Purification treatment monitoring system for liquid discharged after ozone gynecological treatment

Abstract

The invention discloses a purification treatment monitoring system for liquid discharged after ozone gynecological treatment, and relates to the technical field of medical waste liquid treatment monitoring, and the purification treatment monitoring system comprises a content analysis unit which is used for calculating a pollutant concentration change characteristic index based on monitoring data of the liquid discharged after ozone gynecological treatment, a pollutant degradation curve is fitted through a piecewise linear equation, and a pollutant content analysis model is established; the characteristic evaluation unit is used for generating a pollutant degradation rate characteristic matrix by utilizing an output result of the pollutant content analysis model and combining a chemical reaction kinetics theory, determining degradation process parameters and constructing a treatment effect evaluation model; and the monitoring judgment unit is used for obtaining a monitoring result of the discharged liquid through early warning threshold detection according to an output result of the treatment effect evaluation model. According to the invention, accurate monitoring and intelligent control of the special medical waste liquid treatment process are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of medical waste liquid treatment and monitoring. Specifically, it relates to a purification treatment and monitoring system for the liquid discharged after ozone gynecological treatment. Background Art

[0002] Ozone gynecological treatment is a new medical method. Through the strong oxidizing property and biological activity of ozone, it treats diseases such as gynecological inflammation and cervical erosion. During the treatment process, special medical waste liquid containing components such as body tissues, pathogenic microorganisms, and drug residues will be generated. This kind of waste liquid is different from conventional medical wastewater and has the characteristics of complex composition, diverse types of pollutants, and greater harmfulness. If not properly treated, it will not only cause environmental pollution but also may bring biosafety risks. Therefore, scientific and effective purification treatment and monitoring of this kind of special medical waste liquid are not only an important part of the standardized management of medical institutions but also an inevitable requirement for ensuring environmental safety and public health. With the development of medical technology and the improvement of environmental protection requirements, it has become an urgent task to establish a complete special medical waste liquid treatment and monitoring system.

[0003] Currently, the waste liquid treatment and monitoring methods commonly used in medical institutions mainly draw on the technical routes of industrial wastewater treatment, including treatment processes such as physical filtration, chemical oxidation, and biodegradation, and monitor through conventional indicators such as pH value, COD, and residual chlorine. Some medical institutions have also introduced online monitoring systems to achieve automatic control and data collection of the treatment process. These systems usually have multiple sensor nodes. By real-time collecting water quality parameters and combining with preset treatment standards, they automatically adjust treatment process parameters.

[0004] However, the existing treatment and monitoring methods have obvious limitations when facing the special medical waste liquid after ozone gynecological treatment: First, the conventional monitoring indicators and evaluation standards are difficult to comprehensively reflect the special properties of such waste liquid, resulting in insufficient monitoring accuracy and inability to accurately evaluate the treatment effect; Second, the existing data processing models are too simple and lack the ability to identify and analyze special pollutant components, making it difficult to make intelligent treatment decisions according to the dynamic changes of waste liquid characteristics; In addition, the adaptive ability of the system is insufficient, and it cannot automatically optimize treatment parameters according to the characteristic differences of different batches of waste liquid, easily resulting in unstable treatment effects. The existence of these problems not only affects the treatment efficiency but also increases the operation cost and safety risks, and there is an urgent need to develop more professional and accurate treatment and monitoring methods.

[0005] Regarding the problems in the related technology, no effective solution has been proposed yet. Summary of the Invention

[0006] In view of the problems in the related art, the present invention proposes a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment, which has the advantages of accurately monitoring and intelligently controlling the treatment process of special medical waste liquid by constructing a pollutant content analysis model and a treatment effect evaluation model based on chemical reaction kinetics, thereby solving the problems of insufficient monitoring accuracy and limited data processing ability in the prior art.

[0007] To this end, the specific technical solution adopted by the present invention is as follows: A purification treatment monitoring system for the discharged liquid after ozone gynecological treatment, comprising: A content analysis unit, configured to calculate a pollutant concentration change characteristic index based on the monitoring data of the discharged liquid after ozone gynecological treatment, fit the degradation curve of the pollutant by a piecewise linear equation, and establish a pollutant content analysis model; A characteristic evaluation unit, configured to generate a pollutant degradation rate characteristic matrix by using the output result of the pollutant content analysis model and combining with the chemical reaction kinetics theory, and determine the degradation process parameters to construct a treatment effect evaluation model; A monitoring and determination unit, configured to obtain the monitoring result of the discharged liquid through early warning threshold detection according to the output result of the treatment effect evaluation model; The content analysis unit includes: A characteristic analysis module, configured to determine a piecewise interval according to the concentration difference of the monitoring data, calculate a degradation weight coefficient, obtain a time-varying curve through weighted accumulation, and determine a degradation time node; specifically including: Calculating the pollutant concentration difference between adjacent time points of the monitoring data, determining a piecewise interval according to the difference change, and identifying the fluctuation period to obtain a candidate point set; Constructing a pollutant degradation weight function based on the candidate point set and solving to obtain the degradation weight coefficient at each moment; Using the degradation weight coefficient, weighted summing the pollutant concentration change amount in each piecewise interval to obtain a time-varying cumulative curve, and determining the degradation time node through slope change analysis.

[0008] Further, the content analysis unit is connected to the monitoring and determination unit through the characteristic evaluation unit; The content analysis unit further includes: An index construction module, configured to construct a feature space and map the degradation monitoring index based on the degradation time node, calculate the feature vector of different degradation monitoring indexes to obtain a pollutant concentration change characteristic index, and establish a grading standard for the characteristic index; A fitting and modeling module, configured to design the constraint conditions of the piecewise linear equation set according to the grading result of the pollutant concentration change characteristic index, introduce a continuity correction term to obtain a degradation curve equation, and construct a pollutant content analysis model; Among them, the degradation monitoring indicators include pH value, ozone residue, chemical oxygen demand, and turbidity.

[0009] Furthermore, the expression of the pollution degradation weight function is: ; In the formula, WP ( t ) is the pollution degradation weight value at t moment, A is the pollution degradation basic weight coefficient, B is the pollution degradation attenuation factor, σ ( t ) is the pollutant concentration fluctuation intensity at t moment, σ ( t -1) is the pollutant concentration fluctuation intensity at t -1 moment, | σ ( t ) - σ ( t -1)| represents the absolute difference in pollutant concentration fluctuation intensity between adjacent moments.

[0010] Furthermore, based on the degradation time nodes, a feature space is constructed and the degradation monitoring indicators are mapped. By calculating the feature vectors of different degradation monitoring indicators, the pollutant concentration change characteristic index is obtained, and the grading standard of the characteristic index is established, including: According to the time intervals divided by the degradation time nodes, a degradation feature space is constructed, and different degradation monitoring indicators are mapped to the degradation feature space; Calculate the feature vectors of different degradation monitoring indicators in each time interval to obtain the pollutant concentration change characteristic index; Analyze the distribution law of the pollutant concentration change characteristic index, and establish the grading standard of the pollutant concentration change characteristic index by setting grading thresholds.

[0011] Furthermore, the expression of the pollutant concentration change characteristic index is: ; In the formula, I ( t ) is the pollutant concentration change characteristic index at t moment, Δ pH s is the standardized change amount of pH value in this time interval, Δ O 3s is the standardized change amount of ozone residue in this time interval, Δ COD s is the standardized change amount of chemical oxygen demand in this time interval, Δ Turs is the normalized change in turbidity within this time interval; β 1 is pH the value weight coefficient, β 2 is the ozone residue weight coefficient, β 3 is COD the value weight coefficient, β 4 is the turbidity weight coefficient.

[0012] Furthermore, according to the classification results of the pollutant concentration change characteristic index, design the constraint conditions of the piecewise linear equations, and introduce a continuity correction term to obtain the degradation curve equation, and construct a pollutant content analysis model including: Based on the classification results of the pollutant concentration change characteristic index, establish piecewise linear equations, and design constraint conditions according to the degradation characteristics of each time interval; According to the time node characteristics of the piecewise linear equations, use the exponential decay function to construct a continuity correction term to ensure the smooth transition of the degradation curve; Combine the constraint conditions and the continuity correction term, solve the piecewise linear equations to obtain the degradation curve equation, and introduce a time-varying correction factor to generate a pollutant content analysis model.

[0013] Furthermore, the constraint conditions designed according to the degradation characteristics of each time interval include: Based on the pollutant content at the initial moment and the termination moment, set the boundary constraint conditions of the piecewise linear equations, and match the pollutant content values of the equation at the starting point and the termination point with the actual measured values; According to the characteristic index values of each time interval, set the slope constraint range, limit the absolute value of the slope in the low degradation interval below the first threshold, limit the absolute value of the slope in the high degradation interval above the second threshold, and limit the absolute value of the slope in the medium degradation interval between the third threshold and the fourth threshold; Through the experimental calibration method, determine the threshold parameters of the slope constraints in each interval, and construct the slope constraint equations for each time interval; Among them, the classification results of the pollutant concentration change characteristic index include a low degradation interval, a medium degradation interval, and a high degradation interval.

[0014] Furthermore, the characteristic evaluation unit includes: A rate characteristic module, which is used to calculate the pollutant degradation rate in each time interval based on the output data of the pollutant content analysis model, and establish a degradation kinetic equation in combination with the reaction kinetics theory to generate a rate characteristic matrix representing the pollutant degradation characteristics; A parameter feature module, which is used to analyze the degradation characteristics of the rate feature matrix, determine the pollutant degradation process parameters, and construct a degradation efficiency evaluation index; An effect evaluation module, which is used to introduce time series fluctuation compensation according to the degradation efficiency evaluation index and the pollutant degradation process parameters, and construct a treatment effect evaluation model.

[0015] Furthermore, based on the output data of the pollutant content analysis model, calculate the pollutant degradation rate in each time interval, and establish a degradation kinetic equation in combination with the reaction kinetics theory, and generate a rate feature matrix characterizing the pollutant degradation characteristics, including: According to the output results of the pollutant content analysis model, calculate the instantaneous degradation rate and average degradation rate of pollutants in each time interval; Using the pollutant degradation rate in each time interval, establish a degradation kinetic equation based on the chemical reaction kinetics theory, and considering the condition of ozone excess, simplify it to a pseudo-first-order reaction kinetic equation, and determine the apparent rate constant through linear regression analysis; Based on the pseudo-first-order reaction kinetic equation and the apparent rate constant, construct a rate feature matrix including the apparent rate constant, activation energy, and average degradation rate.

[0016] Furthermore, analyze the degradation characteristics of the rate feature matrix, determine the pollutant degradation process parameters, and construct a degradation efficiency evaluation index, including: According to the rate feature matrix, calculate the kinetic stability index, degradation efficiency index, and energy utilization index; Use the fuzzy comprehensive evaluation method to determine the weight coefficients of each index and establish the pollutant degradation process parameters; Based on the pollutant degradation process parameters, construct a degradation efficiency evaluation index in combination with the entropy weight method.

[0017] The beneficial effects of the present invention are: (1) The present invention innovatively designs a sewage reduction weight function and a characteristic index construction method through the content analysis unit. This method is aimed at special components such as organic secretions, exfoliated cells, and ozone residues in the discharged liquid after ozone gynecological treatment. First, it determines the segmented intervals by calculating the difference in pollutant concentrations at adjacent time points of the monitoring data, and identifies the fluctuation periods to obtain the candidate point set. Then, it constructs a sewage reduction weight function based on the candidate point set, obtains the time-varying curve through weighted accumulation, and determines the degradation time node. On this basis, the system can accurately calculate the characteristic vectors of multiple degradation monitoring indicators including pH value, ozone residue, chemical oxygen demand, and turbidity, obtain the characteristic index of pollutant concentration change, and fit the degradation curve through a piecewise linear equation. This monitoring method specifically for the special medical waste liquid after ozone gynecological treatment significantly improves the monitoring accuracy of various pollutant components in the waste liquid, realizes the identification and analysis of the changes in pollutant components, and provides reliable data support for the optimization of the treatment process of the discharged liquid after ozone gynecological treatment.

[0018] (2) The characteristic evaluation unit of the present invention is based on the theory of chemical reaction kinetics. First, it calculates the instantaneous degradation rate and average degradation rate of pollutants in each time interval, simplifies the degradation kinetic equation to a pseudo-first-order reaction kinetic equation under the condition of excessive ozone, determines the apparent rate constant through linear regression analysis, and constructs a rate characteristic matrix including the apparent rate constant, activation energy, and average degradation rate. At the same time, the present invention calculates the kinetic stability index, degradation efficiency index, and energy utilization index, determines the weight coefficients of each index through the fuzzy comprehensive evaluation method, constructs a scientific degradation efficiency evaluation index in combination with the entropy weight method, and establishes a complete treatment effect evaluation model. This evaluation method based on kinetic theory overcomes the defect that the existing data processing model is too simple, significantly improves the system's ability to identify and analyze special pollutants, and provides a theoretical basis for the precise control of the treatment process.

[0019] (3) The monitoring and determination unit of the present invention innovatively introduces an early warning threshold detection mechanism, combines the classification results of the pollutant concentration change characteristic index with the degradation efficiency evaluation index, and constructs a classification standard including a low degradation interval, a medium degradation interval, and a high degradation interval. The system designs constraint conditions by analyzing the degradation characteristics of each time interval, including boundary constraints and slope constraints, and introduces a time series fluctuation compensation mechanism, significantly enhancing the adaptive ability of the treatment system. In addition, this determination method based on multi-level constraints can automatically optimize the treatment parameters according to the characteristic differences of different batches of waste liquid, effectively solves the problem that the treatment effect is unstable due to the insufficient adaptive ability of the existing system, and determines the threshold parameters of the slope constraints in each interval through experimental calibration, ensuring the safety and reliability of the treatment process, and providing technical support for the standardized treatment of special medical waste liquid after ozone gynecological treatment by medical institutions. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 is a schematic block diagram of a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to an embodiment of the present invention; Figure 2 is a schematic flow diagram of a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to an embodiment of the present invention; Figure 3 is a schematic block diagram of a content analysis unit in a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to an embodiment of the present invention; Figure 4 is a schematic block diagram of a characteristic evaluation unit in a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to an embodiment of the present invention.

[0022] In the figure: 1. Content analysis unit; 101. Feature analysis module; 102. Index construction module; 103. Fitting modeling module; 2. Characteristic evaluation unit; 201. Rate characteristic module; 202. Parameter characteristic module; 203. Effect evaluation module; 3. Monitoring and determination unit. Detailed implementation manners

[0023] To further illustrate the embodiments, the present invention provides drawings. These drawings are part of the disclosure of the present invention, mainly used to illustrate the embodiments, and can be used in conjunction with the relevant descriptions in the specification to explain the operation principle of the embodiments. With reference to these contents, those of ordinary skill in the art should be able to understand other possible implementation manners and the advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0024] According to an embodiment of the present invention, a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment is provided.

[0025] Now, the present invention will be further described in combination with the drawings and specific implementation manners. As Figures 1 - 4 shown, according to an embodiment of the present invention, a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment is provided, including: The content analysis unit 1 is used to calculate the characteristic index of pollutant concentration change based on the monitoring data of the discharged liquid after ozone gynecological treatment, fit the degradation curve of pollutants through a piecewise linear equation, and establish a pollutant content analysis model; The characteristic evaluation unit 2 is used to generate a pollutant degradation rate characteristic matrix by using the output result of the pollutant content analysis model and combining the chemical reaction kinetics theory, determine the degradation process parameters, and construct a treatment effect evaluation model; The monitoring and determination unit 3 is used to obtain the monitoring result of the discharged liquid through early warning threshold detection according to the output result of the treatment effect evaluation model; Specifically, the content analysis unit 1 is connected to the characteristic evaluation unit 2 and the monitoring and determination unit 3.

[0026] The content analysis unit 1 includes: The feature analysis module 101 is used to determine the segmented interval according to the concentration difference of the monitoring data, calculate the degradation weight coefficient, obtain the time-varying curve through weighted accumulation, and determine the degradation time node; When the feature analysis module 101 determines the segmented interval according to the concentration difference of the monitoring data, calculates the degradation weight coefficient, obtains the time-varying curve through weighted accumulation, and determines the degradation time node, it includes: Calculate the pollutant concentration difference between adjacent time points of the monitoring data, determine the segmented interval according to the difference change, and identify the fluctuation period to obtain the candidate point set; Construct a pollutant degradation weight function based on the candidate point set and solve to obtain the degradation weight coefficient at each moment; Use the degradation weight coefficient to sum the weighted changes in pollutant concentration within each segmented interval to obtain the time-varying cumulative curve, and determine the degradation time node through slope change analysis.

[0027] Specifically, take the treatment of a special medical waste liquid after ozone treatment in the gynecology outpatient department of a certain tertiary hospital as an example. This batch of waste liquid is about 10 ml, in a slightly yellow and turbid state, and mainly contains components such as body secretions, exfoliated cells, and ozone residues. Specifically, the acquisition frequency of the monitoring system of the present invention is 5 seconds / time, and the monitoring parameters include four indicators: pH value, ozone residue, chemical oxygen demand (COD value), and turbidity. The following is the 60-minute treatment monitoring process of this batch of waste liquid: First, ① the system calculates the difference sequence of the four monitored indicators collected. At the beginning of the treatment, the initial pH value of this batch of medical waste liquid is 6.8, the ozone residue is 2.5 mg / L, the COD value is 95 mg / L, and the turbidity is 45 NTU. Taking the COD value as an example, the difference ΔCOD(t) at time t is obtained by subtracting the value at the previous moment from the current value: ΔCOD(t)=COD(t)-COD(t - 1); Next, sliding variance analysis is performed with a 30 - second window to calculate the local fluctuation intensity σ(t): σ(t)=(1 / n)∑[ΔCOD(t - i)-μ] 2 ; In the formula, n is the number of data points within the window, n=(30 seconds / sampling interval). Since the sampling interval is 5 seconds, n = 6, that is, each window contains 6 data points. μ is the average value of the differences within the window, and the summation range is from i = 0 to n - 1.

[0028] Specifically, in actual processing, it is found that at t = 17 minutes, the color of the medical waste liquid begins to significantly fade, the COD value drops to 65 mg / L, and the fluctuation intensity reaches 0.22; at t = 42 minutes, the medical waste liquid is basically clarified, the COD value drops to 35 mg / L, and the fluctuation intensity is 0.19. These two fluctuation intensities are significantly higher than the set threshold of 0.15. Therefore, these two time points (t 1 and t 2 ) are marked as the candidate point set.

[0029] Specifically, ② Based on the candidate point set, a pollution reduction weight function is established. This function considers the severity and duration of data fluctuations and reflects the dynamic change of data importance through an exponential decay form. The expression of the pollution reduction weight function is: ; In the formula, WP ( t ) is the pollution reduction weight value at t time, A is the pollution reduction basic weight coefficient (taking the value of 0.8), representing the initial weight size, B is the pollution reduction attenuation factor (taking the value of 0.5), controlling the attenuation rate of the weight with fluctuations, σ ( t ) is the pollutant concentration fluctuation intensity at t time, σ ( t - 1) is the pollutant concentration fluctuation intensity at t - 1 time, | σ ( t ) - σ ( t - 1)| represents the absolute difference in pollutant concentration fluctuation intensity between adjacent times.

[0030] Specifically, the calculation results show that the degradation weight coefficient at 17 minutes is 0.75, and the degradation weight coefficient at 42 minutes is 0.82, indicating that the data changes at these two time points have relatively high importance.

[0031] Specifically, ③ using the degradation weight coefficient, the concentration change amounts of the four degradation monitoring indicators in each segmented interval are respectively weighted and summed to calculate the comprehensive time-varying cumulative value A(t): A(t)=∑[WP(t)×(α 1 ×ΔpH(t)+α 2 ×ΔO 3 (t)+α 3 ×ΔCOD(t)+α 4 ×ΔTur(t))]; In the formula, WP(t) is the degradation weight coefficient at time t, ΔpH(t) is the pH value difference at time t, ΔO 3 (t) is the ozone residue difference at time t, ΔCOD(t) is the chemical oxygen demand difference at time t, ΔTur(t) is the turbidity difference at time t, and α 1 , α 2 , α 3 , α 4 are the weighting coefficients of pH value, ozone residue, chemical oxygen demand, and turbidity respectively, and are taken as 0.15, 0.35, 0.35, and 0.15 respectively in this embodiment. Since the sampling interval is 5 seconds, for the candidate point set time interval [t 1 , t 2 , the summation range is from t 1 to t 2 , and the step size is 5 seconds.

[0032] Specifically, in the above embodiment, for the 17 - 42 minute interval: The range of the degradation weight coefficient WP(t) at each moment in this interval is 0.75 - 0.82; The differences of the four indicators are calculated by sampling every 5 seconds; Multiply the weighted differences of all indicators by the degradation weight coefficient and accumulate to obtain the comprehensive time-varying cumulative value of this interval; Finally, by analyzing the slope change of the comprehensive time-varying cumulative curve A(t), it is found that: The slope change rate reaches the maximum value of 0.45 at t = 17 minutes; It reaches the second peak value of 0.38 at t = 42 minutes; Specifically, by analyzing the slope change of the cumulative curve, it is found that the slope change rate reaches the maximum value of 0.45 at t = 17 minutes and reaches the second peak value of 0.38 at t = 42 minutes, further verifying the rationality of these two time points as the degradation time nodes.

[0033] Specifically, the three finally determined time intervals are respectively: 0 - 17 minutes: The initial degradation interval, at this time, the color and turbidity of the waste liquid begin to change significantly; 17 - 42 minutes: Rapid degradation interval, during which the organic matter and ozone residue in the waste liquid are rapidly degraded; 42 - 60 minutes: Stable degradation interval, during which all indicators of the waste liquid tend to be stable.

[0034] In one embodiment, the content analysis unit 1 further includes: An index construction module 102, configured to construct a feature space and map degradation monitoring indicators based on degradation time nodes, obtain a pollutant concentration change feature index by calculating the feature vectors of different degradation monitoring indicators, and establish a grading standard for the feature index; A fitting and modeling module 103, configured to design the constraint conditions of a piecewise linear equation set according to the grading result of the pollutant concentration change feature index, introduce a continuity correction term, obtain a degradation curve equation, and construct a pollutant content analysis model; Wherein, the degradation monitoring indicators include pH value, ozone residue, chemical oxygen demand, and turbidity.

[0035] Specifically, the feature analysis module 101 is connected to the index construction module 102 and the fitting and modeling module 103.

[0036] In one embodiment, when the index construction module 102 constructs a feature space and maps degradation monitoring indicators based on degradation time nodes, obtains a pollutant concentration change feature index by calculating the feature vectors of different degradation monitoring indicators, and establishes a grading standard for the feature index, it includes: Construct a degradation feature space based on the time intervals divided by the degradation time nodes, and map different degradation monitoring indicators to the degradation feature space; Calculate the feature vectors of different degradation monitoring indicators in each time interval to obtain a pollutant concentration change feature index; Analyze the distribution law of the pollutant concentration change feature index, and establish a grading standard for the pollutant concentration change feature index by setting a grading threshold.

[0037] Specifically, ① based on the obtained degradation time nodes (17 minutes and 42 minutes), the entire monitoring process is divided into three time intervals: 0 - 17 minutes, 17 - 42 minutes, and 42 - 60 minutes. A four - dimensional feature space is constructed, and the four degradation monitoring indicators are mapped into this space. To ensure the accuracy of the mapping, the monitoring data is standardized: Y(t)=(X(t)-X min ) / (X max -X min ); In the formula, X(t) is the original value of a certain degradation monitoring indicator at time t, X min and X maxThey are the minimum and maximum values ​​of the indicator during the entire monitoring process.

[0038] Specifically, ② calculate the change in each degradation monitoring indicator in each time interval. Take the second time interval (17-42 minutes) as an example: ΔpH s =|Y_pH(42)-Y_pH(17)| / / pH value change; ΔO 3s =|Y_O3(42)-Y_O3(17)| / / Change in residual ozone; ΔCOD s =|Y_COD(42)-Y_COD(17)| / / COD value change; ΔTur s =|Y_Tur(42)-Y_Tur(17)| / / Change in turbidity.

[0039] Specifically, the pollutant concentration change characteristic index of the time interval is calculated by weighted combination, and the expression of the pollutant concentration change characteristic index is: ; In the formula, I ( t )for t The pollutant concentration change characteristic index at the time represents the overall degradation degree of the pollutant in this time interval, Δ pH s for pH The standardized change of the value in this time interval, Δ O 3s is the standardized change of ozone residual in this time interval, Δ COD s is the standardized change of chemical oxygen demand in the time interval, Δ Tur s The standardized change of turbidity in this time interval is; β 1 =0.2 pH The value weight coefficient reflects the change of the acid-base environment of the discharged liquid and has little effect on the degradation process. β 2 =0.3 is the weight coefficient of ozone residue, which directly reflects the degradation effect of ozone and is one of the important indicators. β 3 =0.3 COD The value weight coefficient characterizes the degree of degradation of organic matter and is the core indicator for evaluating the treatment effect. β 4 =0.2 is the turbidity weight coefficient, which reflects the suspended matter content of the discharged liquid and assists in judging the degradation effect.

[0040] Specifically, ③ statistically analyze the pollutant concentration change characteristic indices calculated within three time intervals, establish a three-level classification standard. In the above embodiments, the classification thresholds are set to 0.4 and 0.8, and the following results are obtained: I(t) < 0.4: The change in pollutant concentration is slow, and the degradation effect is not obvious; 0.4 ≤ I(t) < 0.8: The change in pollutant concentration is moderate, and the degradation effect is good; I(t) ≥ 0.8: The change in pollutant concentration is significant, and the degradation effect is excellent.

[0041] Specifically, the actual treatment results show that: The first time interval (0 - 17 minutes): I(t) = 0.35, indicating that the degradation effect is not obvious in the initial stage; The second time interval (17 - 42 minutes): I(t) = 0.85, indicating that the degradation effect is the best in this stage; The third time interval (42 - 60 minutes): I(t) = 0.55, indicating that the degradation effect tends to be stable in the later stage; The compliance degree between the classification result of the characteristic index and the actual degradation effect reaches 90%, verifying the reliability of this classification method.

[0042] In one embodiment, when the fitting and modeling module 103 designs the constraint conditions of the piecewise linear equation set according to the classification result of the pollutant concentration change characteristic index, introduces a continuity correction term, obtains the degradation curve equation, and constructs the pollutant content analysis model, it includes: Based on the classification result of the pollutant concentration change characteristic index, establish a piecewise linear equation set, and design constraint conditions according to the degradation characteristics of each time interval; According to the time node characteristics of the piecewise linear equation set, use the exponential decay function to construct a continuity correction term to ensure the smooth transition of the degradation curve; Combine the constraint conditions and the continuity correction term, solve the piecewise linear equation set to obtain the degradation curve equation, and introduce a time-varying correction factor to generate the pollutant content analysis model.

[0043] In one embodiment, the classification result of the pollutant concentration change characteristic index includes a low degradation interval, a medium degradation interval, and a high degradation interval; Designing constraint conditions according to the degradation characteristics of each time interval includes: Based on the pollutant contents at the initial moment and the termination moment, set the boundary constraint conditions of the piecewise linear equation set, and match the pollutant content values of the equation at the starting point and the termination point with the actual measured values; According to the characteristic index values of each time interval, set the slope constraint range, limit the absolute value of the slope in the low degradation interval below the first threshold, limit the absolute value of the slope in the high degradation interval above the second threshold, and limit the absolute value of the slope in the medium degradation interval between the third threshold and the fourth threshold; Through the experimental calibration method, determine the threshold parameters of the slope constraints for each interval, and construct the slope constraint equations for each time interval.

[0044] Specifically, ① Based on the obtained characteristic index classification results of the three time intervals (the initial degradation interval of 0 - 17 minutes, the rapid degradation interval of 17 - 42 minutes, and the stable degradation interval of 42 - 60 minutes correspond to the low degradation interval, the medium degradation interval, and the high degradation interval respectively), construct a piecewise linear equation system: C(t)=a 1 ×t + b 1 , 0 ≤ t < 17; C(t)=a 2 ×t + b 2 , 17 ≤ t < 42; C(t)=a 3 ×t + b 3 , 42 ≤ t ≤ 60; In the formula, C(t) is the pollutant content at time t, a j and b j (j = 1, 2, 3) are undetermined coefficients.

[0045] Specifically, design the constraint conditions according to the degradation characteristics of each interval, including: a) Initial condition: C(0)=C 0 (initial pollutant content); b) Termination condition: C(60)=C f (final pollutant content); c) Slope constraint: |a 1 | ≤ k 1 (corresponding to the low degradation interval with I(t)=0.35) |a 2 | ≥ k 2 (corresponding to the high degradation interval with I(t)=0.85) k 3 ≤ |a 3 | ≤ k 4 (corresponding to the medium degradation interval with I(t)=0.55) where k 1 = 0.4, k 2 = 0.8, k 3 = 0.5, k 4 = 0.7 are the slope thresholds calibrated by experiments.

[0046] Specifically, ② construct continuity correction terms at time nodes t = 17 and t = 42: ε 1 (t)=γ 1 ×exp(-λ 1 ×|t - 17|); ε 2 (t)=γ 2 ×exp(-λ 2 ×|t - 42|); In the formula, γ 1 and γ 2 are the first correction coefficient and the second correction coefficient, which are respectively taken as 0.15 and 0.12 in this embodiment; λ 1 and λ 2 are the first attenuation factor and the second attenuation factor, which are respectively taken as 0.5 and 0.4 in this embodiment; |t - 17| and |t - 42| represent the distances from the time nodes.

[0047] Specifically, ③ combine the constraint conditions and the continuity correction terms to obtain the complete degradation curve equation: ; In the formula, C( t ) is the pollutant content at t time, t is the time variable, a 1 , a 2 , a 3 are the slope coefficients of the low degradation interval, the medium degradation interval and the high degradation interval respectively, b 1 , b 2 , b 3 are the intercept coefficients of the low degradation interval, the medium degradation interval and the high degradation interval respectively, ε 1 ( t ) is the continuity correction term of the first time node, ε 2 ( t ) is the continuity correction term of the second time node, H ( t ) is the unit step function. When t≥ 0 H ( t ) = 1. When t <0 H ( t ) = 0.

[0048] Specifically, in this embodiment, the coefficient values are obtained by least squares method: a 1 = -0.35, b 1 = 100; a 2 = -0.82, b 2 = 108; a 3 = -0.48, b 3 = 94; When introducing a time-varying correction factor to construct a pollutant content analysis model, the expression is: M(t) = C(t) + δ(t); In the formula, δ(t) is a time-varying correction term; δ(t) = μ × [1 - exp(-σ × t)]; In the formula, μ is a correction coefficient (taking the value of 0.1), and σ is a time scale factor (taking the value of 0.05) Specifically, the verification result of the final pollutant content analysis model shows that: The degradation curve has a smooth transition at the time node and no obvious jump; The average relative error between the model prediction value and the measured value is 4.2%; The goodness of fit R in three time intervals 2 is 0.92, 0.95 and 0.89 respectively; It shows that the model can effectively describe the variation law of pollutant content with time, providing a theoretical basis for the monitoring and control of the pollutant degradation process.

[0049] In one embodiment, the characteristic evaluation unit 2 includes: A rate characteristic module 201, configured to calculate the pollutant degradation rate in each time interval based on the output data of the pollutant content analysis model, and establish a degradation kinetic equation in combination with the reaction kinetics theory to generate a rate characteristic matrix characterizing the pollutant degradation characteristics; A parameter characteristic module 202, configured to analyze the degradation characteristics of the rate characteristic matrix, determine the pollutant degradation process parameters, and construct a degradation efficiency evaluation index; An effect evaluation module 203, configured to introduce a time series fluctuation compensation according to the degradation efficiency evaluation index and the pollutant degradation process parameters to construct a treatment effect evaluation model.

[0050] Specifically, the rate characteristic module 201 is connected to the effect evaluation module 203 through the parameter characteristic module 202.

[0051] In one embodiment, when the rate feature module 201 calculates the pollutant degradation rate for each time interval based on the output data of the pollutant content analysis model, combines the reaction kinetics theory to establish a degradation kinetics equation, and generates a rate feature matrix characterizing the pollutant degradation characteristics, it includes: Calculate the instantaneous degradation rate and average degradation rate of the pollutant for each time interval according to the output result of the pollutant content analysis model; Using the pollutant degradation rate for each time interval, establish a degradation kinetics equation based on the chemical reaction kinetics theory, and considering the ozone excess condition, simplify it to a pseudo-first-order reaction kinetics equation, and determine the apparent rate constant through linear regression analysis; Based on the pseudo-first-order reaction kinetics equation and the apparent rate constant, construct a rate feature matrix including the apparent rate constant, activation energy, and average degradation rate.

[0052] Specifically, ① First, use the data output by the pollutant content analysis model to calculate the instantaneous degradation rate of the pollutant in each time interval. By comparing the difference in pollutant content between adjacent time points, the change trend of pollutant concentration over time can be obtained. Since the system collects data every 5 seconds, a relatively accurate instantaneous degradation rate can be obtained. The calculation formula for the instantaneous degradation rate is: v(t)=-[M(t)-M(t-Δt)] / Δt; In the formula, v(t) is the instantaneous degradation rate at time t, M(t) is the pollutant content at time t, and Δt is the sampling time interval (5 seconds in this embodiment).

[0053] Specifically, calculate the average degradation rate for three time intervals (0 - 17 minutes, 17 - 42 minutes, and 42 - 60 minutes) respectively: v avg =[M(t 2 )-M(t 1 )] / (t 2 -t 1 ); In the formula, v avg is the average degradation rate, M(t 1 ) and M(t 2 ) are the pollutant contents at the start and end times of the interval respectively. Taking the 17 - 42 minute interval as an example, the pollutant content in this interval drops from the initial value of 85 mg / L to 35 mg / L, and the average degradation rate is 1.96 mg / L·min, which is significantly higher than the other two intervals. Through analysis, it is found that the average degradation rate in the 0 - 17 minute interval is 0.82 mg / L·min, and the average degradation rate in the 42 - 60 minute interval is 0.94 mg / L·min.

[0054] Specifically, ② based on the theory of chemical reaction kinetics, a kinetic equation for ozone oxidation degradation is established: -dM / dt = k × [M]α × [O 3 β; In the formula, k is the reaction rate constant (L / (mg·min)), [M] is the pollutant concentration (mg / L), [O 3 is the ozone concentration (mg / L), and α and β are the reaction orders of the pollutant and ozone, respectively (dimensionless).

[0055] Specifically, considering that ozone is maintained in an excessive state by continuous aeration during the actual treatment process, its concentration is much greater than the pollutant concentration. In this case, the concentration of ozone can be regarded as constant, i.e., [O3] ≈ C (constant), and the equation is simplified to a pseudo-first-order reaction kinetic equation: -dM / dt = kapp × [M]; In the formula, kapp is the apparent rate constant (min-1). Since the ozone concentration remains constant, the k1 × [O 3 β part in the original equation can be combined into a constant kapp, thus obtaining a simpler pseudo-first-order reaction equation.

[0056] Specifically, the apparent rate constant is determined by linear regression analysis. Integrating the pseudo-first-order reaction kinetic equation: ln[M] = ln[M 0 - kapp × t; In the formula, [M 0 is the initial pollutant concentration (mg / L), and t is the reaction time (min); Taking the 17 - 42 minute interval as an example, the logarithm of the experimentally measured pollutant concentration data is plotted against time, and the apparent rate constant kapp for this interval is obtained as 0.082 min-1 through linear regression analysis. The goodness of fit (i.e., the correlation coefficient) of the linear fitting for this interval reaches 0.95, indicating that the experimental data is in good agreement with the pseudo-first-order reaction kinetic equation.

[0057] Specifically, ③ based on the pseudo-first-order reaction kinetic equation and the apparent rate constant, a rate characteristic matrix is constructed. Specifically, this matrix contains three key parameters: the apparent rate constant, the activation energy, and the average degradation rate. The expression of the rate characteristic matrix is: R = [k'Ev avg ; In the formula, k' is the apparent rate constant, E is the activation energy, and v avg is the average degradation rate.

[0058] Specifically, in this embodiment, the activation energy E is calculated by the Arrhenius equation: k' = A·exp(-E / RT); Wherein, A is the pre-exponential factor, R is the gas constant, and T is the reaction temperature.

[0059] Specifically, these parameters are calculated for three time intervals respectively. In the rapid degradation interval of 17 - 42 minutes, the apparent rate constant reaches 0.082 min-1, which is 2.3 times that of the initial interval (0.035 min-1); the activation energy is 22.8 kJ / mol, lower than 25.2 kJ / mol in the initial interval; the average degradation rate is 1.96 mg / L·min. The rate characteristic matrix for this interval is: R = [0.082, 42.8, 1.96]; Through the analysis of the rate characteristic matrix, it can be seen that the interval of 17 - 42 minutes has the optimal kinetic characteristics, providing an important basis for determining the optimal treatment time and optimizing the operating parameters.

[0060] In one embodiment, when the parameter characteristic module 202 analyzes the degradation characteristics of the rate characteristic matrix, determines the pollutant degradation process parameters, and constructs the degradation efficiency evaluation index, it includes: According to the rate characteristic matrix, calculate the kinetic stability index, degradation efficiency index, and energy utilization index; Use the fuzzy comprehensive evaluation method to determine the weight coefficients of each index and establish the pollutant degradation process parameters; Based on the pollutant degradation process parameters, combine the entropy weight method to construct the degradation efficiency evaluation index.

[0061] Specifically, ① First, construct a multi-dimensional evaluation system. The present invention uses the kinetic stability index to reflect the stability degree of the degradation process, which is achieved through the comprehensive characterization of the apparent rate constant and the activation energy. Taking the interval of 17 - 42 minutes as an example, the apparent rate constant k' in this interval is 0.082 min-1, which is the maximum value k' observed max ; the activation energy E is 42.8 kJ / mol, and the minimum activation energy E min is 42.8 kJ / mol. The weight coefficients w 1 and w 2 are determined by the expert scoring method, which are 0.6 and 0.4 respectively; the calculation result is: KSI = 0.6×(0.082 / 0.082) + 0.4×(42.8 / 42.8) = 1.0; It shows that this interval has the best kinetic stability.

[0062] Specifically, the present invention uses the degradation efficiency index to describe the pollutant removal effect. In the interval of 17 - 42 minutes, the average degradation rate v avg is 1.96 mg / L·min, and the maximum degradation rate v maxis 2.1 mg / L·min, and the pollutant concentration drops from 85 mg / L to 35 mg / L; it is calculated that: DEI = (1.96 / 2.1) × (1 - 35 / 85) = 0.69; This value indicates that the degradation efficiency reaches a relatively high level.

[0063] Specifically, the present invention uses the energy utilization index to evaluate the energy conversion efficiency. In this interval, the pollutant removal amount ΔM is 50 mg / L, the energy consumption ΔE is 2.8 kWh, and the energy conversion efficiency η is 0.85; it is calculated that: EUI = (50 / 2.8) × 0.85 = 15.18; It shows that the pollutant removal effect per unit energy consumption is good.

[0064] Specifically, when using the fuzzy comprehensive evaluation to determine the weights, first establish an evaluation index system, including three first-level indicators: kinetic stability (u1), degradation efficiency (u2), and energy utilization (u3). The evaluation grades are divided into four grades: excellent (v1), good (v2), general (v3), and poor (v4). Establish a fuzzy relationship matrix through experimental data analysis: R = [0.8 0.2 0.0 0.0 0.7 0.3 0.0 0.0 0.6 0.3 0.1 0.0]; Specifically, use the analytic hierarchy process to construct a judgment matrix: A = [1.0 2.0 3.0 0.5 1.0 2.0 0.33 0.5 1.0]; Calculate the eigenvalues and eigenvectors to obtain the weight vector W = [0.54, 0.30, 0.16].

[0065] Specifically, when constructing the evaluation index based on the entropy weight method, first standardize the original data. Taking the interval of 17 - 42 minutes as an example, calculate the standardized matrix: X = [1.00 0.69 0.85 0.92 0.65 0.78 0.85 0.58 0.72]; Specifically, calculate the information entropy of the j-th index, and the expression is H j = -k∑(p ij × lnp ij ); where k = 1 / ln(3) = 0.91, and the information entropies of the three indexes are respectively H 1 = 0.82, H 2 = 0.85, H 3= 0.88.

[0066] Specifically, calculate the information utility value and entropy weight: d j = 1 - H j ; w j = d j / ∑d j ; Obtain the final entropy weight vector w = [0.42, 0.35, 0.23].

[0067] Specifically, calculate the comprehensive evaluation index: η = 0.42×1.00 + 0.35×0.69 + 0.23×0.85 = 0.86; It shows that the overall processing effect in the 17 - 42 minute interval is the best.

[0068] In one embodiment, when the effect evaluation module 203 constructs a processing effect evaluation model by introducing time - series fluctuation compensation according to the degradation efficiency evaluation index and the pollutant degradation process parameters, it includes: Establish a fluctuation feature extraction model based on time series to analyze the periodic fluctuations and random perturbations in the pollutant degradation process; Design an adaptive compensation algorithm to dynamically correct and optimize the fluctuation features; Integrate the fluctuation compensation results and the degradation efficiency evaluation index to construct the final processing effect evaluation model.

[0069] Specifically, ① For fluctuation feature extraction, first perform wavelet decomposition on the time - series data. In this embodiment, the db4 wavelet function is selected, and the pollutant concentration time series is decomposed at 3 scales: f(t) = a 3 + d 3 + d 2 + d 1 ; In the formula, a 3 is the low - frequency approximation component, reflecting the overall change trend of the pollutant concentration; d 3 , d 2 , d 1 are the detail components at different scales, characterizing the fluctuation features at different frequencies.

[0070] Specifically, taking the 17 - 42 minute interval as an example, through wavelet decomposition, we get: The low - frequency approximation component a3 shows that the concentration as a whole shows an exponential decay trend; The d3 component shows a periodic fluctuation around 25 minutes, with a period of about 3 minutes; The d2 component reflects short - term fluctuations, with a fluctuation amplitude of ±2.5 mg / L; The d1 component contains high-frequency noise with an amplitude less than 1 mg / L.

[0071] Specifically, ② design an adaptive Kalman filter for dynamic correction. The state equation is: x(k + 1) = 0.95x(k) + u(k) + w(k); y(k) = x(k) + v(k); Where the process noise w(k) ~ N(0, 0.1), and the measurement noise v(k) ~ N(0, 0.2).

[0072] Specifically, the prediction step: x-(k + 1) = 0.95x(k) + u(k); P-(k + 1) = 0.95P(k)0.95T + Q; Where Q is the process noise covariance matrix.

[0073] Specifically, the update step: K(k + 1) = P-(k + 1)HT[HP-(k + 1)HT + R]-1; x(k + 1) = x-(k + 1) + K(k + 1)[y(k + 1) - Hx-(k + 1)]; P(k + 1) = [I - K(k + 1)H]P-(k + 1); Where, R is the measurement noise covariance matrix.

[0074] Specifically, ③ construct the final treatment effect evaluation model: M(t) = 0.45×η(t) + 0.35×[C(t) / C ref + 0.20×F(t) Where, η(t) is the degradation efficiency evaluation index, C(t) is the time series data after Kalman filtering, C ref is the initial pollutant concentration, and F(t) is the prediction correction term based on the ARIMA model.

[0075] Specifically, taking the interval of 17 - 42 minutes as an example: η(t) = 0.86 (degradation efficiency evaluation index); C(t) / C ref = 5 / 15 = 0.33 (the ratio of the current concentration to the initial concentration, dimensionless); F(t) = -0.15 (prediction correction value); Substituting into the model gives: M(t) = 0.45×0.86 + 0.35×0.33 + 0.20×(-0.15) = 0.47; In the present invention, the evaluation result M(t) ∈ [0, 1], where: 0.8 - 1.0 indicates excellent treatment effect; 0.6 - 0.8 indicates good treatment effect; 0.4 - 0.6 indicates average treatment effect; <0.4 indicates that treatment parameters need to be optimized.

[0076] Conclusion: M(t) = 0.47, indicating that the purification treatment effect of the discharged liquid after ozone gynecological treatment in this time interval is average, and the treatment time needs to be appropriately extended or the ozone dosage needs to be adjusted to improve the treatment effect.

[0077] When the monitoring and determination unit 3 obtains the monitoring result of the discharged liquid through early warning threshold detection according to the output result of the treatment effect evaluation model, it includes: Establish a multi-level early warning threshold system and set key index thresholds in combination with medical safety standards; Design an early warning determination method based on fuzzy rules to realize real-time monitoring of the treatment effect; Generate a monitoring early warning report and provide treatment optimization suggestions.

[0078] Specifically, ① when establishing a multi-level early warning threshold system in the present invention, three key monitoring indicators are first determined: pollutant residue, treatment stability, and safety index. According to the characteristics and treatment requirements of the discharged liquid after ozone gynecological treatment, the early warning levels are set: Pollutant residue threshold: Purification completed area: < 30 mg / L; Area for continued treatment: 30 - 50 mg / L; Key treatment area: > 50 mg / L.

[0079] Specifically, the treatment stability score is based on the fluctuation degree of the treatment effect evaluation model: Stable: Treatment process fluctuation < 5% (indicating normal purification treatment process); Fluctuating: Treatment process fluctuation 5% - 15% (treatment parameters need to be adjusted); Unstable: Treatment process fluctuation > 15% (treatment plan needs to be changed).

[0080] Specifically, the purification degree index comprehensively considers the characteristic indexes of the discharged liquid: NI = w 1 ×(pH - 7) 2 + w2 × (O 3 / O 3ref ) + w3 × (T urb / T urb-ref ); In the formula, w1 = 0.4, w 2 = 0.35, w 3 = 0.25 are the weight coefficients; pH is the acidity and alkalinity, O 3 is the ozone residual amount, T urb is the turbidity; O 3ref and T urb-ref are the reference values of the ozone residual amount and the turbidity respectively.

[0081] ② Design fuzzy warning rules for the treatment of the discharged liquid after ozone gynecological treatment. The present invention uses fuzzy rules in the form of If-Then for warning determination, including: Rule 1: IF (residual amount ∈ purification completed area) AND (stability = stable) AND (NI < 0.8) THEN (purification treatment can be terminated); Rule 2: IF (residual amount ∈ area for continued treatment) OR (stability = fluctuating) OR (0.8 ≤ NI < 1.2) THEN (treatment parameters need to be adjusted); Rule 3: IF (residual amount ∈ key treatment area) OR (stability = unstable) OR (NI ≥ 1.2) THEN (treatment plan needs to be changed).

[0082] Specifically, taking the treatment process of the discharged liquid in the 17 - 42 minute interval as an example, the model output results show that: Pollutant residual amount: 35.2 mg / L (area for continued treatment); treatment stability: fluctuating 4.2% (stable); purification degree index: 0.75 (good treatment); According to the fuzzy rule determination, the system prompts that continued treatment is required.

[0083] Specifically, ③ Generate a monitoring report on the treatment of the discharged liquid, and the report includes the following contents: a) Evaluation of treatment status: Current treatment stage: middle stage of purification treatment; treatment progress: 75%; treatment prompt: continue purification treatment; b) Analysis of key indicators: Pollutant residual amount: 35.2 mg / L, continued treatment is required; treatment stability: good, treatment parameters are appropriate; purification degree index: treatment effect meets the standard; c) Suggestions for treatment optimization: It is recommended to continue treatment for 10 - 15 minutes, maintain the current ozone treatment parameters, and focus on monitoring the change of the pH value of the discharged liquid.

[0084] Specifically, this monitoring report is updated every 5 minutes to reflect the treatment status of the discharged liquid in real time. When treatment anomalies are detected, the system promptly provides optimization suggestions to ensure the purification treatment effect of the discharged liquid after ozone gynecological treatment.

[0085] To facilitate the understanding of the above technical solution of the present invention, the following takes the treatment of special medical waste liquid after ozone treatment in the gynecology outpatient department of a certain tertiary hospital as an example for specific description as follows: After a routine ozone gynecological treatment, about 10 ml of special medical waste liquid was collected. The system initially detected the waste liquid through multiple sensor nodes and obtained basic parameters such as a pH value of 6.8, an ozone residual amount of 2.5 mg / L, a COD value of 95 mg / L, and a turbidity of 45 NTU. Based on these measured data, the content analysis unit 1 determined the initial treatment parameters in combination with the preset treatment standards.

[0086] During the 30-minute treatment process, the system collected data every 5 seconds. Through the calculation of the content analysis unit 1, the treatment process was divided into three time intervals: the initial degradation period (0 - 8 minutes), the rapid degradation period (8 - 20 minutes), and the stable period (20 - 30 minutes). During the rapid degradation period, the system detected significant improvements in the various indicators of the waste liquid: the pH value stabilized at 7.2, the ozone residual amount dropped to 0.8 mg / L, the COD value dropped to 35 mg / L, and the turbidity dropped to 15 NTU. The treatment effect evaluation model calculated the M(t) value to be 0.47 based on the real-time monitoring data, indicating good treatment effect.

[0087] At the same time, the system automatically adjusted the treatment process parameters according to the real-time monitoring data. When it detected that the various indicators tended to be stable, it timely adjusted the treatment intensity to avoid over-treatment. At the end of the treatment, the ozone residual amount in the waste liquid dropped below 0.3 mg / L, the COD value dropped to 28 mg / L, and the turbidity dropped to 12 NTU. All indicators met the medical institution sewage discharge standards. Thus, it was verified that the present system can achieve precise monitoring and intelligent treatment for the special medical waste liquid after ozone gynecological treatment, providing reliable technical support for medical institutions to standardize the treatment of such special waste liquid.

[0088] In summary, by means of the above technical solutions of the present invention, the present invention innovatively designs a pollution reduction weight function and a characteristic index construction method through the content analysis unit 1. This method first determines the segmented interval by calculating the difference in pollutant concentrations at adjacent time points of the monitoring data, and identifies the fluctuation period to obtain a candidate point set. Then, based on the candidate point set, a pollution reduction weight function is constructed, and the time-varying curve is obtained through weighted accumulation, and the degradation time node is determined. On this basis, the system can accurately calculate the eigenvectors of multiple degradation monitoring indicators including pH value, ozone residue, chemical oxygen demand, and turbidity, obtain the characteristic index of pollutant concentration change, and fit the degradation curve through a piecewise linear equation, significantly improving the monitoring accuracy of special medical waste liquid after ozone gynecological treatment, solving the technical problem that the existing monitoring system is difficult to comprehensively reflect the special properties of such waste liquid, realizing the accurate identification and analysis of the changes in pollutant components, and providing reliable data support for the optimization of the treatment process. The characteristic evaluation unit 2 of the present invention is based on the theory of chemical reaction kinetics. First, it calculates the instantaneous degradation rate and average degradation rate of pollutants in each time interval, simplifies the degradation kinetic equation to a pseudo-first-order reaction kinetic equation under the condition of excessive ozone, determines the apparent rate constant through linear regression analysis, and constructs a rate characteristic matrix including the apparent rate constant, activation energy, and average degradation rate. At the same time, the present invention calculates the kinetic stability index, degradation efficiency index, and energy utilization index, determines the weight coefficients of each index through the fuzzy comprehensive evaluation method, constructs a scientific degradation efficiency evaluation index in combination with the entropy weight method, and establishes a complete treatment effect evaluation model. This evaluation method based on kinetic theory overcomes the defect that the existing data processing model is too simple, significantly improves the system's ability to identify and analyze special pollutants, and provides a theoretical basis for the precise control of the treatment process. The monitoring and determination unit 3 of the present invention innovatively introduces an early warning threshold detection mechanism, combines the classification results of the pollutant concentration change characteristic index with the degradation efficiency evaluation index, and constructs a classification standard including a low degradation interval, a medium degradation interval, and a high degradation interval. The system designs constraint conditions by analyzing the degradation characteristics of each time interval, including boundary constraints and slope constraints, and introduces a time series fluctuation compensation mechanism, significantly enhancing the adaptive ability of the treatment system. In addition, this determination method based on multi-level constraints can automatically optimize the treatment parameters according to the characteristic differences of different batches of waste liquid, effectively solving the problem that the unstable treatment effect caused by the insufficient adaptive ability of the existing system. At the same time, the threshold parameters of the slope constraints in each interval are determined through experimental calibration, ensuring the safety and reliability of the treatment process, and being able to provide technical support for the standardized treatment of special medical waste liquid after ozone gynecological treatment by medical institutions.

[0089] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A monitoring system for purifying liquid discharged after ozone gynecological treatment, characterized in that: include: The content analysis unit is used to calculate the characteristic index of pollutant concentration change based on the monitoring data of the fluid discharged after ozone gynecological treatment, and to fit the degradation curve of the pollutant through a piecewise linear equation to establish a pollutant content analysis model; The characteristic evaluation unit is used to generate the pollutant degradation rate characteristic matrix by using the output results of the pollutant content analysis model and combining it with the chemical reaction kinetics theory, and to determine the degradation process parameters and construct a treatment effect evaluation model; A monitoring and judging unit, used to obtain a monitoring result of the discharged liquid through early warning threshold detection according to the output result of the treatment effect evaluation model; Content analysis unit includes: The feature analysis module is used to determine the segmentation interval according to the concentration difference of the monitoring data, calculate the degradation weight coefficient, obtain the time-varying curve through weighted accumulation, and determine the degradation time node; specifically includes: Calculate the difference in pollutant concentration between adjacent time points of the monitoring data, determine the segmentation interval based on the difference change, and identify the fluctuation period to obtain the candidate point set; Construct a pollution reduction weight function based on the candidate point set, and solve to obtain the degradation weight coefficient at each moment; The degradation weight coefficient is used to weight the change in pollutant concentration in each segmented interval to obtain a time-varying cumulative curve, and the degradation time node is determined by slope change analysis.

2. A monitoring system for purifying liquid discharged after ozone gynecological treatment according to claim 1, characterized in that: The content analysis unit is connected to the monitoring and determination unit via the characteristic evaluation unit; The content analysis unit also includes: Index construction module, which is used to construct feature space and map degradation monitoring indicators based on degradation time nodes, obtain the characteristic index of pollutant concentration change by calculating the characteristic vectors of different degradation monitoring indicators, and establish the classification standard of characteristic index; The fitting modeling module is used to design the constraint conditions of the piecewise linear equations according to the classification results of the characteristic index of pollutant concentration changes, introduce continuity correction terms, obtain the degradation curve equation, and construct a pollutant content analysis model; Wherein, the degradation monitoring indicators include pH value, ozone residual, chemical oxygen demand and turbidity.

3. A monitoring system for purifying liquid discharged after ozone gynecological treatment according to claim 1, characterized in that: The expression of the pollution reduction weight function is: ； In the formula, WP ( t )for t The pollution reduction weight value at the moment, A is the pollution reduction basic weight coefficient, B is the pollution reduction factor, σ ( t )for t The intensity of the pollutant concentration fluctuation at each moment, σ ( t -1) t -1 moment pollutant concentration fluctuation intensity, | σ ( t )- σ ( t -1)| represents the absolute difference in the intensity of pollutant concentration fluctuations at adjacent moments.

4. A monitoring system for purifying liquid discharged after ozone gynecological treatment according to claim 2, characterized in that: Based on the degradation time node, the feature space is constructed and the degradation monitoring index is mapped. By calculating the feature vectors of different degradation monitoring indicators, the characteristic index of pollutant concentration change is obtained, and the classification standard of the characteristic index is established, including: According to the time intervals divided by the degradation time nodes, a degradation feature space is constructed, and different degradation monitoring indicators are mapped to the degradation feature space; Calculate the characteristic vectors of different degradation monitoring indicators in each time interval to obtain the characteristic index of pollutant concentration change; Analyze the distribution law of the pollutant concentration change characteristic index, and establish the classification standard of the pollutant concentration change characteristic index by setting the classification threshold.

5. A monitoring system for purifying liquid discharged after ozone gynecological treatment according to claim 4, characterized in that: The expression of the pollutant concentration change characteristic index is: ； In the formula, I ( t )for t The pollutant concentration change characteristic index at the moment, Δ pH s for pH The standardized change of the value in this time interval, Δ O 3s is the standardized change of ozone residual in this time interval, Δ COD s is the standardized change of chemical oxygen demand in the time interval, Δ Tur s is the standardized change of turbidity in the time interval; β 1 for pH Value weight coefficient, β 2 is the weight coefficient of ozone residual, β 3 for COD Value weight coefficient, β 4 is the turbidity weight coefficient.

6. A monitoring system for purifying liquid discharged after ozone gynecological treatment according to claim 2, characterized in that: According to the classification results of the characteristic index of pollutant concentration change, the constraint conditions of the piecewise linear equation group are designed, and the continuity correction term is introduced to obtain the degradation curve equation, and the pollutant content analysis model is constructed, which includes: Based on the classification results of the characteristic index of pollutant concentration changes, a piecewise linear equation system is established, and constraint conditions are designed according to the degradation characteristics of each time interval; According to the time node characteristics of the piecewise linear equations, the exponential decay function is used to construct the continuity correction term to ensure the smooth transition of the degradation curve; Combining constraints and continuity correction terms, the piecewise linear equations are solved to obtain the degradation curve equation, and a time-varying correction factor is introduced to generate a pollutant content analysis model.

7. A monitoring system for purifying liquid discharged after ozone gynecological treatment according to claim 6, characterized in that: The design constraints according to the degradation characteristics of each time interval include: Based on the pollutant content at the initial and final time, the boundary constraints of the piecewise linear equations are set to match the pollutant content values ​​at the starting and final points of the equations with the actual measured values; According to the characteristic index value of each time interval, the slope constraint range is set to limit the absolute value of the slope of the low degradation interval to below the first threshold, the absolute value of the slope of the high degradation interval to above the second threshold, and the absolute value of the slope of the medium degradation interval to between the third threshold and the fourth threshold; Through experimental calibration method, the threshold parameters of slope constraints in each interval are determined, and the slope constraint equations in each time interval are constructed; Among them, the classification results of the pollutant concentration change characteristic index include low degradation interval, medium degradation interval and high degradation interval.

8. The monitoring system for purifying the discharged liquid after ozone gynecological treatment according to claim 1 is characterized in that: The characteristic evaluation unit comprises: The rate characteristic module is used to calculate the pollutant degradation rate in each time interval based on the output data of the pollutant content analysis model, and to establish the degradation kinetic equation in combination with the reaction kinetics theory to generate a rate characteristic matrix that characterizes the pollutant degradation characteristics; Parameter characteristic module, which is used to analyze the degradation characteristics of the rate characteristic matrix, determine the parameters of the pollutant degradation process, and construct the degradation efficiency evaluation index; The effect evaluation module is used to introduce time series fluctuation compensation and build a treatment effect evaluation model based on the degradation efficiency evaluation index and pollutant degradation process parameters.

9. A monitoring system for purifying liquid discharged after ozone gynecological treatment according to claim 8, characterized in that: The output data of the pollutant content analysis model is used to calculate the pollutant degradation rate in each time interval, and the degradation kinetic equation is established in combination with the reaction kinetics theory to generate a rate characteristic matrix characterizing the pollutant degradation characteristics, including: According to the output results of the pollutant content analysis model, the instantaneous degradation rate and average degradation rate of pollutants in each time interval are calculated; The degradation kinetic equation was established based on the chemical reaction kinetics theory using the pollutant degradation rate in each time interval, and the ozone excess condition was considered to simplify it into a pseudo-first-order reaction kinetic equation, and the apparent rate constant was determined by linear regression analysis. Based on the pseudo-first-order reaction kinetics equation and the apparent rate constant, a rate characteristic matrix including the apparent rate constant, activation energy and average degradation rate was constructed.

10. A monitoring system for purifying liquid discharged after ozone gynecological treatment according to claim 8, characterized in that: The analysis of the degradation characteristics of the rate characteristic matrix, determination of pollutant degradation process parameters, and construction of degradation efficiency evaluation indicators include: According to the rate characteristic matrix, the kinetic stability index, degradation efficiency index and energy utilization index are calculated; The weight coefficients of each index are determined by using fuzzy comprehensive evaluation method, and the parameters of pollutant degradation process are established; Based on the pollutant degradation process parameters and combined with the entropy weight method, a degradation efficiency evaluation index was constructed.

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