A method for constructing an analytical model for chlorine jet separation

Abstract

The present invention relates to the field of fluid mechanics technology, and in particular to a method for constructing an analytical model for chlorine jet separation. The method comprises the following steps: using a jet device to generate chlorine jet gas, and collecting the chlorine jet gas, thereby obtaining chlorine jet gas; injecting the chlorine jet gas into a preset separation area, and performing chlorine gas flow separation, and performing data monitoring, thereby obtaining chlorine gas flow separation data; constructing a separation model based on the chlorine gas flow separation data to obtain a separation model; performing concentration detection on the chlorine gas flow separation data to obtain concentration data; performing stability evaluation on the concentration data to obtain stability data; inputting the stability data and the concentration data into the separation model, and performing chlorine jet separation effect analysis to generate chlorine jet separation effect data. The present invention improves the jet separation effect based on fluid mechanics technology to ensure that better separation performance is achieved under various conditions.

Description

Technical Field

[0001] The invention relates to the technical field of fluid mechanics, and in particular to a method for constructing an analysis model for chlorine jet separation. Background Art

[0002] In traditional methods, the separation effect of chlorine jets is often evaluated only through preliminary flow and concentration measurements. There is a lack of in-depth analysis of complex airflow distribution and turbulence characteristics, resulting in low accuracy in the evaluation of the separation effect and failure to detect subtle anomalies in a timely manner. Traditional models cannot fully consider the fluctuations in the jet separation effect under different working conditions. For example, changes in jet velocity, temperature, and airflow density can affect the separation efficiency, and traditional methods are insufficiently optimized in these aspects, resulting in the inability to achieve optimal separation efficiency under all conditions. In traditional methods, the establishment of separation models is usually based on simplified fluid dynamics models, ignoring more complex fluid properties such as turbulent layers and velocity gradients. This simplification can affect the true effect of the model, leading to an underestimation or misjudgment of the separation efficiency. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method for constructing an analytical model for chlorine jet separation to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for constructing an analytical model for chlorine jet separation comprises the following steps:

[0005] Step S1: using a jet device to generate a chlorine jet gas, and collecting the chlorine jet gas, thereby obtaining a chlorine jet gas; injecting the chlorine jet gas into a preset separation area, and performing chlorine gas flow separation, and performing data monitoring, thereby obtaining chlorine gas flow separation data;

[0006] Step S2: constructing a separation model based on the chlorine gas flow separation data to obtain a separation model; performing concentration detection on the chlorine gas flow separation data to obtain concentration data; performing stability evaluation on the concentration data to obtain stability data;

[0007] Step S3: inputting the stability data and the concentration data into the separation model, and performing chlorine jet separation effect analysis to generate chlorine jet separation effect data; if the chlorine jet separation effect data does not meet the preset separation standard, it is marked as unreasonable jet separation data;

[0008] Step S4: Perform turbulent diffusion analysis on the unreasonable jet separation data to obtain turbulent diffusion data; perform diffusion area control on the separation model according to the unreasonable jet separation data to obtain diffusion area data; optimize the model separation efficiency of the separation model according to the turbulent diffusion data and the diffusion area data to generate an analysis model.

[0009] The present invention generates chlorine jet gas through a jet device and is accurately injected into a preset separation area for airflow separation. By monitoring the data of the chlorine jet gas flow and concentration, the airflow state in the separation process is tracked in real time. This process ensures the stability and consistency of the airflow, and provides high-quality data support for subsequent data analysis and separation model construction. The key to this step is the control accuracy of the jet device and the accuracy of the data monitoring system, which ensures the reliability of the generated data. When building a separation model based on chlorine gas flow separation data, the flow state of chlorine under different working conditions is considered, and various situations occurring in the separation process are fully simulated. By evaluating the stability of the concentration data, the fluctuation range of the chlorine concentration in the separation process can be determined, and potential unstable factors can be discovered in time. This evaluation result can help optimize the separation process, ensure the stability of the separation process under different environments, and improve the controllability of the chlorine concentration, reducing the impact of external changes on the separation effect. The key to this step is how to scientifically evaluate and quantify the stability data, and how to control the stability range through a reasonable threshold, thereby guiding the adjustment of the subsequent separation process. After the stability data and concentration data are input into the separation model, the chlorine jet separation effect analysis is performed. If the chlorine jet separation effect data does not meet the preset standards, it is marked as unreasonable jet separation data. This process avoids the shortcomings of the traditional method of evaluating the separation effect only through rough airflow data, and timely discovers low separation efficiency or abnormalities. The marked unreasonable data provides a basis for subsequent analysis, especially when the airflow velocity, temperature or airflow density changes, subtle anomalies can be found and adjusted. Through clear separation effect evaluation standards, it is ensured that the separation process meets the established performance standards. Turbulent diffusion analysis is performed on unreasonable jet separation data. First, by identifying turbulent diffusion areas, the diffusion pattern and behavior of the airflow in these areas are further understood, thereby providing data support for subsequent separation model optimization. Turbulent diffusion area data can effectively reveal which areas have larger vortices or uneven airflow, which lead to reduced separation effects. Through in-depth analysis of turbulent diffusion areas, it can provide a basis for adjusting the jet velocity, temperature or direction to ensure the efficient separation process. By precisely adjusting the turbulent diffusion area in the separation model, the jet speed, direction and temperature are optimized in a targeted manner, so that it can adapt to complex airflow distribution conditions and ensure that the separation effect can reach a better level under different operating environments. The final analysis model has higher accuracy and adaptability, avoiding the problem of underestimation or misjudgment of the separation effect caused by simplified fluid models in traditional methods. The entire process ensures that every parameter in the airflow separation process can be fully considered and accurately controlled through multi-level and multi-dimensional data analysis and optimization. Real-time monitoring of turbulent diffusion data and airflow concentration can effectively capture abnormal fluctuations in the separation process and improve the stability and accuracy of separation efficiency.Through this method, the separation process can not only adapt to a variety of working conditions, but also dynamically adjust the model parameters to ensure that the best separation effect can be achieved under any circumstances. This refined management and optimization method significantly improves the application scope and effect of the separation model and makes up for the shortcomings of traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0011] Figure 1 The figure is a schematic flow chart of the steps of the method for constructing the analytical model for chlorine jet separation of the present invention;

[0012] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0013] Figure 3 Detailed step flow diagram of step S13 in the present invention;

[0014] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0017] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0018] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for constructing an analytical model for chlorine jet separation, the method comprising the following steps:

[0019] Step S1: using a jet device to generate a chlorine jet gas, and collecting the chlorine jet gas, thereby obtaining a chlorine jet gas; injecting the chlorine jet gas into a preset separation area, and performing chlorine gas flow separation, and performing data monitoring, thereby obtaining chlorine gas flow separation data;

[0020] In the present embodiment, chlorine is delivered to the airflow nozzle through a gas pipeline using a precisely controlled jet device. The nozzle ejects chlorine gas through the set airflow pressure, flow rate and speed to form a jet gas. In this process, the injection pressure of chlorine needs to be set between 1.2MPa and 1.5MPa, and the flow rate is set to 50-70L per minute to ensure the stability and consistency of the jet gas. The jet gas is injected into the preset separation area through a specified injection channel, and the injected chlorine gas flow is precisely controlled in terms of airflow velocity and distribution. The temperature of the airflow is set at room temperature (about 25 ° C) to ensure that the gaseous state of the chlorine jet gas remains stable. Real-time airflow monitoring is performed by a multi-point gas sensor installed in the separation area. The sensor uses optical concentration sensing technology and thermal conductivity analysis technology to measure the concentration of the chlorine gas flow in real time, thereby obtaining airflow separation data. The data collected in real time by the monitoring system include important parameters such as concentration distribution, airflow velocity, and temperature change. Data recording and transmission use a data acquisition module to feed back the monitored airflow information to the computing system in real time for processing.

[0021] Step S2: constructing a separation model based on the chlorine gas flow separation data to obtain a separation model; performing concentration detection on the chlorine gas flow separation data to obtain concentration data; performing stability evaluation on the concentration data to obtain stability data;

[0022] In the present embodiment, the chlorine concentration data collected from the gas sensor is subjected to a detailed spatiotemporal analysis, the noise in the data is filtered using data preprocessing technology, and the average concentration and local concentration variation trend of chlorine are calculated by weighted average method. Then, based on this series of data, a mathematical model of chlorine jet separation is constructed using regression analysis method, and factors such as airflow density, turbulence degree, and airflow interference in the separation area need to be considered in the model. Concentration detection is completed by a professional chlorine sensor, and the measurement range of the sensor is set to 0 to 10 ppm, the sensitivity is 0.1 ppm, and the sampling frequency is 1 Hz. In order to evaluate stability, the time stability of the concentration data is analyzed, and the root mean square fluctuation of the concentration data is calculated using the fluctuation analysis method. If the stability data fluctuation is less than 0.1 ppm, the concentration is considered to be stable; if the fluctuation is greater than this value, it indicates that the system has instability and needs to further optimize the airflow separation parameters.

[0023] Step S3: inputting the stability data and the concentration data into the separation model, and performing chlorine jet separation effect analysis to generate chlorine jet separation effect data; if the chlorine jet separation effect data does not meet the preset separation standard, it is marked as unreasonable jet separation data;

[0024] In this embodiment, during the input process, the system maps the stability data and the concentration data to the corresponding parameters of the model, such as the stability data will affect the calculation of the airflow turbulence in the model, and the concentration data will affect the judgment of the separation efficiency in the model. On this basis, predictions and evaluations are performed during the operation of the model to generate chlorine jet separation effect data. If the generated separation effect data does not meet the preset standard (for example, the separation efficiency is less than 90%), the data is marked as unreasonable jet separation data by a calculation method. The preset standard is set as: in the area where the chlorine concentration reaches 1 ppm, the separation efficiency should not be less than 95%; when the chlorine concentration is less than 1 ppm, the separation efficiency should be maintained above 90%. If the separation effect does not meet this standard, the system will automatically start a further optimization process.

[0025] Step S4: Perform turbulent diffusion analysis on the unreasonable jet separation data to obtain turbulent diffusion data; perform diffusion area control on the separation model according to the unreasonable jet separation data to obtain diffusion area data; optimize the model separation efficiency of the separation model according to the turbulent diffusion data and the diffusion area data to generate an analysis model.

[0026] In this embodiment, a dynamic image of the airflow in the separation area is taken based on high-speed camera technology, and the turbulent diffusion behavior of the airflow is analyzed in combination with image processing technology. At this time, the turbulent diffusion data will be recorded, and the particle image velocimetry (PIV) technology is used to accurately analyze the airflow velocity field. The turbulent diffusion data includes the turbulence intensity, diffusion velocity and vortex structure of the airflow, and the flow quality of the airflow is evaluated. According to these turbulent data, the diffusion area is controlled, and the airflow injection angle and nozzle configuration in the separation area are accurately adjusted to achieve a uniform distribution of turbulence, thereby controlling the diffusion area of ​​the airflow. The diffusion area data includes information such as the airflow density distribution map and the airflow impact point in the area. These data will be input into the separation model, and the system uses this data to optimize the model. The main goal of the optimization is to improve the separation efficiency and ensure that the chlorine concentration in each area reaches the preset safety standard. The optimized separation model will generate the final analysis model, which will be used as a basis for subsequent operations to ensure that the separation effect is stable and meets the requirements of industrial applications.

[0027] Preferably, step S1 specifically comprises:

[0028] Step S11: using a jet device to generate chlorine jet gas and collect the chlorine jet gas, wherein the chlorine jet velocity is set in the range of 10m / s-20m / s and the nozzle diameter is set in the range of 8mm-15mm, thereby obtaining the chlorine jet gas;

[0029] In this embodiment, it is necessary to use a jet device to generate chlorine gas. The jet device is equipped with an accurate gas flow control system, which can deliver chlorine to the airflow nozzle through a pipeline. The nozzle caliber is set to 8mm to 15mm, and the appropriate nozzle size is selected according to different experimental requirements. To ensure the stability of the jet gas, the speed of the chlorine gas flow is controlled between 10m / s and 20m / s. The pressure regulating system in the jet device ensures that the velocity of the chlorine gas flow ejected is within a set range, and the airflow velocity monitoring system at the nozzle inlet usually adopts an ultrasonic measurement method to ensure that the speed reaches the set value. The gas flow is monitored in real time by a built-in flow meter to ensure that the ejected chlorine gas meets the predetermined flow standard (50-70L per minute). After ejection, the jet gas is transported to the collection area through a connecting pipeline, and the collection device collects the chlorine gas flow quantitatively, and transmits the collected gas data to the data recording device for subsequent analysis.

[0030] Step S12: injecting the chlorine jet gas into the preset separation area, and performing preliminary collection of the jet gas in the separation area, and performing data monitoring to obtain preliminary jet separation data;

[0031] In the present embodiment, an airflow guide device is used to ensure that the gas can be evenly distributed in the separation area. Multiple gas sensors are equipped in the separation area to monitor the concentration, speed and distribution of the airflow in real time. Through these sensors, the preliminary data of the jet gas is obtained, mainly including the flow rate, concentration distribution and temperature of the gas. The gas flow rate is measured by multiple sensors set in the region, and the measurement area is often set to a grid of every 5cm to 10cm to ensure the comprehensiveness of the data. The concentration sensor uses gas chromatography to accurately measure, and the measurement range of the concentration is set between 0.1ppm and 10ppm, and the sensitivity of the sensor is 0.01ppm, which ensures that the slight changes in the chlorine concentration can be monitored. In addition, the temperature sensor monitors the gas temperature in the region in real time, and adjusts the temperature control system of the separation area according to the airflow speed and temperature changes to prevent the temperature from being too high and causing uneven distribution of chlorine. All monitoring data are transmitted to the analysis system in real time by the data acquisition device so that the preliminary data can be analyzed and stored.

[0032] Step S13: performing abnormal separation analysis based on the preliminary jet separation data to obtain abnormal jet separation data;

[0033] In this embodiment, the preliminary data is cleaned to remove abnormal data caused by equipment failure or external factors. Abnormal data is usually manifested as excessive fluctuations in gas concentration (exceeding the set threshold of 0.5ppm) or large deviations in airflow velocity (exceeding the ±10% error range). Abnormal separation analysis mainly uses statistical analysis methods, such as standard deviation analysis and trend analysis, to detect data fluctuations. Abnormal values ​​in the data set need to be marked and isolated. During the analysis process, the normal range of concentration, airflow velocity and temperature is determined by setting thresholds. If the data fluctuates violently or deviates from the normal range, it can be judged as abnormal data. These abnormal data are compared and analyzed with historical data to ensure that invalid or distorted data can be accurately identified and isolated. The identified abnormal data is further analyzed to explore the root causes of its generation, such as equipment problems, airflow disturbances and other factors.

[0034] Step S14: Screening out the preliminary jet separation data according to the abnormal jet separation data, thereby obtaining chlorine gas flow separation data.

[0035] In the present embodiment, a filtering algorithm based on a threshold value is adopted to eliminate abnormal data. During the screening process, the tolerance threshold of the concentration is set to 0.5ppm, and any concentration fluctuation data above this threshold value is regarded as abnormal data. The airflow velocity deviation threshold is set to ±10%, and data exceeding this range will be judged as abnormal. Once all abnormal data are confirmed, they will be eliminated from the preliminary separation data, and valid data that meets the standards will be retained. For the valid data after screening, further processing and correction are performed to ensure that the data finally obtained meet the standard requirements. During the screening process, special attention is paid to the integrity of the data to avoid the loss of useful data caused by excessive cleaning. Therefore, the statistical characteristics of the data and the expected results of the physical model will be considered in the screening process to ensure that the final separation data is reliable and effective. On this basis, the chlorine gas airflow separation data obtained will be used as the basis for subsequent analysis and optimization.

[0036] Preferably, step S13 is specifically as follows:

[0037] Step S131: extracting airflow density features based on preliminary jet separation data to obtain airflow density data;

[0038] In this embodiment, multiple airflow sensor arrays are used to monitor the airflow in the separation area in real time. Each sensor measures the chlorine concentration at different locations using gas sensing technology (such as thermal conductivity method, infrared absorption method). Based on the concentration data, combined with the known gas flow rate information, the airflow density is calculated using the following formula:

[0039] ;

[0040] Where P is air pressure, R is gas constant, T is air temperature, and C is chlorine concentration. The output data of each sensor will obtain the corresponding airflow density value according to the above formula. In this process, the airflow density data is collected per second, and the sampling frequency is set to 100 times per second to ensure the timeliness and accuracy of the data. The obtained airflow density data will be used as the basic data for subsequent analysis, stored in the data recording system, and marked by area and time for subsequent analysis and comparison.

[0041] Step S132: Identify uneven time periods of the airflow density data to obtain uneven airflow density time period data;

[0042] In this embodiment, a reference threshold is set according to the collected airflow density data. Usually, the threshold is set according to the normal fluctuation of the airflow, and the typical value is a fluctuation within the range of ±5%. For example, the airflow density should be between 1.2kg / m³ and 1.5kg / m³ under normal circumstances, and any fluctuation exceeding or below this interval is considered to be an abnormal fluctuation. A sliding window analysis is performed on the data of the entire time period, and the data is divided into multiple time periods, each containing 1000 data points. If the change in airflow density within a certain time period exceeds 10% of the set threshold, the time period is marked as an "uneven time period". This method ensures that large fluctuations in airflow density can be captured in real time, and then the unevenness of airflow distribution can be identified, and these uneven time period data can be accurately identified for subsequent analysis and processing.

[0043] Step S133: performing diffusion statistics on the uneven airflow density time period data to obtain uneven diffusion data;

[0044] In this embodiment, the identified uneven time period data is extracted to perform diffusion analysis. The diffusion can be obtained by calculating the standard deviation of the uneven airflow density, that is:

[0045] ;

[0046] Where D is the diffusion, N is the number of data points in the uneven period, is the airflow density value for each data point, is the average value of the airflow density during the period. This formula is used to obtain the diffusion value in each uneven period, which is used to measure the fluctuation degree of airflow density. The diffusion data is further processed by time series analysis method to screen out the periods with abnormal diffusion, and the diffusion values ​​of these periods exceed the set threshold (for example, 3kg / m³). These high diffusion periods indicate that the airflow distribution is extremely unstable, which usually affects the effect of chlorine separation. All diffusion data will be stored in chronological order and transmitted to the data analysis platform for subsequent processing and optimization.

[0047] Step S134: performing abnormal separation judgment on the preliminary jet separation data according to the non-uniform diffusion data to obtain abnormal jet separation data.

[0048] In this embodiment, it is necessary to combine the uneven diffusion data obtained in step S133 and compare it with the airflow density data in the preliminary separation data. The criterion for abnormal separation judgment is that when the uneven diffusion value of a certain time period is greater than the set threshold value (for example, more than 3kg / m³), it means that there are large fluctuations in the airflow distribution within the time period, resulting in poor jet separation effect. On this basis, by comparing these abnormal diffusion time periods with the airflow density data in the preliminary separation data, marking processing is performed. If the period with high uneven diffusion is accompanied by abnormal changes in airflow density (such as exceeding the set airflow density fluctuation threshold), it can be determined as abnormal jet separation data. These abnormal data need to be eliminated from the preliminary data to avoid interference with subsequent analysis and model optimization. Finally, the eliminated valid data will be further processed as the basic data for optimizing the separation model.

[0049] Preferably, step S134 is specifically as follows:

[0050] When any of the following situations occurs, it is determined to be jet separation abnormality and jet separation abnormality data is obtained: the jet velocity fluctuation exceeds ±10%, the airflow density non-uniformity exceeds the set threshold ±15%, and the airflow thickness in the separation area deviates from the predetermined range by more than 5 cm;

[0051] In this embodiment, the jet velocity is monitored in real time. The jet velocity data is obtained at different positions in the separation area by setting a flow velocity sensor (such as an ultrasonic flow velocity sensor) and calculating its fluctuation amplitude. If the change in jet velocity exceeds ±10%, it is determined that the jet velocity fluctuation is abnormal. In addition, the airflow density in the separation area is collected in real time by an airflow density sensor. If the uniformity fluctuation of the airflow density exceeds ±15% (determined according to the baseline value and deviation limit of the airflow density), it is considered that there is an airflow unevenness problem. Then, the thickness of the airflow is measured at different positions in the separation area using an airflow thickness measuring instrument (such as a laser rangefinder). If the measured airflow thickness deviates from the predetermined range by more than 5 cm, it is marked as abnormal airflow thickness. All data are compared and analyzed in time series. If the above three conditions are met at the same time, it is determined that the jet separation is abnormal, and the corresponding abnormal data is saved in the data recording system.

[0052] When the following conditions occur at the same time, it is determined to be a separation abnormality caused by nozzle blockage, and the nozzle blockage separation abnormality data is obtained: the nozzle outlet pressure drops by more than 15%, the jet velocity is lower than the lower limit of the preset range, the jet range deviation exceeds ±15°, and the airflow diffusion angle deviates from the design angle by more than 10°;

[0053] In this embodiment, a pressure sensor is used to measure the gas pressure at the nozzle outlet and compare it with the preset normal pressure range. If the measured nozzle outlet pressure value is lower than the normal value by more than 15%, it is considered that the nozzle has a blockage problem. At the same time, a flow meter is used to monitor the flow rate of the jet. If the flow rate is lower than the preset lower limit (such as lower than 10m / s), it is further determined that the nozzle is blocked. In addition, a laser radar or a high-precision sensor is used to monitor the injection range of the jet. If the jet deviates from the design angle by more than ±15°, it indicates that the jet range is offset, which further supports the judgment of nozzle blockage. Finally, an angle sensor is used to measure the diffusion angle of the airflow. If the measured airflow diffusion angle deviates from the design angle by more than 10°, it is confirmed that there is a nozzle blockage problem. If the above four conditions are met at the same time, it is determined that the separation is abnormal due to nozzle blockage, and the corresponding abnormal data is recorded.

[0054] When the following conditions occur at the same time, it is determined that the jet separation system has abnormal airflow temperature and abnormal airflow temperature data is obtained: the airflow temperature fluctuation exceeds ±10°C, the temperature in the separation area deviates from the normal range of 20-30°C, and the temperature in the airflow injection area is continuously lower than 10°C or higher than 50°C;

[0055] In this embodiment, the air flow temperature in the separation area is measured in real time by a temperature sensor (such as a thermocouple). If the air flow temperature fluctuates by more than ±10°C, that is, exceeds the set temperature fluctuation threshold, it is determined that the temperature fluctuation is abnormal. Secondly, by arranging multiple temperature measurement points in the air flow injection area, the temperature of the injection area is monitored in real time. If the measured temperature of the injection area deviates from the normal range (20°C to 30°C) and exceeds the upper or lower limit of the range, it is marked as a temperature abnormality. In addition, if the temperature of the air flow injection area is continuously lower than 10°C or higher than 50°C, it is also considered that the air flow temperature is abnormal. All temperature data are recorded and analyzed in real time by the monitoring system. If any of the above conditions is met, it is determined that the air flow temperature is abnormal, and the corresponding abnormal data is saved.

[0056] The abnormal jet separation data, the nozzle blockage separation abnormal data and the airflow temperature abnormal data are integrated to obtain the abnormal jet separation data.

[0057] In this embodiment, after completing the separate determination of jet separation anomaly, nozzle blockage separation anomaly, and airflow temperature anomaly, various types of abnormal data are integrated through the data processing platform. Each type of abnormal data is associated according to its different marking methods (such as timestamp, abnormality type) and merged into a complete abnormal record. The rule engine conducts a comprehensive evaluation of the abnormal data to ensure that multiple abnormal situations occurring in the same time period can be accurately associated and generate abnormal jet separation data. All integrated abnormal data will be stored in the central database and displayed to the operator through a visual interface for subsequent analysis and decision-making.

[0058] Preferably, step S2 specifically comprises:

[0059] Step S21: performing data preprocessing based on the chlorine gas flow separation data to obtain preprocessed chlorine gas flow separation data;

[0060] In this embodiment, an airflow sensor is used to collect chlorine gas flow data, including parameters such as airflow velocity, airflow density, and temperature. A digital signal processor (DSP) with a filtering function is used to perform noise filtering on the original airflow data, and a low-pass filter is used to remove high-frequency noise signals. In addition, the mean smoothing method is used to smooth the data to ensure the stability of the data and remove sudden fluctuations and outliers. During the data preprocessing process, the smoothing parameter is set to a 3-second window length, that is, the data within 3 consecutive seconds is smoothed. Next, the data is standardized and the chlorine gas flow data is mapped to a range of 0-1 for subsequent processing. The pretreated chlorine gas flow separation data will be stored in the data storage system to ensure that its format is unified and easy for subsequent analysis.

[0061] Step S22: extracting chlorine gas flow distribution characteristics according to the pre-processed chlorine gas flow separation data, thereby obtaining chlorine gas flow distribution data;

[0062] In the present embodiment, the airflow data of chlorine is collected at multiple measuring points using a two-dimensional airflow distribution sensor. The airflow sensor adopts a hot film anemometer, which has the characteristics of high precision and rapid response, and can monitor the distribution characteristics of airflow in real time. The average speed, maximum speed, airflow direction and density distribution of the airflow are calculated by a data processing algorithm. The extraction standard set is to perform data sampling every 5 seconds, and the airflow velocity and density at each time point are statistically analyzed to obtain the distribution data of the chlorine gas flow. In the process, the numerical integration method is used to synthesize the data of each measuring point, so as to obtain the distribution characteristics of the chlorine gas flow in the separation area. Finally, the chlorine gas flow distribution data, including parameters such as airflow velocity, density, temperature, etc., are output and stored in the database for subsequent analysis.

[0063] Step S23: performing turbulent layer analysis on the chlorine gas flow distribution data to obtain turbulent data, and constructing a separation model using the Reynolds average equation to obtain a separation model;

[0064] In this embodiment, the flow field of the separation area is analyzed by the turbulence model through the airflow velocity distribution and density data. The turbulence calculation formula and the Reynolds average equation are used to analyze the turbulence characteristics, and the parameters such as the intensity, energy and vortex structure of the turbulence are calculated. When the Reynolds number Re is set to be greater than 4000, it is a turbulent flow, and the turbulence model is used to analyze the velocity fluctuation of the airflow, and the Reynolds average equation is solved by a numerical method to obtain turbulence data. Subsequently, according to the turbulence data, a numerical model of the airflow separation area is established, and the boundary conditions of the model are set, and the boundary conditions include the airflow density, pressure distribution and temperature in the separation area. The separation model is obtained by solving the finite difference method. The velocity threshold value set in the separation model is 20 m / s. If it exceeds this value, it is considered that the airflow enters the turbulent zone, affecting the separation effect. Finally, the model results are output and saved in the separation model database.

[0065] Step S24: performing concentration detection on the chlorine gas flow distribution data to obtain concentration data;

[0066] In this embodiment, the concentration of chlorine is monitored in real time by a chemical gas concentration sensor (such as an electrochemical sensor) installed in the separation area. The detection range of the sensor configuration is 0-1000 ppm, the resolution is 1 ppm, and the detection accuracy is ±3%. The sensor regularly samples and generates concentration data. During the data acquisition process, the sampling interval is set to 5 seconds to ensure the accuracy of the continuity data. The concentration data is processed by an algorithm and compared with the set concentration threshold (such as an alarm when the chlorine concentration is greater than 500 ppm) to obtain the chlorine concentration data at each time point. The concentration data is filtered to remove errors and noise caused by environmental fluctuations to ensure the accuracy of the data. Finally, the concentration data is stored in a centralized monitoring system, and the timestamp of each data point is marked for subsequent analysis and report generation.

[0067] Step S25: Perform stability evaluation on the concentration data to obtain stability data.

[0068] In this embodiment, when the concentration data is evaluated for stability, the concentration fluctuation in each time period is first calculated. The concentration data is evaluated for volatility using statistical analysis methods, such as standard deviation and root mean square error (RMSE), and the threshold value is set to be unstable when the standard deviation exceeds 5ppm. On this basis, the sliding window technology is used to process the concentration data and calculate the concentration fluctuation in each window. If the concentration change exceeds the set stability range within a certain period of time, the concentration in that period is considered unstable. During the evaluation process, the window size is set to 10 minutes, and the average value of the concentration data in each window is calculated. According to the set threshold standard, if the average fluctuation range of the concentration data exceeds the set upper and lower limits (such as ±5 ppm), it is considered that the concentration is unstable. Finally, the stability data is processed and stored in a database for subsequent analysis and monitoring.

[0069] Preferably, step S23 is specifically:

[0070] Step S231: performing speed calculation on the chlorine gas flow distribution data, wherein the speed valid data interval of each node is set to 10 seconds, thereby obtaining the chlorine gas flow distribution speed data;

[0071] In this embodiment, it is necessary to determine the valid data interval of each node. The speed valid data interval of each node is set to 10 seconds. The setting of the interval length is based on the experimental requirements to ensure that the collected speed data has sufficient stability and accuracy. The chlorine gas flow distribution data is collected by sensors and recorded in the airflow data of each node. The airflow distribution data of each node includes airflow intensity, direction and other related parameters. In the data interval of 10 seconds, the airflow parameters of each node are collected, and the speed data is calculated by the difference method. Specifically, the airflow position data of each node in the 10-second time period before and after is first obtained to ensure that these position data are time synchronized and calibrated. Then, the difference formula is used: speed = (current node position-previous node position) / time interval, where the time interval is fixed to 10 seconds. For each node, this calculation method will obtain the corresponding speed data. This process uses high-precision time synchronization tools and differential algorithms to ensure the accurate calculation of airflow data between nodes. Ensure that the time synchronization error of all nodes is less than 1 millisecond to avoid excessive calculation errors. Time synchronization uses high-precision time synchronization instruments to ensure that the time marking of each node is accurate and correct. By controlling the sampling frequency of the airflow sensor to 1Hz, data is collected once per second and the data integrity of each node is ensured. During the calculation process, the timestamp must be recorded for each node and the data must be sorted according to the timestamp so that the differential formula can be correctly applied to calculate the speed. The validity and accuracy of all speed data must be verified through subsequent data quality verification mechanisms, such as comparing different sensor data with actual chlorine gas flow rate model results. After completing the speed calculation, the generated chlorine gas flow distribution speed data will be used for subsequent process optimization and analysis.

[0072] Step S232: performing velocity gradient calculation based on the chlorine gas flow distribution velocity data, wherein the sampling interval is set to 0.1 meters and the calculation range is set to 5-10 meters to obtain velocity gradient data;

[0073] In this embodiment, the velocity data in the 5 to 10 m area is extracted from the air velocity data collected by the chlorine gas flow sensor. In order to ensure the accuracy of the data, the sampling interval is set to 0.1 m, that is, the air velocity value is collected every 0.1 m. Within this range, the velocity data of the air flow is gradually obtained, and the gradient calculation formula is applied:

[0074] ;

[0075] Among them, Δv represents the change in speed, and Δx is the change in distance, that is, the sampling interval (0.1 meter). This formula is used to calculate the speed change rate between each sampling point to obtain the speed gradient data. In this process, the speed change threshold is set to 1m / s. If the speed change exceeds this value, it is considered that there is a significant speed change at that location. Finally, the obtained speed gradient data is stored in the data storage system for subsequent analysis.

[0076] Step S233: performing velocity fluctuation evaluation on the chlorine gas flow distribution velocity data, thereby obtaining velocity fluctuation data;

[0077] In this embodiment, the collected air flow velocity data is subjected to time series analysis to analyze its fluctuations in different time periods. The sampling frequency is set to 1 Hz, that is, the velocity data is acquired once per second. In the velocity fluctuation assessment, the standard deviation and root mean square error (RMSE) methods are used to assess the volatility. Specifically, the average velocity value in each period is calculated, and the deviation is compared with each data point to obtain the fluctuation value in each period. The set fluctuation threshold is 5% (that is, speed fluctuations exceeding 5% are considered large fluctuations). For each time period, if the speed fluctuation is greater than this threshold, the fluctuation data for that time period is recorded. During the evaluation process, a fluctuation calculation is performed on the velocity data within 5 consecutive seconds, and the overall velocity fluctuation data is obtained through cumulative analysis, and these data are finally recorded in the database.

[0078] Step S234: Calculate the vorticity according to the velocity gradient data and the velocity fluctuation data, wherein the fluctuation time of the vorticity data is set to be greater than 3 seconds and regarded as a valid vortex, and obtain the vorticity data;

[0079] In this embodiment, the vorticity is calculated based on the velocity gradient data obtained in step S232 and the velocity fluctuation data obtained in step S233, combined with the basic principles of turbulent flow. The vorticity represents the intensity and direction of rotation in the airflow, and the calculation formula is:

[0080] ;

[0081] in, is the symbol for partial derivative. Partial derivative is a method in mathematics to describe the rate of change of a function of multiple variables, especially when multiple variables are involved. represents the velocity of the airflow in the y direction ( ) to changes in the x-coordinate, represents the velocity of the airflow in the x direction ( ) to changes in the y coordinate, and are the velocity components of the airflow in the x-axis and y-axis directions, respectively. The vorticity calculation process requires discretization calculation based on the velocity difference between different sampling points, and the size of the vorticity is determined in combination with the local velocity change. In the vorticity data calculation, the effective vortex time is set to 3 seconds, that is, if the vortex fluctuation is greater than the set time (3 seconds), the vortex is considered to be an effective vortex, and its impact is further analyzed. In the calculation process, the sliding window technology is used with a window size of 3 seconds to analyze the fluctuation of the vorticity in the window. The obtained vorticity data is filtered to eliminate noise and abnormal data, and stored in the data storage system.

[0082] Step S235: performing turbulent layer identification on the chlorine gas flow distribution data according to the vorticity data to obtain turbulent data, and constructing a separation model using the Reynolds average equation to obtain a separation model.

[0083] In this embodiment, the vortex structure in the airflow is identified by vortex data. Combined with the vortex data, the vortex intensity threshold is set to 0.5. If the vortex is greater than this threshold, it is considered a turbulent zone. By analyzing the spatiotemporal distribution of the vortex data, the turbulent region and the laminar region are divided. Next, a separation model of the airflow is established based on the Reynolds average equation (RANS). The set boundary conditions include airflow velocity, density and temperature distribution, and the Reynolds average equation is solved by a numerical solution method (such as the finite difference method). Through this model, the velocity distribution, pressure distribution and turbulence intensity of the airflow in the turbulent layer are calculated to establish a complete separation model. The separation model is used to simulate the flow characteristics of chlorine gas flow in different regions and further optimize the airflow separation effect. Finally, the obtained turbulent data and separation model are stored in the model database for subsequent application and analysis.

[0084] Preferably, step S24 is specifically as follows:

[0085] Step S241: using a concentration sensor to perform preliminary concentration detection on the chlorine gas flow distribution data, wherein the sampling period is set to once per minute, thereby obtaining preliminary concentration detection data;

[0086] In this embodiment, a suitable concentration sensor is selected, which needs to have high precision and high stability and can detect the chlorine concentration in the airflow. The sampling period of the concentration sensor is set to once per minute, that is, the chlorine concentration is recorded every 60 seconds. The data measured each time should include the chlorine concentration (in ppm) and the relevant characteristics of the airflow (such as temperature, humidity, etc.). In the specific implementation process, the sensor will be installed at a specific position in different airflow areas, and the airflow will be detected at a certain height. The detection data is stored in a local database or cloud server, and further processing will be carried out later. Each time a sample is taken, the data timestamp is recorded for subsequent analysis to ensure the time continuity of the detection process.

[0087] Step S242: dividing the preliminary concentration detection data into excessive concentration data based on the preset reference concentration data, thereby obtaining the preliminary concentration detection data exceeding the standard;

[0088] In this embodiment, the reference concentration value is set as the maximum value of the safe concentration range, and the normal range is usually below 0.5ppm. For each concentration detection value, if it is greater than the reference concentration value (0.5ppm), it is considered to exceed the standard. The concentration data of each detection will be compared with the reference concentration value, and those exceeding 0.5ppm will be marked as exceeding the standard concentration. By setting the concentration threshold to 0.5ppm, filtering and marking are performed to ensure that the exceeding concentration data can be accurately identified and classified. All exceeding data will be recorded and marked to facilitate subsequent regional identification and analysis. At this point, the preliminary detection data of the exceeding concentration has passed the exceeding standard judgment and serves as the basis for the next step of processing.

[0089] Step S243: regional identification is performed on the chlorine gas flow distribution data according to the preliminary detection data of the excessive concentration, wherein the chlorine gas safety concentration limit is set to be less than 0.5 ppm, and the excessive concentration regional data is obtained;

[0090] In the present embodiment, the chlorine safety concentration limit is set to 0.5ppm. During the detection process, all areas where the concentration data value is greater than 0.5ppm will be regarded as areas with excessive concentration. In order to identify these areas, it is necessary to use the spatial distribution information of the concentration sensor data and the airflow model, compare each excessive concentration data point with the data of the surrounding area, find out the adjacent excessive concentration area and mark it. During the specific operation, the data within a short time window (such as 5 minutes) is selected for regional division, and all points exceeding the threshold in the window are regarded as an excessive area, and the coordinate range of each area is recorded, and effective regional data is provided for subsequent analysis. The spatial distribution of each excessive area will be recorded in detail for further analysis.

[0091] Step S244: marking the sensor coordinates based on the preliminary concentration detection data, wherein the neighborhood size is set to 10-15 meters and the minimum number of neighbors is 3, to obtain the sensor coordinate data;

[0092] In this embodiment, the neighborhood size is set to 10-15 meters, and the sensor data within the neighborhood is determined for coordinate marking. The neighborhood size represents the influence range of the sensor within a certain spatial range. The coordinates of each sensor are marked according to its installation location (for example, X, Y, and Z coordinates). The minimum number of neighborhoods is set to 3, that is, within the influence range of each sensor, at least 3 sensor data are required to participate in the coordinate marking. During operation, the coordinates of each sensor are first determined, and then marked according to the coordinates and concentration data of the adjacent sensors within the neighborhood of 10-15 meters to ensure that the sensor coordinate data can reflect the overall distribution of the airflow. All data will be stored and combined with the actual airflow model for subsequent analysis.

[0093] Step S245: marking the target coordinates based on the preliminary concentration detection data to obtain the target coordinate data;

[0094] In this embodiment, the actual coordinates of the target area or target device are determined. During the target coordinate marking process, the coordinate data of the target, such as the X, Y, and Z axis positions of the target, are determined. When marking the target, the coordinate value accuracy is set to 0.01 meters, and the coordinate information of the target is recorded through a high-precision positioning system. According to the positional relationship between the detected concentration data and the target, the concentration data of each target is marked, and the target coordinates are recorded. The purpose of this operation is to ensure that the concentration data is consistent with the spatial coordinates, and to provide accurate position data for subsequent concentration analysis and sensor weight calculation.

[0095] Step S246: performing Euclidean distance calculation according to the sensor coordinate data and the target coordinate data to obtain Euclidean distance data;

[0096] In this embodiment, the target coordinates and sensor coordinates are obtained. For each pair of sensor coordinates and target coordinates, the Euclidean distance is calculated using the following formula:

[0097] ;

[0098] In the formula, (x1, y1, z1) are the sensor coordinates and (x2, y2, z2) are the target coordinates. The distance between each sensor and the target is calculated using this formula. The distance data is used to further analyze the influence of the sensor in the target area and provide a basis for sensor weight calculation and concentration analysis. All calculation results will be stored in the database and combined with other data to facilitate subsequent concentration analysis.

[0099] Step S247: Calculate sensor weight based on the Euclidean distance data to obtain sensor weight data;

[0100] In this embodiment, the weight of the sensor is calculated based on the Euclidean distance between the sensor and the target. The weight calculation formula is set as:

[0101] ;

[0102] The closer the sensor is to the target, the greater its weight. Conversely, the farther the distance, the smaller the weight. Through this formula, the weight value of each sensor is calculated and a weight value is assigned to each sensor. This operation ensures that the impact of the distance of the sensor on its concentration analysis can be reasonably reflected. The weight data of all sensors will be recorded and stored for subsequent concentration analysis.

[0103] Step S248: Perform concentration analysis on the excessive concentration area data according to the sensor weight data to obtain concentration data.

[0104] In this embodiment, the weight of each sensor is used to perform a weighted average on the data of the area with excessive concentration. In specific operation, the concentration data in each area with excessive concentration is multiplied by the weight of the corresponding sensor, and all data are summed to obtain a weighted concentration value. The weighted concentration value reflects the measurement impact of the sensor in the area. Finally, the weighted values ​​of the concentrations of all areas with excessive concentrations are summarized to obtain the concentration analysis results of the entire area for subsequent decision-making and processing.

[0105] Preferably, step S25 is specifically as follows:

[0106] Step S251: performing fast Fourier transform on the concentration data, wherein the sampling frequency is 10 Hz-1000 Hz, to obtain concentration frequency domain data;

[0107] In this embodiment, it is necessary to collect the concentration data of the chlorine gas flow, which is usually collected by a concentration sensor at fixed time intervals (for example, once per minute). In order to ensure that the converted frequency domain data can reflect the periodic fluctuations of the chlorine gas flow concentration, the sampling frequency needs to be set between 10Hz and 1000Hz, and the selection of the specific frequency should consider the characteristics of the change in chlorine concentration and the requirements of data acquisition. The selection of the sampling frequency determines the accuracy and amount of information after the signal conversion. In this step, the fast Fourier transform algorithm (FFT) is used to convert the collected time domain concentration data into frequency domain data to obtain the amplitude and phase information of different frequency components. The FFT algorithm can effectively convert time series data into frequency domain data, revealing the potential periodic changes in the gas flow concentration signal. In order to achieve this conversion, the built-in FFT function of programming tools such as Matlab and Python can be used. By Fourier transforming the concentration signal, the obtained frequency domain data can show the intensity distribution of concentration fluctuations at different frequencies, thereby providing a basis for further analysis of the periodic components in the signal.

[0108] Step S252: extracting the amplitude spectrum based on the concentration frequency domain data, thereby obtaining amplitude spectrum data;

[0109] In this embodiment, it is necessary to process the frequency domain data after the fast Fourier transform. The goal is to extract the amplitude value of each frequency point, which represents the intensity of the concentration signal at the corresponding frequency. The extraction of the amplitude spectrum can be obtained by calculating the modulus of each frequency component. The modulus is the size of the frequency domain data in complex form, which represents the intensity of the concentration fluctuation at this frequency. In the extraction process, special attention should be paid to removing the DC component, that is, the part with zero frequency, because the DC component usually represents the average value of the signal and does not belong to the periodic fluctuation part. By analyzing the amplitude spectrum data, it is possible to further identify which frequency components in the signal have strong fluctuations and which belong to background noise or irrelevant components. The extraction of the amplitude spectrum data helps to screen the subsequent significant amplitudes, clarify which frequency components play a leading role in the concentration changes, and provide data support for the subsequent identification of periodic fluctuations.

[0110] Step S253: performing significant amplitude statistics on the amplitude spectrum data to obtain significant amplitude data;

[0111] In this embodiment, amplitude data is obtained from historical data, and the mean and standard deviation of these data are calculated. For example, assuming that the amplitude value range in the historical data is from 0 to 1, the mean obtained after analysis is 0.3, and the standard deviation is 0.05. Then, according to these statistical values, the amplitude threshold is set to the mean plus three times the standard deviation, i.e. 0.3+3*0.05=0.45. Through this method, those frequency components with smaller amplitudes can be effectively eliminated, which usually represent background noise or irrelevant fluctuations. When the threshold is set, all frequency components with amplitudes greater than 0.45 will be considered significant, representing the main periodic fluctuation components in the concentration signal. These significant frequency components will serve as the basis for subsequent periodic fluctuation analysis to help identify the main fluctuation mode in the chlorine gas flow. The purpose of significant amplitude statistics is to filter out noise and ensure that subsequent analysis can focus on truly meaningful fluctuation data, thereby improving the accuracy of periodic fluctuation identification.

[0112] Step S254: obtaining amplitude threshold data;

[0113] In this embodiment, in the amplitude threshold acquisition step, it is necessary to perform statistical analysis based on the amplitude spectrum data to determine the appropriate amplitude threshold. In order to set this threshold, it is first necessary to fully analyze the amplitude spectrum data, especially its statistical characteristics, such as mean and standard deviation. In actual operation, the mean and standard deviation of its amplitude can be calculated by analyzing historical data or current real-time data collected. For example, when analyzing historical data, assuming that the amplitude data range is 0 to 1, after calculation, the amplitude mean is 0.35 and the standard deviation is 0.04. On this basis, in order to ensure that normal fluctuations can be effectively distinguished from abnormal fluctuations, the threshold can be set to the mean plus three times the standard deviation, that is, 0.35+3 *0.04=0.47. This setting method helps to eliminate most of the background noise and retain the main components in the concentration fluctuation. The setting of the amplitude threshold must ensure that it can effectively distinguish normal fluctuation signals from abnormal fluctuations, so as to avoid misjudging noise as a meaningful signal. In the actual process, the threshold adjustment must be fine to ensure that the main fluctuation components of the airflow can be accurately captured without introducing unnecessary errors or missing important fluctuation information due to excessively high or low thresholds. Therefore, the setting of amplitude threshold plays a crucial role in concentration signal analysis.

[0114] Step S255: identifying periodic fluctuations in the significant amplitude data according to the amplitude threshold data, and obtaining periodic fluctuation concentration frequency domain data;

[0115] In this embodiment, it is necessary to use the amplitude threshold determined in the previous step to screen the significant amplitude data and select all frequency components whose amplitudes exceed the set threshold. At this time, assuming that the amplitude threshold is 0.47, the frequency components in the significant amplitude data will be considered as frequency components with significant fluctuations if their amplitudes are greater than 0.47. Then, for these significant frequency components, the inverse fast Fourier transform (IFFT) is used for processing to convert them from the frequency domain back to the time domain to facilitate the analysis of their performance in the time domain. Specifically, assuming that these frequency components are composed of concentration fluctuation data with sampling points between 10 Hz and 1000 Hz, after IFFT processing, the time domain data will show a periodic fluctuation pattern. By restoring the time domain waveforms of these frequency components, IFFT can reveal the specific characteristics of periodic fluctuations in the concentration signal, thereby helping to identify potential periodic changes in the airflow. The key to this process is to accurately extract and restore the main periodic fluctuation components in the frequency domain to avoid misjudging the noise components as periodic signals. In actual operation, programming tools such as Matlab or Python can be used to effectively convert frequency domain data into time domain data by calling the built-in IFFT function, thereby obtaining the time domain representation of periodic fluctuations. This recognition result provides key data support for subsequent concentration stability evaluation and safety analysis, allowing a more accurate understanding of the dynamic changes of airflow and providing a reference for safety warning or optimized control.

[0116] Step S256: performing range calculation on the concentration data to obtain concentration range data;

[0117] In this embodiment, it is necessary to collect concentration data within a certain period of time, and these data are generally sampled by the sensor every minute, or a higher sampling frequency is used according to the actual situation. For example, assuming that within an hour, concentration data is collected once a minute, so that 60 data points are obtained. Then, traverse these collected concentration data to find the maximum concentration value and the minimum concentration value within the time period. Assuming that within this hour, the maximum value of the concentration data is 0.98ppm and the minimum value is 0.12ppm, then the concentration range is the gap between these two values, that is, 0.98-0.12=0.86 ppm. The size of the concentration range reflects the amplitude of the concentration fluctuation. The larger the range, the more drastic the change in airflow concentration. On the contrary, if the range is small, it means that the concentration changes more steadily. Therefore, this range data has become an important indicator for measuring the intensity of airflow concentration fluctuations, and can provide a basic metric for subsequent stability assessment. Through this method, the fluctuation characteristics of airflow concentration can be effectively captured, and the necessary data support can be provided for subsequent analysis such as periodic fluctuation identification and stability assessment.

[0118] Step S257: Perform stability assessment based on the concentration range data and the periodic fluctuation concentration frequency domain data to obtain stability data.

[0119] In this embodiment, a comprehensive analysis is performed to evaluate the stability of the airflow concentration by combining the concentration range data and the periodic fluctuation concentration frequency domain data. Specifically, the intensity of the concentration fluctuation is first judged based on the concentration range data. Assuming that the range of the collected concentration data is 0.86ppm in a certain period of time, if this value is large, it means that the airflow concentration fluctuates violently and there are unstable factors. A large concentration range usually means that the concentration fluctuates greatly in a short period of time, which affects the safety of the system. Then, the periodic changes of the concentration signal are analyzed based on the periodic fluctuation concentration frequency domain data. For example, if there is an obvious frequency component in the frequency domain data and the fluctuation is regular, the periodic fluctuation will show repeatability and regularity in the time domain data, indicating that the airflow concentration fluctuation has a certain regularity and tends to be stable. If the fluctuation in the frequency domain data is irregular or the periodicity is weak, it means that the concentration change lacks regularity, the fluctuation is relatively random, and the instability of the airflow concentration is high. By combining the concentration range data and the periodic fluctuation concentration frequency domain data, using the preset stability standard, the stability of the airflow concentration can be comprehensively evaluated and the stability evaluation result can be obtained. For example, if the stability standard is set as follows: if the range is greater than 0.8ppm and the periodic fluctuation is weak, it is evaluated as unstable; if the range is less than 0.5ppm and the periodic fluctuation is significant, it is evaluated as stable. Based on these standards, the final stability evaluation results will provide data support for subsequent airflow regulation and safety monitoring, help to timely identify the risk of abnormal concentration fluctuations, take corresponding safety measures in advance, and avoid potential safety hazards.

[0120] Preferably, step S3 specifically comprises:

[0121] Step S31: converting the stability data and the concentration data into CSV files to obtain a stability CSV file and a concentration CSV file;

[0122] In this embodiment, the stability data and concentration data need to be converted into CSV file format. First, two spreadsheets containing relevant data are prepared. The stability data consists of the stability evaluation results obtained in step S257, and the concentration data consists of the concentration values ​​obtained in step S256. First, the two sets of data are organized into two different files, each row of data containing the stability or concentration value at a time point, ensuring that the timestamps are aligned. For each data file, the timestamp is accurate to seconds, and the data accuracy is required to be two decimal places. For example, the stability data file will contain the following: timestamp, stability evaluation value, unit; the concentration data file contains: timestamp, concentration value, unit. The data is finally saved in CSV format, and the file names can be named "stability_data.csv" and "concentration_data.csv" respectively. Make sure that the number of columns and data format in the file meet the upload requirements, and all non-numerical data (such as units) must be clearly marked in the header.

[0123] Step S32: input the stability CSV file and the concentration CSV file into the separation model, and upload the separation model to the simulation software;

[0124] In this embodiment, the stability CSV file and concentration CSV file obtained in step S31 need to be input into the separation model, and the separation model needs to be uploaded to the simulation software. The separation model is constructed based on the stability and concentration data obtained from the previous analysis, and includes the initial parameters and optimization algorithm of the jet separation system. First, confirm that the simulation software supports the import of CSV format data, and import these two CSV files into the separation model input interface through the interface function of the simulation software. Then, confirm that all data columns have been correctly mapped to the input parameters of the model, and check whether additional data processing is required, such as unit conversion or format adjustment. When uploading the separation model to the simulation software, ensure that the model can correctly call the stability and concentration data in the simulation environment for subsequent jet separation simulation.

[0125] Step S33: performing a chlorine jet separation simulation in the simulation software, wherein the jet type is set to a transverse jet, the jet velocity is set to 5 m / s to 20 m / s, and the jet temperature is set to 25° C. to 50° C.;

[0126] In the present embodiment, according to the setting interface of the simulation software, "lateral jet" is selected as the jet type. Next, the jet velocity range is set to 5m / s to 20m / s. The choice of jet velocity can be set according to the actual working conditions, and the flow intensity of the chlorine jet is controlled by adjusting the flow velocity parameter in the simulation software. The speed setting should be selected according to the speed range of the expected airflow, and the numerical accuracy should be ensured to be 1 m / s. Subsequently, the temperature range of the jet is set to 25°C to 50°C, and the appropriate temperature setting is selected through the temperature control module to ensure that the model can simulate the chlorine jet behavior at different temperatures. This temperature range is suitable for most chlorine application scenarios, and the influence of different temperatures on the separation effect of the chlorine jet can be analyzed by gradually adjusting the temperature during simulation.

[0127] Step S34: performing a chlorine jet separation simulation in a simulation software, wherein the separation medium is set to a porous medium, the porosity is set to 40% to 70%, and the separation efficiency parameter is set to 0.85 to 0.95;

[0128] In this embodiment, "porous medium" is selected as the separation medium to ensure that the airflow in the simulated environment passes through a medium with a certain porosity. Next, the porosity range is set to 40% to 70%. Porosity affects the penetration and distribution of airflow in the medium. Selecting this range can simulate different types of porous media, such as filter materials or fillers in reactors. By adjusting the porosity of the medium, the effect of different porosities on the chlorine separation effect can be observed. Finally, the separation efficiency parameter is set to 0.85 to 0.95. This parameter determines the separation ability of the medium for chlorine, and a suitable separation efficiency value can be set through experimental data or historical data. Enter this value in the simulation software to ensure that the simulation can correctly reflect the separation efficiency during the separation process.

[0129] Step S35: running the chlorine jet separation effect analysis module in the simulation software to obtain chlorine jet separation effect data, and recording the chlorine separation effect data every 10 seconds;

[0130] In the present embodiment, the chlorine jet separation effect analysis module is run in the simulation software, and the chlorine separation effect data is recorded regularly. The recording interval is set to 10 seconds so as to obtain the changes in the chlorine jet separation process in time. In the setting interface of the simulation software, select "Effect Analysis Module" and set the data recording frequency to once every 10 seconds. The data recorded each time should include key indicators of the jet separation effect, such as chlorine concentration, separation efficiency, temperature, etc. By analyzing these data, the performance of chlorine jet separation under different setting conditions can be evaluated, and the simulation parameters can be adjusted in real time to optimize the separation effect.

[0131] Step S36: If the chlorine gas jet separation effect data does not meet the preset separation standard, it is marked as unreasonable jet separation data.

[0132] In this embodiment, a reasonable separation standard is set to determine whether the chlorine jet separation effect data meets the requirements. Common separation standards include separation efficiency, degree of reduction of chlorine concentration, pressure change during separation, etc. Specifically, the separation efficiency is set to be greater than 90% as a standard, that is, the concentration of chlorine should reach a preset reduction ratio during the separation process, and the general goal is to reduce the chlorine concentration by at least 90%. This standard can be achieved by the separation efficiency calculation module in the simulation software, which will output the current chlorine concentration and its change trend in real time according to the simulation data. If the simulation results show that the separation efficiency at a certain moment is less than 90%, the data at that moment is considered to be inconsistent with the separation standard. The reduction in chlorine concentration should reach a preset threshold. For example, the concentration drop is set to exceed a certain percentage (such as a drop of more than 50%) as a standard. When the concentration at a certain moment in the simulation is reduced by less than 50%, the data will be judged as unreasonable data. In addition, if under certain conditions, the temperature, speed and other parameters of the chlorine jet fail to reach the expected range, it can also be used as a basis for judging unreasonable data. Data marking is performed using the "output" function of the simulation software. When it is detected that the separation effect data at a certain moment fails to meet the preset separation criteria, the simulation software will automatically mark these data as "abnormal" or "irrational" and store them in a dedicated abnormal data output file. The marked unreasonable data will include specific timestamps, separation efficiency, chlorine concentration and other information to facilitate subsequent analysis and processing. Through the logging function of the simulation software, the occurrence period, parameter settings and other related variables of these unreasonable data can be tracked. These data marked as unreasonable provide a basis for subsequent optimization. Based on these data, users can further analyze the reasons, such as whether it is necessary to adjust parameters such as jet velocity, jet temperature, and medium porosity. Through step-by-step adjustment and re-simulation, the jet separation effect is optimized to ensure higher separation efficiency and better chlorine concentration reduction.

[0133] Preferably, step S4 is specifically:

[0134] Step S41: Identify turbulence characteristics of unreasonable jet separation data, thereby obtaining turbulence characteristic data;

[0135] In this embodiment, it is necessary to analyze the flow field parameters (such as velocity field, pressure field, etc.) obtained from the unreasonable jet separation data. Through the turbulence model module in the simulation software, select a suitable turbulence model (such as k-ε model or k-ω model) for calculation. Then, based on the velocity field and vorticity information in the simulation data, the turbulence characteristics of the jet area are identified using the turbulence model. The turbulent characteristics are mainly reflected in the degree of turbulence of the flow, including indicators such as vorticity, turbulence intensity, and turbulence energy. These characteristics can be obtained by analyzing the pulsating components in the velocity field. For example, by calculating the standard deviation of the velocity component, the turbulence intensity index is obtained. All turbulence characteristic data will be generated and saved in the simulation software for subsequent analysis.

[0136] Step S42: performing diffusion analysis on unreasonable jet separation data based on turbulence characteristic numbers to obtain turbulence diffusion data;

[0137] In this embodiment, a turbulent diffusion model is used to calculate the diffusion characteristics of the airflow in the jet separation region. Turbulent diffusion analysis usually relies on the turbulent kinetic energy and the viscosity coefficient of the fluid. First, the kinetic energy data of the flow field is obtained through the turbulence model in the simulation. Then, by calculating the turbulent kinetic energy gradient, the turbulent diffusion coefficient is obtained, which represents the diffusion rate of the airflow. Thereafter, the diffusion amount at each moment and each position is calculated by the diffusion coefficient to obtain the turbulent diffusion data. Turbulent diffusion data includes diffusion rates and concentration distributions at different positions and times, which are used to determine whether turbulence leads to incomplete separation.

[0138] Step S43: performing intensity statistics according to the turbulent diffusion data to obtain high-intensity turbulent diffusion data;

[0139] In this embodiment, the turbulent diffusion data is statistically analyzed to screen out areas where the diffusion intensity is higher than a certain threshold. For example, by calculating the mean and standard deviation of the turbulent diffusion rate, the threshold is set to the mean plus three times the standard deviation, and the areas where the diffusion rate is greater than this threshold are screened out. These areas usually correspond to locations where turbulent diffusion is strong. Through this method, areas with greater diffusion intensity can be identified and marked as high-intensity turbulent diffusion areas. High-intensity turbulent diffusion data will provide a basis for subsequent regional optimization and model improvement.

[0140] Step S44: identifying the separation point position of the separation model according to the unreasonable jet separation data to obtain separation point position data;

[0141] In this embodiment, the key point of airflow separation, i.e., the separation point, is identified by spatially analyzing the flow field data (such as velocity, pressure, etc.) during the jet separation process. The separation point usually occurs at a location where the fluid velocity changes suddenly and the pressure changes greatly. First, by calculating the velocity gradient in the velocity field, the area with a large velocity change is identified, and the corresponding separation point is determined. Secondly, combined with the pressure field data, the exact position of the separation point is further confirmed at the location with a large pressure gradient. Finally, the separation point position data is obtained, which will be used for subsequent diffusion area analysis.

[0142] Step S45: determining the diffusion area according to the high-intensity turbulence diffusion data and the separation point position data, thereby obtaining the diffusion area data;

[0143] In this embodiment, the turbulent diffusion area is identified by superimposing and analyzing the high-intensity turbulent diffusion area with the separation point position. This process requires matching and comparing the spatial coordinates in the simulation software to determine the boundary of the diffusion area. First, the key area of ​​airflow separation is marked using the separation point position data, and then the location with strong diffusion is marked based on the turbulent diffusion data. Through this superposition analysis, the actual diffusion area is determined, and the relevant regional parameters, such as the area and position of the diffusion area, are recorded to generate the diffusion area data.

[0144] Step S46: adjusting the flow incident angle of the diffusion area data to obtain flow incident angle data;

[0145] In this embodiment, the flow incident angle refers to the angle at which the airflow enters the separation medium in the diffusion region. In this step, the flow direction in the diffusion region is adjusted to optimize the incident angle to improve the separation efficiency. First, the incident angle of the current flow is calculated based on the geometry of the flow path and the diffusion region. Then, according to the simulation results and the requirements of the separation efficiency, the incident angle is adjusted so that the angle of the airflow is close to the optimal incident angle. This adjustment process needs to rely on the fluid dynamics model and the geometric optimization algorithm to ensure that the adjusted incident angle can effectively improve the separation effect. Finally, the adjusted flow incident angle data is obtained.

[0146] Step S47: Optimize the separation efficiency of the separation model according to the turbulent diffusion data and the flow incident angle number, and generate an analysis model.

[0147] In this embodiment, based on the turbulent diffusion data, the behavior of the airflow in the diffusion area during the separation process is analyzed, including parameters such as flow velocity and turbulence intensity, and the factors affecting the separation efficiency are identified. Then, the adjusted flow incident angle data is input into the separation model, and the jet parameters (such as speed, temperature, angle, etc.) are adjusted by the optimization algorithm to improve the separation efficiency. During the optimization process, multiple simulation results are used to correct the optimization strategy, and finally an analysis model with high separation efficiency is generated. This optimization process needs to be based on multiple simulation results and parameter combinations to ensure that the optimized model can achieve better chlorine separation effect under different working conditions.

[0148] It is particularly important that step S41 includes the following steps:

[0149] Step S411: extracting velocity field features from unreasonable jet separation data to obtain unreasonable jet separation velocity field;

[0150] In this embodiment, velocity field characteristics are extracted from unreasonable jet separation experiments or simulation data. Numerical simulation software (such as ANSYS Fluent or OpenFOAM) is used for calculation, and a steady-state or transient flow model is used to simulate the jet separation process. Through refined grid division, the velocity distribution data of the fluid in the jet region is accurately obtained. The velocity field data usually includes the velocity vector of each grid point, and these velocity vector data are clearly marked at each time step. During implementation, the size and resolution of the grid should be selected according to the scale of jet separation to ensure the accuracy of the velocity field data. By setting specific boundary conditions, such as fixed flow velocity or flow rate, the boundary layer conditions of each boundary must be confirmed during the data extraction process, and the flow velocity values ​​at different positions are indicated. For example, the inlet is set to a constant flow velocity, and the outlet is set to a pressure outlet. Finally, through these steps, the velocity data of each grid node in the jet region is obtained, and complete velocity field data is constructed.

[0151] Step S412: Identify the vortex region based on the unreasonable jet separation velocity field to obtain vortex region data;

[0152] In this embodiment, the velocity field data is discretized, and the finite difference or finite volume method is usually used to solve the curl of the velocity field. In numerical calculations, a suitable discretization method is selected to ensure that the vortex area can be accurately identified. When calculating the curl, a threshold value of the curl value is set, and the threshold value (for example, 0.01 s⁻¹) is usually determined by experiment or simulation. When the curl value exceeds the threshold, the area is considered to be a vortex area. This step requires spatial filtering in the vortex area to remove noise and ensure the accuracy of the identified vortex area. The identification basis of the vortex area is mainly the size of the curl value. By comparing the curl values ​​of other areas, the position and range of the vortex area can be clearly distinguished. Finally, the obtained vortex area data includes information such as the size, position, and direction of the vortex.

[0153] Step S413: performing turbulence structure analysis on the vortex region data to obtain large-scale vortex structure data;

[0154] In this embodiment, by calculating the vorticity, strain rate and shear stress distribution parameters in the vortex region, the turbulent structure in the flow field can be effectively described. First, the strain rate tensor and rotation tensor of the vortex region are calculated, and the morphology of the vortex (such as annular vortex, vortex street, etc.) can be identified through these data. By counting the characteristic size, intensity, rotation direction, etc. of each vortex in the vortex region, large-scale vortex structure data is further obtained. The turbulent structure identification method commonly used in fluid mechanics, such as Kolmogorov scale theory, is used to determine the range and intensity of large-scale vortices. In addition, according to the gradient data of the velocity field, vortex information of different scales can be obtained by separating turbulent fluctuations of different scales. Specifically, a specific scale threshold (such as 1 mm) is set to filter out small-scale vortices and focus on large-scale vortex structures. The large-scale vortex structure data output by this step contains information such as the intensity, position, size, and morphology of the vortex.

[0155] Step S414: performing flow field stability analysis based on the large-scale eddy structure data to obtain flow field stability data;

[0156] In this embodiment, by calculating the Reynolds number in the flow field, it is determined whether the flow field is in a stable or unstable state. The Reynolds number is the main indicator of flow stability. A high Reynolds number usually indicates that the flow has turbulent characteristics, while a low Reynolds number indicates that the flow is relatively stable. In this step, a critical Reynolds number is set (for example, Re>2000 indicates that the flow field enters a turbulent state), and the stability of the flow field in different areas is judged by comparing the Reynolds number of each grid unit. In the process of flow field stability analysis, large-scale eddy data is further combined to evaluate the stability of the flow field by solving the local flow velocity gradient and pressure gradient. The stability data of the flow field include the stability index of each area in the flow field, the turbulence intensity, and the strain rate of the flow.

[0157] Step S415: Calculate turbulence energy according to the vortex region data to obtain turbulence energy data;

[0158] In this embodiment, the velocity fluctuation information of the vortex region is first extracted, that is, the deviation from the average flow velocity in the velocity field. By calculating the sum of the squares of these velocity fluctuations, the data of turbulent energy is obtained. In actual operation, a suitable grid division is selected to ensure the accurate capture of velocity fluctuations, and the grid resolution needs to be fine enough (for example, 1 mm or less) to ensure that tiny turbulent fluctuations are captured. In addition, a suitable turbulent energy calculation formula is adopted, and the calculation formula of turbulent kinetic energy is usually used to solve the energy. The specific calculation requires time-series averaging of the velocity components to obtain steady-state turbulent energy. Turbulent energy data include turbulent energy density, turbulent kinetic energy and spatial distribution of vortex regions in the vortex region.

[0159] Step S416: Integrate the flow field stability data and the turbulence energy data to generate turbulence characteristic data.

[0160] In this embodiment, during integration, a weighted average method or a multi-scale analysis method is used to combine the stability data and the energy data. Specifically, according to the stability index in the stability data and the turbulent kinetic energy value in the turbulent energy data, appropriate weights are set (for example, the stability index weight is 0.6, and the turbulent energy data weight is 0.4). Through weighted calculation, a comprehensive turbulence characteristic data is obtained, which reflects the turbulence characteristics of different areas in the flow field. During the integration process, the specific calculation formula and parameter settings (such as weight ratio, scale standard, etc.) should be adjusted according to the actual flow field conditions. Finally, the turbulence characteristic data obtained include information such as the overall turbulence state, stability distribution and turbulent energy distribution of the flow field, and provide a basis for subsequent optimization design.

[0161] It is particularly important that step S47 includes the following steps:

[0162] Step S471: performing turbulent diffusion area identification on the separation model according to the turbulent diffusion data to obtain turbulent diffusion area data;

[0163] In this embodiment, the turbulent diffusion data of the fluid is obtained. These data are usually derived from computational fluid dynamics (CFD) simulations, and the turbulent behavior of the fluid is modeled using a turbulence model (such as a k-ε model or a Reynolds stress model). Then, each region in the flow field is analyzed based on the turbulent diffusion coefficient, and a threshold is set (for example, a turbulent diffusion coefficient greater than 0.1 m² / s) to identify regions with significant turbulent diffusion characteristics. The turbulent diffusion coefficient can be obtained through numerical simulation or experimental data, and is usually calculated in CFD software by setting appropriate boundary conditions and initial conditions. Further, the grid subdivision of specific areas is selected to ensure that the diffusion of turbulence can be accurately captured in these areas. Finally, the identified turbulent diffusion area data will include information such as the diffusion intensity, diffusion direction and range in each area, and these data will provide a basis for subsequent operations.

[0164] Step S472: performing jet velocity adjustment on the turbulent diffusion area data to generate jet velocity data;

[0165] In this embodiment, the velocity distribution of the jet is calculated based on the velocity field data of the turbulent diffusion area. In actual operation, CFD software is used to simulate the movement of the fluid under different jet conditions and adjust the velocity of the jet inlet. The basis for adjusting the jet velocity is the flow characteristics in the turbulent diffusion area, such as the inlet flow velocity, vortex intensity, etc. These parameters determine the distribution range and velocity of the jet. When adjusting, it is necessary to set the range of the inlet flow velocity (for example, from 5 m / s to 10 m / s), and set a threshold value according to the turbulent diffusion characteristics to ensure that the jet velocity is within a controllable range. Through numerical simulation, the jet velocity is gradually adjusted, and it is ensured that the adjusted jet velocity distribution matches the changes in the turbulent diffusion area, and finally the jet velocity data is generated. These data include the velocity, direction, acceleration and other characteristics of the jet, and are used together with the data of the turbulent diffusion area to provide a basis for further separation efficiency adjustment.

[0166] Step S473: input the jet velocity data and the flow incident angle number into the separation model, and adjust the separation efficiency parameters to generate an analysis model.

[0167] In this embodiment, the flow incident angle is obtained, which is determined by the inflow direction and angle of the fluid, and is usually obtained by simulating the flow path of the fluid inside the separator. The CFD model is used to calculate the incident angle to ensure that the angle of the fluid incident is accurately reflected. Usually, this angle needs to be adjusted between 0° and 30° to ensure better flow incidence conditions. Then, the jet velocity data is input into the separation model together with the incident angle to adjust the separation efficiency. By adjusting multiple parameters in the separation model, such as flow rate, incident angle, turbulence intensity, etc., the separation efficiency is finely adjusted using an optimization algorithm (such as a particle swarm optimization algorithm or a genetic algorithm). In this process, it is necessary to gradually adjust the parameters through multiple simulations until the desired separation efficiency is obtained. In the analysis model, the parameter changes of each adjustment are recorded, including jet velocity, incident angle, flow velocity in the separator, vortex intensity, etc. Finally, the generated analysis model not only includes separation efficiency parameters, but also includes the flow characteristics of the fluid, the jet influence area, and the separation effect under different parameter combinations, providing a basis for optimizing the fluid mechanics parameters in the welding process of the industrial robot.

[0168] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0169] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing an analytical model for chlorine jet separation, characterized in that: The following steps are involved: Step S1: using a jet device to generate a chlorine jet gas, and collecting the chlorine jet gas, thereby obtaining a chlorine jet gas; injecting the chlorine jet gas into a preset separation area, and performing chlorine gas flow separation, and performing data monitoring, thereby obtaining chlorine gas flow separation data; Step S2: constructing a separation model based on the chlorine gas flow separation data to obtain a separation model; performing concentration detection on the chlorine gas flow separation data to obtain concentration data; performing stability evaluation on the concentration data to obtain stability data; Step S3: inputting the stability data and the concentration data into the separation model, and performing chlorine jet separation effect analysis to generate chlorine jet separation effect data; if the chlorine jet separation effect data does not meet the preset separation standard, it is marked as unreasonable jet separation data; Step S4: performing turbulence diffusion analysis on the unreasonable jet separation data to obtain turbulence diffusion data; According to the unreasonable jet separation data, the separation model is subjected to diffusion area control to obtain diffusion area data; according to the turbulent diffusion data and the diffusion area data, the separation model is subjected to model separation efficiency optimization to generate an analysis model, and step S4 is specifically as follows: Step S41: Identify turbulence characteristics of unreasonable jet separation data, thereby obtaining turbulence characteristic data; Step S42: performing diffusion analysis on unreasonable jet separation data based on turbulence characteristic numbers to obtain turbulence diffusion data; Step S43: performing intensity statistics according to the turbulent diffusion data to obtain high-intensity turbulent diffusion data; Step S44: identifying the separation point position of the separation model according to the unreasonable jet separation data to obtain separation point position data; Step S45: determining the diffusion area according to the high-intensity turbulence diffusion data and the separation point position data, thereby obtaining the diffusion area data; Step S46: adjusting the flow incident angle of the diffusion area data to obtain flow incident angle data; Step S47: Optimize the separation efficiency of the separation model according to the turbulent diffusion data and the flow incident angle data to generate an analysis model.

2. The method for constructing an analytical model for chlorine jet separation according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: using a jet device to generate chlorine jet gas and collect the chlorine jet gas, wherein the chlorine jet velocity is set in the range of 10m / s-20m / s and the nozzle diameter is set in the range of 8mm-15mm, thereby obtaining the chlorine jet gas; Step S12: injecting the chlorine jet gas into the preset separation area, and performing preliminary collection of the jet gas in the separation area, and performing data monitoring to obtain preliminary jet separation data; Step S13: performing abnormal separation analysis based on the preliminary jet separation data to obtain abnormal jet separation data; Step S14: Screening out the preliminary jet separation data according to the abnormal jet separation data, thereby obtaining chlorine gas flow separation data.

3. The method for constructing an analytical model for chlorine jet separation according to claim 2, characterized in that: Step S13 is specifically as follows: Step S131: extracting airflow density features based on preliminary jet separation data to obtain airflow density data; Step S132: Identify uneven time periods of the airflow density data to obtain uneven airflow density time period data; Step S133: performing diffusion statistics on the uneven airflow density time period data to obtain uneven diffusion data; Step S134: performing abnormal separation judgment on the preliminary jet separation data according to the non-uniform diffusion data to obtain abnormal jet separation data.

4. The method for constructing an analytical model for chlorine jet separation according to claim 3, characterized in that: Step S134 is specifically as follows: When any of the following situations occurs, it is determined to be jet separation abnormality and jet separation abnormality data is obtained: the jet velocity fluctuation exceeds ±10%, the airflow density non-uniformity exceeds the set threshold ±15%, and the airflow thickness in the separation area deviates from the predetermined range by more than 5 cm; When the following conditions occur at the same time, it is determined to be a separation abnormality caused by nozzle blockage, and the nozzle blockage separation abnormality data is obtained: the nozzle outlet pressure drops by more than 15%, the jet velocity is lower than the lower limit of the preset range, the jet range deviation exceeds ±15°, and the airflow diffusion angle deviates from the design angle by more than 10°; When the following conditions occur at the same time, it is determined that the jet separation system has abnormal airflow temperature and abnormal airflow temperature data is obtained: the airflow temperature fluctuation exceeds ±10°C, the temperature in the separation area deviates from the normal range of 20-30°C, and the temperature in the airflow injection area is continuously lower than 10°C or higher than 50°C; The abnormal jet separation data, the nozzle blockage separation abnormal data and the airflow temperature abnormal data are integrated to obtain the abnormal jet separation data.

5. The method for constructing an analytical model for chlorine jet separation according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: performing data preprocessing based on the chlorine gas flow separation data to obtain preprocessed chlorine gas flow separation data; Step S22: extracting chlorine gas flow distribution characteristics according to the pre-processed chlorine gas flow separation data, thereby obtaining chlorine gas flow distribution data; Step S23: performing turbulent layer analysis on the chlorine gas flow distribution data to obtain turbulent data, and constructing a separation model using the Reynolds average equation to obtain a separation model; Step S24: performing concentration detection on the chlorine gas flow distribution data to obtain concentration data; Step S25: Perform stability evaluation on the concentration data to obtain stability data.

6. The method for constructing an analytical model for chlorine jet separation according to claim 5, characterized in that: Step S23 is specifically as follows: Step S231: performing speed calculation on the chlorine gas flow distribution data, wherein the speed valid data interval of each node is set to 10 seconds, thereby obtaining the chlorine gas flow distribution speed data; Step S232: performing velocity gradient calculation based on the chlorine gas flow distribution velocity data, wherein the sampling interval is set to 0.1 meters and the calculation range is set to 5-10 meters to obtain velocity gradient data; Step S233: performing velocity fluctuation evaluation on the chlorine gas flow distribution velocity data, thereby obtaining velocity fluctuation data; Step S234: Calculate the vorticity according to the velocity gradient data and the velocity fluctuation data, wherein the fluctuation time of the vorticity data is set to be greater than 3 seconds and regarded as a valid vortex, and obtain the vorticity data; Step S235: performing turbulent layer identification on the chlorine gas flow distribution data according to the vorticity data to obtain turbulent data, and constructing a separation model using the Reynolds average equation to obtain a separation model.

7. The method for constructing an analytical model for chlorine jet separation according to claim 5, characterized in that: Step S24 is specifically as follows: Step S241: using a concentration sensor to perform preliminary concentration detection on the chlorine gas flow distribution data, wherein the sampling period is set to once per minute, thereby obtaining preliminary concentration detection data; Step S242: dividing the preliminary concentration detection data into excessive concentration data based on the preset reference concentration data, thereby obtaining the preliminary concentration detection data exceeding the standard; Step S243: regional identification is performed on the chlorine gas flow distribution data according to the preliminary detection data of the excessive concentration, wherein the chlorine gas safety concentration limit is set to be less than 0.5 ppm, and the excessive concentration regional data is obtained; Step S244: marking the sensor coordinates based on the preliminary concentration detection data, wherein the neighborhood size is set to 10-15 meters and the minimum number of neighbors is 3, to obtain the sensor coordinate data; Step S245: marking the target coordinates based on the preliminary concentration detection data to obtain the target coordinate data; Step S246: performing Euclidean distance calculation according to the sensor coordinate data and the target coordinate data to obtain Euclidean distance data; Step S247: Calculate sensor weight based on the Euclidean distance data to obtain sensor weight data; Step S248: Perform concentration analysis on the excessive concentration area data according to the sensor weight data to obtain concentration data.

8. The method for constructing an analytical model for chlorine jet separation according to claim 5, characterized in that: Step S25 is specifically as follows: Step S251: performing fast Fourier transform on the concentration data, wherein the sampling frequency is 10 Hz-1000 Hz, to obtain concentration frequency domain data; Step S252: extracting the amplitude spectrum based on the concentration frequency domain data, thereby obtaining amplitude spectrum data; Step S253: performing significant amplitude statistics on the amplitude spectrum data to obtain significant amplitude data; Step S254: obtaining amplitude threshold data; Step S255: identifying periodic fluctuations in the significant amplitude data according to the amplitude threshold data, and obtaining periodic fluctuation concentration frequency domain data; Step S256: performing range calculation on the concentration data to obtain concentration range data; Step S257: Perform stability assessment based on the concentration range data and the periodic fluctuation concentration frequency domain data to obtain stability data.

9. The method for constructing an analytical model for chlorine jet separation according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: converting the stability data and the concentration data into CSV files to obtain a stability CSV file and a concentration CSV file; Step S32: input the stability CSV file and the concentration CSV file into the separation model, and upload the separation model to the simulation software; Step S33: performing a chlorine jet separation simulation in the simulation software, wherein the jet type is set to a transverse jet, the jet velocity is set to 5 m / s to 20 m / s, and the jet temperature is set to 25° C. to 50° C.; Step S34: performing a chlorine jet separation simulation in a simulation software, wherein the separation medium is set to a porous medium, the porosity is set to 40% to 70%, and the separation efficiency parameter is set to 0.85 to 0.95; Step S35: running the chlorine jet separation effect analysis module in the simulation software to obtain chlorine jet separation effect data, and recording the chlorine separation effect data every 10 seconds; Step S36: If the chlorine gas jet separation effect data does not meet the preset separation standard, it is marked as unreasonable jet separation data.

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