A disinfectant production detection method and system based on sampling module

Abstract

The present invention belongs to the technical field of disinfectant detection, and discloses a disinfectant production detection method and system based on a sampling module, wherein the disinfectant production detection method based on the sampling module includes the following steps: acquiring multiple groups of change data for abnormality identification, screening channel consistency samples, setting rate ratio and direction combination based on flow rate ratio, activating a constant temperature environment to extract a pressure curve, identifying time span and gradient morphology, matching volatile profiles to identify sample component labeling content; in the present invention, by synchronously acquiring the pressure change rate, speed fluctuation value and acceleration change value and jointly comparing the peak change amplitude, real-time identification of abnormal status and sampling channel status marking are achieved, high-precision recording of the pressure change curve is achieved, accurate identification of component attribution and quantitative labeling of sample characteristics are achieved, effectively improving the response speed of identification of disinfectant component changes, and improving identification accuracy and real-time adaptability in the production process.

Description

Technical Field

[0001] The present invention belongs to the technical field of disinfectant detection, and in particular, relates to a disinfectant production detection method and system based on a sampling module. Background Art

[0002] The field of disinfectant testing technology involves testing methods and means for monitoring, evaluating and controlling the composition and performance of various types of disinfectant products. Its core issues include quantitative or qualitative testing of the active ingredient content, impurity content, physical and chemical properties, and bactericidal performance of disinfectants, and covers the quality control system of disinfectants throughout the entire process of production, storage and use. The methods used in this technical field include chemical titration analysis, spectrophotometry, high-performance liquid chromatography, etc., supplemented by applicable sample preparation, standard substance comparison and testing equipment to ensure the stability and safety of disinfectant products and their application effectiveness in medical, public health, food processing and other scenarios.

[0003] Among them, the disinfectant production detection method refers to monitoring product quality through manual sampling combined with offline detection using desktop analytical instruments in the production process. This method uses manual sampling for liquid disinfectant samples and then detects the effective chlorine concentration through chemical titration, or uses ultraviolet spectrophotometry to determine the content of effective components in alcohol or quaternary ammonium disinfectants, or can also use gas chromatography to quantify aldehyde-containing components. This existing detection method requires operators to frequently sample, label, transfer and conduct laboratory analysis in multiple production links, which has problems such as detection delay, human error and insufficient sample representativeness. In addition, it is limited by sample transfer and multiple operation links, which can easily cause sample cross-contamination and human error. In addition, when analyzed by chemical titration, spectrophotometry or gas chromatography, the operation steps are cumbersome and the detection cycle is long, which reduces the use effect.

[0004] Based on the above technical problems, in the existing technology, in order to achieve real-time sampling and detection, multiple sampling modules are set on the disinfectant production line. Multiple sampling modules can realize multi-point distributed sampling scenarios, and online analysis instruments are also configured at the sampling module positions. The samples sampled by the sampling module are directly transported to the online analysis instrument to realize real-time sampling and detection processing of the samples, so as to improve the detection efficiency and consistency of the production process.

[0005] However, the existing sampling modules and online analytical instruments are only simply linked and controlled, and are unable to dynamically monitor the fluid status in the disinfectant delivery pipeline in real time. When emergencies such as flow rate fluctuations or component changes occur on the production line, the detection results are delayed, resulting in the inability to screen and replace abnormal samples in a timely manner, which easily leads to batch quality risks. In multi-point distributed sampling scenarios, the lack of consistency comparison of channel structure and guide position results in insufficient sample representativeness, affecting the accuracy of component analysis and the effectiveness of production process control. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a disinfectant production detection method and system based on a sampling module, which is used to solve the problems raised in the background technology, realize accurate sampling of disinfectants, effectively improve the response speed of identifying changes in disinfectant ingredients, and improve accuracy and real-time adaptability in the production process.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] A disinfectant production detection method based on a sampling module comprises the following steps:

[0009] S1: Obtain the pressure change rate at the inlet of the disinfectant delivery pipeline, the speed fluctuation value of the guide rotor device, and the acceleration change value at the sampling module structure. The peak change amplitudes of the three sets of data are called for joint comparison. If there is a single value that continuously deviates from the normal mode, the sampling channel is marked as an abnormal state and an abnormal sampling identification label is generated;

[0010] S2: Using abnormal sampling identification tags, extract the stable rate section in the corresponding diversion structure from the samples within the detection period, identify the sample point of the pressure change rate and extract the fluid guide position, and obtain the sampling replacement record;

[0011] S3: By sampling instead of recording, the fluid flow rate value recorded in the diversion inlet area is extracted, and the flow rate value given at the inlet of the disinfectant delivery channel is collected, the rotor speed ratio and rotation direction combination are set, and the shear channel execution configuration is generated;

[0012] S4: Use the shear channel to execute the configuration, introduce the sampling point into the micro-pressure chamber and activate the constant temperature environment. After the sample is injected into the micro-pressure chamber, extract the pressure change curve according to the time point, locate the slope rising area and the time position of entering the plateau, and obtain the evaporation change map data.

[0013] The following is a further optimization of the above technical solution by the present invention:

[0014] The abnormal sampling identification label includes a single-channel abnormality identifier, an abnormality category label, and an abnormal point sequence number; the sampling replacement record includes the available sample sequence number, channel structure characteristics, and fluid guide position partition; the shear channel execution configuration includes rotor speed ratio parameters, rotation direction combination scheme, and shear rate segment selection results; the evaporation change map data includes the pressure slope change range, the stable period time position, and the pressure gradient change form.

[0015] Further optimization: The specific steps of S1 are:

[0016] S101: Obtain the pressure change rate at the inlet of the disinfectant delivery pipeline and the speed fluctuation value of the guide rotor device, and read the acceleration change value at the sampling module structure, extract the instantaneous change amplitude of the three sets of data for peak value, call the instantaneous change interval, calculate the corresponding peak fluctuation amplitude, and obtain the peak fluctuation amplitude data;

[0017] S102: Based on the peak fluctuation amplitude data, perform interval-synchronous comparison of the fluctuation amplitude data. Using the continuous numerical deviation amplitudes of the pressure fluctuation interval, the speed fluctuation interval, and the acceleration change interval as a benchmark, determine whether there is an abnormal segment in the three data items that continuously deviates from the normal mode threshold. Mark the individual fluctuation segments with deviations to obtain individual deviation marked segment information.

[0018] S103: Call the single deviation mark segment information, for the sampling channel position with a marked state, combine the channel sequence number and the abnormal segment label of the corresponding time point, collect the abnormal channel position, sort out the correspondence between the channel number, time segment and deviation feature type, and generate an abnormal sampling identification label.

[0019] Further optimization: The specific steps of S2 are:

[0020] S201: Calling the abnormal sampling identification tag, combining the corresponding sampling channel number and time segment in the sample set within the detection period, detecting the corresponding pressure change rate data, combining the sample timestamp and the position coordinates in the guide structure, locating the fluid guide position segment point by point, evaluating the correspondence between the sample point and the fluid guide position, and obtaining the fluid guide position information of the sample point;

[0021] S202: Recall the fluid guide position information of the sample point, combine it with the channel number in the abnormal sampling identification label, perform a one-to-one number matching between the fluid guide position and the abnormal sampling channel number, calculate the number matching error score, and select sample points where the fluid guide position channel number is consistent with the abnormal label channel number to form a channel number consistency sample set;

[0022] The specific calculation formula of the number matching error score is:

[0023] ;

[0024] in, represents the number matching error score value, Representative The fluid guide position sample points are The coordinate values ​​on the axis, Representative The fluid guide position sample points are The coordinate values ​​on the axis, The channel number representing the abnormal sampling is The corresponding sample point is The average value in the axis coordinate direction, The channel number representing the abnormal sampling is The corresponding sample point is The average value in the axis coordinate direction, The channel number representing the abnormal sampling is The sample points are The standard deviation of the coordinate values ​​on the axis, The channel number representing the abnormal sampling is The sample points are The standard deviation of the coordinate values ​​on the axis, Representative Fluid-guided sample points and channels The Euclidean distance between the center points, Representative Channel The average value of the Euclidean distance between the sample points and the channel center point;

[0025] S203: According to the channel number consistency sample set, the timestamp, channel number and guide position segment of the sample point are associated and summarized, and the sampling replacement record is obtained by combining the time segment information corresponding to the abnormal state mark.

[0026] Further optimization: The specific steps of S3 are:

[0027] S301: Calling the sampling replacement record, extracting the fluid flow velocity value of the diversion inlet area corresponding to the recorded sample point within the corresponding time segment, combining the timestamp and channel number of the sampling point, and performing point-by-point aggregation processing on the flow velocity parameters of the sample point to obtain the diversion inlet flow velocity sample value;

[0028] S302: Obtain flow velocity detection data at the disinfectant delivery channel inlet based on the diversion inlet flow velocity sample values. Analyze the shear rate variation segment between the diversion inlet flow velocity sample values ​​and the disinfectant delivery channel inlet flow velocity values ​​based on the time segment alignment condition of the two sets of flow velocity values ​​to obtain a shear rate segment comparison feature.

[0029] S303: Invoke the shear rate segment comparison feature, combine the flow velocity difference segments between the diversion inlet area and the delivery channel inlet position, set the combined sequence of rotor speed and rotation direction, and generate the shear channel execution configuration.

[0030] Further optimization: The specific steps of S4 are:

[0031] S401: Calling the shear channel execution configuration, injecting the corresponding sampling point into the micro-pressure chamber, activating the chamber's constant temperature environment, and extracting the pressure change curve data in the micro-pressure chamber point by point based on the time sequence after the sample injection. Using the time point sequence and the pressure change amplitude, the time position of the curve slope rising section and entering the stable section is detected to obtain the pressure slope and time positioning data;

[0032] S402: Based on the pressure slope and time positioning data, combined with the time span parameters of the rising segment and the stable segment, the segment pressure gradient change rate index is calculated, and the pressure change trend characteristics within the time segment are extracted. According to the segment gradient distribution morphology, the relationship between the time span, pressure gradient and change trend is analyzed to obtain the evaporation change map data.

[0033] Further optimization: The specific calculation formula for the section pressure gradient change rate index is:

[0034] ;

[0035] in, Represents the pressure gradient change rate index of the section, Represents the total number of selected time periods, Representative The pressure value corresponding to the starting time of the segment, Representative The pressure value corresponding to the end of the segment, Representative The time span of the segment, Represent all The average value of the time span of the segment, represents the time deviation weighting factor, Represents the absolute deviation between the real-time segment and the average time span. Representative The weight value of the gradient change of the pressure data in the segment.

[0036] Further optimization: the method further comprises step S5:

[0037] S5: Based on the evaporation change map data, the duration structure and waveform trend form from the slope starting point to the stable segment are retrieved, matched to the volatilization profile information corresponding to the differentiated alcohol and quaternary ammonium salt components in the disinfectant, the composition of the disinfectant sample is identified, and the component annotation content of the test sample is generated;

[0038] The test sample component annotation content includes alcohol component type, quaternary ammonium salt component characteristics, and volatility profile matching level.

[0039] Further optimization: The specific steps of S5 are:

[0040] S501: Calling evaporation change map data, extracting the duration structure from the slope starting point to the stable segment, combining the time span parameter with the pressure change curve waveform data in the corresponding segment, and obtaining the time structure and waveform trend characteristics based on the continuous distribution characteristics of the start and end positions of the time period and the pressure change rate;

[0041] S502: Based on the time structure and waveform trend characteristics, the volatility profile information of the differentiated alcohol and quaternary ammonium salt components in the disinfectant is retrieved. The segment duration, waveform gradient characteristics, and pressure change trend are used as matching factors. The time feature intervals and gradient morphological distributions in the volatility profile information are compared group by group to identify component profiles with similar change characteristics, thereby obtaining volatility feature matching results.

[0042] S503: Call the volatile feature matching results, compare the time span, waveform gradient and flow trend relationship between the sample segment features and the corresponding component profile, determine the component attribution of the test sample based on the profile feature number, establish a corresponding relationship between the determination result and the sample number, and generate the test sample component annotation content.

[0043] The present invention also provides a disinfectant production detection system based on a sampling module, which is used to implement the above-mentioned disinfectant production detection method based on a sampling module, and the system includes:

[0044] The abnormal channel marking module obtains the pressure change rate at the inlet of the disinfectant delivery pipeline, the speed fluctuation value of the guide rotor device, and the acceleration change value at the sampling module structure. Based on the peak change amplitude of these three sets of parameters, it determines whether there is a single value that continuously deviates from the equipment startup state curve, and obtains an abnormal sampling identification label;

[0045] The sample channel screening module extracts the stable rate section in the diversion structure from the sample within the detection cycle according to the abnormal sampling identification tag, detects the pressure change rate sample point in the section, reads the fluid guide position number of the sample point, and obtains the sampling replacement record;

[0046] The shear channel configuration module calls the sampling replacement record, extracts the fluid flow rate value of the diversion inlet area, obtains the flow rate value at the inlet of the disinfectant delivery channel, sets the rotor speed ratio and rotation direction combination, and obtains the shear channel execution configuration;

[0047] The evaporation change map module is configured according to the shear channel execution, imports the selected sample points into the micro-pressure chamber, activates the constant temperature environment, and after injecting the sample, extracts the pressure change curve in the micro-pressure chamber according to the time point. The time point when it enters the stable period is detected to obtain the evaporation change map data;

[0048] The component identification and attribution module calls the evaporation change map data, detects the time length and pressure change waveform trend from the starting point of the slope to the stable section, matches it to the volatile contour segment in the volatile characteristic database of disinfectant alcohols and quaternary ammonium salt components, and screens the component category numbers that meet the characteristic segments to obtain the component annotation content of the test sample.

[0049] The present invention adopts the above technical solution, which has at least the following beneficial effects:

[0050] The present invention realizes real-time identification of abnormal states and sampling channel status marking by synchronously acquiring and jointly comparing the pressure change rate, speed fluctuation value and acceleration change value with the peak change amplitude. Combined with the channel consistency screening of the fluid guide position, automatic replacement and compensation of available samples are completed. By comparing the shear rate segments of the flow rate values ​​at different positions, a dynamically adjusted rate ratio and rotation direction parameter configuration are formed. The constant temperature environment in the micro-pressure cavity is used to achieve high-precision recording of the pressure change curve. The fluid evaporation characteristics are obtained by time interval analysis of the slope change and the pressure gradient morphology. Combined with the waveform trend and duration structure, the volatilization profiles of different components in the disinfectant are matched to achieve accurate identification of component attribution and quantitative labeling of sample characteristics, effectively improving the response speed of identifying changes in disinfectant components, and improving the recognition accuracy and real-time adaptability in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of the workflow of the method in an embodiment of the present invention;

[0052] Figure 2 4 is a module diagram of the system in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In the embodiments of the present invention, words such as "example" and "for example" are used to indicate examples, illustrations or explanations; any embodiment or design scheme described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs; to be precise, the use of the word "example" is intended to present concepts in a concrete way; in addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0054] In the embodiments of the present invention, “image” and “picture” may sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent; “of”, “corresponding” and “corresponding” may sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent.

[0055] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0056] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0057] See also Figure 1 A method for detecting the production of disinfectants based on a sampling module, the processing flow of the method includes the following steps:

[0058] S1: Obtain the pressure change rate at the inlet of the disinfectant delivery pipeline, the speed fluctuation value of the guide rotor device, and the acceleration change value at the sampling module structure. The peak change amplitudes of the three sets of data are called for joint comparison. If there is a single value that continuously deviates from the normal mode, the sampling channel is marked as an abnormal state and an abnormal sampling identification label is generated;

[0059] S2: Using abnormal sampling identification tags, extract the stable rate section within the corresponding diversion structure from the samples within the detection period, identify the sample point of the pressure change rate and extract the fluid guide position, compare the channel number of the fluid guide position with the sampling point marked in the abnormal state, select samples with consistent channel structure as available samples, and obtain the sampling replacement record;

[0060] S3: By sampling instead of recording, the fluid flow rate values ​​recorded in the diversion inlet area are extracted, and the flow rate values ​​given at the disinfectant delivery channel inlet are collected. The shear rate segment comparison relationship between the two sets of flow rate values ​​is evaluated, and the rotor speed ratio and rotation direction combination are set to generate the shear channel execution configuration;

[0061] S4: Use the shear channel to execute the configuration, introduce the sampling point into the micro-pressure chamber and activate the constant temperature environment. After the sample is injected into the micro-pressure chamber, extract the pressure change curve according to the time point, locate the slope rising area and the time position of entering the plateau, identify the time span and the corresponding pressure gradient change form, and obtain the evaporation change map data;

[0062] S5: Based on the evaporation change map data, the duration structure and waveform trend shape from the starting point of the slope to the stable segment are retrieved, matched to the volatilization profile information corresponding to the differentiated alcohol and quaternary ammonium salt components in the disinfectant, the component attribution of the disinfectant sample is identified, and the component annotation content of the test sample is generated.

[0063] Abnormal sampling identification labels include single-channel abnormality identification, abnormality category labels, and abnormal point serial numbers; sampling replacement records include available sample serial numbers, channel structural characteristics, and fluid guide position partitions; shear channel execution configurations include rotor speed ratio parameters, rotation direction combination schemes, and shear rate segment selection results; evaporation change map data include pressure slope change range, stable segment time position, and pressure gradient change morphology; detection sample component annotation content includes alcohol component type, quaternary ammonium salt component characteristics, and volatility profile matching level.

[0064] The specific steps of S1 are:

[0065] S101: Obtain the pressure change rate at the inlet of the disinfectant delivery pipeline and the speed fluctuation value of the guide rotor device, and read the acceleration change value at the sampling module structure, extract the instantaneous change amplitudes of the three sets of data for peak value, call the instantaneous change interval, calculate the corresponding peak fluctuation amplitude, and obtain the peak fluctuation amplitude data.

[0066] By reading the data of the vibration sensor sampling device under the running state of the equipment, the three-axis acceleration change value of the structure is extracted. When collecting data, a certain time window is selected for continuous sampling. The commonly used sampling frequency is 1000 times per second, and the set sampling time is 10 seconds to ensure the continuity and integrity of the data. The collected raw acceleration data is preprocessed, including removing zero bias and filtering to improve data quality. It is divided into multiple time segments, divided into intervals of 0.1 seconds, and the instantaneous change amplitudes of the three directions of x, y, and z are extracted in each interval. The data of each interval is scanned to find the maximum and minimum values. The instantaneous peak value change amplitude of the interval is obtained by the difference between the two, and the operation is repeated to obtain the peak value change amplitude sequence within the time period segment by segment. According to the analysis requirements, a larger time span is set as the instantaneous change interval, and a segment is set to 0.5 seconds. Multiple consecutive short-time interval peaks are aggregated, and the fluctuation difference of the peak data of each short-time interval in the segment is calculated. In actual application scenarios, for example, the main shaft of a wind turbine is monitored, and acceleration data within 10 seconds is collected. After segmented extraction, it is found that the x-axis acceleration change amplitude fluctuates greatly in the 2nd to 6th small intervals, and the peak fluctuation amplitude data is obtained.

[0067] S102: Based on the peak fluctuation amplitude data, perform interval synchronization comparison on the fluctuation amplitude data, use the continuous numerical offset amplitude of the pressure fluctuation interval, the speed fluctuation interval and the acceleration change interval as a benchmark, and determine whether there is a single abnormal segment in the three data that continuously deviates from the normal mode threshold. Mark the single fluctuation segment with deviation to obtain the single deviation mark segment information.

[0068] The fluctuation amplitude data in different intervals are synchronously compared section by section, and the acceleration fluctuation amplitude sequence is divided to ensure that each section of data corresponds to the pressure fluctuation and speed fluctuation data of the same time section. After acquisition, the change amplitudes of the three physical quantities of acceleration, pressure and speed in the same time section are numerically compared, and a reference interval is set to ensure that the three sets of data correspond one to one. In actual engineering, for example, in the operation monitoring of underground ventilators in coal mines, the three types of signal fluctuation interval data of acceleration, wind pressure and speed are collected, and the interval values ​​are compared and analyzed to determine whether any of the three data sources have continuously exceeded the value. The threshold range of the normal operating mode is set. The threshold can be set according to the working condition sample data. For example, when the equipment is operating normally, the acceleration fluctuation amplitude does not exceed 1g, then the acceleration abnormality threshold can be set to 1.2g. When the comparison finds that the acceleration in a certain interval continuously deviates from the threshold, and the speed and pressure data do not show similar deviations, that is, a single item is abnormal, the acceleration fluctuation segment is marked. Similarly, for the pressure and speed fluctuation data, if the pressure or speed continuously exceeds the threshold and the other two data remain normal, corresponding marking is also performed. The marking of all single fluctuation abnormal segments is completed, and the single deviation marking segment information is obtained.

[0069] S103: Call the single deviation mark segment information, for the sampling channel position with a marked state, combine the channel sequence number and the abnormal segment label of the corresponding time point, collect the abnormal channel position, sort out the correspondence between the channel number, time segment and deviation feature type, and generate an abnormal sampling identification label.

[0070] The sampling channels in the marked state are classified and collected, and the single deviation mark information is read one by one to obtain the corresponding channel number, time segment and deviation feature type. Different channel numbers are distinguished and sorted, and the abnormal segments are sorted in chronological order. The abnormal segment information of the same channel at different time points is stored separately, and the correspondence between the channel number, abnormal time period and abnormal feature type is established. In an actual application scenario, for example, when processing the multi-channel monitoring data of a water pumping station, it is found that the acceleration channel No. 1 deviates from 3 seconds to 3.5 seconds, and the pressure channel No. 5 also has an abnormality in the same time period. Through the collection process, these two anomalies are classified into two different anomaly labels, and the anomaly type and occurrence time period are marked respectively. At the same time, the channel serial number information is recorded for further analysis by the subsequent fault diagnosis module. During the entire collection process, it is necessary to automatically associate the sampling data source channel configuration table according to the channel number to ensure that each abnormal label corresponds to a specific physical measurement point location to form an abnormal sampling identification label.

[0071] The specific steps of S2 are:

[0072] S201: Call the abnormal sampling identification tag, and in the sample set within the detection period, combine the corresponding sampling channel number and time segment to detect the corresponding pressure change rate data. Combine the sample timestamp and the position coordinates in the guide structure to locate the fluid guide position segment point by point, evaluate the correspondence between the sample point and the fluid guide position, and obtain the fluid guide position information of the sample point.

[0073] The sample data set within the detection period is traversed and screened. According to the channel number and abnormal time segment in the tag information, the sampling channel data range corresponding to the tag is determined, and the data range is associated with the equipment component structure information corresponding to the sampling channel to filter out the sample data segment belonging to the diversion structure. During the screening process, the timestamp and channel number of each sample point are compared one by one to identify the data that is located in the abnormal time period and comes from the sampling channel in the diversion structure. The pressure change rate data of the sample point is detected, and each timestamp point in the sample set is traversed to find the pressure sensor sampling data corresponding to its timestamp. By aligning the time axis, the pressure value change at the same time is obtained, and the pressure change value difference before and after the sampling point is calculated. , determine the pressure change rate of the point, connect the timestamp information of the sample point with the spatial coordinate model of the guide structure, and determine the actual spatial position corresponding to the sample point through the coordinate mapping algorithm. Combined with the fluid flow path model, determine which specific fluid guide position section of the guide structure the sample point is located in. During the process, it is necessary to refer to the three-dimensional structure layout diagram of the equipment to confirm the relationship between the spatial position corresponding to the sampling point and the fluid flow direction. In the water pump guide device detection scenario, the sample point timestamp is 5.6 seconds, and the corresponding spatial position coordinates are the guide blade outlet area. According to the pressure change rate, it is judged that the sample point is in the fluid flow acceleration section. For the sample point, the point-by-point correspondence relationship evaluation between the sample point and the fluid guide section position is completed to obtain the fluid guide position information of the sample point.

[0074] S202: Call the fluid guide position information of the sample point, combine it with the channel number in the abnormal sampling identification label, perform a one-to-one number matching between the fluid guide position and the abnormal sampling channel number, calculate the number matching error score value, and select the sample points where the fluid guide position channel number is consistent with the abnormal label channel number to form a channel number consistency sample set.

[0075] The specific calculation formula of the number matching error score is:

[0076] ;

[0077] in, represents the number matching error score value, Representative The fluid guide position sample points are The coordinate values ​​on the axis, Representative The fluid guide position sample points are The coordinate values ​​on the axis, The channel number representing the abnormal sampling is The corresponding sample point is The average value in the axis coordinate direction, The channel number representing the abnormal sampling is The corresponding sample point is The average value in the axis coordinate direction, The channel number representing the abnormal sampling is The sample points are The standard deviation of the coordinate values ​​on the axis, The channel number representing the abnormal sampling is The sample points are The standard deviation of the coordinate values ​​on the axis, Representative Fluid-guided sample points and channels The Euclidean distance between the center points, Representative Channel The average Euclidean distance between the sample points and the channel center point.

[0078] This formula measures the accuracy of number matching by calculating the spatial deviation between the sample point position and the center position corresponding to the channel number. The numerator measures the sum of the square deviations between the sample point and the channel average position in the two-dimensional coordinates. The denominator combines the sum of the standard deviations of the channel sample points in the coordinate dimension and the degree of deviation from the Euclidean distance from the point to the channel center, and uses the square root for normalization. The lower the score, the closer the sample point is to the channel center in spatial position and the higher the distribution stability, indicating that the number matching is more reasonable.

[0079] The number matching error score value indicates the degree of deviation between the coordinate matching of the sample point and the center position of the abnormal channel. The lower the value, the more accurate the match between the point and the channel number. This score value is used to measure whether the sample point meets the number consistency requirements and serves as a screening basis.

[0080] Parameter meaning and formula calculation derivation process:

[0081] Sample points and It is derived from the two-dimensional coordinate collection in the fluid guide sample data monitoring equipment. The positioning value of each sample point in the actual plane position is extracted through laser ranging and camera. After continuous sampling, a sample point position is extracted: ;

[0082] Channel number , corresponding to the sample point in the channel The coordinate values ​​are derived from a measurement dataset, based on which the mean and standard deviation are calculated as follows:

[0083] Assume that the 5 sample points of channel 4 are The direction coordinate values ​​are: ;

[0084] Then the average value is: ;

[0085] Standard Deviation:

[0086] ;

[0087] ;

[0088] Similarly, let channel 4 The direction coordinate values ​​are: ;

[0089] average value: ;

[0090] Standard Deviation: ;

[0091] ;

[0092] Euclidean distance Represents the spatial distance between the sample point and the channel center point, which is directly calculated by the coordinate difference:

[0093] Assume the center point coordinates of channel 4 are the average position: ;

[0094] but ;

[0095] Channel average Euclidean distance The calculation is based on the distance between the channel sample points and the center. The distance monitoring data between the sample points is as follows:

[0096] Point 1 to center: 3.0mm, point 2 to center: 2.6mm, point 3 to center: 1.5mm, point 4 to center: 5.1mm, point 5 to center: 2.8mm;

[0097] ;

[0098] Substituting into the formula:

[0099] molecular: ;

[0100] Denominator: ;

[0101] Substitute into the formula for calculation:

[0102] ;

[0103] The results show that the number matching error score is 0.674, which reflects the degree of consistency error between the sample point and the channel number. The lower the score, the higher the position fit. Combined with the threshold setting, it can be used to eliminate sample points with large deviations. The numerical result is the basis for judging whether the sample points are included in the channel number consistency sample set; points with error scores lower than the set screening threshold of 0.85 are retained, and the rest are eliminated; the screening threshold is set according to the 80% confidence interval in the distribution of abnormal sample positioning errors and is dynamically adjusted as the data distribution changes.

[0104] S203: According to the channel number consistency sample set, the timestamp, channel number and guide position segment of the sample point are associated and summarized, and the sampling replacement record is obtained by combining the time segment information corresponding to the abnormal state mark.

[0105] The timestamps, channel numbers and guide position segments of the sample points are associated and summarized one by one. The operation process is to extract the timestamp data of each sample point one by one, associate and integrate it with its channel number and the corresponding guide position segment number, ensure that the three information of time, space and channel number are completely corresponding in one record, match and connect the time segments contained in the abnormal status mark, and archive the sample points belonging to the same abnormal time segment. During the summary and collation process, the sample points are sorted according to time for subsequent calls. At the same time, the sample points with different channel numbers and different guide position segments are grouped to form a sampling replacement record set with time series characteristics. In the example of abnormal vibration monitoring of the water pump diversion structure, all CH2 channel sample points in the range of 4 seconds to 5 seconds can be collected through summary, and the sampling replacement record can be obtained by combining its fluid guide position number and abnormal mark segment.

[0106] The specific steps of S3 are:

[0107] S301: Call the sampling replacement record, extract the fluid flow rate value of the diversion inlet area corresponding to the recorded sample point within the corresponding time segment, combine the timestamp and channel number of the sampling point, and perform point-by-point aggregation processing on the flow rate parameters of the sample point to obtain the diversion inlet flow rate sample value.

[0108] The sample point data contained therein is read, and the timestamp, channel number, and corresponding guide position segment of each sample point are matched and confirmed. The sample points belonging to the diversion inlet area are screened out, and the fluid flow rate data corresponding to the sample points are extracted. The extraction process requires calling the flow rate record consistent with the sample point timestamp from the sampling data. For the same time point, the flow rate parameters of the corresponding position are obtained through the channel number correspondence, and the data are collected point by point. During the data sorting process, the sample points are automatically sorted in chronological order to ensure that the flow rate sample points in the same time segment can be arranged in sequence. In actual operation, when monitoring a diversion inlet of a sewage treatment plant, 100 sample points within the time period of 3 seconds to 4 seconds are determined from the sampling replacement record. By matching the channel number and timestamp of each point, the corresponding flow rate data is read from the flow rate monitoring, and the flow rate values ​​are summarized point by point to obtain the diversion inlet flow rate sample value.

[0109] S302: According to the flow velocity sample value at the diversion inlet, the flow velocity detection data at the inlet of the disinfectant delivery channel is obtained. Based on the time segment alignment condition of the two sets of flow velocity values, the shear rate change segment between the flow velocity sample value at the diversion inlet and the flow velocity value at the inlet of the disinfectant delivery channel is analyzed to obtain the shear rate segment comparison feature.

[0110] The flow velocity detection data at the inlet of the disinfectant delivery channel within the corresponding time segment is extracted from the data. The process determines the complete time segment range covered by the diversion inlet flow velocity sample value. The real-time flow velocity detection data of the disinfectant delivery channel is aligned and screened for time segments to ensure that the timestamp of the selected disinfectant channel flow velocity data is consistent with the time interval of the diversion inlet flow velocity sample data. The two sets of flow velocity data are then analyzed point by point. For each diversion inlet sample time point, the disinfectant channel flow velocity value at the same time point is found, and the flow velocity difference between the two is calculated. The flow velocity change segment characteristic sequence is generated based on the time series. In actual engineering applications, the diversion inlet flow velocity sample value is set to gradually change from 0.8 meters per second to 1.2 meters per second during the disinfectant injection control process of a large water plant, while the disinfectant channel inlet flow velocity fluctuates between 0.1 meters per second and 0.15 meters per second. For the two sets of data, multiple shear rate change segments are divided according to the time segment. The flow velocity difference change characteristics between the two flow velocity groups in different time periods are determined, and the segments are marked with shear characteristics to obtain the shear rate segment comparison characteristics.

[0111] S303: Invoke the shear rate segment comparison feature, combine the flow velocity difference segments between the diversion inlet area and the delivery channel inlet position, set the combined sequence of rotor speed and rotation direction, and generate the shear channel execution configuration.

[0112] Call the data feature set, conduct a joint analysis of the flow velocity difference segments between the diversion inlet area and the disinfectant delivery channel inlet, read each shear rate segment data in the comparison feature, confirm the corresponding time segment and spatial position range, and for each difference segment, according to the established rotor operation parameter setting logic, combined with the real-time changes in the diversion flow velocity and the delivery channel flow velocity, set the corresponding rotor speed and rotation direction combination sequence, and execute the configuration generation process. During the configuration process, judge the shear rate segments one by one, and decide the increase or decrease of the rotor speed and the direction switching according to the flow velocity change trend in the difference segment. When a continuous positive shear rate increase section is detected between the diversion inlet and the conveying channel, the rotor speed is set to high-speed forward mode; if a sudden reverse decrease in the shear rate is detected, it is adjusted to low-speed reverse mode. In specific applications, it is set in the municipal sewage disinfection link. According to the shear rate characteristics, the rotor speed is automatically set from 600 rpm to 900 rpm in the 10th to 15th second section and maintains forward rotation. After 15 seconds, the speed is reduced to 500 rpm again according to the shear change, so as to realize dynamic update and real-time adjustment of the shear channel execution configuration and generate the shear channel execution configuration.

[0113] The specific steps of S4 are:

[0114] S401: Call the shear channel execution configuration, inject the corresponding sampling point into the micro-pressure cavity, activate the cavity constant temperature environment, and extract the pressure change curve data in the micro-pressure cavity point by point according to the time sequence after the sample injection. Use the time point sequence and the pressure change amplitude to detect the time position of the curve slope rising section and entering the stable section to obtain the pressure slope and time positioning data.

[0115] According to the configuration content, the injection parameters of the corresponding sampling point are set, and the pre-screened sample fluid is transported to the inside of the micro-pressure cavity through the control. The fluid injection rate and injection time are controlled to strictly follow the shear channel execution configuration sequence, and the injection operation is completed point by point for the sample point to ensure that each sampling point enters the micro-pressure cavity in the established time sequence. After the injection is completed, the cavity constant temperature environment control is immediately started to maintain the set temperature, such as controlling it at a constant state of 25 degrees Celsius. At the same time, the real-time pressure acquisition device in the cavity is activated to record the pressure change process generated by the injection of each sample point. The data acquisition frequency It can be set to 100 times per second to ensure high-precision capture of subtle pressure changes, organize the collected pressure change curve data into time series, compare the pressure change process before and after the injection time point point by point, identify the section in the curve where the pressure slope continues to rise and the subsequent stable section where the pressure gradually stabilizes. In specific application scenarios, such as in the simulation test of the water quality treatment process, it was found that a pressure rising trend began to appear at 0.8 seconds after the injection of a certain sample point, and entered a stable section after 1.5 seconds. The time position within the section was locked to obtain the pressure slope and time positioning data.

[0116] S402: Based on the pressure slope and time positioning data, combined with the time span parameters of the rising segment and the stable segment, the segment pressure gradient change rate index is calculated, and the pressure change trend characteristics within the time segment are extracted. According to the segment gradient distribution morphology, the relationship between the time span, pressure gradient and change trend is analyzed to obtain the evaporation change map data.

[0117] The specific calculation formula of the section pressure gradient change rate index is:

[0118] ;

[0119] in, Represents the pressure gradient change rate index of the section, Represents the total number of selected time periods, Representative The pressure value corresponding to the starting time of the segment, Representative The pressure value corresponding to the end of the segment, Representative The time span of the segment, Represent all The average value of the time span of the segment, represents the time deviation weighting factor, Represents the absolute deviation between the real-time segment and the average time span. Representative The weight value of the gradient change of the pressure data in the segment.

[0120] The calculation logic of the formula is: by weighting the pressure changes in multiple time segments, the rate characteristics of the overall pressure change are quantified, and the absolute difference between the starting and ending pressures of each segment is calculated to represent the pressure change amplitude within the segment. The corrected time span is used as the denominator, and the time deviation and weight factor α are introduced to adjust the impact of the time length difference between different segments. The weight factor is introduced. , weighted according to the pressure change rate and data point density in each segment to reflect the credibility and information content of the data in that segment. The results of the segment are summed and averaged, and the square root of the total number of segments Z is used as the normalization factor to improve the stability and comparability of the overall indicators.

[0121] The pressure gradient change rate index is a numerical expression that measures the amplitude of pressure change per unit time. Taking into account factors such as pressure difference, time span and data density, this index reflects the fluctuation intensity and change rate of pressure in different time periods.

[0122] Parameter meaning and formula calculation derivation process:

[0123] During the monitoring process, four representative time segments (Z=4) were selected. The data acquisition frequency was 1 Hz. Pressure values ​​were extracted using an industrial-grade Bosch BMP390 pressure sensor with a resolution of 0.18 Pa and a sampling accuracy within ±0.5 Pa.

[0124] The monitoring data are recorded as follows:

[0125] Segment 1: Initial pressure =101326Pa, end pressure =101295Pa, time span =12s;

[0126] Segment 2: Initial pressure 101295Pa, end pressure =101260Pa, time span =15s;

[0127] Segment 3: Initial pressure , end pressure =101230Pa, time span =17s;

[0128] Segment 4: Initial pressure =101230Pa, end pressure =101205Pa, time span =14s;

[0129] Calculate the average over a time span:

[0130] ;

[0131] Adjustment factor is the time weight adjustment factor, which is reflected by the ratio of the standard deviation of the time span fluctuation to the maximum span difference and is controlled between 0.1 and 0.3. The standard deviation of the monitoring time span is 2.08s and the maximum difference is 5s, so α = 0.3;

[0132] Weight parameter The pressure change rate is weighted averaged by the data density. The number of data points per unit area is the density benchmark. The standard density is 15 points / second. The weight calculation method is:

[0133] ;

[0134] Among them, d o is the number of data points per unit time in the segment. Since the sampling frequency is 1 Hz, the number of data points in each segment is the same as the time span;

[0135] calculate Values:

[0136] =(31 / 12)×(12 / 15)=2.58;

[0137] =(35 / 15)×(15 / 15)=2.33;

[0138] =(30 / 17)×(17 / 15)=2.00;

[0139] =(25 / 14)×(14 / 15)=1.67;

[0140] Calculate the corrected time items for each segment:

[0141] ;

[0142] ;

[0143] ;

[0144] ;

[0145] Substituting the above values ​​into the formula:

[0146] ;

[0147] Calculate item by item:

[0148] 31÷12.75×2.58=2.43×2.58≈6.27;

[0149] 35÷15.15×2.33=2.31×2.33≈5.39;

[0150] 30÷17.75×2.00=1.69×2.00≈3.38;

[0151] 25÷14.15×1.67=1.77×1.67≈2.96;

[0152] Sum: 6.27+5.39+3.38+2.96=17.99;

[0153] =0.5×17.99=8.995;

[0154] The result shows that the segment pressure gradient change rate index is 8.995, which means that in the current sampling period, the average pressure change rate per second is about 8.995 Pa, reflecting the pressure fluctuation intensity within the time segment. This numerical result is used in the normalization processing of the segment change amplitude of the subsequent trend map as a reference for calculating the standardized interval of the basic intensity index.

[0155] The specific steps of S5 are:

[0156] S501: Call the evaporation change map data, extract the duration structure from the slope starting point to the stable segment, combine the time span parameter with the pressure change curve waveform data in the corresponding segment, and obtain the time structure and waveform trend characteristics based on the continuous distribution characteristics of the start and end positions of the time period and the pressure change rate.

[0157] The evaporation process characteristics of each sample point in the evaporation change map data are extracted in detail, focusing on the duration structure from the starting point of the pressure slope to the entry into the stable section. The starting time point of the slope change and the starting time point of the stable section of each curve are read one by one to determine the time span between the two. The pressure change curve waveform data in this time section is deeply analyzed. By scanning the pressure change rate in the entire interval, its continuous distribution characteristics in the time dimension are identified. The waveform change trend is checked section by section to determine whether it is linear growth, nonlinear slow change or multi-peak fluctuation. At the same time, the rising rate change characteristics of the waveform in each time period are recorded. The pressure in a certain sample section is set to increase continuously and smoothly with a time span of 1.2 seconds. The overall waveform shows a single increasing feature, while another sample has two obvious gradient changes in the same time section, forming a multi-segment hierarchical waveform. The above analysis content is completed point by point according to the sample time series. The time structure and waveform trend characteristics of each sample are classified and identified to obtain the time structure and waveform trend characteristics.

[0158] S502: Based on the time structure and waveform trend characteristics, the volatility profile information of differentiated alcohol and quaternary ammonium salt components in the disinfectant is called, and the segment duration, waveform gradient characteristics and pressure change trend are used as matching factors. The time feature interval and gradient morphological distribution in the volatility profile information are compared group by group, and the component profiles with similar change characteristics are identified to obtain the volatility feature matching results.

[0159] The stored volatile profile information data of disinfectant components is called, and the differentiated alcohol and quaternary ammonium salt component profile data related to the test samples are selected. The comparison process reads the time feature interval, waveform gradient change characteristics and pressure change trend classification attributes in each set of volatile profile information. The data is used as the target comparison object, and the three parameters of the time segment duration, waveform gradient characteristics and pressure change trend of the test sample are matched one-to-one with the profile information. The two are compared group by group to see whether the time interval length is similar, whether the waveform gradient distribution morphology is consistent, and whether the overall pressure change trend has the same characteristics. For a test sample, if its time interval duration is between 1 second and 1.5 seconds, the waveform gradient change shows a single-peak linear growth characteristic, and the quaternary ammonium salt component profile also shows exactly the same time characteristics and gradient morphology distribution, then the two are judged to have high feature similarity. During the entire matching process, batch comparison is performed on a one-to-many basis between the sample and profile data to screen out the volatile profile numbers with highly consistent characteristics, provide data support for sample component determination, and obtain volatile feature matching results.

[0160] S503: Call the volatile feature matching results, compare the time span, waveform gradient and flow trend relationship between the sample segment features and the corresponding component profile, determine the component attribution of the test sample based on the profile feature number, establish a corresponding relationship between the determination result and the sample number, and generate the test sample component annotation content.

[0161] An in-depth comparison is conducted between the time segment characteristics of the test sample and the time span, waveform gradient, and flow trend relationship between the successfully matched volatilization profile. The time span and waveform gradient characteristic parameters corresponding to each test sample are read one by one, and synchronously compared with the matched component profile standard characteristics. The correspondence and deviation range of the sample curve and the profile characteristic interval are analyzed point by point to determine whether the sample fully meets a specific component profile number in terms of time length, slope gradient, and flow trend. If so, the component profile number is used as the basis for determining the composition of the current test sample. A one-to-one association is established between the test sample number and its corresponding component profile number, and the samples are archived and organized. In the disinfectant monitoring application scenario, the sample number S1001 is classified as the quaternary ammonium salt type number QAS-03 through comparison, while the sample number S1002 corresponds to the alcohol component number ALC-05, and the component annotation content of the test sample is generated.

[0162] See also Figure 2 The present invention also provides a disinfectant production detection system based on a sampling module, the system is used to implement the above-mentioned disinfectant production detection method based on a sampling module, and the system includes:

[0163] The abnormal channel marking module obtains the pressure change rate at the inlet of the disinfectant delivery pipeline, the speed fluctuation value of the guide rotor device, and the acceleration change value at the sampling module structure. Based on the peak change amplitude of these three sets of parameters, it determines whether there is a single value that continuously deviates from the equipment startup state curve, and obtains an abnormal sampling identification label;

[0164] The sample channel screening module extracts the stable rate section in the diversion structure from the sample within the detection cycle according to the abnormal sampling identification tag, detects the pressure change rate sample point in the section, reads the fluid guide position number of the sample point, and obtains the sampling replacement record;

[0165] The shear channel configuration module calls the sampling replacement record, extracts the fluid flow rate value of the diversion inlet area, obtains the flow rate value at the inlet of the disinfectant delivery channel, sets the rotor speed ratio and rotation direction combination, and obtains the shear channel execution configuration;

[0166] The evaporation change map module is configured according to the shear channel execution, imports the selected sample points into the micro-pressure chamber, activates the constant temperature environment, and after injecting the sample, extracts the pressure change curve in the micro-pressure chamber according to the time point. The time point when it enters the stable period is detected to obtain the evaporation change map data;

[0167] The component identification and attribution module calls the evaporation change map data, detects the time length and pressure change waveform trend from the starting point of the slope to the stable section, matches it to the volatile contour segment in the volatile characteristic database of disinfectant alcohols and quaternary ammonium salt components, and screens the component category numbers that meet the characteristic segments to obtain the component annotation content of the test sample.

[0168] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A disinfectant production detection method based on a sampling module, characterized in that: The following steps are involved: S1: Obtain the pressure change rate at the inlet of the disinfectant delivery pipeline, the speed fluctuation value of the guide rotor device, and the acceleration change value at the sampling module structure. The peak change amplitudes of the three sets of data are called for joint comparison. If there is a single value that continuously deviates from the normal mode, the sampling channel is marked as an abnormal state and an abnormal sampling identification label is generated; S2: Using abnormal sampling identification tags, extract the stable rate section in the corresponding diversion structure from the samples within the detection period, identify the sample point of the pressure change rate and extract the fluid guide position, and obtain the sampling replacement record; S3: By sampling instead of recording, the fluid flow rate value recorded in the diversion inlet area is extracted, and the flow rate value given at the inlet of the disinfectant delivery channel is collected, the rotor speed ratio and rotation direction combination are set, and the shear channel execution configuration is generated; S4: Use the shear channel to execute the configuration, introduce the sampling point into the micro-pressure chamber and activate the constant temperature environment. After the sample is injected into the micro-pressure chamber, extract the pressure change curve according to the time point, locate the slope rising area and the time position of entering the plateau, and obtain the evaporation change map data.

2. A disinfectant production detection method based on a sampling module according to claim 1, characterized in that: The abnormal sampling identification label includes a single-channel abnormality identifier, an abnormality category label, and an abnormal point sequence number; the sampling replacement record includes the available sample sequence number, channel structure characteristics, and fluid guide position partition; the shear channel execution configuration includes rotor speed ratio parameters, rotation direction combination scheme, and shear rate segment selection results; the evaporation change map data includes the pressure slope change range, the stable period time position, and the pressure gradient change form.

3. A disinfectant production detection method based on a sampling module according to claim 1, characterized in that: The specific steps of S1 are: S101: Obtain the pressure change rate at the inlet of the disinfectant delivery pipeline and the speed fluctuation value of the guide rotor device, and read the acceleration change value at the sampling module structure, extract the instantaneous change amplitude of the three sets of data for peak value, call the instantaneous change interval, calculate the corresponding peak fluctuation amplitude, and obtain the peak fluctuation amplitude data; S102: Based on the peak fluctuation amplitude data, perform interval-synchronous comparison of the fluctuation amplitude data. Using the continuous numerical deviation amplitudes of the pressure fluctuation interval, the speed fluctuation interval, and the acceleration change interval as a benchmark, determine whether there is an abnormal segment in the three data items that continuously deviates from the normal mode threshold. Mark the individual fluctuation segments with deviations to obtain individual deviation marked segment information. S103: Call the single deviation mark segment information, for the sampling channel position with a marked state, combine the channel sequence number and the abnormal segment label of the corresponding time point, collect the abnormal channel position, sort out the correspondence between the channel number, time segment and deviation feature type, and generate an abnormal sampling identification label.

4. A disinfectant production detection method based on a sampling module according to claim 3, characterized in that: The specific steps of S2 are: S201: Calling the abnormal sampling identification tag, combining the corresponding sampling channel number and time segment in the sample set within the detection period, detecting the corresponding pressure change rate data, combining the sample timestamp and the position coordinates in the guide structure, locating the fluid guide position segment point by point, evaluating the correspondence between the sample point and the fluid guide position, and obtaining the fluid guide position information of the sample point; S202: Recall the fluid guide position information of the sample point, combine it with the channel number in the abnormal sampling identification label, perform a one-to-one number matching between the fluid guide position and the abnormal sampling channel number, calculate the number matching error score, and select sample points where the fluid guide position channel number is consistent with the abnormal label channel number to form a channel number consistency sample set; The specific calculation formula of the number matching error score is: ； in, represents the number matching error score value, Representative The fluid guide position sample points are The coordinate values ​​on the axis, Representative The fluid guide position sample points are The coordinate values ​​on the axis, The channel number representing the abnormal sampling is The corresponding sample point is The average value in the axis coordinate direction, The channel number representing the abnormal sampling is The corresponding sample point is The average value in the axis coordinate direction, The channel number representing the abnormal sampling is The sample points are The standard deviation of the coordinate values ​​on the axis, The channel number representing the abnormal sampling is The sample points are The standard deviation of the coordinate values ​​on the axis, Representative Fluid-guided sample points and channels The Euclidean distance between the center points, Representative Channel The average value of the Euclidean distance between the sample points and the channel center point; S203: According to the channel number consistency sample set, the timestamp, channel number and guide position segment of the sample point are associated and summarized, and the sampling replacement record is obtained by combining the time segment information corresponding to the abnormal state mark.

5. A disinfectant production detection method based on a sampling module according to claim 4, characterized in that: The specific steps of S3 are: S301: Calling the sampling replacement record, extracting the fluid flow velocity value of the diversion inlet area corresponding to the recorded sample point within the corresponding time segment, combining the timestamp and channel number of the sampling point, and performing point-by-point aggregation processing on the flow velocity parameters of the sample point to obtain the diversion inlet flow velocity sample value; S302: Obtain flow velocity detection data at the disinfectant delivery channel inlet based on the diversion inlet flow velocity sample values. Analyze the shear rate variation segment between the diversion inlet flow velocity sample values ​​and the disinfectant delivery channel inlet flow velocity values ​​based on the time segment alignment condition of the two sets of flow velocity values ​​to obtain a shear rate segment comparison feature. S303: Invoke the shear rate segment comparison feature, combine the flow velocity difference segments between the diversion inlet area and the delivery channel inlet position, set the combined sequence of rotor speed and rotation direction, and generate the shear channel execution configuration.

6. A disinfectant production detection method based on a sampling module according to claim 5, characterized in that: The specific steps of S4 are: S401: Calling the shear channel execution configuration, injecting the corresponding sampling point into the micro-pressure chamber, activating the chamber's constant temperature environment, and extracting the pressure change curve data in the micro-pressure chamber point by point based on the time sequence after the sample injection. Using the time point sequence and the pressure change amplitude, the time position of the curve slope rising section and entering the stable section is detected to obtain the pressure slope and time positioning data; S402: Based on the pressure slope and time positioning data, combined with the time span parameters of the rising segment and the stable segment, the segment pressure gradient change rate index is calculated, and the pressure change trend characteristics within the time segment are extracted. According to the segment gradient distribution morphology, the relationship between the time span, pressure gradient and change trend is analyzed to obtain the evaporation change map data.

7. A disinfectant production detection method based on a sampling module according to claim 6, characterized in that: The specific calculation formula of the section pressure gradient change rate index is: ； in, Represents the pressure gradient change rate index of the section, Represents the total number of selected time periods, Representative The pressure value corresponding to the starting time of the segment, Representative The pressure value corresponding to the end of the segment, Representative The time span of the segment, Represent all The average value of the time span of the segment, represents the time deviation weighting factor, Represents the absolute deviation between the real-time segment and the average time span. Representative The weight value of the gradient change of the pressure data in the segment.

8. The method for detecting production of disinfectant based on a sampling module according to claim 1, characterized in that: The method also Including S5 step: S5: Based on the evaporation change map data, the duration structure and waveform trend form from the slope starting point to the stable segment are retrieved, matched to the volatilization profile information corresponding to the differentiated alcohol and quaternary ammonium salt components in the disinfectant, the composition of the disinfectant sample is identified, and the component annotation content of the test sample is generated; The test sample component annotation content includes alcohol component type, quaternary ammonium salt component characteristics, and volatility profile matching level.

9. A disinfectant production detection method based on a sampling module according to claim 8, characterized in that: The specific steps of S5 are: S501: Calling evaporation change map data, extracting the duration structure from the slope starting point to the stable segment, combining the time span parameter with the pressure change curve waveform data in the corresponding segment, and obtaining the time structure and waveform trend characteristics based on the continuous distribution characteristics of the start and end positions of the time period and the pressure change rate; S502: Based on the time structure and waveform trend characteristics, the volatility profile information of the differentiated alcohol and quaternary ammonium salt components in the disinfectant is retrieved. The segment duration, waveform gradient characteristics, and pressure change trend are used as matching factors. The time feature intervals and gradient morphological distributions in the volatility profile information are compared group by group to identify component profiles with similar change characteristics, thereby obtaining volatility feature matching results. S503: Call the volatile feature matching results, compare the time span, waveform gradient and flow trend relationship between the sample segment features and the corresponding component profile, determine the component attribution of the test sample based on the profile feature number, establish a corresponding relationship between the determination result and the sample number, and generate the test sample component annotation content.

10. A disinfectant production detection system based on a sampling module, characterized by: The system is used to implement a disinfectant production detection method based on a sampling module according to any one of claims 1 to 9, and the system comprises: The abnormal channel marking module obtains the pressure change rate at the inlet of the disinfectant delivery pipeline, the speed fluctuation value of the guide rotor device, and the acceleration change value at the sampling module structure. Based on the peak change amplitude of these three sets of parameters, it determines whether there is a single value that continuously deviates from the equipment startup state curve, and obtains an abnormal sampling identification label; The sample channel screening module extracts the stable rate section in the diversion structure from the sample within the detection cycle according to the abnormal sampling identification tag, detects the pressure change rate sample point in the section, reads the fluid guide position number of the sample point, and obtains the sampling replacement record; The shear channel configuration module calls the sampling replacement record, extracts the fluid flow rate value of the diversion inlet area, obtains the flow rate value at the inlet of the disinfectant delivery channel, sets the rotor speed ratio and rotation direction combination, and obtains the shear channel execution configuration; The evaporation change map module is configured according to the shear channel execution, imports the selected sample points into the micro-pressure chamber, activates the constant temperature environment, and after injecting the sample, extracts the pressure change curve in the micro-pressure chamber according to the time point. The time point when it enters the stable period is detected to obtain the evaporation change map data; The component identification and attribution module calls the evaporation change map data, detects the time length and pressure change waveform trend from the starting point of the slope to the stable section, matches it to the volatile contour segment in the volatile characteristic database of disinfectant alcohols and quaternary ammonium salt components, and screens the component category numbers that meet the characteristic segments to obtain the component annotation content of the test sample.

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