A purification treatment monitoring system for the discharged liquid after ozone gynecological treatment

Abstract

The present invention discloses a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment, which relates to the technical field of medical waste liquid treatment monitoring, and includes: a content analysis unit, which 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; a characteristic evaluation unit, which is used to utilize the output result of the pollutant content analysis model, combine the chemical reaction kinetics theory, generate a characteristic matrix of pollutant degradation rate, and determine the degradation process parameters to construct a treatment effect evaluation model; a monitoring and determination unit, which 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. The present invention realizes the precise monitoring and intelligent control of the special medical waste liquid treatment process.

Description

Technical Field

[0001] The present invention relates to the technical field of medical waste liquid treatment and monitoring, and more specifically, 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 that treats gynecological inflammations, cervical erosion and other diseases through the strong oxidizing property and biological activity of ozone. 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, which automatically adjust the treatment process parameters by collecting water quality parameters in real time and combining preset treatment standards.

[0004] However, the existing treatment and monitoring methods have obvious limitations when dealing with the special medical waste liquid after ozone gynecological treatment. First of all, conventional monitoring indicators and evaluation criteria 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. Secondly, 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 based on 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 operating cost and safety risk, and there is an urgent need to develop more professional and accurate treatment and monitoring methods.

[0005] No effective solutions have been proposed for the problems in the related technologies. 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 special medical waste liquid treatment process 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] For this reason, the specific technical solution adopted by the present invention is as follows:

[0008] A purification treatment monitoring system for the discharged liquid after ozone gynecological treatment, comprising:

[0009] A content analysis unit, configured to calculate the pollutant concentration change characteristic index based on the monitoring data of the discharged liquid after ozone gynecological treatment, and fit the degradation curve of the pollutant by a piecewise linear equation to establish a pollutant content analysis model;

[0010] 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 theory of chemical reaction kinetics, and determine the degradation process parameters to construct a treatment effect evaluation model;

[0011] A monitoring and determination unit, configured to obtain the monitoring result of the discharged liquid by detecting the warning threshold according to the output result of the treatment effect evaluation model;

[0012] The content analysis unit includes:

[0013] A feature analysis module, configured to determine the segmentation interval according to the concentration difference of the monitoring data, calculate the degradation weight coefficient, obtain the time-varying curve by weighted accumulation, and determine the degradation time node; specifically including:

[0014] Calculate the pollutant concentration difference between adjacent time points of the monitoring data, determine the segmentation interval according to the change of the difference, and identify the fluctuation period to obtain the candidate point set;

[0015] Construct a pollutant degradation weight function based on the candidate point set, and solve to obtain the degradation weight coefficient at each moment;

[0016] Use the degradation weight coefficient to sum the pollutant concentration changes in each segmentation interval by weighted summation to obtain the time-varying cumulative curve, and determine the degradation time node by analyzing the slope change.

[0017] Furthermore, the content analysis unit is connected to the monitoring and determination unit through the characteristic evaluation unit;

[0018] The content analysis unit further includes:

[0019] An exponential construction module, which is used to construct a feature space and map degradation monitoring indicators based on degradation time nodes, obtain a pollutant concentration change characteristic index by calculating the feature vectors of different degradation monitoring indicators, and establish a grading standard for the characteristic index;

[0020] A fitting and modeling module, which is used to design the constraint conditions of a piecewise linear equation set according to the grading result of the pollutant concentration change characteristic index, introduce a continuity correction term, obtain a degradation curve equation, and construct a pollutant content analysis model;

[0021] Among them, the degradation monitoring indicators include pH value, ozone residue, chemical oxygen demand, and turbidity.

[0022] Furthermore, the expression of the pollution degradation weight function is:

[0023] ;

[0024] In the formula, WP ( t ) is the pollution degradation weight value at t time, 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 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.

[0025] Furthermore, based on the degradation time nodes, construct a feature space and map the degradation monitoring indicators, obtain the pollutant concentration change characteristic index by calculating the feature vectors of different degradation monitoring indicators, and the establishment of the grading standard for the characteristic index includes:

[0026] Construct a degradation feature space according to the time interval divided by the degradation time nodes, and map different degradation monitoring indicators to the degradation feature space;

[0027] Calculate the feature vectors of different degradation monitoring indicators in each time interval to obtain the pollutant concentration change characteristic index;

[0028] Analyze the distribution law of the pollutant concentration change characteristic index, and establish a grading standard for the pollutant concentration change characteristic index by setting grading thresholds.

[0029] Furthermore, the expression of the pollutant concentration change characteristic index is:

[0030] ;

[0031] In the formula, I ( t ) is the pollutant concentration change characteristic index at t time, and Δ pH s is the standardized change amount of pH value within this time interval, and Δ O 3s is the standardized change amount of ozone residue within this time interval, and Δ COD s is the standardized change amount of chemical oxygen demand within this time interval, and Δ Tur s is the standardized change amount of turbidity within this time interval; β 1 is the pH value weight coefficient, β 2 is the ozone residue weight coefficient, β 3 is the COD value weight coefficient, β 4 is the turbidity weight coefficient.

[0032] Furthermore, according to the classification results of the pollutant concentration change characteristic index, the constraint conditions of the piecewise linear equations are designed, and a continuity correction term is introduced to obtain the degradation curve equation, and a pollutant content analysis model is constructed, including:

[0033] Based on the classification results of the pollutant concentration change characteristic index, a piecewise linear equation set is established, and the constraint conditions are designed according to the degradation characteristics of each time interval;

[0034] According to the time node characteristics of the piecewise linear equation set, an exponential decay function is used to construct a continuity correction term to ensure the smooth transition of the degradation curve;

[0035] Combining the constraint conditions and the continuity correction term, the piecewise linear equation set is solved to obtain the degradation curve equation, and a time-varying correction factor is introduced to generate the pollutant content analysis model.

[0036] Furthermore, the constraint conditions designed according to the degradation characteristics of each time interval include:

[0037] Based on the pollutant contents at the initial time and the termination time, the boundary constraint conditions of the piecewise linear equation set are set, and the pollutant content values of the equation at the starting point and the termination point are matched with the actual measured values;

[0038] 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;

[0039] 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;

[0040] 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.

[0041] Furthermore, the characteristic evaluation unit includes:

[0042] A rate characteristic module, which is used to calculate the pollutant degradation rate of each time interval based on the output data of the pollutant content analysis model, establish a degradation kinetic equation in combination with the reaction kinetics theory, and generate a rate characteristic matrix representing the pollutant degradation characteristics;

[0043] A parameter characteristic module, which is used to analyze the degradation characteristics of the rate characteristic matrix, determine the pollutant degradation process parameters, and construct a degradation efficiency evaluation index;

[0044] An effect evaluation module, which is used to construct a treatment effect evaluation model by introducing time series fluctuation compensation according to the degradation efficiency evaluation index and the pollutant degradation process parameters.

[0045] Furthermore, based on the output data of the pollutant content analysis model, calculating the pollutant degradation rate of each time interval, and establishing a degradation kinetic equation in combination with the reaction kinetics theory, the rate characteristic matrix representing the pollutant degradation characteristics includes:

[0046] According to the output results of the pollutant content analysis model, calculate the instantaneous degradation rate and average degradation rate of the pollutants in each time interval;

[0047] Using the pollutant degradation rate of 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;

[0048] Based on the pseudo-first-order reaction kinetic equation and the apparent rate constant, construct a rate characteristic matrix including the apparent rate constant, activation energy, and average degradation rate.

[0049] Furthermore, analyzing the degradation characteristics of the rate characteristic matrix, determining the pollutant degradation process parameters, and constructing the degradation efficiency evaluation index include:

[0050] Calculate the kinetic stability index, degradation efficiency index, and energy utilization index according to the rate characteristic matrix;

[0051] Use the fuzzy comprehensive evaluation method to determine the weight coefficients of each index and establish the parameters of the pollutant degradation process;

[0052] Based on the parameters of the pollutant degradation process, combine the entropy weight method to construct the degradation efficiency evaluation index.

[0053] The beneficial effects of the present invention are as follows:

[0054] (1) Through the content analysis unit, the present invention innovatively designs the sewage reduction weight function and the construction method of the characteristic index. This method is aimed at the special components such as organic secretions, exfoliated cells, and ozone residues in the discharged liquid after ozone gynecological treatment. First, determine the segmented interval by calculating the difference in pollutant concentrations at adjacent time points of the monitoring data, and identify the fluctuation period to obtain the candidate point set. Then, based on the candidate point set, construct the sewage reduction weight function, and obtain the time-varying curve and determine the degradation time node through weighted accumulation; 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 pollutant concentration change characteristic index, 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.

[0055] (2) Based on the theory of chemical reaction kinetics, the characteristic evaluation unit of the present invention first 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, combines the entropy weight method to construct a scientific degradation efficiency evaluation index, 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.

[0056] (3) By innovatively introducing an early warning threshold detection mechanism, the monitoring and determination unit of the present invention 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 processing system; in addition, this determination method based on multi-level constraints can automatically optimize the processing parameters according to the characteristic differences of different batches of waste liquid, effectively solving the problem of unstable treatment effects 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 providing technical support for the standardized treatment of special medical waste liquid after ozone gynecological treatment in medical institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order 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 be obtained based on these drawings.

[0058] 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;

[0059] Figure 2 is a schematic flow chart of a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to an embodiment of the present invention;

[0060] 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;

[0061] 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.

[0062] In the figure:

[0063] 1. Content analysis unit; 101. Feature analysis module; 102. Index construction module; 103. Fitting modeling module; 2. Characteristic evaluation unit; 201. Rate feature module; 202. Parameter feature module; 203. Effect evaluation module; 3. Monitoring and determination unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] To further illustrate each embodiment, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principle of the embodiments. With reference to these contents, those of ordinary skill in the art should be able to understand other possible embodiments and the advantages of the present invention. The components in the drawings are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0065] According to an embodiment of the present invention, a purification treatment monitoring system for the discharged liquid after ozone gynecological treatment is provided.

[0066] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. 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:

[0067] A content analysis unit 1, 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 through a piecewise linear equation, and establish a pollutant content analysis model;

[0068] A characteristic evaluation unit 2, configured to generate a pollutant degradation rate characteristic matrix by combining the output result of the pollutant content analysis model with the chemical reaction kinetics theory, determine the degradation process parameters, and construct a treatment effect evaluation model;

[0069] A monitoring and determination unit 3, 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;

[0070] Specifically, the content analysis unit 1 is connected to the characteristic evaluation unit 2 and the monitoring and determination unit 3.

[0071] The content analysis unit 1 includes:

[0072] A feature analysis module 101, configured to determine a segmented 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;

[0073] When the feature analysis module 101 determines a segmented interval according to the concentration difference of the monitoring data, calculates a degradation weight coefficient, obtains a time-varying curve through weighted accumulation, and determines a degradation time node, it includes:

[0074] Calculating the pollutant concentration difference between adjacent time points of the monitoring data, determining a segmented interval according to the difference change, and identifying the fluctuation period to obtain a candidate point set;

[0075] Constructing a pollutant degradation weight function based on the candidate point set and solving to obtain the degradation weight coefficient at each moment;

[0076] Using the degradation weight coefficient, the change amount of pollutant concentration in each segmented interval is weighted and summed to obtain a time-varying cumulative curve, and the degradation time node is determined by analyzing the slope change.

[0077] Specifically, taking 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, showing a slightly yellowish 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:

[0078] First, ① the system calculates the difference sequence of the four collected monitoring indicators. 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:

[0079] ΔCOD(t)=COD(t)-COD(t - 1);

[0080] Next, a sliding variance analysis is performed with a 30-second window to calculate the local fluctuation intensity σ(t):

[0081] σ(t)=(1 / n)∑[ΔCOD(t - i)-μ] 2 ;

[0082] In the formula, n is the number of data points in 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 in the window, and the summation range is from i = 0 to n - 1.

[0083] Specifically, in actual treatment, it is found that at t = 17 minutes, the color of this medical waste liquid begins to significantly lighten, the COD value drops to 65 mg / L, and the fluctuation intensity reaches 0.22; at t = 42 minutes, this medical waste liquid has basically become clear, 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 (t1 and t2) are marked as the candidate point set.

[0084] Specifically, ② a pollution degradation weight function is established based on the candidate point set. 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 degradation weight function is:

[0085] ;

[0086] In the formula, WP ( t ) is t the pollution reduction weight value at the moment of A is the pollution reduction basic weight coefficient (with a value of 0.8), indicating the initial weight size, B is the pollution reduction attenuation factor (with a value of 0.5), controlling the attenuation rate of the weight with fluctuations, σ ( t ) is t the pollutant concentration fluctuation intensity at the moment of σ ( t -1) is t the pollutant concentration fluctuation intensity at the moment of -1, | σ ( t ) - σ ( t -1)| represents the absolute difference in the pollutant concentration fluctuation intensity between adjacent moments.

[0087] 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 are of relatively high importance.

[0088] Specifically, ③ using the degradation weight coefficient, the concentration changes of the four degradation monitoring indicators in each segmented interval are weighted and summed respectively to calculate the comprehensive time-varying cumulative value A(t):

[0089] A(t)=∑[WP(t)×(α1×ΔpH(t)+α2×ΔO3(t)+α3×ΔCOD(t)+α4×ΔTur(t))];

[0090] In the formula, WP(t) is the degradation weight coefficient at time t, ΔpH(t) is the difference in pH value at time t, ΔO3(t) is the difference in ozone residue at time t, ΔCOD(t) is the difference in chemical oxygen demand at time t, ΔTur(t) is the difference in turbidity at time t, and α1, α2, α3, and α4 are the weighting coefficients of pH value, ozone residue, chemical oxygen demand, and turbidity respectively. In this embodiment, they are taken as 0.15, 0.35, 0.35, and 0.15 respectively. Since the sampling interval is 5 seconds, for the candidate point set time interval [t1, t2], the summation range is from t = t1 to t2, with a step size of 5 seconds.

[0091] Specifically, in the above embodiment, for the interval of 17 - 42 minutes:

[0092] The range of the degradation weight coefficient WP(t) at each moment in this interval is 0.75 - 0.82;

[0093] The differences of the four indicators are calculated by sampling every 5 seconds;

[0094] Multiply the weighted differences of all indicators by the degradation weight coefficient and accumulate them to obtain the comprehensive time-varying cumulative value for this interval;

[0095] Finally, by analyzing the slope change of the comprehensive time-varying cumulative curve A(t), it is found that:

[0096] The slope change rate reaches the maximum value of 0.45 at t = 17 minutes;

[0097] The second peak value of 0.38 is reached at t = 42 minutes;

[0098] 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 the second peak value of 0.38 is reached at t = 42 minutes, further verifying the rationality of these two time points as the degradation time nodes.

[0099] Specifically, the three finally determined time intervals are respectively:

[0100] 0 - 17 minutes: Initial degradation interval, at this time, the color and turbidity of the waste liquid begin to change significantly;

[0101] 17 - 42 minutes: Rapid degradation interval, at this time, the organic matter and ozone residue in the waste liquid degrade rapidly;

[0102] 42 - 60 minutes: Stable degradation interval, at this time, all indicators of the waste liquid tend to be stable.

[0103] In one embodiment, the content analysis unit 1 further includes:

[0104] An index construction module 102, configured to construct a feature space and map the degradation monitoring indicators based on the degradation time nodes, obtain the pollutant concentration change characteristic index by calculating the feature vectors of different degradation monitoring indicators, and establish a grading standard for the feature index;

[0105] 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 characteristic index, introduce a continuity correction term, obtain a degradation curve equation, and construct a pollutant content analysis model;

[0106] Wherein, the degradation monitoring indicators include pH value, ozone residue, chemical oxygen demand, and turbidity.

[0107] Specifically, the feature analysis module 101 is connected to the index construction module 102 and the fitting and modeling module 103.

[0108] In one embodiment, when the exponential construction module 102 constructs a feature space and maps degradation monitoring indicators based on degradation time nodes, and obtains the pollutant concentration change characteristic index by calculating the feature vectors of different degradation monitoring indicators and establishes a grading standard for the characteristic index, it includes:

[0109] 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;

[0110] Calculate the feature vectors of different degradation monitoring indicators in each time interval to obtain the pollutant concentration change characteristic index;

[0111] Analyze the distribution law of the pollutant concentration change characteristic index, and establish a grading standard for the pollutant concentration change characteristic index by setting grading thresholds.

[0112] 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. Construct a four-dimensional feature space and map the four degradation monitoring indicators into this space. To ensure the accuracy of the mapping, standardize the monitoring data:

[0113] Y(t)=(X(t)-X min ) / (X max -X min );

[0114] In the formula, X(t) is the original value of a certain degradation monitoring indicator at time t, and X min and X max are the minimum and maximum values of this indicator during the entire monitoring process, respectively.

[0115] Specifically, ② Calculate the change amount of each degradation monitoring indicator in each time interval. Taking the second time interval (17 - 42 minutes) as an example:

[0116] ΔpH s =|Y_pH(42)-Y_pH(17)| / / pH value change amount;

[0117] ΔO 3s =|Y_O3(42)-Y_O3(17)| / / ozone residue change amount;

[0118] ΔCOD s =|Y_COD(42)-Y_COD(17)| / / COD value change amount;

[0119] ΔTur s =|Y_Tur(42)-Y_Tur(17)| / / turbidity change amount.

[0120] Specifically, the characteristic index of pollutant concentration change in this time interval is calculated by weighted combination, and the expression of the characteristic index of pollutant concentration change is:

[0121] ;

[0122] In the formula, I ( t ) is the characteristic index of pollutant concentration change at t moment, which characterizes the overall degradation degree of pollutants in this time interval. Δ pH s is the standardized change of pH value in this time interval, Δ O 3s is the standardized change of ozone residual amount in this time interval, Δ COD s is the standardized change of chemical oxygen demand in this time interval, Δ Tur s is the standardized change of turbidity in this time interval; β 1 = 0.2 is the weight coefficient of pH value, which reflects the change of acid-base environment of the discharged liquid and has little influence on the degradation process. β 2 = 0.3 is the weight coefficient of ozone residual amount, which directly reflects the degradation effect of ozone and is one of the important indicators. β 3 = 0.3 is the weight coefficient of COD value, which characterizes the degradation degree of organic matter and is the core index for evaluating the treatment effect. β 4 = 0.2 is the weight coefficient of turbidity, which reflects the suspended solid content of the discharged liquid and helps to judge the degradation effect.

[0123] Specifically, ③ statistically analyze the characteristic indexes of pollutant concentration change calculated in three time intervals, establish a three-level classification standard, and set the classification threshold to 0.4 and 0.8 in the above embodiments, and obtain:

[0124] I(t) < 0.4: The pollutant concentration changes slowly and the degradation effect is not obvious;

[0125] 0.4 ≤ I(t) < 0.8: The pollutant concentration changes moderately and the degradation effect is good;

[0126] I(t) ≥ 0.8: The pollutant concentration changes significantly and the degradation effect is excellent.

[0127] Specifically, the actual treatment results show that:

[0128] The first time interval (0 - 17 minutes): I(t) = 0.35, indicating that the degradation effect is not obvious in the initial stage;

[0129] The second time interval (17 - 42 minutes): I(t) = 0.85, indicating that the degradation effect is the best in this stage;

[0130] The third time interval (42 - 60 minutes): I(t) = 0.55, indicating that the degradation effect tends to be stable in the later stage;

[0131] The compliance degree between the characteristic index grading result and the actual degradation effect reaches 90%, verifying the reliability of this grading method.

[0132] In one embodiment, when the fitting and modeling module 103 designs the constraint conditions of the piecewise linear equations according to the grading result of the characteristic index of the pollutant concentration change, introduces the continuity correction term, obtains the degradation curve equation, and constructs the pollutant content analysis model, it includes:

[0133] Based on the grading result of the characteristic index of the pollutant concentration change, establish a piecewise linear equation set, and design the constraint conditions according to the degradation characteristics of each time interval;

[0134] According to the time node characteristics of the piecewise linear equation set, use the exponential decay function to construct the continuity correction term to ensure the smooth transition of the degradation curve;

[0135] Combine the constraint conditions and the continuity correction term, solve the piecewise linear equation set to obtain the degradation curve equation, and introduce the time-varying correction factor to generate the pollutant content analysis model.

[0136] In one embodiment, the grading result of the characteristic index of the pollutant concentration change includes a low degradation interval, a medium degradation interval, and a high degradation interval;

[0137] Designing the constraint conditions according to the degradation characteristics of each time interval includes:

[0138] Based on the pollutant content 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;

[0139] 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;

[0140] Through the experimental calibration method, determine the threshold parameters of the slope constraint of each interval, and construct the slope constraint equation of each time interval.

[0141] Specifically, ① based on the characteristic index grading results of the three time intervals obtained (the low degradation interval, the medium degradation interval, and the high degradation interval correspond to 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 respectively), a piecewise linear equation system is constructed:

[0142] C(t)=a1×t + b1, 0 ≤ t < 17;

[0143] C(t)=a2×t + b2, 17 ≤ t < 42;

[0144] C(t)=a3×t + b3, 42 ≤ t ≤ 60;

[0145] In the formula, C(t) is the pollutant content at time t, and a j and b j (j = 1, 2, 3) are undetermined coefficients.

[0146] Specifically, according to the degradation characteristics of each interval, constraint conditions are designed, including:

[0147] a) Initial condition: C(0) = C0 (initial pollutant content);

[0148] b) Termination condition: C(60) = C f (final pollutant content);

[0149] c) Slope constraint: |a1| ≤ k1 (corresponding to the low degradation interval where I(t) = 0.35), |a2| ≥ k2 (corresponding to the high degradation interval where I(t) = 0.85), k3 ≤ |a3| ≤ k4 (corresponding to the medium degradation interval where I(t) = 0.55), where k1 = 0.4, k2 = 0.8, k3 = 0.5, and k4 = 0.7 are slope thresholds calibrated by experiments.

[0150] Specifically, ② continuity correction terms are constructed at time nodes t = 17 and t = 42:

[0151] ε1(t)=γ1×exp(-λ1×|t - 17|);

[0152] ε2(t)=γ2×exp(-λ2×|t - 42|);

[0153] In the formula, γ1 and γ2 are the first correction coefficient and the second correction coefficient, which are respectively taken as 0.15 and 0.12 in this embodiment; λ1 and λ2 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.

[0154] Specifically, ③ combining the constraint conditions and the continuity correction terms, the complete degradation curve equation is obtained:

[0155] ;

[0156] Wherein, C( t ) is the pollutant content at t moment, t is the time variable, a 1, a 2, a 3 are the slope coefficients of the low degradation interval, medium degradation interval and high degradation interval respectively, b 1, b 2, b 3 are the intercept coefficients of the low degradation interval, medium degradation interval and high degradation interval respectively, ε 1( t ) is the continuity correction term at the first time node, ε 2( t ) is the continuity correction term at the second time node, H ( t ) is the unit step function. When t≥ 0, H ( t ) = 1. When t < 0, H ( t ) = 0.

[0157] Specifically, in this embodiment, the values of each coefficient are obtained by least squares method:

[0158] a 1 = -0.35, b 1 = 100;

[0159] a 2 = -0.82, b 2 = 108;

[0160] a 3 = -0.48, b 3 = 94;

[0161] Introduce a time-varying correction factor to construct a pollutant content analysis model, and the expression is:

[0162] M(t) = C(t) + δ(t);

[0163] Wherein, δ(t) is the time-varying correction term;

[0164] δ(t) = μ × [1 - exp(-σ × t)];

[0165] Wherein, μ is the correction coefficient (taking the value of 0.1), and σ is the time scale factor (taking the value of 0.05)

[0166] Specifically, the verification results of the final pollutant content analysis model show that:

[0167] The degradation curve has a smooth transition at the time nodes and no obvious jumps;

[0168] The average relative error between the model prediction value and the measured value is 4.2%;

[0169] The goodness of fit R 2 in three time intervals are 0.92, 0.95, and 0.89 respectively;

[0170] This indicates 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.

[0171] In one embodiment, the characteristic evaluation unit 2 includes:

[0172] 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, and generate a rate characteristic matrix representing the pollutant degradation characteristics;

[0173] 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;

[0174] An effect evaluation module 203, configured 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.

[0175] Specifically, the rate characteristic module 201 is connected to the effect evaluation module 203 through the parameter characteristic module 202.

[0176] In one embodiment, when the rate characteristic module 201 calculates the pollutant degradation rate in each time interval based on the output data of the pollutant content analysis model, and establishes a degradation kinetic equation in combination with the reaction kinetics theory, and generates a rate characteristic matrix representing the pollutant degradation characteristics, it includes:

[0177] According to the output result of the pollutant content analysis model, calculate the instantaneous degradation rate and average degradation rate of the pollutant in each time interval;

[0178] Using the pollutant degradation rate in each time interval, establish a degradation kinetic equation based on the chemical reaction kinetics theory, and considering the ozone excess condition, simplify it to a pseudo-first-order reaction kinetic equation, and determine the apparent rate constant through linear regression analysis;

[0179] Based on the pseudo-first-order reaction kinetic equation and the apparent rate constant, construct a rate characteristic matrix including the apparent rate constant, activation energy, and average degradation rate.

[0180] Specifically, ① First, using the data output by the pollutant content analysis model, calculate the instantaneous degradation rate of pollutants in each time interval. By comparing the difference in pollutant content at adjacent time points, the change trend of pollutant concentration over time is 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:

[0181] v(t)= -[M(t) - M(t - Δt)] / Δt;

[0182] 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).

[0183] Specifically, calculate the average degradation rate for three time intervals (0 - 17 minutes, 17 - 42 minutes, and 42 - 60 minutes) respectively:

[0184] v avg = [M(t2) - M(t1)] / (t2 - t1);

[0185] In the formula, v avg is the average degradation rate, and M(t1) and M(t2) 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.

[0186] Specifically, ② Based on the theory of chemical reaction kinetics, establish the ozone oxidation degradation kinetic equation:

[0187] -dM / dt = k×[M]α×[O3]β;

[0188] In the formula, k is the reaction rate constant (L / (mg·min)), [M] is the pollutant concentration (mg / L), [O3] is the ozone concentration (mg / L), and α and β are the reaction orders of the pollutant and ozone respectively (dimensionless).

[0189] Specifically, considering that in the actual treatment process, ozone is maintained in an excessive state through continuous aeration, and its concentration is much greater than the pollutant concentration. In this case, the concentration of ozone can be regarded as constant, that is, [O3]≈C (constant), then the equation is simplified to a pseudo-first-order reaction kinetic equation:

[0190] -dM / dt = kapp×[M];

[0191] In the formula, kapp is the apparent rate constant (min-1). Since the ozone concentration remains constant, the k1×[O3]β part in the original equation can be combined into a constant kapp, thus obtaining a simpler pseudo-first-order reaction equation.

[0192] Specifically, the apparent rate constant is determined by linear regression analysis. Integrating the pseudo-first-order reaction kinetic equation:

[0193] ln[M]=ln[M0]-kapp×t;

[0194] In the formula, [M0] is the initial pollutant concentration (mg / L), and t is the reaction time (min);

[0195] Taking the interval of 17 - 42 minutes as an example, after taking the logarithm of the experimentally measured pollutant concentration data and plotting it against time, the apparent rate constant kapp for this interval obtained through linear regression analysis is 0.082 min-1. The goodness of linear fit (i.e., the correlation coefficient) for this interval reaches 0.95, indicating that the experimental data is in good agreement with the pseudo-first-order reaction kinetic equation.

[0196] 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:

[0197] R = [k' E v avg ;

[0198] In the formula, k' is the apparent rate constant, E is the activation energy, and v avg is the average degradation rate.

[0199] Specifically, in this embodiment, the activation energy E is calculated by the Arrhenius equation:

[0200] k' = A·exp(-E / RT);

[0201] In the formula, A is the pre-exponential factor, R is the gas constant, and T is the reaction temperature.

[0202] 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:

[0203] R = [0.082, 42.8, 1.96];

[0204] Analysis of the throughput rate characteristic matrix shows that the interval from 17 to 42 minutes has the optimal kinetic characteristics, providing an important basis for determining the optimal treatment time and optimizing the operating parameters.

[0205] In one embodiment, when analyzing the degradation characteristics of the rate characteristic matrix, determining the pollutant degradation process parameters, and constructing the degradation efficiency evaluation index, the parameter feature module 202 includes:

[0206] Calculating the kinetic stability index, degradation efficiency index, and energy utilization index according to the rate characteristic matrix;

[0207] Determining the weight coefficients of each index using the fuzzy comprehensive evaluation method to establish the pollutant degradation process parameters;

[0208] Constructing the degradation efficiency evaluation index based on the pollutant degradation process parameters in combination with the entropy weight method.

[0209] 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 activation energy. Taking the interval from 17 to 42 minutes as an example, the apparent rate constant k' in this interval is 0.082 min-1, which is the maximum observed k' 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 w1 and w2 are determined by the expert scoring method and are 0.6 and 0.4 respectively; the calculation results are:

[0210] KSI = 0.6×(0.082 / 0.082) + 0.4×(42.8 / 42.8) = 1.0;

[0211] It shows that this interval has the best kinetic stability.

[0212] Specifically, the present invention uses the degradation efficiency index to describe the pollutant removal effect. In the interval from 17 to 42 minutes, the average degradation rate v avg is 1.96 mg / L·min, and the maximum degradation rate v max is 2.1 mg / L·min, and the pollutant concentration drops from 85 mg / L to 35 mg / L; the calculation results are:

[0213] DEI = (1.96 / 2.1)×(1 - 35 / 85) = 0.69;

[0214] This value indicates that the degradation efficiency reaches a relatively high level.

[0215] Specifically, the present invention uses the energy utilization index to evaluate the energy conversion efficiency. The amount of pollutant removed ΔM in this interval 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:

[0216] EUI = (50 / 2.8) × 0.85 = 15.18;

[0217] It shows that the pollutant removal effect per unit energy consumption is good.

[0218] Specifically, when using the fuzzy comprehensive evaluation to determine the weights, first establish an evaluation index system, including three first-level indexes: kinetic stability (u1), degradation efficiency (u2), and energy utilization (u3). The evaluation levels are divided into four levels: excellent (v1), good (v2), general (v3), and poor (v4). Establish a fuzzy relation matrix through experimental data analysis:

[0219] 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];

[0222] Specifically, use the analytic hierarchy process to construct a judgment matrix:

[0223] A = [1.0 2.0 3.0 0.5 1.0 2.0 0.33 0.5 1.0];

[0226] Calculate the eigenvalue and eigenvector to obtain the weight vector W = [0.54, 0.30, 0.16].

[0227] 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, the standardized matrix is calculated as:

[0228] X = [1.00 0.69 0.85 0.92 0.65 0.78 0.85 0.58 0.72];

[0231] Specifically, calculate the information entropy of the j-th index, and the expression is H j = -k∑(p ij × lnp ij );

[0232] Where k = 1 / ln(3) = 0.91, and the information entropies of the three indexes are H1 = 0.82, H2 = 0.85, and H3 = 0.88 respectively.

[0233] Specifically, calculate the information utility value and entropy weight:

[0234] d j = 1 - H j ;

[0235] w j = d j / ∑d j ;

[0236] The final entropy weight vector w = [0.42, 0.35, 0.23] is obtained.

[0237] Specifically, the comprehensive evaluation index is calculated as follows:

[0238] η = 0.42×1.00 + 0.35×0.69 + 0.23×0.85 = 0.86;

[0239] It shows that the overall treatment effect in the 17 - 42 - minute interval is the best.

[0240] In one embodiment, when the effect evaluation module 203 constructs the treatment effect evaluation model by introducing time - series fluctuation compensation according to the degradation efficiency evaluation index and the pollutant degradation process parameters, it includes:

[0241] Establish a fluctuation feature extraction model based on time series to analyze the periodic fluctuations and random perturbations in the pollutant degradation process;

[0242] Design an adaptive compensation algorithm to dynamically correct and optimize the fluctuation features;

[0243] Integrate the fluctuation compensation results and the degradation efficiency evaluation index to construct the final treatment effect evaluation model.

[0244] Specifically, ① For the extraction of fluctuation features, the time - series data is first decomposed by wavelet. In this embodiment, the db4 wavelet function is selected, and the pollutant concentration time series is decomposed at 3 scales:

[0245] f(t) = a3 + d3 + d2 + d1;

[0246] In the formula, a3 is the low - frequency approximation component, reflecting the overall change trend of the pollutant concentration; d3, d2, and d1 are the detail components at different scales, characterizing the fluctuation features at different frequencies.

[0247] Specifically, taking the 17 - 42 - minute interval as an example, through wavelet decomposition, we get:

[0248] The low - frequency approximation component a3 shows that the concentration as a whole shows an exponential decay trend;

[0249] The d3 component shows that there are periodic fluctuations around 25 minutes, and the period is about 3 minutes;

[0250] The d2 component reflects short-term fluctuations with a fluctuation amplitude of ±2.5 mg / L;

[0251] The d1 component contains high-frequency noise with an amplitude less than 1 mg / L.

[0252] Specifically, ② design an adaptive Kalman filter for dynamic correction. The state equation is:

[0253] x(k + 1) = 0.95x(k) + u(k) + w(k);

[0254] y(k) = x(k) + v(k);

[0255] Where the process noise w(k) ~ N(0, 0.1) and the measurement noise v(k) ~ N(0, 0.2).

[0256] Specifically, the prediction step:

[0257] x-(k + 1) = 0.95x(k) + u(k);

[0258] P-(k + 1) = 0.95P(k)0.95T + Q;

[0259] Where Q is the process noise covariance matrix.

[0260] Specifically, the update step:

[0261] K(k + 1) = P-(k + 1)HT[HP-(k + 1)HT + R]-1;

[0262] x(k + 1) = x-(k + 1) + K(k + 1)[y(k + 1) - Hx-(k + 1)];

[0263] P(k + 1) = [I - K(k + 1)H]P-(k + 1);

[0264] Where R is the measurement noise covariance matrix.

[0265] Specifically, ③ construct the final treatment effect evaluation model:

[0266] M(t) = 0.45×η(t) + 0.35×[C(t) / C ref +0.20×F(t)

[0267] 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.

[0268] Specifically, taking the 17 - 42 minute interval as an example:

[0269] η(t) = 0.86 (degradation efficiency evaluation index);

[0270] C(t) / C ref = 5 / 15 = 0.33 (ratio of current concentration to initial concentration, dimensionless);

[0271] F(t) = -0.15 (prediction correction value);

[0272] Substituting into the model gives:

[0273] M(t) = 0.45×0.86 + 0.35×0.33 + 0.20×(-0.15) = 0.47;

[0274] In the present invention, the evaluation result M(t) ∈ [0, 1], where:

[0275] 0.8 - 1.0 indicates excellent treatment effect;

[0276] 0.6 - 0.8 indicates good treatment effect;

[0277] 0.4 - 0.6 indicates average treatment effect;

[0278] < 0.4 indicates that treatment parameters need to be optimized.

[0279] 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.

[0280] 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:

[0281] Establish a multi-level early warning threshold system and set key index thresholds in combination with medical safety standards;

[0282] Design an early warning determination method based on fuzzy rules to realize real-time monitoring of the treatment effect;

[0283] Generate a monitoring early warning report and provide treatment optimization suggestions.

[0284] 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:

[0285] Pollutant residue threshold:

[0286] Purification completed area: < 30 mg / L;

[0287] Continuous treatment area: 30 - 50 mg / L;

[0288] Key treatment area: > 50 mg / L.

[0289] Specifically, the treatment stability score is based on the fluctuation degree of the treatment effect evaluation model:

[0290] Stable: The treatment process fluctuation < 5% (indicating that the purification treatment process is normal);

[0291] Fluctuating: The treatment process fluctuation is 5% - 15% (treatment parameters need to be adjusted);

[0292] Unstable: The treatment process fluctuation > 15% (treatment plan needs to be changed).

[0293] Specifically, the purification degree index comprehensively considers the characteristic indexes of the discharged liquid:

[0294] NI = w1×(pH - 7) 2 + w2×(O3 / O 3ref ) + w3×(T urb / T urb-ref );

[0295] In the formula, w1 = 0.4, w2 = 0.35, w3 = 0.25 are weight coefficients; pH is the acidity and alkalinity, O3 is the ozone residue, and T urb is the turbidity; O 3ref and T urb-ref are the reference values of the ozone residue and the turbidity respectively.

[0296] 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:

[0297] Rule 1: IF (residue amount ∈ purification completion area) AND (stability = stable) AND (NI < 0.8) THEN (purification treatment can be terminated);

[0298] Rule 2: IF (residue amount ∈ continuous treatment area) OR (stability = fluctuating) OR (0.8 ≤ NI < 1.2) THEN (treatment parameters need to be adjusted);

[0299] Rule 3: IF (residue amount ∈ key treatment area) OR (stability = unstable) OR (NI ≥ 1.2) THEN (treatment plan needs to be changed).

[0300] Specifically, taking the treatment process of the discharged liquid in the 17 - 42 - minute interval as an example, the model output results show:

[0301] Residual pollutant amount: 35.2 mg / L (continuing treatment area); Treatment stability: fluctuating by 4.2% (stable); Purification degree index: 0.75 (good treatment); According to the fuzzy rules, the system prompts that further treatment is required.

[0302] Specifically, ③ generate a monitoring report on the treatment of the discharged liquid, and the report includes the following content:

[0303] a) Treatment status assessment:

[0304] Current treatment stage: mid-stage of purification treatment; Treatment progress: 75%; Treatment prompt: continue purification treatment;

[0305] b) Analysis of key indicators:

[0306] Residual pollutant amount: 35.2 mg / L, further treatment is required; Treatment stability: good, treatment parameters are appropriate; Purification degree index: treatment effect meets the standard;

[0307] c) Suggestions for treatment optimization:

[0308] 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.

[0309] Specifically, this monitoring report is updated every 5 minutes to reflect the treatment status of the discharged liquid in real time. When treatment abnormalities are detected, the system provides optimization suggestions in a timely manner to ensure the purification treatment effect of the discharged liquid after ozone gynecological treatment.

[0310] To facilitate the understanding of the above technical solutions 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:

[0311] 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. The content analysis unit 1 determined the initial treatment parameters based on these measured data and in combination with the preset treatment standards.

[0312] 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, significant improvements in various indicators of the waste liquid were detected by the system: the pH value was stabilized at 7.2, the ozone residue was reduced to 0.8 mg / L, the COD value was reduced to 35 mg / L, and the turbidity was reduced 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.

[0313] Meanwhile, 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 residue in the waste liquid was reduced to below 0.3 mg / L, the COD value was reduced to 28 mg / L, and the turbidity was reduced to 12 NTU. All the indicators met the medical institution sewage discharge standards. This verified that the 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.

[0314] 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 the 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 characteristic vectors of multiple degradation monitoring indicators including pH value, ozone residue, chemical oxygen demand, and turbidity, obtain the pollutant concentration change characteristic index, 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 precise 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, the instantaneous degradation rate and average degradation rate of pollutants in each time interval are calculated. Considering the condition of excessive ozone, the degradation kinetic equation is simplified to a pseudo-first-order reaction kinetic equation, and the apparent rate constant is determined through linear regression analysis, constructing 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, and determines the weight coefficients of each index through the fuzzy comprehensive evaluation method, combines the entropy weight method to construct a scientific degradation efficiency evaluation index, 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 a warning threshold detection mechanism, combines the classification result 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 existing system has insufficient adaptive ability resulting in unstable treatment effects. 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.

[0315] 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 purification treatment monitoring system for the discharged liquid after ozone gynecological treatment, characterized in that, Comprising: A content analysis unit, which 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; A characteristic evaluation unit, which is used to generate a characteristic matrix of pollutant degradation rate and determine the degradation process parameters by using the output result of the pollutant content analysis model and combining with the theory of chemical reaction kinetics, and construct a treatment effect evaluation model; A monitoring and determination unit, which 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; The content analysis unit includes: A feature analysis module, which is used to determine the piecewise 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 including: Calculating the pollutant concentration difference between adjacent time points of the monitoring data, determining the piecewise interval according to the change of the difference, and identifying the fluctuation period to obtain the 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 the time-varying cumulative curve, and determining the degradation time node through slope change analysis.

2. The purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to claim 1, wherein, 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, which is used to construct a feature space and map the degradation monitoring index based on the degradation time node, calculate the feature vectors of different degradation monitoring indexes to obtain the characteristic index of pollutant concentration change, and establish a grading standard for the characteristic index; A fitting and modeling module, which is used to design the constraint conditions of the piecewise linear equation set according to the grading result of the characteristic index of pollutant concentration change, introduce a continuity correction term to obtain the degradation curve equation, and construct a pollutant content analysis model; Wherein, the degradation monitoring indexes include pH value, ozone residue, chemical oxygen demand and turbidity.

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

4. A purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to claim 2, characterized in that, The constructing a feature space and mapping the degradation monitoring index based on the degradation time node, calculating the feature vectors of different degradation monitoring indexes to obtain the characteristic index of pollutant concentration change, and establishing a grading standard for the characteristic index includes: Constructing a degradation feature space according to the time interval divided by the degradation time node, and mapping different degradation monitoring indexes to the degradation feature space; Calculating the feature vectors of different degradation monitoring indexes in each time interval to obtain the characteristic index of pollutant concentration change; Analyzing the distribution law of the characteristic index of pollutant concentration change, and establishing a grading standard for the characteristic index of pollutant concentration change by setting a grading threshold.

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

6. The purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to claim 2, wherein The designing the constraint conditions of the piecewise linear equation set according to the grading result of the characteristic index of pollutant concentration change, introducing a continuity correction term to obtain the degradation curve equation, and constructing a pollutant content analysis model includes: Based on the classification results of the pollutant concentration change characteristic index, establish a piecewise linear equation system, and design constraint conditions according to the degradation characteristics of each time interval; According to the time node characteristics of the piecewise linear equation system, use the exponential decay function to construct a continuity correction term to ensure the smooth transition of the degradation curve; Combined with the constraint conditions and the continuity correction term, solve the piecewise linear equation system to obtain the degradation curve equation, and introduce a time-varying correction factor to generate a pollutant content analysis model.

7. A purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to claim 6, characterized in that, The design of the constraint conditions according to the degradation characteristics of each time interval includes: Based on the pollutant content at the initial time and the termination time, set the boundary constraint conditions of the piecewise linear equation system, 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 equation of 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.

8. A purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to claim 1, characterized in that, The characteristic evaluation unit includes: A rate characteristic module, which is used to calculate the pollutant degradation rate of 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 characteristic module, which is used 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, which is used to construct a treatment effect evaluation model by introducing time series fluctuation compensation according to the degradation efficiency evaluation index and the pollutant degradation process parameters.

9. The purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to claim 8, wherein, The calculation of the pollutant degradation rate of each time interval based on the output data of the pollutant content analysis model, and the establishment of a degradation kinetic equation in combination with the reaction kinetics theory to generate a rate characteristic matrix representing the pollutant degradation characteristics includes: According to the output results of the pollutant content analysis model, calculate the instantaneous degradation rate and average degradation rate of the pollutant in each time interval; Using the pollutant degradation rate of each time interval, establish a degradation kinetic equation based on the chemical reaction kinetics theory, and considering the ozone excess condition, 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 characteristic matrix including the apparent rate constant, activation energy, and average degradation rate.

10. A purification treatment monitoring system for the discharged liquid after ozone gynecological treatment according to claim 8, characterized in that, The analysis of the degradation characteristics of the rate characteristic matrix, the determination of the pollutant degradation process parameters, and the construction of the degradation efficiency evaluation index include: 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, construct a degradation efficiency evaluation index in combination with the entropy weight method.

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