System and method for intelligently monitoring silt blockage of filling pipeline based on ERT technology

Abstract

The invention relates to the technical field of chemical or physical analysis, in particular to an ERT technology-based system and method for intelligently monitoring silt blockage of a filling pipeline, and the system comprises an ERT monitoring module, a fluid mechanics optimization module, a blockage risk early warning module, an intelligent scheduling and adjusting module and a self-cleaning and repairing module. According to the method, the conductivity change of fluid in the pipeline is monitored in real time, fluid distribution is accurately mastered, parameters such as flow velocity, pressure and temperature are optimized, the flowing stability of the pipeline is ensured, blockage and deposition are avoided, flowing parameters are dynamically adjusted, flowing abnormity can be rapidly recognized and corrected, the efficiency of the filling process is improved, and meanwhile the self-cleaning function is combined. Sediments are removed through the micro-vibration technology, the maintenance cost and frequency are reduced, the service life of a pipeline is prolonged, the automation and intelligence level of the filling process is improved through the overall scheme, and mine safety and operation stability are improved.

Description

Technical Field

[0001] The present invention relates to the field of chemical or physical analysis technology, and in particular to a system and method for intelligently monitoring siltation in a filling pipeline based on ERT technology. Background Art

[0002] The technical field of goaf filling includes technical means of filling goafs with filling materials, aiming to prevent ground subsidence in mining areas, reduce safety hazards, maintain the stability of mines, etc. This technology mainly involves transporting filling materials to the goaf of mines through pipelines, and using mechanical equipment, pumping devices and other means to utilize specific physical and chemical properties to achieve the purpose of filling voids and restoring ground stability. The core content of goaf filling technology is to reasonably select filling materials and their proportions according to the actual situation of the mining area to ensure the stability and safety of the filling effect. The entire technical field not only covers the selection and use of filling materials, but also includes the monitoring of the filling process, the management of pipeline transportation devices, the flow control of filling liquids and other technical means.

[0003] Among them, the system for intelligent monitoring of filling pipe blockage and siltation based on ERT technology refers to the use of resistivity imaging technology (ERT) to monitor the filling pipe in real time to detect whether blockage or siltation problems occur. The system monitors the resistivity changes of the fluid in the filling pipe in real time, identifies possible blockage or siltation areas, and takes corresponding treatment measures. The technical matters of the patent subject mainly involve real-time monitoring and abnormal detection of fluids in filling pipes, using ERT technology to accurately sense the state of the fluid in the pipe, and using resistivity differences to determine whether the pipe is blocked or silted. The system collects and analyzes data on resistivity changes to further diagnose whether there are flow anomalies in the pipe, and provides a technical basis for subsequent maintenance and treatment. This technology achieves dynamic tracking of pipeline conditions through resistivity measurement, thereby effectively avoiding the impact of blockage and siltation.

[0004] In the process of monitoring the filling pipeline, the existing technology mainly relies on a single pressure and flow monitoring method, lacking a comprehensive perception of the fluid state. Traditional technology fails to capture subtle changes in the flow state inside the pipeline in real time, and often fails to detect potential problems in time when the flow rate, pressure and temperature change abnormally. When dealing with complex geological conditions and pipeline wear, existing methods tend to ignore the dynamic changes in fluid behavior, resulting in the gradual aggravation of problems such as blockage and siltation, which ultimately affects the filling effect and mine safety. Due to the lack of an effective adaptive adjustment mechanism, traditional methods often rely on manual judgment or delayed response when faced with pipeline anomalies, thereby increasing the risk of the filling process. In addition, the existing technology is relatively simplified in terms of pipeline cleaning and maintenance, and fails to effectively solve the problem of sediment accumulation, making it difficult to remove the deposits inside the pipeline, affecting long-term work efficiency and filling quality. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a system and method for intelligently monitoring siltation in filling pipelines based on ERT technology.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A system for intelligently monitoring siltation in filling pipelines based on ERT technology comprises:

[0007] The ERT monitoring module collects data based on resistivity sensors at multiple key locations in the filling pipeline, analyzes the distribution and changes of fluid conductivity in the pipeline, identifies flow pattern areas, draws conductivity spatial distribution maps based on fluid flow characteristics, extracts and evaluates conductivity data, and classifies and evaluates data based on differentiated flow patterns to generate conductivity spatial distribution results in the pipeline;

[0008] The fluid mechanics optimization module extracts flow rate, pressure and temperature data based on the spatial distribution results of the electrical conductivity in the pipeline, evaluates the fluid state, calculates the flow rate change and pressure distribution, optimizes the flow parameters in combination with the pipeline design and fluid characteristics, adjusts the fluid flow, and generates fluid flow optimization results;

[0009] The blockage risk warning module analyzes the fluid flow optimization results, analyzes the real-time flow rate, pressure and temperature data, evaluates the flow state in the pipeline, identifies abnormal flow rate or resistance points, evaluates the blockage risk based on viscosity changes, fluid properties, and pipeline wear, and generates a blockage risk prediction value;

[0010] The intelligent scheduling and adjustment module analyzes real-time data according to the blockage risk prediction value, compares differentiated flow parameters, adjusts the filling operation process, optimizes pipeline flow rate, pressure, and fluid delivery direction, switches flow patterns to adjust pipeline operation status, and generates dynamic flow adjustment results;

[0011] The self-cleaning and repair module analyzes the dynamic flow adjustment results. When there is abnormal flow in the pipeline, the micro-vibration unit is activated, the vibration frequency is adjusted to match the flow parameters, and the sediment is removed by using the hydrodynamic force to generate a cleaning effect evaluation result.

[0012] As a further scheme of the present invention, the spatial distribution results of conductivity in the pipeline include conductivity distribution, flow area division, flow feature identification and flow state classification; the fluid flow optimization results include flow velocity change, pressure distribution, fluid property optimization, and pipeline flow parameter adjustment; the blockage risk prediction value includes flow velocity change, pressure anomaly, viscosity change, fluid property evaluation, and pipeline wear degree; the dynamic flow adjustment results include flow velocity adjustment, pressure optimization, fluid transport direction adjustment, flow state switching and pipeline state adjustment; the cleaning effect evaluation results include sediment removal, vibration frequency adjustment, flow parameter coordination, and cleaning effect evaluation.

[0013] As a further solution of the present invention, the ERT monitoring module includes a data acquisition submodule, a conductivity distribution identification submodule and a flow pattern area assessment submodule;

[0014] The data acquisition submodule is based on resistivity sensors at multiple key positions in the filling pipeline. By measuring the fluid conductivity at differentiated positions in the pipeline, the resistivity data of each sensor is obtained and stored to form the original conductivity data.

[0015] The conductivity distribution identification submodule performs spatial distribution analysis of the conductivity data based on the original conductivity data, identifies the change trend of the conductivity in the pipeline based on the resistivity difference between the data points, and graphically processes the distribution of the fluid conductivity in the pipeline through data interpolation and fitting methods to generate a conductivity distribution map;

[0016] The flow pattern area assessment submodule is based on the conductivity distribution map. By comparing the conductivity changes in differentiated areas and combining the flow characteristics of the fluid with the sensor collected data, it divides the differentiated flow pattern areas, classifies the differentiated flow pattern areas, and generates conductivity spatial distribution results.

[0017] As a further solution of the present invention, the numerical calculation formula of the change trend is specifically:

[0018] ;

[0019] in, Represents the trend value, R i Represents the resistivity of the ith data point, R ref represents the resistivity of the reference point, represents the spatial distance between the ith data point and the reference point, and N is the total number of measurement points.

[0020] As a further solution of the present invention, the fluid mechanics optimization module includes a data extraction submodule, a conductivity spatial distribution evaluation submodule and a flow parameter optimization submodule;

[0021] The data extraction submodule extracts the flow rate, pressure and temperature data based on the conductivity spatial distribution result, and uses each data recorded by the sensor to perform spatial positioning through the fluid mechanics model to generate a fluid state data set;

[0022] The conductivity spatial distribution evaluation submodule evaluates the flow velocity, pressure and temperature data based on the fluid state data set, calculates the flow velocity distribution value, and analyzes the pressure change and temperature gradient to identify the fluid state at each position in the pipeline and generate a fluid state evaluation result;

[0023] The flow parameter optimization submodule optimizes the flow parameters based on the fluid state evaluation results, adjusts the pipeline flow parameters by comparing the effects of differentiated parameter settings on flow velocity and pressure distribution, and generates fluid flow optimization results.

[0024] As a further solution of the present invention, the flow velocity distribution value calculation formula is specifically:

[0025]

[0027] Where FlowState represents the velocity distribution value at the kth position of the pipeline, v k represents the value of the flow velocity at the kth position in the pipeline, p k represents the pressure of the fluid at the kth position in the pipeline, T k represents the temperature of the fluid at the kth position in the pipeline, v m Represents the value of the flow velocity at the mth position in the pipeline, represents the pressure of the fluid at the mth position in the pipeline, and W represents the total number of positions in the pipeline.

[0028] As a further solution of the present invention, the blockage risk warning module includes a fluid state assessment submodule, an abnormal flow rate identification submodule and a blockage risk assessment submodule;

[0029] The fluid state evaluation submodule extracts and analyzes the real-time flow rate, pressure and temperature data based on the fluid flow optimization result, and generates the fluid state evaluation result by comparing the real-time data with the recorded data;

[0030] The abnormal flow rate identification submodule performs abnormal detection on the flow rate data based on the fluid state evaluation result, identifies abnormal flow rate points and resistance points by setting a threshold and comparing with the flow rate change, and generates an abnormal flow rate identification result;

[0031] The blockage risk assessment submodule evaluates the impact of flow characteristics on blockage risk based on the flow rate anomaly identification result, combined with viscosity changes, fluid properties and pipeline wear, and generates a blockage risk prediction value.

[0032] As a further solution of the present invention, the intelligent scheduling and adjustment module includes a data analysis submodule, a flow parameter comparison submodule and a pipeline adjustment submodule;

[0033] The data analysis submodule extracts real-time flow rate, pressure and temperature data based on the blockage risk prediction value, performs data cleaning and preprocessing, compares the real-time data with the recorded trends, identifies flow state changes, and generates real-time data analysis results;

[0034] The flow parameter comparison submodule compares the flow parameters based on the real-time data analysis results, analyzes the changes in flow velocity and pressure under differentiated pipeline conditions, identifies differentiated flow conditions by comparing the parameters with pipeline design standards, and generates flow parameter comparison results;

[0035] The pipeline adjustment submodule adjusts the pipeline filling operation process based on the flow parameter comparison result, optimizes the flow rate, pressure, and fluid delivery direction of the pipeline, switches the flow state and adjusts the pipeline operation state, and generates dynamic flow adjustment results.

[0036] As a further solution of the present invention, the self-cleaning and repair module includes a flow state analysis submodule, a vibration adjustment submodule and a sediment removal submodule;

[0037] The flow state analysis submodule extracts the flow velocity, pressure and temperature data in the pipeline based on the dynamic flow adjustment result, compares the recorded data with the real-time monitoring data, analyzes the pipeline flow state, and generates a flow anomaly identification result;

[0038] The vibration adjustment submodule activates the micro-vibration unit based on the flow anomaly identification result, adjusts the matching of the vibration frequency and the flow parameter, analyzes the synergistic effect of the micro-vibration and the hydrodynamic force, and generates a vibration frequency adjustment result;

[0039] The sediment removal submodule removes sediments in the pipeline based on the vibration frequency adjustment result, combines vibration and fluid dynamics, evaluates the removal effect and analyzes changes in the pipeline flow state, and generates a cleaning effect evaluation result.

[0040] A method for intelligently monitoring siltation in a filling pipeline based on ERT technology comprises the following steps:

[0041] S1: Based on the resistivity sensors at multiple key locations in the filling pipeline, the data is collected to analyze the distribution and changes of the conductivity of the fluid in the pipeline, identify the flow pattern area, extract and evaluate the conductivity data based on the fluid flow characteristics, and classify and evaluate the data according to the differentiated flow patterns to generate the spatial distribution results of the conductivity in the pipeline;

[0042] S2: Based on the results of the spatial distribution of electrical conductivity in the pipeline, the flow rate, pressure and temperature data are extracted, the fluid state is evaluated, the flow rate change and pressure distribution are calculated, the fluid flow is adjusted, and the fluid flow optimization results are generated;

[0043] S3: By analyzing the fluid flow optimization result, analyzing the real-time flow velocity, pressure and temperature data, and evaluating the flow state in the pipeline, identifying abnormal flow velocity or resistance points, evaluating the blockage risk, and generating a blockage risk prediction value;

[0044] S4: Analyze real-time data according to the blockage risk prediction value, compare differentiated flow parameters, adjust the filling operation process, optimize pipeline flow rate, pressure, and fluid delivery direction, and generate dynamic flow adjustment results;

[0045] S5: Analyze the dynamic flow adjustment result, activate the micro-vibration unit, adjust the vibration frequency and flow parameters, use hydrodynamics to remove sediments, and generate a cleaning effect evaluation result.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, by real-time monitoring of the conductivity changes of the fluid in the pipeline, the fluid distribution is accurately grasped, and parameters such as flow rate, pressure and temperature are optimized to ensure pipeline flow stability and avoid blockage and siltation. By dynamically adjusting the flow parameters, flow anomalies can be quickly identified and corrected to improve the efficiency of the filling process. At the same time, combined with the self-cleaning function, sediments are removed through micro-vibration technology to reduce maintenance costs and frequency, and extend the service life of the pipeline. The overall solution improves the automation and intelligence level of the filling process, and improves mine safety and operational stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 is a system flow chart of the present invention;

[0050] Figure 2 It is a submodule flow chart of the present invention;

[0051] Figure 3 It is a flow chart of the ERT monitoring module of the present invention;

[0052] Figure 4 It is a flow chart of the fluid mechanics optimization module of the present invention;

[0053] Figure 5 This is a flow chart of the congestion risk warning module of the present invention;

[0054] Figure 6 This is a flow chart of the intelligent scheduling and adjustment module of the present invention;

[0055] Figure 7 This is a flow chart of the self-cleaning and repairing module of the present invention;

[0056] Figure 8The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0057] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0058] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0059] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] See also Figure 1 and Figure 2 , an intelligent monitoring system for filling pipeline clogging based on ERT technology includes:

[0062] The ERT monitoring module collects data based on resistivity sensors at multiple key locations in the filling pipeline, analyzes the distribution and changes of fluid conductivity in the pipeline, identifies flow pattern areas, draws conductivity spatial distribution maps based on fluid flow characteristics, extracts and evaluates conductivity data, and classifies and evaluates data based on differentiated flow patterns to generate conductivity spatial distribution results in the pipeline;

[0063] The fluid mechanics optimization module extracts flow velocity, pressure and temperature data based on the spatial distribution results of electrical conductivity in the pipeline, evaluates the fluid state, calculates flow velocity changes and pressure distribution, optimizes flow parameters based on pipeline design and fluid characteristics, adjusts fluid flow, and generates fluid flow optimization results;

[0064] The blockage risk warning module analyzes the results of fluid flow optimization, analyzes real-time flow rate, pressure and temperature data, evaluates the flow state in the pipeline, identifies abnormal flow rate or resistance points, and evaluates the blockage risk based on viscosity changes, fluid properties and pipeline wear, and generates a blockage risk prediction value;

[0065] The intelligent scheduling and adjustment module analyzes real-time data based on the predicted value of blockage risk, compares differentiated flow parameters, adjusts the filling operation process, optimizes pipeline flow rate, pressure and fluid delivery direction, switches flow patterns to adjust pipeline operation status, and generates dynamic flow adjustment results;

[0066] The self-cleaning and repair module analyzes the dynamic flow adjustment results. When there is abnormal flow in the pipeline, the micro-vibration unit is activated, the vibration frequency is adjusted to match the flow parameters, and the hydrodynamic force is used to remove sediments to generate a cleaning effect evaluation result.

[0067] The results of the spatial distribution of conductivity in the pipeline include conductivity distribution, flow area division, flow feature identification and flow state classification. The results of fluid flow optimization include flow velocity change, pressure distribution, fluid characteristics optimization and pipeline flow parameter adjustment. The blockage risk prediction values ​​include flow velocity change, pressure anomaly, viscosity change, fluid property evaluation and pipeline wear degree. The results of dynamic flow adjustment include flow velocity adjustment, pressure optimization, fluid transport direction adjustment, flow state switching and pipeline state adjustment. The results of cleaning effect evaluation include sediment removal, vibration frequency adjustment, flow parameter coordination and cleaning effect evaluation.

[0068] See also Figure 3 and Figure 2 ,The ERT monitoring module includes a data acquisition submodule, a conductivity distribution identification submodule, and a flow regime area assessment submodule;

[0069] The data acquisition submodule is based on resistivity sensors at multiple key positions in the filling pipeline. By measuring the fluid conductivity at differentiated positions in the pipeline, the resistivity data of each sensor is obtained and stored to form the original conductivity data.

[0070] First, according to the conductivity data at different locations in the pipeline, the resistivity values ​​provided by multiple sensors are measured to obtain the resistivity data of each sensor. After the data is collected, it is stored to generate the original conductivity data. The data collection of these sensors is accurate and distributed in different key locations. The conductivity values ​​are obtained through the layout of the sensors. The collected data are further stored and subsequently analyzed through the processing module of the equipment, providing basic data for the subsequent identification of conductivity distribution and regional evaluation. Finally, after the data is stored, the raw data formed can be used for further analysis.

[0071] The conductivity distribution identification submodule performs spatial distribution analysis of conductivity data based on the original conductivity data. Relying on the resistivity difference between data points, it calculates the change trend value of conductivity in the pipeline. Through data interpolation and fitting methods, it graphically processes the distribution of fluid conductivity in the pipeline and generates a conductivity distribution map.

[0072] The specific calculation formula for the change trend value is:

[0073] ;

[0074] in, Represents the trend value, R iRepresents the resistivity of the ith data point, R ref represents the resistivity of the reference point, represents the spatial distance between the ith data point and the reference point, and N is the total number of measurement points.

[0075] This formula is used to calculate the changing trend of conductivity in the pipeline and present this change in the form of weighted average.

[0076] It represents the changing trend of conductivity and is the final result that needs to be calculated. It reflects the distribution change of conductivity in the pipeline.

[0077] R i The resistivity data representing the i-th measurement point is usually measured by a resistivity sensor at different locations and is used to represent the conductivity characteristics of the fluid at that location.

[0078] R ref Resistivity data representing a reference location, used as a comparison standard to calculate resistivity differences at other locations.

[0079] It is the spatial distance between the ith data point and the reference point, which is usually calculated by the layout of the sensors.

[0080] N represents the total number of measured data points and is usually known during the data acquisition process.

[0081] Parameter quantization process:

[0082] Resistivity R i and reference resistivity R ref :

[0083] Resistivity is an important physical property of fluid in a pipeline, which is usually collected in real time by installed resistivity sensors. For each sensor location, the resistivity will be different, reflecting the physical state of the fluid at that location.

[0084] Example: Assume that at some measurement points, the resistivities are , , ,Reference resistivity .

[0085] Spatial distance :

[0086] The distance between each measurement point and the reference point is usually determined by the specific location where the sensor is installed.

[0087] Example: Assume the spatial distance between sensors is:

[0088] , , .

[0089] Calculation process:

[0090] Assuming there are 3 measurement points N=3, after substituting the parameters, the formula is derived as follows:

[0091] Calculate the resistivity difference:

[0092] ;

[0093] ;

[0094] ;

[0095] Compute the spatial weighting factor for each data point:

[0096] ;

[0097] ;

[0098] ;

[0099] Enter the formula to calculate:

[0100] ;

[0101] Compute the numerator and denominator:

[0102] ;

[0103] What the results mean:

[0104] The calculated conductivity trend It reflects the difference in the spatial distribution of conductivity in the pipeline and indicates the degree of change of fluid conductivity at each location. A larger difference value usually indicates a larger change in the fluid state, which may be caused by changes in factors such as temperature and pressure. This value provides basic data for subsequent flow optimization, anomaly identification and pipeline adjustment.

[0105] The flow pattern area assessment submodule is based on the conductivity distribution map. By comparing the conductivity changes of the differentiated areas, combined with the flow characteristics of the fluid and the sensor collection data, the differentiated flow pattern areas are divided, and the differentiated flow pattern areas are classified and processed to generate the conductivity spatial distribution results;

[0106] First, the conductivity distribution map is obtained, and then the conductivity change trend in the map is analyzed, and the fluid area is divided based on the change characteristics. The flow area is obtained by comparing the conductivity differences in different areas. After data analysis, further processing is performed based on the conductivity change trend and the division of flow areas to obtain the classification results and the corresponding conductivity spatial distribution results.

[0107] See also Figure 4 and Figure 2 ,The fluid mechanics optimization module includes a data extraction submodule, a conductivity spatial distribution evaluation submodule, and a flow parameter optimization submodule;

[0108] The data extraction submodule extracts the flow rate, pressure and temperature data based on the conductivity spatial distribution results, and uses each data recorded by the sensor to perform spatial positioning through the fluid mechanics model to generate a fluid state data set;

[0109] First, these data are acquired and spatially analyzed. The fluid dynamics model plays a key role in this process. The model simulates the behavior of the fluid and generates the spatial position of the corresponding data such as flow rate, pressure and temperature. The data collected by each sensor is further processed by the fluid dynamics model to locate the fluid state at different positions in the pipeline. Based on these data, the fluid state data set is further constructed. These data sets will be used for subsequent fluid state evaluation and optimization work to ensure the accurate positioning and characterization of each key parameter.

[0110] The conductivity spatial distribution assessment submodule evaluates the flow velocity, pressure and temperature data based on the fluid state data set. It calculates the flow velocity distribution value and analyzes the pressure change and temperature gradient to identify the fluid state at each location in the pipeline and generate the fluid state assessment result.

[0111] The specific calculation formula for flow velocity distribution value is:

[0112]

[0114] Where FlowState represents the velocity distribution value at the kth position of the pipeline, v k represents the value of the flow velocity at the kth position in the pipeline, p k represents the pressure of the fluid at the kth position in the pipeline, T k represents the temperature of the fluid at the kth position in the pipeline, v m Represents the value of the flow velocity at the mth position in the pipeline, represents the pressure of the fluid at the mth position in the pipeline, and W represents the total number of positions in the pipeline.

[0115] This formula is used to calculate the flow state of the fluid in the pipeline and evaluate the influence of flow velocity, pressure and temperature data on the flow state. Each parameter in the formula represents the physical quantity at different positions in the pipeline.

[0116] v k Represents the value of the flow velocity at the kth position in the pipeline, usually measured by a flow velocity sensor. Flow velocity v k It can be obtained through the flow velocity instrument in the pipeline.

[0117] It is the pressure difference at the kth position in the pipeline, obtained by the pressure sensor, which indicates the pressure change in the pipeline.

[0118] T k is the temperature at the kth position in the pipeline, and the fluid temperature inside the pipeline is usually measured by a temperature sensor.

[0119] w represents the total number of positions considered in the pipeline.

[0120] v m is the flow velocity at the mth position in the pipeline, and the contribution of the flow velocity at each position is calculated.

[0121] Represents the pressure difference at the mth position in the pipeline, similar to , which can be obtained through the pressure sensor at the corresponding position.

[0122] Parameter quantization process:

[0123] Flow rate k and v m :

[0124] Flow velocity is the speed of the fluid in a pipe, and the unit of measurement is usually meters per second (m / s). In practical applications, flow velocity sensors are used to measure the velocity of the fluid in a pipe in real time.

[0125] Example: Assume that the flow rates at different locations in the pipeline are , , .

[0126] Pressure difference and :

[0127] The pressure difference reflects the pressure change of the fluid in the pipeline, and the measurement unit is usually Pascal (Pa). The pressure at each location is obtained through the pressure sensor, and then the pressure difference is calculated.

[0128] Example: Assume that the pressure differences at different locations are , , .

[0129] Temperature T k :

[0130] Temperature refers to the temperature of the fluid in the pipe, and is measured in degrees Celsius (°C). Temperature sensors are usually used to monitor the temperature of the fluid in the pipe in real time.

[0131] Example: Assume the temperatures are , , .

[0132] Calculation process:

[0133] Assuming the total number of positions W = 3, after substituting it into the formula, the formula is derived as follows:

[0134] Calculate the weighted contributions of flow rate, pressure, and temperature at each location:

[0135] ;

[0136] ;

[0137] ;

[0138] Calculate the weighted sum of the flow velocity at each location:

[0139] ;

[0140] The final calculated flow velocity distribution value is:

[0141] ;

[0142] What the results mean:

[0143] The calculated result FlowState≈12.17 represents the flow state evaluation result of the fluid in the pipeline, reflecting the comprehensive influence of multiple factors such as flow velocity, pressure and temperature on the flow state of the pipeline. The higher this value is, the more complex the flow state of the fluid in the pipeline is, and more adjustment measures may be needed to optimize pipeline operation.

[0144] The flow parameter optimization submodule optimizes the flow parameters based on the fluid state evaluation results. By comparing the effects of differentiated parameter settings on flow velocity and pressure distribution, it adjusts the pipeline flow parameters and generates fluid flow optimization results.

[0145] First, the flow velocity and pressure distribution at different pipeline locations are compared, and then the pipeline flow parameters are adjusted. During the comparison process, calculations are performed based on the data of fluid states such as flow velocity and pressure to optimize the impact of each parameter setting on the flow, and then the optimized fluid flow results are generated by adjusting the pipeline flow parameters.

[0146] See also Figure 5 and Figure 2,The clogging risk warning module includes a fluid state assessment submodule, an abnormal flow rate identification submodule and a clogging risk assessment submodule;

[0147] The fluid state assessment submodule extracts and analyzes real-time flow rate, pressure and temperature data based on the fluid flow optimization results, and generates fluid state assessment results by comparing real-time data with recorded data;

[0148] First, extract these data, compare them with real-time data and historical data, and evaluate the current state of the fluid through the comparison results. In specific operations, real-time flow rate, pressure and temperature data will be matched with historical data. With the help of these comparison information, the system will generate fluid state evaluation results to ensure real-time monitoring and analysis of fluid state.

[0149] The abnormal flow rate identification submodule performs abnormal detection on the flow rate data based on the fluid state assessment results, identifies abnormal flow rate points and resistance points by setting thresholds and comparing them with flow rate changes, and generates abnormal flow rate identification results;

[0150] Flow rate anomaly identification, according to the formula:

[0151] ;

[0152] Calculate the flow velocity anomaly identification results.

[0153] In the formula, v i represents the flow velocity at the ith position, V threshold is the set flow rate threshold, is the time interval of the ith measurement position, and n is the number of data points.

[0154] In the formula, velocity anomaly identification is based on the difference between the measured velocity and the set threshold. The velocity difference of each data point is multiplied by the time interval, and the weighted average is performed to obtain the overall anomaly value. In the formula, V abnormal Represents the overall flow rate abnormality value. The higher the value, the more significant the flow rate abnormality detected by the system.

[0155] Assume there are 5 measurement points, where the difference between the flow velocity measurement and the threshold is:

[0156] Measurement point 1: flow rate is 1.5 m / s, threshold is 2.0 m / s, time interval is 1 second

[0157] Measurement point 2: flow rate is 1.8 m / s, threshold is 2.0 m / s, time interval is 2 seconds

[0158] Measurement point 3: flow rate is 2.1 m / s, threshold is 2.0 m / s, time interval is 1.5 seconds

[0159] Measurement point 4: flow rate is 1.9 m / s, threshold is 2.0 m / s, time interval is 2.5 seconds

[0160] Measurement point 5: flow rate is 2.0 m / s, threshold is 2.0 m / s, time interval is 1 second

[0161] Calculate the flow rate anomaly according to the formula:

[0162] ;

[0163] Each item in the calculation is:

[0164] ;

[0165] ;

[0166] ;

[0167] ;

[0168] ;

[0169] Combine the calculations and bring in the total time interval (1+2+1.5+2.5+1=8 seconds):

[0170] ;

[0171] The result shows that the velocity anomaly value is -0.125 m / s, indicating that the difference between the measured flow velocity and the set threshold value occupies a smaller weight in the overall flow velocity calculation, indicating that the flow velocity at these measurement points does not significantly exceed the threshold and is within the normal range.

[0172] The blockage risk assessment submodule evaluates the impact of flow characteristics on blockage risk based on the flow rate anomaly identification results, combined with viscosity changes, fluid properties, and pipeline wear, and generates a blockage risk prediction value;

[0173] First, the abnormal flow rate identification results are combined with the viscosity of the fluid, the fluid properties and the wear of the pipeline for analysis. Through comparison and calculation, the blockage risk prediction value is obtained. Based on the calculation results, the system generates a corresponding blockage risk assessment report.

[0174] See also Figure 6 and Figure 2 ,The intelligent scheduling and adjustment module includes a data analysis submodule, a flow parameter comparison submodule, and a pipeline adjustment submodule;

[0175] The data analysis submodule extracts real-time flow rate, pressure and temperature data based on the blockage risk prediction value, performs data cleaning and preprocessing, compares the real-time data with the recorded trends, identifies flow state changes, and generates real-time data analysis results;

[0176] First, the real-time monitored flow rate, pressure and temperature data are extracted from the system, and these data are initially cleaned and preprocessed. During the data cleaning process, invalid data and noise are removed, and the changes in flow state are analyzed by comparing the trends of real-time data with historical records. By comparing the trends of these real-time data with historical records, the changes in flow state are identified, and finally the real-time data analysis results are generated to provide a reference for subsequent flow parameter comparison and pipeline adjustment.

[0177] The flow parameter comparison submodule compares the flow parameters based on the real-time data analysis results, analyzes the changes in flow velocity and pressure under differentiated pipeline conditions, identifies differentiated flow conditions by comparing the parameters with pipeline design standards, and generates flow parameter comparison results;

[0178] Comparison of flow parameters, according to the formula:

[0179] ;

[0180] Compute flow parameters and compare results.

[0181] In the formula, v1 represents the flow velocity measured for the first time, v0 represents the reference flow velocity, and R is the resistance coefficient of the pipeline. is the time interval.

[0182] In the formula Indicates the change in flow parameters. The change in flow state is obtained by calculating the ratio of flow velocity difference to resistance coefficient and multiplying it by the time interval. The calculation process is as follows:

[0183] Flow rate difference calculation:

[0184] The flow rate difference is calculated by the difference between the measured flow rate and the reference flow rate, that is:

[0185] ;

[0186] Among them, v1 is the measured flow velocity at a certain pipeline position, and v0 is the reference flow velocity.

[0187] Drag coefficient:

[0188] The resistance coefficient (R) is determined by the pipe characteristics and is usually obtained through experiments or theoretical models. It is usually related to the pipe diameter, roughness and flow rate.

[0189] Time interval:

[0190] Time interval It is the time interval between two flow rate measurements in seconds.

[0191] Calculate flow parameter changes:

[0192] The change in flow parameters can be obtained by multiplying the ratio of the flow velocity difference to the resistance coefficient by the time interval:

[0193] ;

[0194] This formula reflects the relationship between flow velocity and pipe resistance, and obtains the change of flow parameters by weighting by time.

[0195] example:

[0196] Assume that the flow velocity at the measuring point is V1=3.0m / s, the reference flow velocity is V0=2.5m / s, the resistance coefficient is R=0.8 (assumed), and the time interval is .

[0197] First calculate the flow rate difference:

[0198] ;

[0199] Then substitute the formula to calculate the flow parameter changes:

[0200] ;

[0201] The results show that the flow parameter variation in this section of the pipeline is 1.25 Pa, indicating that the relationship between flow velocity and pipeline resistance has an impact on the pipeline flow state.

[0202] The pipeline adjustment submodule adjusts the pipeline filling process based on the flow parameter comparison results, optimizes the flow rate, pressure and fluid delivery direction of the pipeline, switches the flow state and adjusts the pipeline operation state, and generates dynamic flow adjustment results;

[0203] First, according to the flow parameter comparison results, the pipeline filling process is adjusted to further optimize the pipeline flow rate, pressure and fluid delivery direction. By switching the flow state and adjusting the pipeline operation state, the operation efficiency of the pipeline system is optimized, and finally the dynamic flow adjustment results are generated to ensure that the pipeline reaches the best state under different operating conditions.

[0204] See also Figure 7 and Figure 2 ,The self-cleaning and repair module includes a flow state analysis submodule, a vibration regulation submodule and a sediment removal submodule;

[0205] The flow state analysis submodule extracts the flow velocity, pressure and temperature data in the pipeline based on the dynamic flow adjustment results, compares the recorded data with the real-time monitoring data, analyzes the pipeline flow state, and generates flow anomaly identification results;

[0206] This process first cleans the real-time monitoring data, removes noise and invalid data, and verifies the integrity of the data. After data cleaning, advanced algorithms are used to compare the trend changes of real-time data with historical data and analyze the changes in flow status. By identifying the fluctuations and change patterns of flow velocity, pressure and temperature in the pipeline, it is further confirmed whether there are flow anomalies or faults. The results of these comparisons and analyses will generate a flow anomaly identification report, provide the real-time status of pipeline operation, and provide a basis for subsequent adjustments, especially in the vibration adjustment and sediment removal process, to ensure the timeliness and accuracy of adjustment measures.

[0207] The vibration adjustment submodule activates the micro-vibration unit based on the flow anomaly identification results, adjusts the matching of the vibration frequency and flow parameters, analyzes the synergistic effect of micro-vibration and hydrodynamics, and generates the vibration frequency adjustment results;

[0208] Vibration regulation, through the following formula:

[0209] ;

[0210] In the formula, F represents the vibration force, m represents the mass of the vibration unit, a is the vibration acceleration, and k is the spring constant. is the deformation of the spring.

[0211] Vibration adjustment is based on the results of flow anomaly identification. The working principle of the vibration unit follows the basic laws of mechanics. The vibration frequency adjustment is calculated by the above formula. The vibration force F is calculated from the mass m and the acceleration a. Using the vibration characteristics of the spring, the vibration frequency is calculated from the spring constant k and the deformation In the specific calculation process, the flow rate, pressure and other flow parameters that need to be adjusted are first determined through the flow anomaly identification results, and then the vibration unit is appropriately adjusted.

[0212] Assume that the mass of the vibration unit is m=2.5kg and the acceleration is a=4.0m / s 2 , the spring constant is k=1500N / m, and the deformation is , substitute into the formula to calculate the vibration force F:

[0213] ;

[0214] Next, we can calculate the elastic force of the vibration force by inputting the spring constant and deformation:

[0215] ;

[0216] By comparing vibration forces:

[0217] 10.0N≠30.0N

[0218] The vibration frequency can be further adjusted to achieve optimization of flow parameters.

[0219] The sediment removal submodule removes sediments in the pipeline based on the vibration frequency adjustment results, combines vibration and fluid dynamics, evaluates the removal effect, analyzes changes in pipeline flow conditions, and generates cleaning effect evaluation results;

[0220] Micro-vibration and hydrodynamics work together to effectively activate sediments and detach them from the pipe wall by changing the dynamic characteristics of the fluid in the pipe. The adjusted vibration frequency interacts with the fluid flow state, allowing the sediment in the pipe to be completely removed. In the process of removing sediments, the system will monitor the changes in the flow state of the pipe in real time, and evaluate the cleaning effect through analysis of data such as flow rate and pressure to determine whether further adjustments are needed. The cleaning effect evaluation results will provide feedback to ensure that the pipeline remains clean, the flow efficiency is maximized, and the overall pipeline operation performance and safety are improved.

[0221] See also Figure 8 , a method for intelligently monitoring siltation in a filling pipeline based on ERT technology, comprising the following steps:

[0222] S1: Based on the resistivity sensors at multiple key locations in the filling pipeline, the data is collected to analyze the distribution and changes of the conductivity of the fluid in the pipeline, identify the flow pattern area, extract and evaluate the conductivity data based on the fluid flow characteristics, and classify and evaluate the data according to the differentiated flow patterns to generate the spatial distribution results of the conductivity in the pipeline;

[0223] S2: Based on the results of the spatial distribution of electrical conductivity in the pipeline, the flow rate, pressure and temperature data are extracted to evaluate the fluid state, calculate the flow rate change and pressure distribution, adjust the fluid flow, and generate the fluid flow optimization results;

[0224] S3: By analyzing the results of fluid flow optimization, analyzing the real-time flow rate, pressure and temperature data, and evaluating the flow state in the pipeline, identifying abnormal flow rate or resistance points, evaluating the blockage risk, and generating a blockage risk prediction value;

[0225] S4: Analyze real-time data based on the blockage risk prediction value, compare differentiated flow parameters, adjust the filling operation process, optimize pipeline flow rate, pressure, and fluid delivery direction, and generate dynamic flow adjustment results;

[0226] S5: Analyze the dynamic flow adjustment results, activate the micro-vibration unit, adjust the vibration frequency and flow parameters, use hydrodynamics to remove sediments, and generate cleaning effect evaluation results.

[0227] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent monitoring system for filling pipe blockage based on ERT technology, characterized by: The system comprises: The ERT monitoring module collects data based on resistivity sensors at multiple key locations in the filling pipeline, analyzes the distribution and changes of fluid conductivity in the pipeline, identifies flow pattern areas, draws conductivity spatial distribution maps based on fluid flow characteristics, extracts and evaluates conductivity data, and performs classification and evaluation processing based on differentiated flow patterns to generate conductivity spatial distribution results in the pipeline; The fluid mechanics optimization module extracts flow rate, pressure and temperature data based on the spatial distribution results of the electrical conductivity in the pipeline, evaluates the fluid state, calculates the flow rate change and pressure distribution, optimizes the flow parameters in combination with the pipeline design and fluid characteristics, adjusts the fluid flow, and generates fluid flow optimization results; The blockage risk warning module analyzes the fluid flow optimization results, analyzes the real-time flow rate, pressure and temperature data, evaluates the flow state in the pipeline, identifies abnormal flow rate or resistance points, evaluates the blockage risk based on viscosity changes, fluid properties, and pipeline wear, and generates a blockage risk prediction value; The intelligent scheduling and adjustment module analyzes real-time data according to the blockage risk prediction value, compares differentiated flow parameters, adjusts the filling operation process, optimizes pipeline flow rate, pressure, and fluid delivery direction, switches flow patterns to adjust pipeline operation status, and generates dynamic flow adjustment results; The self-cleaning and repair module analyzes the dynamic flow adjustment results. When there is abnormal flow in the pipeline, the micro-vibration unit is activated, the vibration frequency is adjusted to match the flow parameters, and the sediment is removed by using the hydrodynamic force to generate a cleaning effect evaluation result.

2. The system for intelligently monitoring siltation in filling pipes based on ERT technology according to claim 1 is characterized in that: The results of the spatial distribution of conductivity in the pipeline include conductivity distribution, flow area division, flow feature identification and flow classification; the results of fluid flow optimization include flow velocity change, pressure distribution, fluid property optimization and pipeline flow parameter adjustment; the blockage risk prediction value includes flow velocity change, pressure anomaly, viscosity change, fluid property evaluation and pipeline wear degree; the dynamic flow adjustment results include flow velocity adjustment, pressure optimization, fluid transport direction adjustment, flow state switching and pipeline state adjustment; the cleaning effect evaluation results include sediment removal, vibration frequency adjustment, flow parameter coordination and cleaning effect evaluation.

3. The system for intelligently monitoring siltation in filling pipes based on ERT technology according to claim 1 is characterized in that: The ERT monitoring module includes a data acquisition submodule, a conductivity distribution identification submodule and a flow pattern area assessment submodule; The data acquisition submodule is based on resistivity sensors at multiple key positions in the filling pipeline. By measuring the fluid conductivity at differentiated positions in the pipeline, the resistivity data of each sensor is obtained and stored to form the original conductivity data. The conductivity distribution identification submodule performs spatial distribution analysis of the conductivity data based on the original conductivity data, identifies the change trend of the conductivity in the pipeline based on the resistivity difference between the data points, and graphically processes the distribution of the fluid conductivity in the pipeline through data interpolation and fitting methods to generate a conductivity distribution map; The flow pattern area assessment submodule is based on the conductivity distribution map. By comparing the conductivity changes in differentiated areas and combining the flow characteristics of the fluid with the sensor collected data, it divides the differentiated flow pattern areas, classifies the differentiated flow pattern areas, and generates conductivity spatial distribution results.

4. The system for intelligently monitoring siltation in filling pipes based on ERT technology according to claim 3 is characterized in that: The numerical calculation formula of the change trend is specifically: ； in, Represents the trend value, R i Represents the resistivity of the ith data point, R ref represents the resistivity of the reference point, represents the spatial distance between the ith data point and the reference point, and N is the total number of measurement points.

5. The system for intelligent monitoring of filling pipeline blockage based on ERT technology according to claim 1 is characterized by: The fluid mechanics optimization module includes a data extraction submodule, a conductivity spatial distribution evaluation submodule and a flow parameter optimization submodule; The data extraction submodule extracts the flow rate, pressure and temperature data based on the conductivity spatial distribution result, and uses each data recorded by the sensor to perform spatial positioning through the fluid mechanics model to generate a fluid state data set; The conductivity spatial distribution evaluation submodule evaluates the flow velocity, pressure and temperature data based on the fluid state data set, calculates the flow velocity distribution value, and analyzes the pressure change and temperature gradient to identify the fluid state at each position in the pipeline and generate a fluid state evaluation result; The flow parameter optimization submodule optimizes the flow parameters based on the fluid state evaluation results, adjusts the pipeline flow parameters by comparing the effects of differentiated parameter settings on flow velocity and pressure distribution, and generates fluid flow optimization results.

6. The system for intelligently monitoring siltation in filling pipes based on ERT technology according to claim 5 is characterized in that: The flow velocity distribution value calculation formula is specifically: Where FlowState represents the velocity distribution value at the kth position of the pipeline, v k represents the value of the flow velocity at the kth position in the pipeline, p k represents the pressure of the fluid at the kth position in the pipeline, T k represents the temperature of the fluid at the kth position in the pipeline, v m Represents the value of the flow velocity at the mth position in the pipeline, represents the pressure of the fluid at the mth position in the pipeline, and W represents the total number of positions in the pipeline.

7. The system for intelligently monitoring siltation in filling pipes based on ERT technology according to claim 1 is characterized by: The blockage risk warning module includes a fluid state assessment submodule, an abnormal flow rate identification submodule and a blockage risk assessment submodule; The fluid state evaluation submodule extracts and analyzes the real-time flow rate, pressure and temperature data based on the fluid flow optimization result, and generates the fluid state evaluation result by comparing the real-time data with the recorded data; The abnormal flow rate identification submodule performs abnormal detection on the flow rate data based on the fluid state evaluation result, identifies abnormal flow rate points and resistance points by setting a threshold and comparing with the flow rate change, and generates an abnormal flow rate identification result; The blockage risk assessment submodule evaluates the impact of flow characteristics on blockage risk based on the flow rate anomaly identification result, combined with viscosity changes, fluid properties, and pipeline wear, and generates a blockage risk prediction value.

8. The system for intelligently monitoring siltation in filling pipes based on ERT technology according to claim 1 is characterized by: The intelligent scheduling and adjustment module includes a data analysis submodule, a flow parameter comparison submodule and a pipeline adjustment submodule; The data analysis submodule extracts real-time flow rate, pressure and temperature data based on the blockage risk prediction value, performs data cleaning and preprocessing, compares the real-time data with the recorded trends, identifies flow state changes, and generates real-time data analysis results; The flow parameter comparison submodule compares the flow parameters based on the real-time data analysis results, analyzes the changes in flow velocity and pressure under differentiated pipeline conditions, identifies differentiated flow conditions by comparing the parameters with pipeline design standards, and generates flow parameter comparison results; The pipeline adjustment submodule adjusts the pipeline filling operation process based on the flow parameter comparison result, optimizes the flow rate, pressure and fluid delivery direction of the pipeline, switches the flow state and adjusts the pipeline operation state, and generates dynamic flow adjustment results.

9. The system for intelligently monitoring siltation in filling pipes based on ERT technology according to claim 1 is characterized by: The self-cleaning and repair module includes a flow state analysis submodule, a vibration adjustment submodule and a sediment removal submodule; The flow state analysis submodule extracts the flow velocity, pressure and temperature data in the pipeline based on the dynamic flow adjustment result, compares the recorded data with the real-time monitoring data, analyzes the pipeline flow state, and generates a flow anomaly identification result; The vibration adjustment submodule activates the micro-vibration unit based on the flow anomaly identification result, adjusts the matching of the vibration frequency and the flow parameter, analyzes the synergistic effect of the micro-vibration and the hydrodynamic force, and generates a vibration frequency adjustment result; The sediment removal submodule removes sediments in the pipeline based on the vibration frequency adjustment result, combines vibration and fluid dynamics, evaluates the removal effect and analyzes changes in the pipeline flow state, and generates a cleaning effect evaluation result.

10. A method for intelligently monitoring siltation in filling pipes based on ERT technology, characterized in that: According to any one of claims 1 to 9, the system for intelligently monitoring siltation in filling pipelines based on ERT technology is implemented, comprising the following steps: S1: Based on the resistivity sensors at multiple key locations in the filling pipeline, the data is collected to analyze the distribution and changes of the conductivity of the fluid in the pipeline, identify the flow pattern area, extract and evaluate the conductivity data based on the fluid flow characteristics, and classify and evaluate the data according to the differentiated flow patterns to generate the spatial distribution results of the conductivity in the pipeline; S2: Based on the results of the spatial distribution of electrical conductivity in the pipeline, the flow rate, pressure and temperature data are extracted, the fluid state is evaluated, the flow rate change and pressure distribution are calculated, the fluid flow is adjusted, and the fluid flow optimization result is generated; S3: By analyzing the fluid flow optimization result, analyzing the real-time flow velocity, pressure and temperature data, and evaluating the flow state in the pipeline, identifying abnormal flow velocity or resistance points, evaluating the blockage risk, and generating a blockage risk prediction value; S4: Analyze real-time data according to the blockage risk prediction value, compare differentiated flow parameters, adjust the filling operation process, optimize pipeline flow rate, pressure, and fluid delivery direction, and generate dynamic flow adjustment results; S5: Analyze the dynamic flow adjustment result, activate the micro-vibration unit, adjust the vibration frequency and flow parameters, use hydrodynamics to remove sediments, and generate a cleaning effect evaluation result.

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