An emergency safety warning platform for special scenarios

Abstract

The present invention discloses an emergency safety warning platform for special scenarios, which relates to the field of filtration safety warning technology. The platform includes: detecting the pressure on both sides of the filter element in the high-pressure backwash filter station, calculating and obtaining the filtration pressure difference information, and obtaining the filtration rupture safety risk information; obtaining the clogging parameters of the filter element; obtaining the optimal backwash pressure and the optimal filter element damage probability; obtaining the flushing pressure difference information, conducting a pipeline rupture safety risk analysis, obtaining the flushing rupture safety risk information, calculating and obtaining the safety risk coefficient of the high-pressure backwash filter station, and issuing an emergency safety warning. The platform solves the technical problem of the existing high-pressure backwash filter station lacking real-time and accurate monitoring and evaluation of the filter element status, pipeline rupture risk and backwash effect, which leads to low filtration efficiency and inaccurate safety risk assessment of the high-pressure backwash filter station, and achieves the technical effect of improving filtration efficiency and the accuracy of safety risk assessment.

Description

Technical Field

[0001] The present application relates to the technical field related to filtering safety warnings, and specifically to an emergency safety warning platform for special scenarios. Background Art

[0002] High-pressure backwash filter stations play a vital role in industrial production and water treatment. They are mainly responsible for removing impurities from fluids to ensure the stable operation of subsequent processes and the safety of equipment. However, in high-pressure, high-flow operating environments, filter elements are susceptible to problems such as wear and clogging, which not only affects filtration efficiency but may also cause serious safety accidents such as pipe ruptures. Traditional filter station monitoring systems can often only provide basic pressure, flow and other parameter monitoring. They lack in-depth analysis and early warning of filter element blockage, backwashing effects and potential safety risks. As a result, in actual operation, manual intervention is often required to determine whether the filter element needs to be replaced or cleaned, and whether the backwash pressure setting is reasonable. This makes the entire production process inefficient and difficult to accurately assess the safety risks of high-pressure backwash filter stations, posing a huge hidden danger to production.

[0003] Therefore, at the current stage, the safety warning technologies related to high-pressure backwash filter stations lack real-time and accurate monitoring and evaluation of the status of filter elements, pipeline rupture risks and backwashing effects, which leads to technical problems such as low filtration efficiency and inaccurate safety risk assessment of high-pressure backwash filter stations. Summary of the Invention

[0004] This application solves the technical problems of low filtration efficiency and inaccurate safety risk assessment in existing high-pressure backwash filter stations by providing an emergency safety warning platform for special scenarios, thereby achieving the technical effect of improving filtration efficiency and the accuracy of safety risk assessment.

[0005] The present application provides an emergency safety warning platform for special scenarios, which includes: a pipeline rupture safety risk analysis module, which is used to detect the pressure on both sides of the filter element in the high-pressure backwash filter station during operation of the high-pressure backwash filter station, obtain initial pressure information and filtration pressure information, calculate filtration pressure difference information, perform pipeline rupture safety risk analysis, and obtain filtration rupture safety risk information; a filter element blockage analysis module, which is used to perform blockage analysis of the filter element based on the filtration pressure difference information and obtain blockage parameters of the filter element; a backwash optimization module, which is used to obtain a backwash pressure space for backwashing the filter element, and perform backwash optimization within the backwash pressure space to maximize the cleanliness of the blockage flushing and minimize the probability of filter element damage, obtain the optimal backwash pressure, and the optimal filter element damage probability; a safety risk coefficient acquisition module, which is used to simulate backwashing according to the optimal backwash pressure, obtain flushing pressure difference information, perform pipeline rupture safety risk analysis, obtain flushing rupture safety risk information, and calculate the safety risk coefficient of the high-pressure backwash filter station in combination with the filtration rupture safety risk information and the filter element damage probability, and issue an emergency safety warning.

[0006] In a possible implementation, the pipeline rupture safety risk analysis module further performs the following processing: detecting the pressure on both sides of the filter element in the high-pressure backwash filter station to obtain initial pressure information and filtration pressure information; calculating the filtration pressure difference information of the filter element based on the initial pressure information and the filtration pressure information; pre-training a pipeline rupture risk predictor; using the pipeline rupture risk predictor, performing pipeline rupture safety risk analysis and prediction on the filtration pressure difference information input to obtain filtration rupture safety risk information, wherein the filtration rupture safety risk information includes the pipeline rupture probability.

[0007] In a possible implementation, the pipeline rupture safety risk analysis module further performs the following processing: based on the historical maintenance data of the pipeline in the high-pressure backwash filter station, a set of sample pressure difference information intervals is collected, and the proportion of pipeline rupture in different sample pressure difference information intervals is collected and marked as a sample pipeline rupture probability set; the sample pressure difference information interval set and the sample pipeline rupture probability set are constructed as supervised training data and verification data, and the pipeline rupture risk predictor is trained until convergence.

[0008] In a possible implementation, the filter element blockage analysis module also performs the following processing: based on the historical maintenance data of the filter element in the high-pressure backwash filter station, a set of sample filter pressure difference information intervals is collected, and the blockage ratio of the filter element under different sample filter pressure difference information intervals is collected, which is marked as a sample blockage parameter set; a mapping relationship between the sample filter pressure difference information interval set and the sample blockage parameter set is constructed to obtain a blockage classifier; the filter pressure difference information is input into the blockage classifier, and the blockage parameters of the filter element are obtained by mapping and classification. In a possible implementation, the backwash optimization module also performs the following processing: obtaining a backwash pressure space for backwashing the filter element; randomly extracting a first backwash pressure in the backwash pressure space; performing a flushing prediction based on the first backwash pressure and the blockage parameter to obtain a first blockage flushing cleaning level and a first filter element damage probability; and calculating a first flushing fitness based on the first blockage flushing cleaning level and the first filter element damage probability, as shown in the following formula:

[0009] Among them, RA is the flushing adaptability, and is the weight, R is the clogging flushing cleaning level, Q is a positive integer, and K is the probability of filter element damage; the backwash pressure is continuously optimized in the backwash pressure space until the convergence optimization times are reached, the backwash pressure with the maximum flushing fitness is output, the optimal backwash pressure is obtained, and the probability of filter element damage at the optimal backwash pressure is obtained as the optimal filter element damage probability.

[0010] In a possible implementation, the backwash optimization module further performs the following processing: based on the backwash history data of the filter element, a sample blockage parameter set and a sample backwash pressure set are collected, and the blockage flushing cleaning level and the filter element damage ratio under backwashing with different sample blockage parameters and sample backwash pressures are collected, and the sample blockage flushing cleaning level set and the sample filter element damage probability set are obtained by marking; the sample blockage parameter set, the sample backwash pressure set, the sample blockage flushing cleaning level set and the sample filter element damage probability set are used as supervised training data and verification data to train a backwash predictor until convergence; the backwash predictor is used to perform flushing prediction on the first backwash pressure and blockage parameter input to obtain a first blockage flushing cleaning level and a first filter element damage probability.

[0011] In a possible implementation, the safety risk coefficient acquisition module also performs the following processing: based on the backwash history data of the filter element, the pressure difference of the filter element under backwashing with different sample blockage parameters and sample backwash pressures is collected to obtain a sample flushing pressure difference information set; the sample blockage parameter set, sample backwash pressure set and sample flushing pressure difference information set are used to train a backwash pressure difference simulator; the backwash pressure difference simulator is used to simulate backwashing of the first backwash pressure and blockage parameter to obtain flushing pressure difference information.

[0012] In a possible implementation, the safety risk coefficient acquisition module also performs the following processing: inputting the flushing pressure difference information into the pipeline rupture risk predictor, analyzing and predicting the pipeline rupture safety risk, and obtaining the flushing rupture safety risk information; performing a weighted calculation on the flushing rupture safety risk information, the filtration rupture safety risk information, and the probability of filter element damage to obtain the safety risk coefficient of the high-pressure backwash filter station; judging whether the safety risk coefficient is greater than or equal to the safety risk threshold value, and if so, issuing an emergency safety warning, and if not, not issuing an emergency safety warning.

[0013] This application proposes an emergency safety warning platform for a special scenario. It is intended to detect the pressure on both sides of the filter element in the high-pressure backwash filter station, calculate the filter pressure difference information, obtain the filter rupture safety risk information; obtain the filter element blockage parameters; obtain the optimal backwash pressure and the optimal filter element damage probability; obtain the flushing pressure difference information, conduct a pipeline rupture safety risk analysis, obtain the flushing rupture safety risk information, calculate the safety risk coefficient of the high-pressure backwash filter station, and issue an emergency safety warning. This solves the technical problem of the existing high-pressure backwash filter station's lack of real-time and accurate monitoring and evaluation of the filter element status, pipeline rupture risk, and backwash effect, which leads to low filtration efficiency and inaccurate safety risk assessment of the high-pressure backwash filter station, and achieves the technical effect of improving filtration efficiency and the accuracy of safety risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. On the contrary, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A schematic diagram of the structure of an emergency safety warning platform for a special scenario provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the execution flow of a filter element blockage analysis module in an emergency safety warning platform for a special scenario provided in an embodiment of the present application.

[0017] Description of the reference numerals: pipeline rupture safety risk analysis module 10 , filter element blockage analysis module 20 , backwash optimization module 30 , safety risk coefficient acquisition module 40 . DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or components that are not clearly listed or that are inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The embodiment of the present application provides an emergency safety warning platform for special scenarios, such as Figure 1 As shown, the emergency safety warning platform for a special scenario includes: a pipeline rupture safety risk analysis module 10, a filter element blockage analysis module 20, a backwash optimization module 30, and a safety risk coefficient acquisition module 40.

[0022] The pipeline rupture safety risk analysis module 10 is used to detect the pressure on both sides of the filter element in the high-pressure backwash filter station during the operation of the high-pressure backwash filter station, obtain initial pressure information and filtration pressure information, calculate the filtration pressure difference information, perform pipeline rupture safety risk analysis, and obtain filtration rupture safety risk information.

[0023] Preferably, the operating principle of the high-pressure backwash filter station includes filtration and backwashing. Specifically, the high-pressure pump pushes the fluid to be filtered to the filter station through the water inlet. The fluid carries impurities, particles or suspended matter into the filtration system. The fluid passes through multiple layers or filter media with specific precision. Impurities and particles are intercepted on the surface or inside of the filter medium, and the filtered clean fluid passes through the filter medium and enters the water outlet. The filter station is usually equipped with a differential pressure sensor to monitor the pressure changes on both sides of the filter medium in real time. When impurities gradually accumulate and the pressure difference on both sides of the medium increases to a certain set value, it is judged that the filter needs to be cleaned, that is, when the filter When the impurities in the filter accumulate to a certain extent, hindering the normal flow of the fluid, the backwash mode is turned on by the automatic control device when the pressure difference reaches the set threshold. At this time, the normal filtration fluid channel is closed and the backwash channel is opened. The backwash fluid (usually clean water or gas) enters the filter medium from the opposite direction of the filter at high pressure, and the reverse fluid pressure is used to flush out impurities and particles from the surface and inside of the filter medium. The backwash high-pressure fluid carries these impurities out of the filter station through the sewage outlet. After the backwash process is completed, the backwash channel is closed, the normal filtration process is restored, and the filter medium is cleaned and put into use again.

[0024] Preferably, before the filtration operation of the high-pressure backwash filter station begins, a pressure sensor installed on the inlet side of the filter element is used to detect and record the fluid pressure at that time as initial pressure information, and a pressure sensor installed on the outlet side of the filter element is used to continuously detect and record the fluid pressure at that time as filtration pressure information. Then, the initial pressure information and the filtration pressure information are used to calculate the pressure difference between the inlet side and the outlet side of the filter element, i.e., the filtration pressure difference information, which reflects the resistance change of the filter element during the operation of the high-pressure backwash filter station. When a certain amount of pollutants accumulates inside the filter element, the normal flow of the fluid will be hindered, resulting in an increase in the pressure on the inlet side and a decrease in the pressure on the outlet side. Based on the filtration pressure difference information, combined with the operating parameters of the filtration station (such as flow rate, temperature, etc.) and the material, structure and other characteristics of the filter element, a pipeline rupture safety risk analysis is performed. That is, by analyzing the changing trend and fluctuation range of the filtration pressure difference and whether it exceeds the preset safety threshold, it is judged whether there is a safety risk of pipeline rupture. According to the results of the pipeline rupture safety risk analysis, the pipeline rupture probability is obtained as the filtration rupture safety risk information; among them, when the actual filtration pressure difference increases abnormally or exceeds the safety threshold, the early warning mechanism is automatically triggered to remind the operator to take timely measures to intervene, such as cleaning or replacing the filter element, adjusting the operating parameters, etc., to prevent the occurrence of safety accidents such as pipeline rupture.

[0025] The filter element clogging analysis module 20 is used to perform clogging analysis on the filter element according to the filter pressure difference information to obtain clogging parameters of the filter element.

[0026] Preferably, the real-time changes in the filter pressure differential are recorded based on the filter pressure differential information analysis, that is, the clogging of the filter element is analyzed. Generally speaking, as the filter element is used for an extended period of time, the filter pressure differential will gradually increase. When the filter pressure differential exceeds a preset safety threshold, it is considered that the filter element is at risk of clogging. The clogging parameters of the filter element are obtained through the clogging analysis. Specifically, the clogging parameters mainly include a clogging level, such as clogging levels 1 to 10. The clogging degree of the filter element is assessed by comparing the difference between the current filter pressure differential and the initial filter pressure differential (or the historical average filter pressure differential). The larger the difference in the filter pressure differential, the more severe the clogging degree and the higher the clogging level. The clogging speed, clogging location, and other clogging-related parameters, such as clogging frequency and clogging period, may also be included. Among them, by analyzing the change trend of the filter pressure differential over time, the clogging speed, that is, the increase in the filter pressure differential per unit time, can be calculated, reflecting the speed of clogging of the filter element. In some cases, by analyzing the change of the filter pressure differential at different locations, the location of the clogging can also be roughly determined. For example, if the filter pressure differential in a certain area increases abnormally, it may mean that the filter element in that area is clogged. During the operation of the high-pressure backwash filter station, the operating parameters of the filter station (such as flow rate, pressure, etc.) can be adjusted according to the analysis results of the blockage parameters to slow down the blockage speed and extend the service life of the filter elements.

[0027] The backwash optimization module 30 is used to obtain a backwash pressure space for backwashing the filter element, and perform backwash optimization within the backwash pressure space to maximize the degree of cleanliness of the blockage flushing and minimize the probability of damage to the filter element, thereby obtaining an optimal backwash pressure and an optimal probability of damage to the filter element.

[0028] Preferably, the backwashing pressure space for backwashing the filter element is determined based on the actual situation of the filter station, the material and characteristics of the filter element, and the properties of the fluid. The backwashing pressure space refers to the range in which the backwashing pressure may vary during the backwashing operation. When determining the backwashing pressure space, ensure that the backwashing pressure does not exceed the pressure limit of the filter element to prevent damage to the element. At the same time, consider the properties of different fluids and the operating parameters of the filter station, such as flow rate, temperature, etc. For example, high-viscosity fluids require higher backwashing pressure to remove pollutants. The greater the backwashing pressure for backwashing the filter element, the greater the degree of cleaning, but the greater the probability of damaging the filter element. Conversely, the smaller the backwashing pressure, the lower the degree of cleaning, and the lower the probability of damaging the filter element. Backwashing optimization is performed within the backwashing pressure space to find the optimal The optimal backwash pressure and the optimal filter element damage probability, that is, the maximum clogging flushing cleanliness and the minimum filter element damage probability, specifically, an initial backwash pressure is selected within the backwash pressure space for testing, and the parameters such as the cleanliness of the filter element, flushing time, and flushing water volume during the backwashing process are recorded to evaluate the backwash effect, and the damage probability of the filter element is evaluated by observing and analyzing the status of the filter element after backwashing, such as checking whether the element has cracks, deformation or wear, etc. According to the evaluation results of the backwash effect and the filter element damage probability, the backwash pressure is adjusted and repeated backwashing is performed to find the optimal backwash pressure and the optimal filter element damage probability that can maximize the clogging flushing cleanliness and minimize the filter element damage probability within the backwash pressure space, thereby maximizing the filtration efficiency and minimizing the cost of the high-pressure backwash filter station.

[0029] The safety risk coefficient acquisition module 40 is used to simulate backwashing according to the optimal backwashing pressure, obtain flushing pressure difference information, perform pipeline rupture safety risk analysis, obtain flushing rupture safety risk information, combine the filtration rupture safety risk information and the probability of filter element damage, calculate the safety risk coefficient of the high-pressure backwashing filter station, and issue an emergency safety warning.

[0030] Preferably, a simulated backwash is performed using an optimal backwash pressure, and the pressure differential changes across the filter element, i.e., the flushing pressure differential information, are monitored and recorded in real time to reflect the changes in the resistance of the filter element during the backwash process. Then, based on the flushing pressure differential information and in combination with the operating parameters of the filter station (such as flow rate, temperature, etc.) and the characteristics of the pipeline material and structure, a pipeline rupture safety risk analysis is performed. For example, by analyzing the changing trend and fluctuation range of the flushing pressure differential and whether it exceeds a preset safety threshold, it is determined whether there is a safety risk of pipeline rupture. Based on the results of the pipeline rupture safety risk analysis, flushing rupture safety risk information, i.e., the pipeline rupture probability, is obtained. Combining the filtration rupture safety risk information and the probability of filter element damage, a safety risk coefficient of the high-pressure backwash filter station is calculated based on multiple factors such as the degree of filter element blockage, the stability of the backwash pressure, and the strength of the pipeline material. This coefficient reflects the possibility and severity of a safety accident occurring in the current operating state of the filter station. Based on the calculated safety risk coefficient, an emergency safety warning is issued, such as by adjusting the backwash pressure, inspecting and replacing damaged filter elements, and other measures to provide early warning intervention, thereby reducing the probability and severity of accidents and ensuring the safe operation of the high-pressure backwash filter station.

[0031] Furthermore, the specific configuration of the pipeline rupture safety risk analysis module 10 also includes detecting the pressure on both sides of the filter element in the high-pressure backwash filter station to obtain initial pressure information and filtration pressure information; calculating the filtration pressure difference information of the filter element based on the initial pressure information and the filtration pressure information; pre-training a pipeline rupture risk predictor; using the pipeline rupture risk predictor to perform pipeline rupture safety risk analysis and prediction on the filtration pressure difference information input to obtain filtration rupture safety risk information, wherein the filtration rupture safety risk information includes the pipeline rupture probability.

[0032] Preferably, in a high-pressure backwash filter station, pressure sensors on both sides of the filter element (such as a filter screen or a filter element) are used to detect the pressure before and after the filter element, which are used as initial pressure information and filter pressure information respectively. By comparing the initial pressure information and the filter pressure information, the pressure difference before and after the filter element is calculated, that is, the filter pressure difference information, which reflects the resistance of the filter element to the fluid (such as gas or liquid); then a prediction model is constructed based on machine learning (such as decision trees, random forests, neural networks, etc.), and the prediction model is trained using historical data (such as past pipeline rupture events and related factor data) to obtain a pipeline rupture risk predictor, which can more accurately predict future pipeline rupture events. The calculated filter pressure difference information is input into the trained pipeline rupture risk predictor. The pipeline rupture risk predictor performs safety risk analysis and prediction based on the input filter pressure difference information and other possible related factors (such as pipeline material, usage time, environmental factors, etc.), and outputs filter rupture safety risk information. Among them, the most important part of the filter rupture safety risk information is the probability of pipeline rupture. The pipeline rupture probability indicates the possibility of pipeline rupture under current conditions. The pipeline rupture risk predictor predicts and evaluates the pipeline rupture risk, helps to timely discover and deal with potential pipeline rupture risks, and ensures the safe operation of the filtration station.

[0033] Furthermore, the specific configuration of the pipeline rupture safety risk analysis module 10 also includes collecting a set of sample pressure difference information intervals based on the historical maintenance data of the pipelines in the high-pressure backwash filter station, and collecting the proportion of pipeline ruptures under different sample pressure difference information intervals, which are marked as a sample pipeline rupture probability set; constructing the sample pressure difference information interval set and the sample pipeline rupture probability set as supervised training data and verification data, and training the pipeline rupture risk predictor until convergence.

[0034] Preferably, based on the historical maintenance data of the pipeline in the high-pressure backwash filter station, a sample pressure difference information interval set is collected and a sample pipeline rupture probability set is labeled, and these data are used to train a pipeline rupture risk predictor. Specifically, the pressure difference information of the pipeline is extracted from the historical maintenance data of the high-pressure backwash filter station, reflecting the pressure change of the fluid inside the pipeline. The extracted pressure difference information is then divided into different intervals according to certain rules, such as the size and range of the pressure difference. The divided pressure difference information intervals are used as a sample pressure difference information interval set. This set contains multiple different pressure difference information intervals, each of which represents the pressure state of the pipeline under certain conditions; based on the historical maintenance records, the pipeline rupture events occurring in each pressure difference information interval are found, and each pressure difference information interval is matched with the corresponding pipeline rupture event. For each pressure difference information interval, the proportion of pipeline ruptures in the interval is calculated, and the calculated pipeline rupture proportion is used as a sample pipeline rupture probability set, which contains the pipeline rupture probability corresponding to each pressure difference information interval, for subsequent risk prediction. The sample pressure difference information interval set and the sample pipeline rupture probability set are divided into supervised training data and validation data. A model is constructed based on machine learning models such as logistic regression, decision tree, random forest, support vector machine or neural network. The supervised training data is used to train the prediction model to learn the relationship between pressure difference information and pipeline rupture probability. The trained pipeline rupture risk predictor is verified using the validation data until the performance of the prediction model reaches the preset standard or no longer improves significantly. The training process is considered to have converged, and a pipeline rupture risk predictor is obtained.

[0035] Further, such as Figure 2 As shown, the specific configuration of the filter element blockage analysis module 20 also includes, based on the historical maintenance data of the filter elements in the high-pressure backwash filter station, collecting a set of sample filter pressure difference information intervals, and collecting the blockage ratios of the filter elements under different sample filter pressure difference information intervals, which are marked as sample blockage parameter sets; constructing a mapping relationship between the sample filter pressure difference information interval set and the sample blockage parameter set to obtain a blockage classifier; inputting the filter pressure difference information into the blockage classifier, mapping and classifying to obtain the blockage parameters of the filter element.

[0036] Preferably, the filtration differential pressure information of the filter element is extracted from the historical maintenance data of the high-pressure backwash filter station, the extracted filtration differential pressure information is divided into different intervals, and the divided filtration differential pressure information intervals are used as a sample filtration differential pressure information interval set, which includes multiple different filtration differential pressure information intervals, each interval represents the filtration state of the filter element under certain conditions, and the filter element blockage events occurring in each filtration differential pressure information interval are found through historical maintenance records, and each filtration differential pressure information interval is mapped and matched with the corresponding filter element blockage event. For each filtration differential pressure information interval, the proportion of filter element blockage in the interval is calculated, and the calculated blockage event is used as the filter element blockage event. The blockage ratio is used as a sample blockage parameter set; a mapping relationship is constructed between the sample filter pressure difference information interval set and the sample blockage parameter set, and a blockage classifier is constructed based on machine learning (such as decision tree, random forest, support vector machine, etc.). The sample filter pressure difference information interval set and the sample blockage parameter set are used as training data to train the blockage classifier. The filter pressure difference information of the filter element in the current high-pressure backwash filter station is then input into the trained blockage classifier. The blockage classifier searches for the corresponding blockage parameter in the internal mapping relationship based on the input filter pressure difference information, and outputs the blockage parameter of the filter element through mapping and classification, reflecting the current blockage status of the filter element. Furthermore, the specific configuration of the backwash optimization module 30 also includes obtaining a backwash pressure space for backwashing the filter element; randomly extracting a first backwash pressure within the backwash pressure space; performing a flushing prediction based on the first backwash pressure and the blockage parameter to obtain a first blockage flushing cleaning level and a first filter element damage probability; and calculating a first flushing fitness based on the first blockage flushing cleaning level and the first filter element damage probability, as shown in the following formula: ;

[0037] Among them, RA is the flushing adaptability, and is the weight, R is the clogging flushing cleaning level, Q is a positive integer, and K is the probability of filter element damage; the backwash pressure is continuously optimized in the backwash pressure space until the convergence optimization times are reached, the backwash pressure with the maximum flushing fitness is output, the optimal backwash pressure is obtained, and the probability of filter element damage at the optimal backwash pressure is obtained as the optimal filter element damage probability.

[0038] Preferably, all possible backwash pressure values used when backwashing the filter element are used as the backwash pressure space, with the minimum and maximum backwash pressure values forming the boundaries of the backwash pressure space to ensure that the backwash process is both effective and does not cause excessive damage to the filter element. Then, within the backwash pressure space, a backwash pressure value is randomly selected as the first backwash pressure, and the effectiveness and potential risks of backwashing at this pressure are evaluated, which helps to determine the optimal backwash pressure value to balance the cleaning effect and the risk of filter element damage. Based on machine learning, a flushing prediction model is constructed, taking the first backwash pressure and blockage parameters as inputs to perform flushing predictions and output a first blockage flushing cleaning level and a first filter element damage probability, wherein the first blockage flushing cleaning level represents the degree or efficiency of filter element blockage removal at a given backwash pressure, and the first filter element damage probability represents the probability of filter element damage (such as rupture, deformation, etc.) at a given backwash pressure. Preferably, based on the first blockage flushing cleaning level and the first filter element damage probability, a first flushing fitness is calculated, and the calculation formula is as follows: ; Among them, RA is the flushing adaptability, and is the weight, R is the clogging flushing cleaning level, Q is a positive integer, and K is the probability of filter element damage. Then, the backwash pressure is optimized in the backwash pressure space until the convergence optimization times are reached. The backwash pressure with the maximum flushing fitness is output as the optimal backwash pressure, and the probability of filter element damage corresponding to the optimal backwash pressure is obtained as the optimal probability of filter element damage, thereby achieving a balance between the cleaning effect and the risk of filter element damage.

[0039] Furthermore, the specific configuration of the backwash optimization module 30 also includes, based on the backwash history data of the filter element, collecting a sample blockage parameter set and a sample backwash pressure set, and collecting the blockage flushing cleaning level and the filter element damage ratio under backwashing with different sample blockage parameters and sample backwashing pressures, marking and obtaining the sample blockage flushing cleaning level set and the sample filter element damage probability set; using the sample blockage parameter set, sample backwash pressure set, sample blockage flushing cleaning level set and sample filter element damage probability set as supervised training data and verification data to train a backwash predictor until convergence; using the backwash predictor to perform flushing prediction on the first backwash pressure and blockage parameter input to obtain a first blockage flushing cleaning level and a first filter element damage probability.

[0040] Preferably, the clogging parameters before each backwash are extracted from the backwash history data of the filter element as a sample clogging parameter set, and the backwash pressure value used in each backwash is recorded to form a sample backwash pressure set. At the same time, the degree or efficiency of the blockage being flushed out during each backwash is evaluated and recorded, that is, the blockage flushing cleaning level, such as a qualitative classification (such as excellent, good, average, poor), and the damage of the filter element after each backwash (including rupture, deformation, wear, etc.), including the number or proportion of damage, is recorded. These data are then annotated to obtain a sample clogging flushing cleaning level set and a sample filter element damage probability set; using this These sample sets are divided into supervised training data and validation data, and a prediction model is constructed based on machine learning (such as neural networks, decision trees, etc.). The prediction model is trained using the training data to learn the relationship between the clogging parameters, backwash pressure, clogging flushing cleaning level, and filter element damage probability, and a backwash predictor is obtained. The trained backwash predictor is verified using the validation data, and then the first backwash pressure and clogging parameters are input into the trained backwash predictor, and the first clogging flushing cleaning level and the first filter element damage probability are output as prediction results, which helps to optimize the backwash operation and improve the cleaning effect and service life of the filter element.

[0041] Furthermore, the specific configuration of the safety risk factor acquisition module 40 also includes, based on the backwash history data of the filter element, collecting the pressure difference of the filter element under backwashing with different sample blockage parameters and sample backwash pressures to obtain a sample flushing pressure difference information set; using the sample blockage parameter set, sample backwash pressure set and sample flushing pressure difference information set to train a backwash pressure difference simulator; using the backwash pressure difference simulator to simulate backwashing of the first backwash pressure and blockage parameter to obtain flushing pressure difference information.

[0042] Preferably, the blockage parameters before each backwash are extracted from the backwash history data of the filter element, which may include the degree of blockage, the type of blockage material, the blockage distribution, etc., and the backwash pressure value used in each backwash is recorded to form a sample backwash pressure set. When backwashing is performed under different sample blockage parameters and sample backwash pressures, the pressure difference before and after the filter element is measured and recorded, and compiled into a sample flushing pressure difference information set. A backwash pressure difference simulator is constructed based on machine learning models such as linear regression, decision tree, and neural network. The above-collected sample blockage parameter set, sample backwash pressure set, and sample flushing pressure difference information set are used as training data sets to train the backwash pressure difference simulator, learn the relationship between the blockage parameters, backwash pressure, and flushing pressure difference, and obtain the final backwash pressure difference simulator. Then, the first backwash pressure and blockage parameters are input into the backwash pressure difference simulator to simulate backwashing. The backwash pressure difference simulator outputs flushing pressure difference information as a prediction result based on the input parameters. This flushing pressure difference information reflects the change in resistance of the filter element during the backwash process under given blockage parameters and backwash pressure.

[0043] Furthermore, the specific configuration of the safety risk coefficient acquisition module 40 also includes inputting the flushing pressure difference information into the pipeline rupture risk predictor, analyzing and predicting the pipeline rupture safety risk, and obtaining the flushing rupture safety risk information; weighted calculation of the flushing rupture safety risk information, the filtration rupture safety risk information and the probability of filter element damage to obtain the safety risk coefficient of the high-pressure backwash filter station; judging whether the safety risk coefficient is greater than or equal to the safety risk threshold value, and if so, issuing an emergency safety warning, and if not, not issuing an emergency safety warning.

[0044] Preferably, the flushing pressure difference information obtained from the backwashing pressure difference simulator is input into the trained pipeline rupture risk predictor. The pipeline rupture risk predictor analyzes and predicts the pipeline rupture safety risk based on the input flushing pressure difference information and other possible pipeline parameters (such as pipeline material, wall thickness, working pressure, etc.), and outputs the pipeline rupture safety risk information under a given flushing pressure difference, that is, the flushing rupture safety risk information, which indicates the possibility of pipeline rupture. Different weights are then assigned to the flushing rupture safety risk information, the filtration rupture safety risk information and the probability of filter element damage. These weights are used to perform weighted calculation on the flushing rupture safety risk information, the filtration rupture safety risk information and the probability of filter element damage to obtain the high pressure backwashing safety risk information. The safety risk coefficient of the flushing filter station reflects the overall safety risk level of the high-pressure backwashing filter station under the current operating conditions; the calculated safety risk coefficient is compared with a preset safety risk threshold value. If the safety risk coefficient is greater than or equal to the safety risk threshold value, it indicates that the safety risk under the current operating conditions is high and an emergency safety warning is required immediately, such as stopping the backwash operation, reducing the working pressure, checking and repairing potential safety hazards, etc.; if the safety risk coefficient is less than the safety risk threshold value, it indicates that the safety risk under the current operating conditions is within an acceptable range and no emergency safety warning is required; among them, the safety risk threshold value is set according to the safety standards and acceptable risk level of the high-pressure backwash filter station.

[0045] Although the present application makes various references to certain components in the platform according to the embodiments of the present application, any number of different components can be used and run on the user terminal and / or server, and the various units and components included are only divided according to functional logic, but are not limited to the above divisions, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention. The above specific implementation methods do not constitute limitations on the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the scope of protection of this application.

Claims

1. An emergency safety warning platform for special scenarios, characterized by: The platform includes: The pipeline rupture safety risk analysis module is used to detect the pressure on both sides of the filter element in the high-pressure backwash filter station during operation, obtain initial pressure information and filter pressure information, calculate the filter pressure difference information, perform pipeline rupture safety risk analysis, and obtain filter rupture safety risk information; a filter element clogging analysis module, configured to perform a clogging analysis on the filter element based on the filter pressure difference information and obtain a clogging parameter of the filter element; A backwash optimization module is used to obtain a backwash pressure space for backwashing the filter element, and perform backwash optimization within the backwash pressure space to maximize the degree of cleanliness of the flushing and minimize the probability of damage to the filter element, thereby obtaining an optimal backwash pressure and an optimal probability of damage to the filter element; The safety risk coefficient acquisition module is used to simulate backwashing according to the optimal backwashing pressure, obtain flushing pressure difference information, perform pipeline rupture safety risk analysis, obtain flushing rupture safety risk information, combine the filter rupture safety risk information and the probability of filter element damage, calculate the safety risk coefficient of the high-pressure backwash filter station, and issue an emergency safety warning.

2. The emergency safety warning platform for special scenarios according to claim 1 is characterized in that: Detect the pressure on both sides of the filter element in the high-pressure backwash filter station to obtain initial pressure information and filter pressure information, calculate the filter pressure difference information, perform pipeline rupture safety risk analysis, and obtain filter rupture safety risk information, including: Detecting the pressure on both sides of the filter element in the high-pressure backwash filter station to obtain initial pressure information and filtering pressure information; Calculating and obtaining filtering pressure difference information of the filter element according to the initial pressure information and the filtering pressure information; Pre-trained pipeline rupture risk predictor; The pipeline rupture risk predictor is used to perform pipeline rupture safety risk analysis and prediction on the filter pressure difference information input to obtain filter rupture safety risk information, wherein the filter rupture safety risk information includes pipeline rupture probability.

3. The emergency safety warning platform for special scenarios according to claim 2 is characterized in that: Pre-trained pipeline rupture risk predictor, including: Based on the historical maintenance data of the pipelines in the high-pressure backwash filter station, a set of sample pressure difference information intervals is collected, and the proportion of pipeline rupture in different sample pressure difference information intervals is collected and marked as a sample pipeline rupture probability set; The sample pressure difference information interval set and the sample pipeline rupture probability set are constructed as supervised training data and verification data, and the pipeline rupture risk predictor is trained until convergence.

4. The emergency safety warning platform for special scenarios according to claim 1 is characterized in that: Performing a clogging analysis on the filter element according to the filter pressure difference information to obtain a clogging parameter of the filter element includes: Based on the historical maintenance data of the filter elements in the high-pressure backwash filter station, a set of sample filter pressure difference information intervals is collected, and the blockage ratio of the filter elements under different sample filter pressure difference information intervals is collected and marked as a sample blockage parameter set; Constructing a mapping relationship between the sample filtered pressure difference information interval set and the sample blockage parameter set to obtain a blockage classifier; The filter pressure difference information is input into the blockage classifier, and the blockage parameter of the filter element is obtained by mapping and classifying.

5. The emergency safety warning platform for special scenarios according to claim 1 is characterized in that: Obtaining a backwash pressure space for backwashing the filter element, and performing backwash optimization within the backwash pressure space to maximize the degree of flushing cleanliness and minimize the probability of damage to the filter element, including: Obtaining a backwash pressure space for backwashing the filter element; Randomly extracting a first backwash pressure in the backwash pressure space; Based on the first backwash pressure and the clogging parameter, a flushing prediction is performed to obtain a first clogging flushing cleaning level and a first filter element damage probability. Based on the first clogging flushing cleaning level and the first filter element damage probability, a first flushing fitness is calculated as follows: ; Among them, RA is the flushing adaptability, and is the weight, R is the clogging flushing cleaning level, Q is a positive integer, and K is the probability of filter element damage; the backwash pressure is continuously optimized in the backwash pressure space until the convergence optimization times are reached, the backwash pressure with the maximum flushing fitness is output, the optimal backwash pressure is obtained, and the probability of filter element damage at the optimal backwash pressure is obtained as the optimal filter element damage probability.

6. The emergency safety warning platform for special scenarios according to claim 5 is characterized in that: Performing flushing prediction based on the first backwash pressure and the blockage parameter includes: Based on the backwash history data of the filter element, a sample blockage parameter set and a sample backwash pressure set are collected. The blockage flushing cleaning level and the filter element damage ratio under backwashing with different sample blockage parameters and sample backwashing pressures are collected, and the sample blockage flushing cleaning level set and the sample filter element damage probability set are obtained by annotation. Using the sample clogging parameter set, the sample backwash pressure set, the sample clogging flushing cleaning level set, and the sample filter element damage probability set as supervised training data and validation data, training a backwash predictor until convergence; The backwash predictor is used to perform a flushing prediction on the first backwash pressure and the clogging parameter input to obtain a first clogging flushing cleaning level and a first filter element damage probability.

7. The emergency safety warning platform for special scenarios according to claim 5 is characterized in that: Performing simulated backwashing according to the optimal backwashing pressure to obtain backwashing pressure difference information includes: Based on the backwash history data of the filter element, the pressure difference of the filter element under different sample clogging parameters and sample backwash pressures is collected to obtain a sample flushing pressure difference information set; Using the sample blockage parameter set, the sample backwash pressure set, and the sample flushing pressure differential information set, a backwash pressure differential simulator is trained; The backwash differential pressure simulator is used to simulate backwashing on the first backwash pressure and the blockage parameter to obtain backwash differential pressure information.

8. The emergency safety warning platform for special scenarios according to claim 3 is characterized in that: Conduct a pipeline rupture safety risk analysis to obtain flushing rupture safety risk information. Combine the filtration rupture safety risk information and the probability of filter element damage to calculate the safety risk factor of the high-pressure backwash filter station, including: Inputting the flushing pressure difference information into the pipeline rupture risk predictor, performing pipeline rupture safety risk analysis and prediction, and obtaining flushing rupture safety risk information; The flushing rupture safety risk information, the filtration rupture safety risk information and the probability of filter element damage are weightedly calculated to obtain the safety risk coefficient of the high-pressure backwash filter station; determine whether the safety risk coefficient is greater than or equal to the safety risk threshold value. If so, an emergency safety warning is issued; if not, no emergency safety warning is issued.

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