecting real-time water quality data and real-time equipment operation data of the medical wastewater to be treated in real time, analyzing historical treatment data and treatment process data, constructing a decision tree for each treatment process in the treatment process data, calculating the strategy evaluation value of each historical treatment strategy in the treatment strategy set of each treatment process, and determining and executing real-time control commands. Furthermore, by deploying intelligent sensors with multi-parameter synchronous monitoring and data interaction capabilities, real-time, comprehensive, and multi-dimensional water quality data and equipment operating parameters of the medical wastewater to be treated can be collected in real time. This provides high-precision and timely data support for dynamic matching of the treatment process, decision tree construction, and strategy evaluation. Relying on the real-time data feedback capability of intelligent sensors, the response time to water quality fluctuations can be further shortened, enabling dynamic and precise correlation and adaptation between the treatment process, water quality, treatment strategy, and equipment operation. This quantifies the effect of strategy execution, improves the accuracy and economy of control, enhances the efficiency and stability of medical wastewater treatment, reduces manual intervention and operating costs, ensures stable water quality compliance while reducing resource waste, achieves intelligent and refined control of medical wastewater treatment, improves the safety of wastewater treatment, facilitates the iterative upgrading of water pollution prevention and control technologies and equipment, and contributes to environmental protection.

[0039] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0040] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 This is a schematic diagram of a medical wastewater treatment monitoring and control system according to an embodiment of the present invention. Detailed Implementation

[0043] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. Example 1:

[0044] This invention provides a medical wastewater treatment monitoring and control system, with reference to Figure 1 ,include:

[0045] Acquisition module: Acquires basic information data and historical treatment data of the medical wastewater to be treated, and determines the treatment process data of the medical wastewater to be treated based on the basic information data;

[0046] Data Acquisition Module: Real-time acquisition of water quality data of medical wastewater to be treated; based on the real-time water quality data and treatment process data of the medical wastewater to be treated, real-time acquisition of equipment operation data of the medical wastewater to be treated.

[0047] Analysis module: Analyzes historical processing data and processing flow data to determine the historical flow data and processing strategy set for each processing flow in the processing flow data of the medical wastewater to be treated, and constructs a decision tree for each processing flow in the processing flow data;

[0048] Calculation module: Based on the historical process data, processing strategy set, and decision tree of each processing process in the processing flow data, calculate the strategy evaluation value of each historical processing strategy in the processing strategy set of each processing process;

[0049] Control module: Based on the historical process data of all processing processes in the processing process data, the strategy evaluation values ​​of all historical treatment strategies in the processing strategy set of all processing processes, real-time water quality data, and real-time equipment operation data, it determines and executes real-time control commands to realize real-time monitoring and control of the wastewater to be treated.

[0050] In this embodiment, the acquisition module comprehensively collects two types of key data: one type is basic information data of the medical wastewater to be treated. This type of data covers the core characteristics of the wastewater itself, the types of pollutants contained in the wastewater (such as organic matter, pathogens, heavy metals, etc.), the approximate concentration range of each pollutant, the type of hospital that generates the wastewater (such as general hospitals, infectious disease hospitals, specialized hospitals, etc., the composition of wastewater from different types of hospitals is significantly different, for example, the concentration of pathogens in wastewater from infectious disease hospitals is much higher than that from ordinary hospitals), and the final discharge destination of the wastewater (whether it is directly discharged into natural water bodies or enters urban sewage treatment plants for further treatment, which directly determines the stringency of the standards that the treated water quality needs to meet); the other type is historical treatment data, that is, all records accumulated when treating wastewater of similar types to the current wastewater to be treated in the past.

[0051] In this embodiment, the basic information data is analyzed in depth, and the treatment process data that the wastewater to be treated needs to go through is accurately determined by combining factors such as wastewater characteristics, hospital type and discharge destination. This is a series of orderly treatment stages, such as pretreatment (removal of large particulate impurities), biological treatment (decomposition of organic matter), disinfection treatment (killing pathogens), and advanced treatment (removal of residual pollutants).

[0052] In this embodiment, the acquisition module collects real-time water quality data of the medical wastewater to be treated through sensors deployed at different nodes of the wastewater treatment system. This data includes, but is not limited to, chemical oxygen demand (COD, reflecting the degree of organic pollution), pH value (acidity / alkalinity, affecting the effectiveness of treatment agents), suspended solids concentration (SS), residual chlorine content, total nitrogen content, and total phosphorus content. On the other hand, the acquisition module combines the real-time water quality data with the treatment process data determined by the acquisition module, specifically collecting real-time operating data of all equipment involved in each treatment process. For example, in the pretreatment process, the module collects the operating speed of the bar screen and the amount of impurities intercepted; in the biological treatment process, it collects the airflow of the aeration equipment, the temperature of the bioreactor, and the dissolved oxygen concentration; in the disinfection process, it collects the dosage of disinfectant and the power of the ultraviolet lamps. In this way, it ensures that the collected equipment operating data is highly matched with the currently ongoing treatment process, providing accurate real-time data for subsequent analysis and control.

[0053] In this embodiment, the analysis module performs a systematic and in-depth analysis of the historical processing data provided by the acquisition module and the processing flow data determined by the acquisition module. First, it extracts the historical flow data corresponding to each processing flow from the historical processing data; this data is a detailed record of all past processing iterations of that flow. Second, the analysis module sorts out all the processing strategies used by each processing flow from the historical flow data, forming a set of processing strategies for that flow. For example, in a disinfection process, it may include different strategies such as ultraviolet disinfection, chlorine disinfection, and ozone disinfection. Finally, a decision tree is constructed for each processing flow.

[0054] In this embodiment, the calculation module comprehensively evaluates each historical processing strategy in the processing strategy set of each processing process based on the historical process data, processing strategy set, and constructed decision tree of each processing process, and calculates the corresponding strategy evaluation value.

[0055] In this embodiment, the control module continuously monitors the effect after the command is executed, acquires new real-time water quality data and equipment operation data through the acquisition module, and continuously feeds back and adjusts the control commands to form a dynamic closed-loop control process, ensuring that the medical wastewater to be treated can be treated in real time and accurately, and ultimately reach the predetermined treatment standards.

[0056] The beneficial effects of the above technologies are as follows: By acquiring basic information data and historical treatment data of the medical wastewater to be treated, the treatment process data of the medical wastewater to be treated is determined; real-time water quality data and real-time equipment operation data of the medical wastewater to be treated are collected; historical treatment data and treatment process data are analyzed; a decision tree for each treatment process in the treatment process data is constructed; the strategy evaluation value of each historical treatment strategy in the treatment strategy set of each treatment process is calculated; and real-time control commands are determined and executed. This enables dynamic and precise correlation and adaptation between the treatment process, water quality, treatment strategy, and equipment operation, quantifies the strategy execution effect, improves the accuracy and economy of control, enhances the efficiency and stability of medical wastewater treatment, reduces manual intervention and operating costs, ensures stable water quality compliance while reducing resource waste, and achieves intelligent and refined control of medical wastewater treatment. Example 2:

[0057] Based on Example 1, a medical wastewater treatment monitoring and control system includes an acquisition module, comprising:

[0058] Basic information data unit: Acquire basic information data of the medical wastewater to be treated, including the characteristics of the wastewater, the type of hospital, and the discharge destination;

[0059] Processing flow data unit: Based on the basic information data of the medical wastewater to be treated, the processing flow data of the medical wastewater to be treated is determined. The processing flow data includes multiple processing flows and the processing standard data for each processing flow. The processing standard data includes the parameter standards for multiple water quality parameters.

[0060] Historical treatment data unit: Acquires historical treatment data of medical wastewater to be treated. The historical treatment data includes historical treatment sub-data from multiple historical treatments. The historical treatment sub-data includes historical equipment operation data, historical initial water quality data, historical treated water quality data, and historical treatment strategies for each treatment process.

[0061] In this embodiment, wastewater characteristics refer to the types, concentrations, and water volume variation patterns of pollutants in wastewater, such as COD, BOD, ammonia nitrogen, pathogenic microorganisms, heavy metals, and drug residues.

[0062] In this embodiment, the hospital types include general hospitals, infectious disease hospitals, and specialized hospitals, and the composition and degree of harm of the sewage generated by different types of hospitals are different.

[0063] In this embodiment, the discharge destinations include direct discharge into natural water bodies (such as rivers and lakes), reuse (such as for landscaping and toilet flushing), and entry into urban sewage treatment plants.

[0064] In this embodiment, the processing flow may include a pretreatment process (grid filtration, equalization tank homogenization), a main treatment process (biological treatment, coagulation sedimentation), and a deep treatment and disinfection process (filtration, ultraviolet / chemical disinfection), etc.

[0065] In this embodiment, specific water quality standards are set for each treatment process, specifically embodied in "parameter standards for multiple water quality parameters". For example, standards for the pretreatment stage may include "solid impurity diameter ≤ 5mm" and "pH stable at 6-9"; standards for the disinfection stage may include "fecal coliform count ≤ 500 CFU / L" and "residual chlorine concentration 0.5-3 mg / L". These standards ensure that the treatment effect of each process is measurable, avoiding the impact of non-compliance in previous processes on subsequent treatments.

[0066] In this embodiment, the historical device operation data record for each processing flow records the specific operating parameters of each device used during the processing.

[0067] In this embodiment, the historical initial water quality data represents the initial state of the wastewater before each treatment, such as COD, pH, pathogen concentration, etc. before treatment.

[0068] In this embodiment, historical water quality data represents the water quality results after each process, such as COD removal rate, pathogen inactivation rate, and whether the standards are met.

[0069] In this embodiment, the historical treatment strategy refers to the specific treatment methods used in the past for different water qualities and processes, such as "increasing the aeration rate by 30% when COD exceeds the standard" and "activating secondary disinfection when the pathogen concentration is high".

[0070] The beneficial effects of the above technologies are as follows: the acquisition of basic information data and historical treatment data of the medical wastewater to be treated, the determination of treatment process data of the medical wastewater to be treated based on the basic information data, the formulation of personalized treatment processes, the realization of data-driven intelligent processing, and the provision of data support for determining real-time control commands. Example 3:

[0071] Based on Example 1, a medical wastewater treatment monitoring and control system includes a data acquisition module, comprising:

[0072] Process Equipment Set Unit: Obtain the set of equipment for treating medical wastewater, and based on the set of equipment, all treatment processes, and the treatment standards for each process, determine the set of process equipment for each treatment process;

[0073] Real-time water quality data unit: Based on the first sensor group, real-time water quality data of the medical wastewater to be treated is collected in real time. The real-time water quality data includes the real-time parameter values ​​of multiple water quality parameters.

[0074] In this embodiment, a dedicated equipment set is matched for each treatment process. First, it comprehensively acquires all equipment used for treating medical wastewater, forming a complete equipment set. This set covers various stages from pretreatment to advanced treatment and disinfection, including bar screens, equalization tanks, aeration equipment, disinfection devices, and filtration systems. Then, considering all treatment processes and their respective treatment standards, the equipment in the set is screened and allocated. Different treatment processes have different treatment objectives and standards. For example, pretreatment processes require the removal of large particulate impurities, necessitating bar screens; biological treatment processes require aeration equipment and biological reactors to decompose organic matter; and disinfection processes, aimed at eliminating pathogens, may require ultraviolet sterilizers and chlorine dioxide generators. Based on the specific needs of each treatment process, equipment that meets its treatment standards is selected from the equipment set, forming a unique process equipment set for each process.

[0075] In this embodiment, the first sensor group includes a spectrophotometer for detecting COD, an ion-selective electrode sensor for detecting ammonia nitrogen, and an electrochemical sensor for detecting residual chlorine, which can monitor various key water quality parameters in medical wastewater in real time.

[0076] In this embodiment, real-time water quality data is collected using a first sensor group. This first sensor group consists of various sensors specifically designed to detect water quality parameters. The real-time water quality data includes multiple parameters, specifically: real-time values ​​of Chemical Oxygen Demand (COD), reflecting the degree of organic pollution in the wastewater; real-time values ​​of Biochemical Oxygen Demand (BOD), reflecting the content of organic matter that can be decomposed by microorganisms in the wastewater; real-time values ​​of pH, indicating the acidity or alkalinity of the wastewater; real-time values ​​of pathogen concentration, directly related to the biosafety of the wastewater; and possibly real-time values ​​of parameters such as ammonia nitrogen, total phosphorus, and residual chlorine.

[0077] The beneficial effects of the above technologies are: real-time acquisition of real-time water quality data of medical wastewater to be treated can achieve precise matching between equipment and processes, avoid equipment redundancy or insufficient functionality, improve process adaptability, and achieve flexible response and efficient operation of the wastewater treatment process. Example 4:

[0078] Based on Example 3, a medical wastewater treatment monitoring and control system, including a data acquisition module, further includes:

[0079] Real-time processing unit: Compare the real-time water quality data of the medical wastewater to be treated with the treatment standard data of each treatment process in the treatment process data to determine the real-time treatment process of the medical wastewater to be treated.

[0080] Real-time equipment operation sub-data unit: Based on the real-time treatment process of the medical wastewater to be treated and the set of process equipment for all treatment processes, real-time equipment operation sub-data of each device in the set of process equipment for the real-time treatment process of the medical wastewater to be treated is collected.

[0081] Real-time equipment operation data unit: Based on the real-time equipment operation data of all equipment in the process equipment set of the real-time treatment process for medical wastewater, the real-time equipment operation data of the medical wastewater to be treated is determined.

[0082] In this embodiment, real-time water quality data is compared in detail with the treatment standard data for each treatment process in the treatment process data. For example, if the COD value in the real-time water quality data is 180 mg / L, while the treatment standard for the pretreatment process requires the COD to be reduced to below 150 mg / L, then it means that the pretreatment process needs to be performed first. After the real-time water quality data after pretreatment meets the pretreatment standard, it is compared with the standard of the next treatment process (such as biological treatment), and so on, thereby determining which treatment process should be performed at the current time and ensuring that each step of the treatment is closely related to the real-time water quality status.

[0083] In this embodiment, based on the real-time processing flow and the set of process devices corresponding to the real-time processing flow, the real-time operating information of each device in the set of process devices corresponding to the real-time processing flow is collected in real time, i.e., real-time device operating sub-data. For example, if the real-time processing flow is the biological treatment in the main processing flow, and its corresponding set of process devices includes aerators, agitators, etc., then this unit will collect the real-time air volume and air pressure of the aerator, the real-time speed and operating current of the agitator, and other specific operating parameters of each device. This sub-data can accurately reflect the real-time working status of each device in the current processing flow.

[0084] In this embodiment, the real-time equipment operation sub-data of all devices in the process equipment set of the real-time processing flow are summarized and integrated to form the real-time equipment operation data of the medical wastewater to be treated. For example, the real-time operating parameters of all relevant equipment such as aerators and agitators are collected to form a data set that comprehensively reflects the overall operation of the equipment in the current processing flow.

[0085] The beneficial effects of the above technologies are as follows: Based on the real-time water quality data and treatment process data of the medical wastewater to be treated, the real-time equipment operation data of the medical wastewater to be treated can be collected in real time, which can realize the real-time linkage between the treatment process and the equipment status, break through the limitations of fixed processes, and improve the dynamic adaptability and precise control capability of wastewater treatment. Example 5:

[0086] Based on Example 4, a medical wastewater treatment monitoring and control system includes an analysis module, comprising:

[0087] Historical process data unit: The historical treatment data of the medical wastewater to be treated is preprocessed. Based on the historical treatment sub-data of all historical treatments in the preprocessed historical treatment data, the historical equipment operation data, historical initial water quality data, historical treated water quality data and historical treatment strategies of each treatment process are extracted to determine the historical process data of each treatment process of the medical wastewater to be treated.

[0088] Historical Equipment Feature Vector Unit: For each treatment process of medical wastewater to be treated, feature extraction is performed on the historical equipment operation data of each equipment in the historical process data of each historical treatment, and the historical equipment feature vector of each equipment in the historical process data of each treatment process is determined.

[0089] Historical initial water quality feature vector and historical treated water quality feature vector unit: feature extraction is performed on the historical initial water quality data and historical treated water quality data of each historical treatment process in the historical process data of each treatment process of the medical wastewater to be treated, and the historical initial water quality feature vector and historical treated water quality feature vector of each historical treatment process in the historical process data of each treatment process are determined.

[0090] Processing strategy set unit: Based on the historical processing strategies of all previous processes in the historical process data of each processing flow, determine the processing strategy set for each processing flow;

[0091] Decision tree unit: Based on the historical initial water quality feature vectors of all historical treatments in the historical process data of each treatment process as input, and based on the historical treatment strategies of all historical treatments in the historical process data of each treatment process as output, a decision tree is constructed for each treatment process in the process data.

[0092] In this embodiment, historical processing data is preprocessed, such as removing outliers and filling in missing data, to make the data more standardized and reliable. Then, from the preprocessed historical processing sub-data, historical equipment operation data, historical initial water quality data, historical treated water quality data, and historical processing strategies corresponding to each processing step are extracted. This data is then integrated to form historical process data for each processing step. This ensures that each processing step has a complete historical record, clearly showing the past processing status of that step.

[0093] In this embodiment, features of the historical equipment operation data for each historical process are extracted from the historical process data of each processing flow. For example, for aeration equipment, features such as average air volume, air volume fluctuation range, and maximum air pressure during its historical operation may be extracted, and these features are combined into a historical equipment feature vector for that equipment. The feature vector can more concisely reflect the operating characteristics of the equipment in historical processing.

[0094] In this embodiment, feature extraction is performed on the historical initial water quality data and historical treated water quality data for each historical process in the historical process data of each treatment process. For the historical initial water quality data, features such as the average COD, the fluctuation range of pH value, and the peak value of pathogen concentration may be extracted to form a historical initial water quality feature vector; similarly, the historical treated water quality data is processed in a similar way to obtain a historical treated water quality feature vector.

[0095] In this embodiment, historical processing strategies used in all previous processes are collected from the historical process data of each processing flow. After removing duplicate strategies, a set of processing strategies for that processing flow is formed. This set contains all effective strategies used by that process in the past.

[0096] In this embodiment, the initial water quality feature vectors (such as the feature values ​​of indicators such as COD, pH, and pathogen concentration) of all historical treatments are first extracted from the historical process data of a certain treatment process as input, and the corresponding historical treatment strategies are used as output. The CART algorithm is adopted to select the optimal partitioning features and thresholds by calculating the Gini impurity (such as first partitioning according to "COD≤200mg / L", and then further partitioning the subset that meets the condition according to "pH≥6.8"). The nodes are recursively split until all samples belong to the same strategy or the termination condition is reached (such as sample size <5). Finally, a tree structure is formed with leaf nodes corresponding to specific treatment strategies. For example, the decision tree of a certain pretreatment process may present a hierarchical partition of "root node → COD≤150mg / L → pH≥7.0 → leaf node (strategy 1)", "root node → COD≤150mg / L → pH<7.0 → leaf node (strategy 2)", "root node → COD>150mg / L → leaf node (strategy 3)", and each leaf node is clearly associated with a specific treatment strategy.

[0097] The beneficial effects of the above technologies are as follows: by analyzing historical processing data and processing flow data, the historical flow data and set of processing strategies for each processing flow in the processing flow data of the medical wastewater to be treated are determined, and a decision tree for each processing flow in the processing flow data is constructed, so as to achieve precise correlation between processing flow, equipment and strategies, break through the reliance on traditional experience, and make wastewater treatment decisions more data-supported, dynamic, adaptive and scientific. Example 6:

[0098] Based on Example 5, a medical wastewater treatment monitoring and control system includes a computing module, comprising:

[0099] Path condition unit: Traverse the decision tree of each processing flow to determine all leaf nodes of each historical processing strategy in the processing strategy set of each processing flow and the path condition of each leaf node.

[0100] Initial water quality characteristic range set unit: Based on the path conditions of all leaf nodes of each historical treatment strategy in the treatment strategy set of each treatment process, determine the initial water quality characteristic range set of each historical treatment strategy in the treatment strategy set of each treatment process;

[0101] Historical Strategy Data Unit: Determine whether the historical initial water quality feature vectors of each historical treatment in the historical process data of each treatment process satisfy the set of initial water quality feature ranges of each historical treatment strategy in the treatment strategy set of each treatment process. Based on the historical initial water quality feature vectors, historical treatment water quality feature vectors, and historical equipment feature vectors of all equipment of all historical treatments that meet the requirements, determine the historical strategy data of each historical treatment strategy in the treatment strategy set of each treatment process. Among them, the historical strategy data includes historical treatment sub-data of multiple historical treatments.

[0102] Strategy Evaluation Value Unit: Based on the historical initial water quality feature vectors, historical treatment water quality feature vectors, historical equipment feature vectors of all equipment, and historical strategy data of all historical treatment strategies in the treatment strategy set of each treatment process for each historical treatment in the historical strategy data of each historical treatment strategy in the treatment strategy set of each treatment process, calculate the sewage treatment effect value, equipment stability value, and cost value for each historical treatment in the historical strategy data of each historical treatment strategy in the treatment strategy set of each treatment process, and calculate the strategy evaluation value of each historical treatment strategy in the treatment strategy set of each treatment process.

[0103] In this embodiment, check each decision tree constructed for each treatment process one by one, carefully sort out the structure of the decision tree, find all the leaf nodes corresponding to each historical treatment strategy in the treatment strategy set of each treatment process in the decision tree. Based on these path conditions, further organize and summarize the initial water quality feature ranges applicable to each historical treatment strategy to form a set of initial water quality feature ranges. For example: The type of hospital for the medical sewage to be treated is an infectious disease hospital, the treatment process is an enhanced treatment process, and a certain treatment strategy is ozone disinfection + secondary filtration. In the decision tree of the enhanced treatment process, the treatment strategy of ozone disinfection + secondary filtration includes leaf node 1 and leaf node 2. Leaf node 1: The path conditions are "COD ≤ 300mg / L" and "total coliforms ≤ CFU / L" and "pH ∈ [6.5, 8.0]"; Leaf node 2: The path conditions are "COD ∈ (300, 500]mg / L" and "total coliforms ≤ CFU / L" and "pH ∈ [6.5, 8.0]" and "initial chlorine residual concentration ≤ 0.3mg / L". Combine the path conditions of leaf node 1 and leaf node 2 to obtain the conditional expression of the initial water quality features applicable to the treatment strategy of ozone disinfection + secondary filtration in the enhanced treatment process as (COD ≤ 300mg / L and total coliforms ≤ CFU / L and pH ∈ [6.5, 8.0]) OR (300mg / L < COD ≤ 500mg / L and total coliforms ≤ CFU / L and pH∈[6.5,8.0] and initial residual chlorine concentration≤0.3mg / L); the corresponding initial water quality characteristic range set is: COD: [0,500]mg / L (where above 300mg / L must meet the initial residual chlorine concentration constraint); fecal coliform count: [0, CFU / L; pH: [6.5, 8.0]; Initial residual chlorine concentration: ≤0.3 mg / L only when COD>300 mg / L.

[0104] In this embodiment, the historical initial water quality feature vectors of each historical treatment process are checked in the historical process data of each treatment process to determine whether they conform to the initial water quality feature range set of each historical treatment strategy in the treatment strategy set of that process. For all historical treatments corresponding to historical initial water quality feature vectors that meet the conditions, their historical initial water quality feature vectors, historical treatment water quality feature vectors, and historical equipment feature vectors of all devices are extracted. These data are integrated to form the historical strategy data for each historical treatment strategy. This historical strategy data contains detailed data from multiple historical treatments.

[0105] In this embodiment, the strategy evaluation value unit calculates the wastewater treatment effect value, equipment stability value, and cost value for each historical treatment strategy in the historical strategy data of each historical treatment strategy in the treatment strategy set of each treatment process, and calculates the strategy evaluation value for each historical treatment strategy in the treatment strategy set of each treatment process. The calculation formulas for the wastewater treatment effect value, equipment stability value, cost value, and strategy evaluation value can be expressed as follows:

[0106] ;

[0107] in, This represents the strategy evaluation value of the j-th historical processing strategy in the set of processing strategies for the i-th processing flow. This represents the shared factor of the k-th historical processing in the historical strategy data of the j-th historical processing strategy in the processing strategy set of the i-th processing flow. Let represent the k-th historical processing instance of the j-th historical processing strategy in the processing strategy set of the i-th processing flow, and let iN2 represent the number of historical processing strategies in the processing strategy set of the i-th processing flow. This represents the wastewater treatment effect value of the k-th historical treatment in the historical strategy data of the j-th historical treatment strategy in the set of treatment strategies for the i-th treatment process. This represents the average wastewater treatment effect of all historical treatments within the historical strategy data of the j-th historical treatment strategy in the set of treatment strategies for the i-th treatment process. This represents the standard deviation of the wastewater treatment effect values ​​for all historical treatments within the historical strategy data of the j-th historical treatment strategy in the set of treatment strategies for the i-th treatment process. This represents the device stability value of the k-th historical processing in the historical strategy data of the j-th historical processing strategy in the processing strategy set of the i-th processing flow. Let represent the cost value of the k-th historical treatment in the historical strategy data of the j-th historical treatment strategy in the treatment strategy set of the i-th treatment process. Ae, As, and Ac represent the wastewater treatment adjustment factor, equipment stability adjustment factor, and cost adjustment factor, respectively. ijN1 represents the number of historical treatments in the historical strategy data of the j-th historical treatment strategy in the treatment strategy set of the i-th treatment process. This represents the k-th historical processing instance within the historical strategy data of the j-th historical processing strategy in the processing strategy set of the i-th processing flow. This represents the historical strategy data of the m-th historical processing strategy in the set of processing strategies for the i-th processing flow. This represents the historical water quality feature vector of the k-th historical treatment in the historical strategy data of the j-th historical treatment strategy in the set of treatment strategies for the i-th treatment process. This represents the historical initial water quality feature vector of the k-th historical treatment in the historical strategy data of the j-th historical treatment strategy in the set of treatment strategies for the i-th treatment process. Let b be the feature value of the device feature in the historical device feature vector of the a-th device in the k-th historical processing of the j-th historical processing strategy in the processing strategy set of the i-th processing flow. Let represent the average feature value of the b-th device feature in the historical device feature vector of the a-th device in the historical policy data of all historical processing strategies in the processing strategy set of the i-th processing flow. Let iaN3 represent the standard deviation of the eigenvalues ​​of the b-th device feature in the historical device feature vector of the a-th device in the historical strategy data of all historical treatment strategies in the i-th treatment process set, VT represent the actual treatment volume of the medical wastewater to be treated. Let represent the rated processing volume of the 'a'th device in the 'i'th processing flow, and let iN4 represent the number of devices in the process device set of the 'i'th processing flow. This represents the drug cost of the j-th historical processing strategy in the set of processing strategies for the i-th processing flow. This represents the operating cost of the a-th device in the j-th historical processing strategy set of the i-th processing flow.

[0108] In this embodiment, This represents the number of times the k-th historical processing occurs in the historical strategy data of the j-th historical processing strategy in the processing strategy set of the i-th processing flow.

[0109] In this embodiment, when the k-th historical processing in the historical strategy data of the j-th historical processing strategy in the processing strategy set of the i-th processing flow belongs to the historical strategy data of the m-th historical processing strategy in the processing strategy set of the i-th processing flow, the first indicator function... The value is 1; otherwise, the value is 0.

[0110] In this embodiment, The shared weight is used to reduce the weight of the kth historical processing in the historical strategy data of the jth historical processing strategy in the processing strategy set of the i-th processing flow.

[0111] In this embodiment, ln(1+ijN1) is used to suppress the score inflation caused by "fake big data". For example, when ijN1 is too small (such as only 1-2 data points), accidental factors may cause the strategy evaluation value to be inflated. ln(1+ijN1) can significantly suppress the strategy evaluation value.

[0112] In this embodiment, The wastewater treatment effect of all historical treatments in the historical strategy data of the j-th historical treatment strategy in the set of treatment strategies for the i-th treatment process is used to enhance the wastewater treatment effect.

[0113] The beneficial effects of the above technologies are as follows: Based on the historical process data, processing strategy set, and decision tree of each processing process in the processing process data, the strategy evaluation value of each historical processing strategy in the processing strategy set of each processing process can be calculated. This can achieve dynamic matching between strategies and water quality, break through the blindness of traditional strategy selection, quantify the strategy execution effect, improve the comprehensiveness of strategy evaluation value, provide a scientific quantitative basis for the selection of wastewater treatment strategies, and improve the accuracy and economy of decision-making. Example 7:

[0114] Based on Example 6, a medical wastewater treatment monitoring and control system includes a computing module, comprising:

[0115] Process control model unit: Based on the processing standard data, historical process data, and policy evaluation values ​​of all historical processing strategies in the processing strategy set of each processing process in the processing process data, a process control model for each processing process in the processing process data is constructed.

[0116] Real-time control model unit: Based on the real-time treatment process of the medical wastewater to be treated, determine the real-time control model of the medical wastewater to be treated;

[0117] Real-time control command unit: Input the real-time water quality data and real-time equipment operation data of the medical wastewater to be treated into the real-time control model of the medical wastewater to be treated, determine the real-time control command of the medical wastewater to be treated based on the output results of the real-time control model, and execute the real-time control command to realize the real-time monitoring and control of the medical wastewater to be treated.

[0118] In this embodiment, a process control model is constructed based on the standard data of each treatment process, combined with historical process data and the strategy evaluation values ​​of all historical treatment strategies in the process's strategy set. For example, by dividing water quality intervals (e.g., low / medium / high based on initial COD), the optimal strategy and corresponding equipment parameter range are matched for each interval (e.g., strategy A is matched for the low COD interval, corresponding to a dosing pump flow rate of 10-15 L / h and an aeration intensity of 1.2-1.5 mg / L). Simultaneously, feedback adjustment logic is embedded (e.g., when the treated water quality deviates from the standard, equipment parameters are adjusted according to historical correction records; for example, the dosing pump flow rate is increased proportionally when COD exceeds the standard). This ultimately forms a closed-loop model of "initial water quality → optimal strategy → equipment parameters → effect feedback → parameter correction," ensuring that the model output accurately points to the control logic that meets the treatment standards. The constructed process control model clearly defines how to adjust the treatment strategy and equipment parameters under different water quality conditions and equipment operating states to ensure that the treatment process always proceeds in a direction that meets the treatment standards, providing a theoretical framework and model support for real-time control.

[0119] In this embodiment, based on the real-time treatment process of the medical wastewater to be treated, that is, the currently ongoing treatment steps, the model corresponding to the real-time treatment process is selected from the process control models previously constructed for each treatment process, and this model is used as the real-time control model.

[0120] In this embodiment, real-time water quality data and real-time equipment operation data of the medical wastewater to be treated are input into a real-time control model. The real-time control model analyzes and processes this input data, and outputs real-time control commands based on its internal logic and algorithms. These commands, such as adjusting the operating parameters of a device or switching treatment strategies, are then sent to the corresponding devices for execution. This achieves real-time monitoring and precise control of the medical wastewater, ensuring that the treatment process is efficient and stable, and ultimately enabling the treated wastewater to meet predetermined standards.

[0121] The beneficial effects of the above technologies are as follows: based on the historical process data of all processing processes in the processing process data, the strategy evaluation values ​​of all historical treatment strategies in the set of treatment strategies of all processing processes, real-time water quality data, and real-time equipment operation data, real-time control commands are determined and executed to realize real-time monitoring and control of the wastewater to be treated. This can realize a closed loop from historical data to real-time control, break through the limitations of traditional static control, and improve the dynamic accuracy and efficiency of wastewater treatment. Example 8:

[0122] The present invention provides a medical wastewater treatment monitoring and control device for executing any one of the medical wastewater treatment monitoring and control systems in Examples 1 to 7.

[0123] The beneficial effects of the above technologies are as follows: By acquiring basic information data and historical treatment data of the medical wastewater to be treated, the treatment process data of the medical wastewater to be treated is determined; real-time water quality data and real-time equipment operation data of the medical wastewater to be treated are collected; historical treatment data and treatment process data are analyzed; a decision tree for each treatment process in the treatment process data is constructed; the strategy evaluation value of each historical treatment strategy in the treatment strategy set of each treatment process is calculated; and real-time control commands are determined and executed. This enables dynamic and precise correlation and adaptation between the treatment process, water quality, treatment strategy, and equipment operation, quantifies the strategy execution effect, improves the accuracy and economy of control, enhances the efficiency and stability of medical wastewater treatment, reduces manual intervention and operating costs, ensures stable water quality compliance while reducing resource waste, and achieves intelligent and refined control of medical wastewater treatment.

[0124] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A medical wastewater treatment monitoring control system, characterized by, include: Acquisition module: Acquires basic information data and historical treatment data of the medical wastewater to be treated, and determines the treatment process data of the medical wastewater to be treated based on the basic information data; Data Acquisition Module: Real-time acquisition of water quality data of medical wastewater to be treated; based on the real-time water quality data and treatment process data of the medical wastewater to be treated, real-time acquisition of equipment operation data of the medical wastewater to be treated. Analysis module: Analyzes historical processing data and processing flow data to determine the historical flow data and processing strategy set for each processing flow in the processing flow data of the medical wastewater to be treated, and constructs a decision tree for each processing flow in the processing flow data; Calculation module: Based on the historical process data, processing strategy set, and decision tree of each processing process in the processing flow data, calculate the strategy evaluation value of each historical processing strategy in the processing strategy set of each processing process; Control module: Based on the historical process data of all processing processes in the processing process data, the strategy evaluation values ​​of all historical treatment strategies in the processing strategy set of all processing processes, real-time water quality data, and real-time equipment operation data, it determines and executes real-time control commands to realize real-time monitoring and control of the wastewater to be treated. The analysis module includes: Historical process data unit: The historical treatment data of the medical wastewater to be treated is preprocessed. Based on the historical treatment sub-data of all historical treatments in the preprocessed historical treatment data, the historical equipment operation data, historical initial water quality data, historical treated water quality data and historical treatment strategies of each treatment process are extracted to determine the historical process data of each treatment process of the medical wastewater to be treated. Processing strategy set unit: Based on the historical processing strategies of all previous processes in the historical process data of each processing flow, determine the processing strategy set for each processing flow; Decision tree unit: Based on the historical initial water quality feature vectors of all historical treatments in the historical process data of each treatment process as input, and based on the historical treatment strategies of all historical treatments in the historical process data of each treatment process as output, a decision tree is constructed for each treatment process in the process data. The calculation module includes: Path condition unit: Traverse the decision tree of each processing flow to determine all leaf nodes of each historical processing strategy in the processing strategy set of each processing flow and the path condition of each leaf node. Initial water quality characteristic range set unit: Based on the path conditions of all leaf nodes of each historical treatment strategy in the treatment strategy set of each treatment process, determine the initial water quality characteristic range set of each historical treatment strategy in the treatment strategy set of each treatment process; Historical strategy data unit: Determine whether the historical initial water quality feature vector of each historical treatment in the historical process data of each treatment process satisfies the initial water quality feature range set of each historical treatment strategy in the treatment strategy set of each treatment process. Based on the historical initial water quality feature vectors of all historical treatments that satisfy the historical initial water quality feature vectors, the historical treatment water quality feature vectors, and the historical equipment feature vectors of all equipment, determine the historical strategy data of each historical treatment strategy in the treatment strategy set of each treatment process. The historical strategy data includes historical treatment sub-data of multiple historical treatments. Strategy Evaluation Value Unit: Based on the historical initial water quality feature vector, historical treated water quality feature vector, historical equipment feature vector of all equipment, and historical strategy data of all historical treatment strategies in the treatment strategy set of each treatment process, calculate the wastewater treatment effect value, equipment stability value, cost value, and sharing factor of each historical treatment strategy in the historical strategy data of each historical treatment strategy in the treatment strategy set of each treatment process. Also calculate the strategy evaluation value of each historical treatment strategy in the treatment strategy set of each treatment process. The sharing factor of each historical treatment is the weight of that historical treatment, representing the penalty value received by the historical treatment sub-data corresponding to that historical treatment due to being referenced by multiple historical treatment strategies.

2. The medical wastewater treatment monitoring control system according to claim 1, wherein The acquisition module includes: Basic information data unit: Acquire basic information data of the medical wastewater to be treated, including the characteristics of the wastewater, the type of hospital, and the discharge destination; Processing flow data unit: Based on the basic information data of the medical wastewater to be treated, the processing flow data of the medical wastewater to be treated is determined. The processing flow data includes multiple processing flows and the processing standard data for each processing flow. The processing standard data includes the parameter standards for multiple water quality parameters. Historical treatment data unit: Acquires historical treatment data of medical wastewater to be treated. The historical treatment data includes historical treatment sub-data from multiple historical treatments. The historical treatment sub-data includes historical equipment operation data, historical initial water quality data, historical treated water quality data, and historical treatment strategies for each treatment process.

3. The medical wastewater treatment monitoring control system according to claim 2, wherein The data acquisition module includes: Process Equipment Set Unit: Obtain the set of equipment for treating medical wastewater, and based on the set of equipment, all treatment processes, and the treatment standards for each process, determine the set of process equipment for each treatment process; Real-time water quality data unit: Based on the first sensor group, real-time water quality data of the medical wastewater to be treated is collected in real time. The real-time water quality data includes the real-time parameter values ​​of multiple water quality parameters.

4. The medical wastewater treatment monitoring control system according to claim 3, wherein The data acquisition module also includes: Real-time processing unit: Compare the real-time water quality data of the medical wastewater to be treated with the treatment standard data of each treatment process in the treatment process data to determine the real-time treatment process of the medical wastewater to be treated. Real-time equipment operation sub-data unit: Based on the real-time treatment process of the medical wastewater to be treated and the set of process equipment for all treatment processes, real-time equipment operation sub-data of each device in the set of process equipment for the real-time treatment process of the medical wastewater to be treated is collected. Real-time equipment operation data unit: Based on the real-time equipment operation data of all equipment in the process equipment set of the real-time treatment process for medical wastewater, the real-time equipment operation data of the medical wastewater to be treated is determined.

5. The medical wastewater treatment monitoring control system according to claim 4, wherein The analysis module also includes: Historical Equipment Feature Vector Unit: For each treatment process of medical wastewater to be treated, feature extraction is performed on the historical equipment operation data of each equipment in the historical process data of each historical treatment, and the historical equipment feature vector of each equipment in the historical process data of each treatment process is determined. Historical initial water quality feature vector and historical treated water quality feature vector unit: feature extraction is performed on the historical initial water quality data and historical treated water quality data of each historical treatment in the historical process data of each treatment process of the medical wastewater to be treated, and the historical initial water quality feature vector and historical treated water quality feature vector of each historical treatment in the historical process data of each treatment process are determined.

6. The medical wastewater treatment monitoring control system according to claim 1, wherein The calculation module includes: Process control model unit: Based on the processing standard data, historical process data, and policy evaluation values ​​of all historical processing strategies in the processing strategy set of each processing process in the processing process data, a process control model for each processing process in the processing process data is constructed. Real-time control model unit: Based on the real-time treatment process of the medical wastewater to be treated, determine the real-time control model of the medical wastewater to be treated; Real-time control command unit: Input the real-time water quality data and real-time equipment operation data of the medical wastewater to be treated into the real-time control model of the medical wastewater to be treated, determine the real-time control command of the medical wastewater to be treated based on the output results of the real-time control model, and execute the real-time control command to realize the real-time monitoring and control of the medical wastewater to be treated.

7. A medical wastewater treatment monitoring control device characterized by comprising: Used to implement any one of the medical wastewater treatment monitoring and control systems of claims 1 to 6.

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