A method and system for controlling a pneumatic diaphragm pump for an emulsion matrix transport vehicle

Abstract

The application discloses a kind of pneumatically operated diaphragm pump control method and system for emulsion matrix transport vehicle, it is related to intelligent fluid delivery control technical field.The present application realizes the accurate control and dynamic optimization of key parameters in the emulsion matrix transport process by integrating temperature monitoring and adjustment, viscosity prediction and parameter setting and multi-parameter coupling optimization and other means, can monitor and adjust the temperature distribution in the storage tank in real time, according to the dynamic adjustment pumping parameter of matrix viscosity, and by multi-parameter optimization algorithm coordination pumping pressure, flow and vibration frequency, so as to solve the problem of low efficiency of traditional rotor pump, high equipment failure rate, which not only improves the transport efficiency and construction quality of emulsion matrix, but also reduces energy consumption and maintenance cost, significantly improves the reliability and service life of equipment, provides strong guarantee for road construction and maintenance.

Description

Technical Field

[0001] This invention relates to the field of intelligent fluid transport control technology, specifically a pneumatic diaphragm pump control method and system for an emulsion matrix transport vehicle. Background Technology

[0002] Emulsified matrix is ​​a key material in road construction and maintenance, and its efficient transportation directly affects project quality and construction progress. With the continuous expansion of infrastructure construction, the performance requirements for emulsified matrix transportation equipment are becoming increasingly stringent, and the reliability and efficiency of transportation systems have become important factors restricting the industry's development.

[0003] Currently, most emulsion matrix transport vehicles use rotary pumps as the core pumping equipment. However, this traditional solution has revealed significant shortcomings in practical applications. During pumping, the rotary pump generates considerable friction with the matrix, which can easily damage the internal structure of the emulsion matrix and affect material quality. Furthermore, existing control systems lack intelligent adjustment capabilities and cannot dynamically optimize operating parameters based on matrix characteristics, resulting in low transport efficiency and a high equipment failure rate. Summary of the Invention

[0004] The purpose of this invention is to provide a pneumatic diaphragm pump control method and system for emulsion matrix transport vehicles. Through intelligent temperature monitoring, viscosity prediction and multi-parameter optimization control, the efficiency, stability and equipment reliability of emulsion matrix transport are significantly improved.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] This application provides a pneumatic diaphragm pump control method for an emulsion matrix transport vehicle, comprising the following steps:

[0007] Temperature distribution data of the emulsified matrix at different locations in the storage tank are collected by a multi-point temperature sensor array, and the temperature signal is processed by an ambient temperature compensation algorithm to obtain a matrix temperature gradient distribution map.

[0008] Based on the matrix temperature gradient distribution map, the current matrix viscosity coefficient is calculated using a viscosity prediction model. The corresponding optimal pumping pressure range and speed control parameters are obtained by matching through the rheological parameter library, and the initial pumping condition setpoints are determined.

[0009] The vibration frequency signal and power consumption data during the operation of the pumping system are obtained. The vibration characteristic spectrum is extracted by frequency domain analysis algorithm. When the vibration amplitude increases abnormally and the power consumption fluctuation exceeds the normal range, it is determined that there is an intensified mechanical friction phenomenon.

[0010] Based on the detected friction anomalies, the speed control parameters are dynamically adjusted through an adaptive control algorithm. The speed correction is calculated based on the deviation between the real-time flow monitoring data and the target flow, and the optimized pumping speed setpoint is obtained.

[0011] The pressure difference data of each section of the pipeline is obtained by using an online pipeline resistance monitoring device. The risk points of pipeline blockage or matrix solidification are identified by the resistance change trend analysis algorithm. Then, the heating power distribution is adjusted according to the temperature distribution of each section of the pipeline to obtain a uniform pipeline temperature field distribution.

[0012] The cumulative operating data of the pumping system is analyzed by the equipment wear assessment model. The remaining life of key components is calculated by the wear prediction algorithm. Then, the coordination of pumping pressure, flow monitoring and vibration frequency is comprehensively analyzed based on the operation stability evaluation index. The optimal combination of system operating parameters is obtained by multi-parameter coupling optimization algorithm.

[0013] Furthermore, a matrix temperature gradient distribution map was obtained, specifically including:

[0014] The original temperature data of the emulsified matrix at different locations in the storage tank is collected in real time by a multi-point temperature sensor array to generate an initial temperature dataset. Then, combined with the external ambient temperature data, the original temperature data is corrected by applying an ambient temperature compensation algorithm to obtain a corrected temperature dataset.

[0015] By calculating the temperature differences between various points inside the storage tank, a preliminary temperature gradient matrix is ​​formed, and a continuous temperature gradient distribution map inside the storage tank is generated by interpolation method to determine the visualization distribution result.

[0016] The gradient values ​​of key areas are extracted from the temperature gradient distribution map. When the gradient value of the key area exceeds the preset threshold, a trigger signal is generated to activate the heating system and perform local heating adjustment on the abnormal temperature gradient area in the storage tank, and obtain the adjusted temperature feedback data.

[0017] By adjusting the temperature feedback data, the initial temperature dataset is updated, and the correction and gradient calculation process is repeated to determine whether the temperature distribution tends to stabilize.

[0018] Furthermore, the initial pumping condition setpoints are determined, specifically including:

[0019] By comparing matrix temperature data and gradient distribution information with a pre-established temperature spectrum database, the characteristic values ​​of the current matrix temperature distribution are obtained, the temperature gradient change trend is determined, and then input into the viscosity prediction model to calculate the viscosity coefficient of the current matrix. It is then determined whether the viscosity coefficient is within the preset threshold range. If it exceeds the threshold, the temperature input value is adjusted through data smoothing, and the viscosity coefficient is recalculated.

[0020] The calculated viscosity coefficient is used to perform parameter matching in conjunction with the rheological parameter database to obtain the corresponding rheological parameter set, determine the optimal pumping pressure range, and perform secondary matching in conjunction with the speed control parameter database to obtain the appropriate speed control parameter values, thus obtaining a preliminary operating condition setting scheme.

[0021] Based on the preliminary operating condition setting scheme, and combined with historical operating data for verification, if the verification results show that the pressure range or speed control parameters deviate from the preset safe range, the parameter values ​​are adjusted through regression analysis model to determine the final operating condition setting value.

[0022] The final operating condition setpoints are input into the pumping control system, which automatically adjusts the equipment's operating status and obtains real-time feedback data of the initial pumping conditions. It continuously monitors changes in matrix temperature and gradient distribution. When the temperature distribution characteristic value is detected to deviate from the preset range, the process of recalculating the viscosity coefficient is triggered, and the operating condition setpoints are updated.

[0023] Furthermore, the extraction of vibration characteristic spectra using frequency domain analysis algorithms also includes:

[0024] The vibration frequency signal and power consumption data of the pumping system under operating conditions are obtained. The raw signals are collected in real time by sensors, and noise interference is removed through data preprocessing to obtain the vibration signal and power data after preliminary cleaning.

[0025] The Fast Fourier Transform algorithm is used for frequency domain analysis to extract the vibration characteristic spectrum, determine the distribution of vibration amplitude in different frequency bands, and analyze whether the vibration amplitude exceeds the preset threshold range. When the vibration amplitude is continuously higher than the threshold in multiple frequency bands, it is determined that there is an abnormal vibration phenomenon.

[0026] By analyzing the power consumption data over time, the power fluctuation range is calculated. When the power fluctuation range exceeds the preset normal range, it is determined that there is abnormal fluctuation in power consumption. Combining the results of abnormal vibration and abnormal power consumption fluctuation, if both are abnormal at the same time, it is further determined that there may be an increase in mechanical friction in the system.

[0027] The system assesses the increase in mechanical friction by comparing historical operating data with current data, analyzes trends to determine the potential direction of friction intensification, generates dynamic adjustment strategies for system monitoring, and continuously tracks the system's operating status by updating monitoring parameters in real time.

[0028] Furthermore, the optimized pumping speed setpoint is obtained, specifically including:

[0029] The sensor data acquisition module acquires friction anomaly signals during equipment operation. The acquired signals are initially filtered to obtain noise-reduced friction feature data. A pre-established anomaly detection model is used for analysis to determine whether there are significant friction anomalies. When the detected anomaly signal intensity exceeds a preset threshold, anomaly identification data is generated.

[0030] By acquiring real-time flow monitoring data and comparing it with the target flow value, the flow deviation value is calculated to obtain the flow deviation result. Then, an adaptive control algorithm is used to dynamically adjust the speed parameter, determine the speed correction amount, and output the adjusted speed parameter value.

[0031] By combining the adjusted speed parameter value with the current operating status data of the equipment, the optimized pumping speed setpoint is calculated, the final speed control command is obtained, and the operating parameters of the equipment are updated in real time. The updated operating status data is obtained, and it is determined whether the operating status data meets the preset stable range. If the stable range is not reached, the process returns to the flow deviation calculation stage for further adjustment.

[0032] By continuously monitoring the friction characteristics and flow changes of the equipment through updated operating status data, long-term operating trend data is obtained for dynamic adjustment and optimization.

[0033] Furthermore, the resistance change trend analysis algorithm identifies potential risks of pipe blockage or matrix solidification, specifically including:

[0034] The pressure difference data of each section of the pipeline is continuously acquired by the online monitoring device and stored as an initial dataset. Then, the initial dataset is processed by a pre-established resistance change calculation model to calculate the resistance change value of each section of the pipeline, thus obtaining the resistance change dataset.

[0035] Time series analysis algorithms are used to identify abnormal trends and determine potential pipeline blockage or matrix solidification risk points. When the identified abnormal trends exceed a preset threshold, the corresponding pipeline section is marked as a high-risk area and a list of high-risk areas is output.

[0036] By acquiring relevant data on the insulation effect of the corresponding pipeline section and comparing and analyzing it with historical operation records, it is determined whether the insulation effect meets the transportation standards. Based on the comparison and analysis results, if the insulation effect is lower than the preset standard, a targeted data acquisition command is triggered to obtain more detailed pipeline environmental parameters and form a supplementary dataset.

[0037] By using a supplementary dataset and a resistance change dataset for joint analysis, the specific causes of pipe blockage or matrix solidification are determined and the classification results are output.

[0038] Furthermore, a uniform temperature field distribution in the pipe is obtained, specifically including:

[0039] By collecting resistance data of each section of the pipeline in real time through sensors and analyzing its changing trend, a preliminary evaluation result of the insulation effect is obtained. When it is determined that the insulation effect is lower than the preset threshold, the start signal of the segmented insulation control system is triggered to determine the range of pipeline sections that need to be adjusted.

[0040] A segmented control mechanism is adopted to collect temperature distribution data for each pipe segment, obtain the current temperature value of each segment, determine whether there is a temperature unevenness, and when the temperature distribution of a certain pipe segment deviates from the uniform temperature target, the required heating power adjustment for that segment is calculated to obtain a targeted power allocation scheme.

[0041] By dynamically adjusting the output power of each section of the heating equipment, the adjusted temperature distribution data is obtained to determine whether the temperature field is becoming uniform. If the temperature field is still not uniform, the temperature distribution data is further analyzed based on the support vector machine algorithm to obtain the optimized power allocation parameters. The heating power is then adjusted again, and the temperature distribution and resistance change trends of each section of the pipeline are continuously monitored.

[0042] Furthermore, a wear prediction algorithm is used to calculate the remaining life of key components, specifically including:

[0043] By acquiring accumulated operating data from the pumping system, the data is cleaned and formatted using a pre-established data processing module to obtain a structured operating dataset. Then, a support vector machine model is applied to assess the wear degree of key components and determine the quantitative indicators of wear status.

[0044] Based on the quantitative indicators of wear status, when the value approaches the preset threshold, an automatic early warning mechanism is triggered. By analyzing the real-time values ​​of the current operating intensity parameters, a new operating intensity parameter is calculated using preset adjustment rules, and the adjusted parameter configuration is obtained.

[0045] Based on the adjusted parameter configuration, the operation control commands of the pumping system are automatically updated, a new set value for the system's operating intensity is determined, and the operating data and wear status of key components are continuously monitored to determine whether they are stable within a safe range. If the monitored wear status is not stable within a safe range, the support vector machine model is called again to recalculate the remaining lifespan and obtain the updated lifespan prediction result.

[0046] Furthermore, the optimal combination of system operating parameters is obtained through a multi-parameter coupled optimization algorithm, specifically including:

[0047] By collecting pumping pressure, flow monitoring, and vibration frequency data during equipment operation, and using sensors to acquire multi-dimensional information in real time, an initial operating dataset is obtained. The correlation between pumping pressure, flow monitoring, and vibration frequency is analyzed, and the coordination measure between each parameter is determined using correlation analysis.

[0048] Based on the coordination metric, when the coordination degree of a certain parameter is lower than the preset threshold, the weights of the relevant parameters are adjusted to obtain an optimized parameter combination dataset. The genetic algorithm is then applied to simulate and optimize the running state to obtain the optimal state configuration scheme of the system.

[0049] By adjusting the control values ​​of pumping pressure and flow monitoring in real time, it is determined whether the vibration frequency is within the safe range, and the adjusted operating status data is obtained. If the vibration frequency still exceeds the safe range, the parameter combination is recalculated through the feedback mechanism to obtain the updated control instruction set.

[0050] By dynamically adjusting the system operating parameters using the updated control instruction set, it is determined whether the emulsion matrix delivery has reached a stable state, thus obtaining the final stable delivery control result.

[0051] This application provides a pneumatic diaphragm pump control system for an emulsion matrix transport vehicle, used to implement a pneumatic diaphragm pump control method for an emulsion matrix transport vehicle, including:

[0052] The temperature monitoring and regulation module collects and processes the temperature data of the emulsion matrix in real time through a multi-point temperature sensor array, generates a temperature gradient distribution map, and starts the heating system to regulate the temperature when necessary.

[0053] The viscosity prediction and parameter setting module calculates the matrix viscosity coefficient based on the temperature gradient distribution map, matches the rheological parameter library to determine the optimal pumping pressure and speed control parameters, and sets the initial pumping conditions.

[0054] The vibration and power analysis module monitors the vibration frequency and power consumption of the pumping system and identifies the phenomenon of increased mechanical friction through frequency domain analysis algorithms.

[0055] The adaptive speed control module dynamically adjusts the pumping speed based on real-time flow monitoring data and target flow deviation to optimize pumping efficiency.

[0056] The monitoring and control module monitors the pressure difference of the delivery pipeline online, analyzes the trend of resistance change, identifies the risk of blockage or solidification, and activates the segmented insulation control system to maintain uniform pipeline temperature.

[0057] The prediction and evaluation module analyzes the operating data of the pumping system, predicts the remaining life of key components, and automatically adjusts the operating intensity when wear approaches the warning threshold.

[0058] The multi-parameter coupling optimization module comprehensively analyzes the coordination of pumping pressure, flow rate and vibration frequency, and obtains the optimal combination of system operating parameters through optimization algorithms.

[0059] The beneficial effects of this invention are as follows:

[0060] By using a multi-point temperature sensor array and an ambient temperature compensation algorithm, the problem of uneven temperature distribution of the emulsion matrix in the storage tank is solved. By real-time monitoring and temperature adjustment, an accurate temperature gradient distribution map is generated to ensure the quality and stability of the emulsion matrix. When the temperature gradient exceeds the preset threshold, the system automatically starts the heating system for local adjustment, thereby avoiding changes in material properties caused by uneven temperature and improving the transportation efficiency and construction quality of the emulsion matrix.

[0061] By utilizing viscosity prediction models and rheological parameter libraries, the pumping pressure and speed are dynamically adjusted according to the viscosity changes of the emulsion matrix, solving the problem of the lack of intelligent adjustment capabilities in existing control systems. By accurately matching the optimal pumping parameters, the system can adapt to matrices of different viscosities, reduce friction and wear during the pumping process, lower equipment failure rate, and improve conveying efficiency, thereby ensuring the efficient and stable delivery of the emulsion matrix.

[0062] By comprehensively analyzing multiple parameters such as pumping pressure, flow monitoring, and vibration frequency, and employing a multi-parameter coupled optimization algorithm, the optimal operating state of the pumping system was achieved. This solved the problem of insufficient parameter coordination during the pumping process. By adjusting and optimizing operating parameters in real time, the system can automatically adapt to changes in the characteristics of the emulsion matrix, reduce energy consumption, extend equipment life, and improve the stability and reliability of the conveying process, ultimately achieving efficient and stable conveying of the emulsion matrix. Attached Figure Description

[0063] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0064] Figure 1 A schematic flowchart of a pneumatic diaphragm pump control method for an emulsion matrix transport vehicle provided in Embodiment 1 of this application;

[0065] Figure 2 A schematic flowchart illustrating the determination of initial pumping condition setpoints for a pneumatic diaphragm pump control method for an emulsion matrix transport vehicle, as provided in Embodiment 1 of this application.

[0066] Figure 3 A schematic flowchart illustrating the process of extracting vibration characteristic spectrum using a frequency domain analysis algorithm for a pneumatic diaphragm pump control method for an emulsion matrix transport vehicle, as provided in Embodiment 1 of this application.

[0067] Figure 4This is a schematic diagram of the structure of a pneumatic diaphragm pump control system for an emulsion matrix transport vehicle, provided in Embodiment 2 of this application. Detailed Implementation

[0068] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0069] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0070] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0071] Example 1

[0072] Please see Figures 1-3 This embodiment provides a pneumatic diaphragm pump control method for an emulsion matrix transport vehicle, including the following steps:

[0073] S1. Collect temperature distribution data of the emulsified matrix at different locations in the storage tank through a multi-point temperature sensor array, process the temperature signal in combination with the ambient temperature compensation algorithm to obtain the matrix temperature gradient distribution map, and start the heating system to regulate when the temperature gradient exceeds the preset threshold.

[0074] Furthermore, a matrix temperature gradient distribution map was obtained, specifically including:

[0075] The original temperature data of the emulsified matrix at different locations in the storage tank is collected in real time by a multi-point temperature sensor array to generate an initial temperature dataset. Then, combined with the external ambient temperature data, the original temperature data is corrected by applying an ambient temperature compensation algorithm to obtain a corrected temperature dataset.

[0076] By calculating the temperature differences between various points inside the storage tank, a preliminary temperature gradient matrix is ​​formed, and a continuous temperature gradient distribution map inside the storage tank is generated by interpolation method to determine the visualization distribution result.

[0077] The gradient values ​​of key areas are extracted from the temperature gradient distribution map. When the gradient value of the key area exceeds the preset threshold, a trigger signal is generated to activate the heating system and perform local heating adjustment on the abnormal temperature gradient area in the storage tank, and obtain the adjusted temperature feedback data.

[0078] By adjusting the temperature feedback data, the initial temperature dataset is updated, and the correction and gradient calculation process is repeated to determine whether the temperature distribution tends to stabilize.

[0079] Specifically, by precisely controlling the temperature distribution within the emulsion matrix storage tank, the material quality problem caused by uneven temperature is effectively solved, ensuring the quality and stability of the emulsion matrix. At the same time, the automated adjustment process improves energy efficiency, reduces energy consumption, and ensures the continuity of construction and the overall quality of the project.

[0080] S2. Calculate the current matrix viscosity coefficient using a viscosity prediction model based on the matrix temperature gradient distribution map, obtain the corresponding optimal pumping pressure range and speed control parameters through rheological parameter library matching, and determine the initial pumping condition setpoint.

[0081] Furthermore, the initial pumping condition setpoints are determined, specifically including:

[0082] S21. By comparing the matrix temperature data and gradient distribution information with a pre-established temperature spectrum database, the characteristic value of the current matrix temperature distribution is obtained, the temperature gradient change trend is determined, and then input into the viscosity prediction model to calculate the viscosity coefficient of the current matrix. It is determined whether the viscosity coefficient is within the preset threshold range. If it exceeds the threshold, the temperature input value is adjusted through data smoothing and the viscosity coefficient is recalculated.

[0083] S22. Using the calculated viscosity coefficient, perform parameter matching in conjunction with the rheological parameter database to obtain the corresponding rheological parameter set, determine the optimal pumping pressure range, and perform secondary matching in conjunction with the speed control parameter database to obtain the appropriate speed control parameter value, thus obtaining a preliminary working condition setting scheme.

[0084] S23. Based on the preliminary operating condition setting scheme, and combined with historical operating data, the verification is carried out. When the verification results show that the pressure range or speed control parameters deviate from the preset safe range, the parameter values ​​are adjusted through regression analysis model to determine the final operating condition setting value.

[0085] S24. Input the final operating condition setpoint into the pumping control system, automatically adjust the equipment operating status, obtain real-time feedback data of the initial pumping operating condition, continuously monitor the changes in matrix temperature and gradient distribution, and when the temperature distribution characteristic value is detected to deviate from the preset range, trigger the process of recalculating the viscosity coefficient and updating the operating condition setpoint.

[0086] Specifically, by employing an advanced viscosity prediction model and rheological parameter library, the system can accurately calculate the viscosity coefficient of the emulsion matrix and match it with the optimal pumping pressure and speed parameters, thereby determining the efficient initial pumping condition setpoints. This process not only ensures the efficient operation of the pumping system, but also ensures rapid response and optimization of pumping parameters when the matrix characteristics change through real-time monitoring and automatic adjustment mechanisms. This effectively solves the problems of low conveying efficiency and equipment failure caused by parameter mismatch in traditional pumping systems, and significantly improves the reliability and economy of emulsion matrix transportation.

[0087] S3. Obtain the vibration frequency signal and power consumption data during the operation of the pumping system, and use the frequency domain analysis algorithm to extract the vibration characteristic spectrum. When the vibration amplitude increases abnormally and the power consumption fluctuation exceeds the normal range, it is determined that there is an intensified mechanical friction phenomenon.

[0088] Furthermore, the extraction of vibration characteristic spectra using frequency domain analysis algorithms also includes:

[0089] S31. Obtain the vibration frequency signal and power consumption data of the pumping system under operating conditions. Use sensors to collect raw signals in real time, remove noise interference through data preprocessing, and obtain vibration signals and power data after preliminary cleaning.

[0090] S32. Use the Fast Fourier Transform algorithm to perform frequency domain analysis, extract the vibration characteristic spectrum, determine the distribution of vibration amplitude in different frequency bands, analyze whether the vibration amplitude exceeds the preset threshold range, and determine that there is an abnormal vibration phenomenon when the vibration amplitude is continuously higher than the threshold in multiple frequency bands.

[0091] S33. By analyzing the time series of power consumption data, calculate the power fluctuation range. When the power fluctuation range exceeds the preset normal range, it is determined that there is abnormal fluctuation in power consumption. Combining the results of abnormal vibration and abnormal power consumption fluctuation, when both are abnormal at the same time, it is further determined that there may be an increase in mechanical friction in the system.

[0092] S34. Based on the judgment of increased mechanical friction, obtain the difference between historical operating status data and current data, determine the potential development direction of increased friction through trend analysis, generate a dynamic adjustment strategy for system monitoring, and obtain continuous tracking results of system operating status by updating monitoring parameters in real time.

[0093] Specifically, frequency domain analysis technology is used to effectively monitor vibration and power consumption during the pumping process of emulsified matrix, accurately identify the phenomenon of increased mechanical friction, and optimize system operation through dynamic adjustment strategies, thereby improving equipment reliability and reducing maintenance requirements.

[0094] S4. Based on the detected friction anomaly, the speed control parameters are dynamically adjusted through an adaptive control algorithm. The speed correction is calculated based on the deviation between the real-time flow monitoring data and the target flow to obtain the optimized pumping speed setpoint.

[0095] Furthermore, the optimized pumping speed setpoint is obtained, specifically including:

[0096] The sensor data acquisition module acquires friction anomaly signals during equipment operation. The acquired signals are initially filtered to obtain noise-reduced friction feature data. A pre-established anomaly detection model is used for analysis to determine whether there are significant friction anomalies. When the detected anomaly signal intensity exceeds a preset threshold, anomaly identification data is generated.

[0097] By acquiring real-time flow monitoring data and comparing it with the target flow value, the flow deviation value is calculated to obtain the flow deviation result. Then, an adaptive control algorithm is used to dynamically adjust the speed parameter, determine the speed correction amount, and output the adjusted speed parameter value.

[0098] By combining the adjusted speed parameter value with the current operating status data of the equipment, the optimized pumping speed setpoint is calculated, the final speed control command is obtained, and the operating parameters of the equipment are updated in real time. The updated operating status data is obtained, and it is determined whether the operating status data meets the preset stable range. If the stable range is not reached, the process returns to the flow deviation calculation stage for further adjustment.

[0099] By continuously monitoring the friction characteristics and flow changes of the equipment through updated operating status data, long-term operating trend data can be obtained for subsequent dynamic adjustment and optimization processes.

[0100] Specifically, the pumping speed is dynamically adjusted through an adaptive control algorithm to cope with abnormal friction phenomena and achieve an optimized pumping speed setpoint. By collecting abnormal friction signals through sensors and performing noise reduction processing, and combining the deviation between real-time flow monitoring data and the target flow rate, the pumping speed can be intelligently calculated and adjusted to ensure the stability and efficiency of the pumping process. This method can not only respond to abnormal friction in real time and reduce equipment wear, but also optimize long-term operating trends through continuous monitoring and adjustment, significantly improving the reliability and economy of emulsion matrix transportation.

[0101] S5. Use an online pipeline resistance monitoring device to obtain pressure difference data of each section of the conveying pipeline. Use a resistance change trend analysis algorithm to identify pipeline blockage or matrix solidification risk points and determine whether the pipeline insulation effect meets the conveying requirements. When the pipeline resistance change trend shows that the insulation effect is insufficient, start the segmented insulation control system and adjust the heating power distribution according to the temperature distribution of each section of the pipeline to obtain a uniform pipeline temperature field distribution.

[0102] Furthermore, the resistance change trend analysis algorithm identifies potential risks of pipe blockage or matrix solidification, specifically including:

[0103] The pressure difference data of each section of the pipeline is continuously acquired by the online monitoring device and stored as an initial dataset. Then, the initial dataset is processed by a pre-established resistance change calculation model to calculate the resistance change value of each section of the pipeline, thus obtaining the resistance change dataset.

[0104] Time series analysis algorithms are used to identify abnormal trends and determine potential pipeline blockage or matrix solidification risk points. When the identified abnormal trends exceed a preset threshold, the corresponding pipeline section is marked as a high-risk area and a list of high-risk areas is output.

[0105] By acquiring relevant data on the insulation effect of the corresponding pipeline section and comparing and analyzing it with historical operation records, it is determined whether the insulation effect meets the transportation standards. Based on the comparison and analysis results, if the insulation effect is lower than the preset standard, a targeted data acquisition command is triggered to obtain more detailed pipeline environmental parameters and form a supplementary dataset.

[0106] By using a supplementary dataset and a resistance change dataset for joint analysis, the specific causes of pipe blockage or matrix solidification are determined and the classification results are output.

[0107] Specifically, by monitoring and analyzing the pressure difference data of each section of the pipeline online, the system can effectively identify and warn of the risk of pipeline blockage or matrix solidification, ensuring that the pipeline insulation effect meets the transportation requirements. When insufficient insulation is detected, the system will automatically start segmented insulation control and intelligently adjust the heating power to achieve a uniform distribution of the temperature field inside the pipeline, thereby ensuring the smooth transportation of the emulsified matrix, avoiding construction delays and material waste caused by pipeline problems, and significantly improving transportation efficiency and project quality.

[0108] Furthermore, a uniform temperature field distribution in the pipe is obtained, specifically including:

[0109] By collecting resistance data of each section of the pipeline in real time through sensors and analyzing its changing trend, a preliminary evaluation result of the insulation effect is obtained. When it is determined that the insulation effect is lower than the preset threshold, the start signal of the segmented insulation control system is triggered to determine the range of pipeline sections that need to be adjusted.

[0110] A segmented control mechanism is adopted to collect temperature distribution data for each pipe segment, obtain the current temperature value of each segment, determine whether there is a temperature unevenness, and when the temperature distribution of a certain pipe segment deviates from the uniform temperature target, the required heating power adjustment for that segment is calculated to obtain a targeted power allocation scheme.

[0111] By dynamically adjusting the output power of each section of the heating equipment, the adjusted temperature distribution data is obtained to determine whether the temperature field is becoming uniform. If the temperature field is still not uniform, the temperature distribution data is further analyzed based on the support vector machine algorithm to obtain the optimized power distribution parameters. The heating power is then adjusted again, and the temperature distribution and resistance change trends of each section of the pipeline are continuously monitored to determine whether the overall insulation effect meets expectations.

[0112] Specifically, through real-time sensor monitoring and segmented control mechanisms, the temperature of each section of the pipeline is precisely adjusted, ensuring a uniform temperature field distribution along the pipeline. When poor insulation is detected, the system automatically calculates and adjusts the heating power of each section to optimize the temperature distribution until a uniform state is achieved. In addition, the support vector machine algorithm is used to further analyze and optimize the power allocation, ultimately ensuring that the overall insulation effect meets expectations. This effectively solves the problems of reduced conveying efficiency and damaged matrix quality caused by uneven pipeline temperature, and significantly improves the stability and reliability of emulsified matrix conveying.

[0113] S6. Analyze the accumulated operating data of the pumping system through the equipment wear assessment model, calculate the remaining life of key components using the wear prediction algorithm, and automatically reduce the operating intensity parameters when the wear level approaches the warning threshold. Then, based on the operation stability evaluation index, comprehensively analyze the coordination of pumping pressure, flow monitoring, and vibration frequency, and obtain the optimal combination of system operating state parameters through a multi-parameter coupling optimization algorithm to achieve stable delivery control of the emulsion matrix.

[0114] Furthermore, a wear prediction algorithm is used to calculate the remaining life of key components, specifically including:

[0115] By acquiring accumulated operating data from the pumping system, the data is cleaned and formatted using a pre-established data processing module to obtain a structured operating dataset. Then, a support vector machine model is applied to assess the wear degree of key components and determine the quantitative indicators of wear status.

[0116] Based on the quantitative indicators of wear status, when the value approaches the preset threshold, an automatic early warning mechanism is triggered. By analyzing the real-time values ​​of the current operating intensity parameters, a new operating intensity parameter is calculated using preset adjustment rules, and the adjusted parameter configuration is obtained.

[0117] Based on the adjusted parameter configuration, the operation control commands of the pumping system are automatically updated, a new set value for the system's operating intensity is determined, and the operating data and wear status of key components are continuously monitored to determine whether they are stable within a safe range. If the monitored wear status is not stable within a safe range, the support vector machine model is called again to recalculate the remaining lifespan and obtain the updated lifespan prediction result.

[0118] Specifically, by analyzing the operating data of the pumping system and predicting the wear level of key components, the system can automatically adjust operating parameters when component wear approaches a warning threshold, reducing operational intensity and thus extending component lifespan. Furthermore, the system comprehensively evaluates the coordination of pumping pressure, flow rate, and vibration frequency, using a multi-parameter coupled optimization algorithm to determine the optimal operating state, achieving stable delivery of the emulsion matrix. This not only improves the reliability and safety of the pumping system but also reduces maintenance costs and downtime, ensuring long-term efficient operation.

[0119] Furthermore, the optimal combination of system operating parameters is obtained through a multi-parameter coupled optimization algorithm, specifically including:

[0120] By collecting pumping pressure, flow monitoring, and vibration frequency data during equipment operation, and using sensors to acquire multi-dimensional information in real time, an initial operating dataset is obtained. The correlation between pumping pressure, flow monitoring, and vibration frequency is analyzed, and the coordination measure between each parameter is determined using correlation analysis.

[0121] Based on the coordination metric, when the coordination degree of a certain parameter is lower than the preset threshold, the weights of the relevant parameters are adjusted to obtain an optimized parameter combination dataset. The genetic algorithm is then applied to simulate and optimize the running state to obtain the optimal state configuration scheme of the system.

[0122] By adjusting the control values ​​of pumping pressure and flow monitoring in real time, it is determined whether the vibration frequency is within the safe range, and the adjusted operating status data is obtained. If the vibration frequency still exceeds the safe range, the parameter combination is recalculated through the feedback mechanism to obtain the updated control instruction set.

[0123] By dynamically adjusting the system operating parameters using the updated control instruction set, it is determined whether the emulsion matrix delivery has reached a stable state, thus obtaining the final stable delivery control result.

[0124] Specifically, by employing a multi-parameter coupled optimization algorithm, the system achieves precise control and coordinated optimization of key parameters such as pumping pressure, flow rate, and vibration frequency during the emulsion matrix transportation process. This method significantly improves the stability and efficiency of the transportation system, reduces energy consumption and equipment wear, and ensures the continuous and uniform transportation of the emulsion matrix, thereby improving project quality and construction progress, and providing strong support for road construction and maintenance.

[0125] Example 2

[0126] Please see Figure 4 This embodiment provides a pneumatic diaphragm pump control system for an emulsion matrix transport vehicle, used to implement a pneumatic diaphragm pump control method for an emulsion matrix transport vehicle, including:

[0127] The temperature monitoring and regulation module collects and processes the temperature data of the emulsion matrix in real time through a multi-point temperature sensor array, generates a temperature gradient distribution map, and starts the heating system to regulate the temperature when necessary.

[0128] The viscosity prediction and parameter setting module calculates the matrix viscosity coefficient based on the temperature gradient distribution map, matches the rheological parameter library to determine the optimal pumping pressure and speed control parameters, and sets the initial pumping conditions.

[0129] The vibration and power analysis module monitors the vibration frequency and power consumption of the pumping system, and identifies the phenomenon of increased mechanical friction through frequency domain analysis algorithms to ensure the safe operation of the system.

[0130] The adaptive speed control module dynamically adjusts the pumping speed based on real-time flow monitoring data and target flow deviation to optimize pumping efficiency.

[0131] The monitoring and control module monitors the pressure difference of the delivery pipeline online, analyzes the trend of resistance change, identifies the risk of blockage or solidification, and activates the segmented insulation control system to maintain uniform pipeline temperature.

[0132] The prediction and evaluation module analyzes the operating data of the pumping system, predicts the remaining life of key components, and automatically adjusts the operating intensity when wear approaches the warning threshold.

[0133] The multi-parameter coupling optimization module comprehensively analyzes the coordination of pumping pressure, flow rate and vibration frequency, and obtains the optimal combination of system operating parameters through optimization algorithms to achieve stable delivery of emulsion matrix.

[0134] Specifically, the system can monitor and adjust the temperature distribution inside the storage tank in real time, dynamically adjust the pumping parameters according to the matrix viscosity, and coordinate the pumping pressure, flow rate and vibration frequency through a multi-parameter optimization algorithm. This solves the problems of low conveying efficiency and high equipment failure rate of traditional rotor pumps, improves the transportation efficiency and construction quality of emulsified matrix, reduces energy consumption and maintenance costs, and significantly improves the reliability and service life of the equipment.

[0135] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A pneumatic diaphragm pump control method for an emulsion matrix transport vehicle, characterized in that: Includes the following steps: Temperature distribution data of the emulsified matrix at different locations in the storage tank are collected by a multi-point temperature sensor array, and the temperature signal is processed by an ambient temperature compensation algorithm to obtain a matrix temperature gradient distribution map. Based on the matrix temperature gradient distribution map, the current matrix viscosity coefficient is calculated using a viscosity prediction model. The corresponding optimal pumping pressure range and speed control parameters are obtained by matching through the rheological parameter library, and the initial pumping condition setpoints are determined. The vibration frequency signal and power consumption data during the operation of the pumping system are obtained. The vibration characteristic spectrum is extracted by frequency domain analysis algorithm. When the vibration amplitude increases abnormally and the power consumption fluctuation exceeds the normal range, it is determined that there is an intensified mechanical friction phenomenon. Based on the detected friction anomalies, the speed control parameters are dynamically adjusted through an adaptive control algorithm. The speed correction is calculated based on the deviation between the real-time flow monitoring data and the target flow, and the optimized pumping speed setpoint is obtained. The pressure difference data of each section of the pipeline is obtained by using an online pipeline resistance monitoring device. The risk points of pipeline blockage or matrix solidification are identified by the resistance change trend analysis algorithm. Then, the heating power distribution is adjusted according to the temperature distribution of each section of the pipeline to obtain a uniform pipeline temperature field distribution. The cumulative operating data of the pumping system is analyzed by the equipment wear assessment model. The remaining life of key components is calculated by the wear prediction algorithm. Then, the coordination of pumping pressure, flow monitoring and vibration frequency is comprehensively analyzed based on the operation stability evaluation index. The optimal combination of system operating parameters is obtained by multi-parameter coupling optimization algorithm.

2. The pneumatic diaphragm pump control method for an emulsion matrix transport vehicle according to claim 1, characterized in that: The matrix temperature gradient distribution map was obtained, specifically including: The original temperature data of the emulsified matrix at different locations in the storage tank is collected in real time by a multi-point temperature sensor array to generate an initial temperature dataset. Then, combined with the external ambient temperature data, the original temperature data is corrected by applying an ambient temperature compensation algorithm to obtain a corrected temperature dataset. By calculating the temperature differences between various points inside the storage tank, a preliminary temperature gradient matrix is ​​formed, and a continuous temperature gradient distribution map inside the storage tank is generated by interpolation method to determine the visualization distribution result. The gradient values ​​of key areas are extracted from the temperature gradient distribution map. When the gradient value of the key area exceeds the preset threshold, a trigger signal is generated to activate the heating system and perform local heating adjustment on the abnormal temperature gradient area in the storage tank, and obtain the adjusted temperature feedback data. By adjusting the temperature feedback data, the initial temperature dataset is updated, and the correction and gradient calculation process is repeated to determine whether the temperature distribution tends to stabilize.

3. The pneumatic diaphragm pump control method for an emulsion matrix transport vehicle according to claim 1, characterized in that: Determine the initial pumping condition setpoints, specifically including: By comparing matrix temperature data and gradient distribution information with a pre-established temperature spectrum database, the characteristic values ​​of the current matrix temperature distribution are obtained, the temperature gradient change trend is determined, and then input into the viscosity prediction model to calculate the viscosity coefficient of the current matrix. It is then determined whether the viscosity coefficient is within the preset threshold range. If it exceeds the threshold, the temperature input value is adjusted through data smoothing, and the viscosity coefficient is recalculated. The calculated viscosity coefficient is used to perform parameter matching in conjunction with the rheological parameter database to obtain the corresponding rheological parameter set, determine the optimal pumping pressure range, and perform secondary matching in conjunction with the speed control parameter database to obtain the appropriate speed control parameter values, thus obtaining a preliminary operating condition setting scheme. Based on the preliminary operating condition setting scheme, and combined with historical operating data for verification, if the verification results show that the pressure range or speed control parameters deviate from the preset safe range, the parameter values ​​are adjusted through regression analysis model to determine the final operating condition setting value. The final operating condition setpoints are input into the pumping control system, which automatically adjusts the equipment's operating status and obtains real-time feedback data of the initial pumping conditions. It continuously monitors changes in matrix temperature and gradient distribution. When the temperature distribution characteristic value is detected to deviate from the preset range, the process of recalculating the viscosity coefficient is triggered, and the operating condition setpoints are updated.

4. The pneumatic diaphragm pump control method for an emulsion matrix transport vehicle according to claim 1, characterized in that: Extracting vibration characteristic spectra using frequency domain analysis algorithms also includes: The vibration frequency signal and power consumption data of the pumping system under operating conditions are obtained. The raw signals are collected in real time by sensors, and noise interference is removed through data preprocessing to obtain the vibration signal and power data after preliminary cleaning. The Fast Fourier Transform algorithm is used for frequency domain analysis to extract the vibration characteristic spectrum, determine the distribution of vibration amplitude in different frequency bands, and analyze whether the vibration amplitude exceeds the preset threshold range. When the vibration amplitude is continuously higher than the threshold in multiple frequency bands, it is determined that there is an abnormal vibration phenomenon. By analyzing the power consumption data over time, the power fluctuation range is calculated. When the power fluctuation range exceeds the preset normal range, it is determined that there is abnormal fluctuation in power consumption. Combining the results of abnormal vibration and abnormal power consumption fluctuation, if both are abnormal at the same time, it is further determined that there is an increase in mechanical friction in the system. The system assesses the increase in mechanical friction by comparing historical operating data with current data, analyzes trends to determine the potential direction of friction intensification, generates dynamic adjustment strategies for system monitoring, and continuously tracks the system's operating status by updating monitoring parameters in real time.

5. The pneumatic diaphragm pump control method for an emulsion matrix transport vehicle according to claim 1, characterized in that: The optimized pumping speed setpoints are obtained, specifically including: The sensor data acquisition module acquires friction anomaly signals during equipment operation. The acquired signals are initially filtered to obtain noise-reduced friction feature data. A pre-established anomaly detection model is used for analysis to determine whether there are significant friction anomalies. When the detected anomaly signal intensity exceeds a preset threshold, anomaly identification data is generated. By acquiring real-time flow monitoring data and comparing it with the target flow value, the flow deviation value is calculated to obtain the flow deviation result. Then, an adaptive control algorithm is used to dynamically adjust the speed parameter, determine the speed correction amount, and output the adjusted speed parameter value. By combining the adjusted speed parameter value with the current operating status data of the equipment, the optimized pumping speed setpoint is calculated, the final speed control command is obtained, and the operating parameters of the equipment are updated in real time. The updated operating status data is obtained, and it is determined whether the operating status data meets the preset stable range. If the stable range is not reached, the process returns to the flow deviation calculation stage for further adjustment. By continuously monitoring the friction characteristics and flow changes of the equipment through updated operating status data, long-term operating trend data is obtained for dynamic adjustment and optimization.

6. The pneumatic diaphragm pump control method for an emulsion matrix transport vehicle according to claim 1, characterized in that: The algorithm for analyzing resistance change trends identifies potential risks of pipe blockage or matrix solidification, specifically including: The pressure difference data of each section of the pipeline is continuously acquired by the online monitoring device and stored as an initial dataset. Then, the initial dataset is processed by a pre-established resistance change calculation model to calculate the resistance change value of each section of the pipeline, thus obtaining the resistance change dataset. Time series analysis algorithms are used to identify abnormal trends and determine potential pipeline blockage or matrix solidification risk points. When the identified abnormal trends exceed a preset threshold, the corresponding pipeline section is marked as a high-risk area and a list of high-risk areas is output. By acquiring relevant data on the insulation effect of the corresponding pipeline section and comparing and analyzing it with historical operation records, it is determined whether the insulation effect meets the transportation standards. Based on the comparison and analysis results, if the insulation effect is lower than the preset standard, a targeted data acquisition command is triggered to obtain more detailed pipeline environmental parameters and form a supplementary dataset. By using a supplementary dataset and a resistance change dataset for joint analysis, the specific causes of pipe blockage or matrix solidification are determined and the classification results are output.

7. The pneumatic diaphragm pump control method for an emulsion matrix transport vehicle according to claim 1, characterized in that: A uniform temperature field distribution in the pipe is obtained, specifically including: By collecting resistance data of each section of the pipeline in real time through sensors and analyzing its changing trend, a preliminary evaluation result of the insulation effect is obtained. When it is determined that the insulation effect is lower than the preset threshold, the start signal of the segmented insulation control system is triggered to determine the range of pipeline sections that need to be adjusted. A segmented control mechanism is adopted to collect temperature distribution data for each pipe segment, obtain the current temperature value of each segment, determine whether there is a temperature unevenness, and when the temperature distribution of a certain pipe segment deviates from the uniform temperature target, the required heating power adjustment for that segment is calculated to obtain a targeted power allocation scheme. By dynamically adjusting the output power of each section of the heating equipment, the adjusted temperature distribution data is obtained to determine whether the temperature field is becoming uniform. If the temperature field is still not uniform, the temperature distribution data is further analyzed based on the support vector machine algorithm to obtain the optimized power allocation parameters. The heating power is then adjusted again, and the temperature distribution and resistance change trends of each section of the pipeline are continuously monitored.

8. The pneumatic diaphragm pump control method for an emulsion matrix transport vehicle according to claim 1, characterized in that: The remaining life of critical components is calculated using a wear prediction algorithm, specifically including: By acquiring accumulated operating data from the pumping system, the data is cleaned and formatted using a pre-established data processing module to obtain a structured operating dataset. Then, a support vector machine model is applied to assess the wear degree of key components and determine the quantitative indicators of wear status. Based on the quantitative indicators of wear status, when the value approaches the preset threshold, an automatic early warning mechanism is triggered. By analyzing the real-time values ​​of the current operating intensity parameters, a new operating intensity parameter is calculated using preset adjustment rules, and the adjusted parameter configuration is obtained. Based on the adjusted parameter configuration, the operation control commands of the pumping system are automatically updated, a new set value for the system's operating intensity is determined, and the operating data and wear status of key components are continuously monitored to determine whether they are stable within a safe range. If the monitored wear status is not stable within a safe range, the support vector machine model is called again to recalculate the remaining lifespan and obtain the updated lifespan prediction result.

9. A pneumatic diaphragm pump control method for an emulsion matrix transport vehicle according to claim 1, characterized in that: The optimal combination of system operating parameters is obtained through a multi-parameter coupled optimization algorithm, specifically including: By collecting pumping pressure, flow monitoring, and vibration frequency data during equipment operation, and using sensors to acquire multi-dimensional information in real time, an initial operating dataset is obtained. The correlation between pumping pressure, flow monitoring, and vibration frequency is analyzed, and the coordination measure between each parameter is determined using correlation analysis. Based on the coordination metric, when the coordination degree of a certain parameter is lower than the preset threshold, the weights of the relevant parameters are adjusted to obtain an optimized parameter combination dataset. The genetic algorithm is then applied to simulate and optimize the running state to obtain the optimal state configuration scheme of the system. By adjusting the control values ​​of pumping pressure and flow monitoring in real time, it is determined whether the vibration frequency is within the safe range, and the adjusted operating status data is obtained. If the vibration frequency still exceeds the safe range, the parameter combination is recalculated through the feedback mechanism to obtain the updated control instruction set. By dynamically adjusting the system operating parameters using the updated control instruction set, it is determined whether the emulsion matrix delivery has reached a stable state, thus obtaining the final stable delivery control result.

10. A pneumatic diaphragm pump control system for an emulsion matrix transport vehicle, used to implement the pneumatic diaphragm pump control method for an emulsion matrix transport vehicle as described in any one of claims 1-9, characterized in that: include: The temperature monitoring and regulation module collects and processes the temperature data of the emulsion matrix in real time through a multi-point temperature sensor array, generates a temperature gradient distribution map, and starts the heating system to regulate the temperature when necessary. The viscosity prediction and parameter setting module calculates the matrix viscosity coefficient based on the temperature gradient distribution map, matches the rheological parameter library to determine the optimal pumping pressure and speed control parameters, and sets the initial pumping conditions. The vibration and power analysis module monitors the vibration frequency and power consumption of the pumping system and identifies the phenomenon of increased mechanical friction through frequency domain analysis algorithms. The adaptive speed control module dynamically adjusts the pumping speed based on real-time flow monitoring data and target flow deviation to optimize pumping efficiency. The monitoring and control module monitors the pressure difference of the delivery pipeline online, analyzes the trend of resistance change, identifies the risk of blockage or solidification, and activates the segmented insulation control system to maintain uniform pipeline temperature. The prediction and evaluation module analyzes the operating data of the pumping system, predicts the remaining life of key components, and automatically adjusts the operating intensity when wear approaches the warning threshold. The multi-parameter coupling optimization module comprehensively analyzes the coordination of pumping pressure, flow rate and vibration frequency, and obtains the optimal combination of system operating parameters through optimization algorithms.

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