Pilot-operated type conveying system and control method

Abstract

The invention relates to the technical field of operation and maintenance of power plant equipment, and provides a pilot-operated type conveying system and a control method, aiming at the operation and maintenance requirements of the power plant equipment, real-time monitoring and accurate control of the conveying process are achieved by integrating an advanced sensor, a controller and an executing mechanism. The system comprises a data acquisition unit, a simulation prediction unit, a data acquisition precision detection and comparison unit, a data correction unit and the like, data errors can be automatically detected and corrected, and the accuracy of a scheduling model is improved. By optimizing pilot airflow conveying adjusting parameters and dynamically adjusting an equipment scheduling model, the conveying efficiency and stability are improved, and the energy consumption and the failure rate are reduced. According to the invention, the automation level and the intelligence level of the system are improved, and effective support is provided for energy conservation, emission reduction and environmental protection of the power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant equipment operation and maintenance, and in particular to a pilot conveying system and a control method. Background Art

[0002] With the increasing attention paid to energy conservation, emission reduction and environmental protection around the world, power plants, as major energy consumers and emitters, are facing tremendous pressure. The pilot conveying system reduces energy consumption during the conveying process by optimizing the airflow organization, while reducing material breakage and pipeline wear caused by high-speed airflow, thereby reducing dust emissions and meeting environmental protection requirements. Modern power plants are increasingly inclined to achieve automated and intelligent production to improve production efficiency and safety. The pilot conveying system integrates advanced sensors, controllers and actuators to achieve real-time monitoring and precise control of the conveying process, which not only improves the stability and reliability of the pilot conveying system, but also provides strong support for the intelligent management of power plants.

[0003] Modern power plants are increasingly inclined to achieve automated and intelligent production to improve production efficiency and safety. The pilot conveying system integrates advanced sensors, controllers and actuators to achieve real-time monitoring and precise control of the conveying process. In the actual use of the pilot conveying system, due to the wide variety of materials that need to be transported in power plants, including coal powder, ash, limestone, etc., these materials have differences in particle size, density, humidity and other characteristics. The pilot conveying system adjusts the speed and pressure of the pilot airflow, as well as the opening and closing timing of the plug valve. When the data processed and analyzed by the pilot conveying system is wrong, it will cause the pilot conveying system to control and adjust the equipment in the system incorrectly, resulting in low operating efficiency or failure of the pilot conveying system. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a pilot conveying system and a control method to solve the problem that when errors exist in the data processed and analyzed by the pilot conveying system, errors will occur in the control and adjustment of the equipment within the system by the pilot conveying system.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0006] In a first aspect, the present invention provides a pilot conveying system, comprising:

[0007] A data acquisition unit, used to acquire the operation data of the pilot conveying system and the pilot conveying demand information. The operation data of the pilot conveying system includes material level signals, air pressure data, material flow data, blockage location information, conveying equipment status and environmental parameters;

[0008] A simulation prediction unit is used to classify the operation data of the pilot conveying system according to the pilot conveying demand, obtain the pilot conveying demand classification result, match the pilot conveying demand information in a preset model library, obtain the simulation prediction model corresponding to the pilot conveying demand information, substitute the pilot conveying demand classification result into the corresponding simulation prediction model, and obtain the pilot conveying demand simulation result data set;

[0009] A data acquisition accuracy detection unit is used to retrieve error data and abnormal data in the pilot transport demand simulation result data set, collect associated devices corresponding to the error data and abnormal data, detect the operating status of the associated devices corresponding to the error data and abnormal data, and if the operating status of the associated devices corresponding to the error data and abnormal data is normal, detect the data acquisition accuracy of the associated devices corresponding to the error data and abnormal data to obtain the data acquisition accuracy results of the associated devices corresponding to the error data and abnormal data;

[0010] The data collection accuracy comparison unit is used to compare the equipment data collection standard accuracy with the associated equipment data collection accuracy results corresponding to the error data and the abnormal data according to the equipment data collection standard accuracy corresponding to the pilot conveying demand information in the database, and obtain the equipment data collection accuracy comparison result, and the equipment data collection accuracy comparison result includes the equipment data collection accuracy being qualified and the equipment data collection accuracy being unqualified;

[0011] A data correction unit is used for, if the equipment data collection accuracy is unqualified, retrieving the historical operation data of the equipment with unqualified equipment data collection accuracy, establishing an equipment data collection virtual model according to the historical operation data of the equipment with unqualified equipment data collection accuracy, using the equipment data collection virtual model to generate equipment data collection simulation data, replacing the unqualified data in the equipment data collection accuracy comparison result with the equipment data collection simulation data, obtaining the corrected pilot conveying system operation data, substituting the pilot conveying system operation data into the preset pilot conveying system equipment scheduling model, obtaining the pilot conveying equipment scheduling result, and sending the pilot conveying equipment scheduling result to the corresponding equipment in the pilot conveying system.

[0012] Furthermore, the pilot conveying system provided by the present invention, the data correction unit comprises:

[0013] A pilot air flow conveying adjustment module is used to obtain the pilot conveying system operation data including the speed and pressure of the pilot air flow, and to process the obtained pilot conveying system operation data including the speed and pressure of the pilot air flow to obtain the pilot air flow conveying adjustment parameters;

[0014] The control and adjustment optimization module is used to use the pilot airflow conveying adjustment parameters to perform data processing and analysis to obtain an optimized pilot conveying system equipment scheduling model, and use the optimized pilot conveying system equipment scheduling model to replace the preset pilot conveying system equipment scheduling model.

[0015] Furthermore, the pilot airflow conveying adjustment module of the pilot conveying system provided by the present invention is also used for:

[0016] The pilot conveying system operation data also includes the speed and pressure of the pilot airflow, and the speed and pressure of the pilot airflow are substituted into the preset pilot conveying system equipment scheduling model to obtain the opening and closing time information of the plug valve;

[0017] According to the opening and closing time information of the control plug valve, the air flow velocity, pressure and material flow rate of the material in the pipeline transportation process are collected;

[0018] The airflow velocity, pressure and material flow rate of the material in the pipeline transportation process are preprocessed to obtain the preprocessed pipeline transportation monitoring data, and the current transportation state is determined according to the preset material pipeline transmission detection threshold. The corresponding airflow control algorithm is matched in the preset airflow control algorithm knowledge base according to the current transportation state, and the preprocessed pipeline transportation monitoring data is processed using the airflow control algorithm corresponding to the current transportation state to obtain the pilot airflow transportation adjustment parameters.

[0019] Furthermore, the pilot conveying system provided by the present invention, the control and adjustment optimization module is also used for:

[0020] receiving a pilot air flow delivery adjustment parameter, modifying a sensor configuration according to the pilot air flow delivery adjustment parameter to obtain a modified sensor configuration, and loading the modified sensor configuration into the sensor;

[0021] receiving data collected by the sensor loaded with the modified sensor configuration to obtain real-time sensor collected data, and preprocessing the real-time sensor collected data to obtain preprocessed real-time sensor collected data;

[0022] The preprocessed real-time sensor collected data is substituted into the preset pilot conveying system equipment scheduling model to obtain the real-time conveying equipment scheduling result, and the real-time conveying equipment scheduling result is compared with the pilot conveying equipment scheduling result. If the comparison result of the real-time conveying equipment scheduling result with the pilot conveying equipment scheduling result is greater than the preset error range value, the pilot conveying system equipment scheduling model is optimized to obtain the optimized pilot conveying system equipment scheduling model, and the optimized pilot conveying system equipment scheduling model is used to replace the preset pilot conveying system equipment scheduling model.

[0023] In a second aspect, the present invention provides a pilot conveying method, which is applied to the pilot conveying system, and is characterized by comprising:

[0024] Step S101, obtaining pilot conveying system operation data and pilot conveying demand information, the pilot conveying system operation data including material level signal, air pressure data, material flow data, blockage location information, conveying equipment status and environmental parameters;

[0025] Step S102, classifying the pilot conveying system operation data according to the pilot conveying demand to obtain the pilot conveying demand classification result, matching the pilot conveying demand information in a preset model library to obtain a simulation prediction model corresponding to the pilot conveying demand information, substituting the pilot conveying demand classification result into the corresponding simulation prediction model to obtain a pilot conveying demand simulation result data set;

[0026] Step S103, retrieve the error data and abnormal data in the pilot transport demand simulation result data set, collect the associated devices corresponding to the error data and abnormal data, detect the operating status of the associated devices corresponding to the error data and abnormal data, and if the operating status of the associated devices corresponding to the error data and abnormal data is normal, detect the data collection accuracy of the associated devices corresponding to the error data and abnormal data, and obtain the data collection accuracy result of the associated devices corresponding to the error data and abnormal data;

[0027] Step S104, according to the equipment data collection standard accuracy corresponding to the pilot conveying demand information in the database, the equipment data collection standard accuracy is compared with the associated equipment data collection accuracy results corresponding to the error data and the abnormal data to obtain the equipment data collection accuracy comparison result, and the equipment data collection accuracy comparison result includes the equipment data collection accuracy being qualified and the equipment data collection accuracy being unqualified;

[0028] Step S105, if the equipment data collection accuracy is unqualified, retrieve the historical operation data of the equipment with unqualified equipment data collection accuracy, establish an equipment data collection virtual model based on the historical operation data of the equipment with unqualified equipment data collection accuracy, use the equipment data collection virtual model to generate equipment data collection simulation data, replace the unqualified data in the equipment data collection accuracy comparison result with the equipment data collection simulation data, obtain the corrected pilot conveying system operation data, substitute the pilot conveying system operation data into the preset pilot conveying system equipment scheduling model, obtain the pilot conveying equipment scheduling result, and send the pilot conveying equipment scheduling result to the corresponding equipment in the pilot conveying system.

[0029] Furthermore, in the pilot conveying control method provided by the present invention, step S105 comprises:

[0030] Step 501, obtaining the pilot conveying system operation data including the speed and pressure of the pilot airflow, and performing data processing on the obtained pilot conveying system operation data including the speed and pressure of the pilot airflow to obtain the pilot airflow conveying adjustment parameters;

[0031] Step 502, using the pilot airflow transport adjustment parameters to perform data processing and analysis to obtain an optimized pilot transport system equipment scheduling model, and using the optimized pilot transport system equipment scheduling model to replace the preset pilot transport system equipment scheduling model.

[0032] Furthermore, in the pilot conveying control method provided by the present invention, step 501 comprises:

[0033] The pilot conveying system operation data also includes the speed and pressure of the pilot airflow, and the speed and pressure of the pilot airflow are substituted into the preset pilot conveying system equipment scheduling model to obtain the opening and closing time information of the plug valve;

[0034] According to the opening and closing time information of the control plug valve, the air flow velocity, pressure and material flow rate of the material in the pipeline transportation process are collected;

[0035] The airflow velocity, pressure and material flow rate of the material in the pipeline transportation process are preprocessed to obtain the preprocessed pipeline transportation monitoring data, and the current transportation state is determined according to the preset material pipeline transmission detection threshold. The corresponding airflow control algorithm is matched in the preset airflow control algorithm knowledge base according to the current transportation state, and the preprocessed pipeline transportation monitoring data is processed using the airflow control algorithm corresponding to the current transportation state to obtain the pilot airflow transportation adjustment parameters.

[0036] Furthermore, in the pilot conveying control method provided by the present invention, step 502 includes:

[0037] receiving a pilot air flow delivery adjustment parameter, modifying a sensor configuration according to the pilot air flow delivery adjustment parameter to obtain a modified sensor configuration, and loading the modified sensor configuration into the sensor;

[0038] receiving data collected by the sensor loaded with the modified sensor configuration to obtain real-time sensor collected data, and preprocessing the real-time sensor collected data to obtain preprocessed real-time sensor collected data;

[0039] The preprocessed real-time sensor collected data is substituted into the preset pilot conveying system equipment scheduling model to obtain the real-time conveying equipment scheduling result, and the real-time conveying equipment scheduling result is compared with the pilot conveying equipment scheduling result. If the comparison result of the real-time conveying equipment scheduling result with the pilot conveying equipment scheduling result is greater than the preset error range value, the pilot conveying system equipment scheduling model is optimized to obtain the optimized pilot conveying system equipment scheduling model, and the optimized pilot conveying system equipment scheduling model is used to replace the preset pilot conveying system equipment scheduling model.

[0040] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0041] Through the data acquisition accuracy detection unit and the data acquisition accuracy comparison unit, the present invention can timely discover and correct errors and anomalies in the system operation data, ensuring that the data used for decision-making is accurate and reliable. This greatly improves the data processing accuracy of the system and avoids control and adjustment errors caused by data errors.

[0042] The data correction unit constructs a virtual model for equipment data collection and generates simulated data to replace unqualified actual data, thereby correcting the system operation data. These corrected data are substituted into the equipment scheduling model to obtain more accurate equipment scheduling results, thus optimizing the equipment scheduling process of the entire conveying system.

[0043] The pilot air flow conveying adjustment module obtains optimized conveying parameters by finely adjusting the speed and pressure of the pilot air flow, thereby improving the stability and efficiency of material conveying. At the same time, the control and adjustment optimization module ensures that the system always operates in the optimal state through real-time data processing and model optimization, further improving the overall performance and efficiency of the system.

[0044] By optimizing airflow organization and equipment scheduling, the present invention reduces energy consumption during the transportation process. In addition, due to the accuracy of data processing and the optimization of equipment scheduling, the failure rate of the system is also significantly reduced, improving the overall operation and maintenance level.

[0045] The present invention integrates advanced sensors, controllers and actuators to achieve real-time monitoring and precise control of the transportation process. This provides strong support for the intelligent management of power plants, enabling the system to automatically adapt to various operating conditions and improve production efficiency and safety.

[0046] Through the preset model library and airflow control algorithm knowledge base, the present invention can be flexibly configured and optimized according to the characteristics and transportation requirements of different materials. At the same time, the system also supports real-time data processing and model optimization functions, so that the system can continuously optimize and improve performance as the operation data accumulates.

[0047] In summary, the beneficial effects of the present invention are that the data processing accuracy, equipment scheduling efficiency, system stability and intelligent management level of the pilot conveying system are improved, energy consumption and failure rate are reduced, and the adaptability and scalability of the system are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0049] Figure 1 Schematic diagram of a production system for a pilot conveying system and a control method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0051] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0052] In a first aspect, the present invention provides a pilot conveying system, comprising:

[0053] A data acquisition unit, used to acquire the operation data of the pilot conveying system and the pilot conveying demand information. The operation data of the pilot conveying system includes material level signals, air pressure data, material flow data, blockage location information, conveying equipment status and environmental parameters;

[0054] The data acquisition unit is an important component of the pilot conveying system in the present invention, and its main responsibility is to collect and acquire various data generated during the operation of the pilot conveying system, as well as the conveying demand information specified by the user or the system.

[0055] Material level signal: an important parameter reflecting the stock or position of materials in the conveying equipment, which plays a key role in the system's decision on whether to continue feeding or adjust the conveying speed.

[0056] Air pressure data: In the pilot conveying system, air pressure is one of the key factors to ensure smooth material transportation. By monitoring the air pressure data, we can understand the pressure conditions inside the system and then determine whether the air pressure needs to be adjusted to ensure transportation efficiency.

[0057] Material flow data: reflects the amount of material passing through the conveying system per unit time, which is of great significance for evaluating the system's conveying capacity and efficiency and monitoring material consumption.

[0058] Blockage location information: During the transportation process, materials may get blocked at certain locations in the pipeline or equipment. Blockage location information helps the system quickly locate the blockage point so that timely measures can be taken to clear it and avoid affecting the overall transportation efficiency.

[0059] Conveying equipment status: including the operating status information of various conveying equipment, such as motor speed, bearing temperature, vibration status, etc. This information helps to monitor the health of system equipment, prevent failures, and quickly locate the problem when a failure occurs.

[0060] Environmental parameters: The environmental conditions of the conveying system, such as temperature, humidity, etc., may also have an impact on the conveying process. Therefore, obtaining and monitoring these environmental parameters is crucial to ensure the stability and efficiency of the conveying process.

[0061] In addition, the data acquisition unit is also responsible for obtaining leading conveying demand information, which may come from user instructions, preset production plans or the results of automatic analysis by the system, providing a basis for subsequent decisions of the system.

[0062] In summary, the data acquisition unit provides a solid foundation for subsequent key links such as data processing, simulation prediction, and equipment scheduling by comprehensively collecting and analyzing the operating data and demand information of the pilot conveying system.

[0063] A simulation prediction unit is used to classify the operation data of the pilot conveying system according to the pilot conveying demand, obtain the pilot conveying demand classification result, match the pilot conveying demand information in a preset model library, obtain the simulation prediction model corresponding to the pilot conveying demand information, substitute the pilot conveying demand classification result into the corresponding simulation prediction model, and obtain the pilot conveying demand simulation result data set;

[0064] The simulation prediction unit plays a vital role in the pilot conveying system. Its main function is to guide the optimal operation of the system through simulation prediction based on the real-time operation data of the pilot conveying system and the specific conveying requirements. The following is the specific workflow of the simulation prediction unit:

[0065] The simulation prediction unit first receives the pilot conveying system operation data provided by the data acquisition unit, including material level signals, air pressure data, material flow data, blockage location information, conveying equipment status, and environmental parameters.

[0066] According to the specific needs of the pilot transportation, the simulation prediction unit classifies the operation data to identify the data subset related to the current demand, that is, the classification result of the pilot transportation demand.

[0067] The simulation prediction unit then matches the leading transportation demand information (such as the type of transported material, target location, time requirements, etc.) with the model library preset in the system. The model library contains a variety of prediction models, each of which is optimized for specific transportation needs and scenarios. Through the matching process, the simulation prediction model that best matches the current transportation demand information is found.

[0068] Substitute the previously obtained pilot transportation demand classification results into the matched simulation prediction model. The model performs simulation calculations based on the input data and its own algorithm logic to predict the operating parameters or operation strategies that meet the current transportation demand. Output the pilot transportation demand simulation result data set, which contains various prediction results, such as the optimal transportation path, the optimal operating parameters of each equipment, etc., to provide a basis for subsequent data processing and scheduling optimization.

[0069] In this way, the simulation prediction unit can effectively integrate the real-time operation data and specific needs of the pilot conveying system, and provide strong support for the optimized operation of the system through scientific simulation prediction methods. This not only helps to improve the transmission efficiency, but also reduces energy consumption and failure rate, thereby improving the operation and maintenance level of the entire power plant.

[0070] A data acquisition accuracy detection unit is used to retrieve error data and abnormal data in the pilot transport demand simulation result data set, collect associated devices corresponding to the error data and abnormal data, detect the operating status of the associated devices corresponding to the error data and abnormal data, and if the operating status of the associated devices corresponding to the error data and abnormal data is normal, detect the data acquisition accuracy of the associated devices corresponding to the error data and abnormal data to obtain the data acquisition accuracy results of the associated devices corresponding to the error data and abnormal data;

[0071] The data acquisition accuracy detection unit plays an important role in ensuring data quality in the pilot conveying system. It detects and analyzes errors and anomalies in the system operation data, and then evaluates the accuracy of the data acquisition equipment to ensure the accuracy of subsequent data processing and decision-making. The following is the specific workflow of this unit:

[0072] The data acquisition accuracy detection unit first identifies and filters out erroneous data and abnormal data from the pilot transport demand simulation result data set output by the simulation prediction unit. These data may be caused by sensor failure, signal processing error or data transmission problem.

[0073] For each erroneous or abnormal data point identified, the unit tracks and locates the associated devices corresponding to these data points. These associated devices are key components that generate these data, such as sensors, flow meters, pressure gauges, etc.

[0074] The data acquisition accuracy detection unit will then detect the current operating status of these associated devices. The purpose of this step is to confirm whether the error or abnormal data is caused by a fault in the device itself. If the device status is abnormal (such as shutdown, fault alarm, etc.), it may not be necessary to further detect the data acquisition accuracy, but to directly perform equipment maintenance.

[0075] If the device status is normal, the data acquisition accuracy detection unit proceeds to the next step of data acquisition accuracy detection.

[0076] For the associated equipment in normal condition, the data acquisition accuracy detection unit will evaluate its data acquisition accuracy. This usually involves comparing the actual measurement value of the equipment with the standard value or known accurate value, or directly calibrating the equipment using special calibration equipment.

[0077] Through this step, the data acquisition accuracy detection unit can quantitatively evaluate the data acquisition accuracy of the equipment and generate a corresponding accuracy result report.

[0078] Finally, the data acquisition accuracy detection unit will organize and output the data acquisition accuracy results of each associated device. These results will serve as an important basis for subsequent data processing, model optimization or equipment maintenance.

[0079] The data acquisition accuracy detection unit can effectively identify and solve the data quality problems existing in the pilot conveying system, enabling the system to make decisions and optimizations based on high-quality data, thereby improving overall operational efficiency and reliability.

[0080] The data collection accuracy comparison unit is used to compare the equipment data collection standard accuracy with the associated equipment data collection accuracy results corresponding to the error data and the abnormal data according to the equipment data collection standard accuracy corresponding to the pilot conveying demand information in the database, and obtain the equipment data collection accuracy comparison result, and the equipment data collection accuracy comparison result includes the equipment data collection accuracy being qualified and the equipment data collection accuracy being unqualified;

[0081] The data acquisition accuracy comparison unit plays a vital role in the pilot conveying system. Its main function is to find the corresponding equipment data acquisition standard accuracy in the database according to the pilot conveying demand information. Then, this standard accuracy is compared and analyzed with the data acquisition accuracy results of the associated equipment corresponding to the error data and abnormal data.

[0082] Specifically, the data collection accuracy comparison unit will check the data collection accuracy of each associated device one by one to determine whether it has reached the preset standard accuracy requirements. The comparison results will clearly mark whether the data collection accuracy of each device is "qualified" or "unqualified".

[0083] If the data collection accuracy of a certain device is not up to standard, it means that there may be problems with the data collection of the device, which may be due to the performance degradation of the device itself, or there may be deviations in some links of the data collection process. At this time, the system will take further corrective measures based on the comparison results, such as correcting these unqualified data through the data correction unit to ensure the accuracy and stability of the entire pilot conveying system.

[0084] In summary, the data acquisition accuracy comparison unit can effectively identify possible problems in the data acquisition process through strict standard comparison, providing a reliable basis for subsequent corrections to the system.

[0085] A data correction unit is used for, if the equipment data collection accuracy is unqualified, retrieving the historical operation data of the equipment with unqualified equipment data collection accuracy, establishing an equipment data collection virtual model according to the historical operation data of the equipment with unqualified equipment data collection accuracy, using the equipment data collection virtual model to generate equipment data collection simulation data, replacing the unqualified data in the equipment data collection accuracy comparison result with the equipment data collection simulation data, obtaining the corrected pilot conveying system operation data, substituting the pilot conveying system operation data into the preset pilot conveying system equipment scheduling model, obtaining the pilot conveying equipment scheduling result, and sending the pilot conveying equipment scheduling result to the corresponding equipment in the pilot conveying system.

[0086] The data correction unit plays a key role in data correction and optimization in the pilot conveying system. When the data acquisition accuracy comparison unit finds that the data acquisition accuracy of some equipment is unqualified, the data correction unit will intervene and perform a series of automatic correction processes.

[0087] First, the data correction unit will retrieve the historical operation data of those devices whose data collection accuracy is unqualified. These data record the status information of the equipment when it was operating normally in the past, which is an important basis for building a virtual model.

[0088] Then, using these historical operation data, the data correction unit will build a corresponding equipment data acquisition virtual model. This model can simulate the data acquisition process of the equipment under ideal conditions, thereby generating reliable data acquisition simulation data.

[0089] Then, the data correction unit will use the generated equipment data acquisition simulation data to replace the actual data that is judged as unqualified in the data acquisition accuracy comparison results. This step ensures the consistency and accuracy of the data and eliminates the system errors that may be caused by data acquisition accuracy problems.

[0090] After the data replacement is completed, the revised pilot conveyor system operation data will be re-entered into the preset pilot conveyor system equipment scheduling model. This model will recalculate and obtain more accurate pilot conveyor equipment scheduling results based on the latest and revised data set.

[0091] Finally, the data correction unit will send the calculated pilot conveying equipment scheduling results to the corresponding equipment in the pilot conveying system. In this way, the equipment can be adjusted and optimized accordingly according to the new scheduling results, thereby ensuring the stable operation and efficient operation of the entire conveying system.

[0092] Through this series of complex and sophisticated data correction processes, the data correction unit effectively solves the problem of unqualified data collection accuracy and provides strong support for the intelligent management and optimization of the pilot conveying system.

[0093] Specifically, the pilot conveying system provided by the present invention, the data correction unit comprises:

[0094] A pilot air flow conveying adjustment module is used to obtain the pilot conveying system operation data including the speed and pressure of the pilot air flow, and to process the obtained pilot conveying system operation data including the speed and pressure of the pilot air flow to obtain the pilot air flow conveying adjustment parameters;

[0095] The control and adjustment optimization module is used to use the pilot airflow conveying adjustment parameters to perform data processing and analysis to obtain an optimized pilot conveying system equipment scheduling model, and use the optimized pilot conveying system equipment scheduling model to replace the preset pilot conveying system equipment scheduling model.

[0096] In the pilot conveying system described in the present invention, the data correction unit is further subdivided into a pilot airflow conveying adjustment module and a control adjustment optimization module, and these two modules work together to achieve further optimization and adjustment of the conveying system.

[0097] The main function of the pilot air flow control module is to obtain and analyze the pilot air flow speed and pressure information in the pilot conveying system operation data. Through the precise processing of these key parameters, the module can calculate the pilot air flow control parameters. These parameters are directly related to the optimization of air flow organization and the improvement of material conveying efficiency, and are the key to ensure the smooth and efficient operation of the conveying process.

[0098] Data Acquisition: The module first captures the velocity and pressure data of the pilot air flow in the pilot conveying system.

[0099] Data processing: In-depth analysis of the acquired air flow velocity and pressure data to extract valuable information.

[0100] Parameter calculation: Based on the processed data, the pilot air flow transmission adjustment parameters are calculated. These parameters provide a scientific basis for subsequent air flow adjustment and transmission optimization.

[0101] The control and adjustment optimization module is responsible for further optimizing the pilot conveying system equipment scheduling model by using the pilot air flow conveying adjustment parameters. By dynamically adjusting the model parameters, the system can better adapt to the actual operating conditions and improve the conveying efficiency and stability.

[0102] The specific steps include:

[0103] Parameter reception: receiving the adjustment parameters calculated by the pilot air flow transport adjustment module.

[0104] Data Analysis: Perform in-depth analysis of the received adjustment parameters to evaluate their impact on system performance.

[0105] Model optimization: Based on the analysis results, necessary adjustments and optimizations are made to the preset pilot conveying system equipment scheduling model to better match the actual operation requirements.

[0106] Model replacement: Use the optimized equipment scheduling model to replace the original preset model to ensure that the system can schedule and control equipment according to the new and more optimized strategy.

[0107] Through the close cooperation between the pilot airflow conveying adjustment module and the control adjustment optimization module, the present invention can not only effectively improve the data acquisition accuracy and data processing capability of the pilot conveying system, but also dynamically optimize the equipment scheduling model according to the real-time operation data, thereby significantly improving the conveying efficiency, reducing energy consumption and reducing the failure rate. This not only meets the needs of modern power plants for automated and intelligent production, but also makes a positive contribution to energy conservation, emission reduction and environmental protection.

[0108] Specifically, the pilot conveying system provided by the present invention, the pilot airflow conveying adjustment module is also used for:

[0109] The pilot conveying system operation data also includes the speed and pressure of the pilot airflow, and the speed and pressure of the pilot airflow are substituted into the preset pilot conveying system equipment scheduling model to obtain the opening and closing time information of the plug valve;

[0110] According to the opening and closing time information of the control plug valve, the air flow velocity, pressure and material flow rate of the material in the pipeline transportation process are collected;

[0111] The airflow velocity, pressure and material flow rate of the material in the pipeline transportation process are preprocessed to obtain the preprocessed pipeline transportation monitoring data, and the current transportation state is determined according to the preset material pipeline transmission detection threshold. The corresponding airflow control algorithm is matched in the preset airflow control algorithm knowledge base according to the current transportation state, and the preprocessed pipeline transportation monitoring data is processed using the airflow control algorithm corresponding to the current transportation state to obtain the pilot airflow transportation adjustment parameters.

[0112] The pilot air flow conveying adjustment module in the present invention undertakes the important task of finely regulating the pilot conveying system. The following is a detailed analysis of the specific functions of this module:

[0113] First, the module obtains real-time operating data from the pilot conveying system, especially the speed and pressure information of the pilot air flow. These data are the basis for subsequent adjustment and optimization.

[0114] The plug valve time information calculation substitutes the pilot air velocity and pressure data into the preset pilot conveying system equipment scheduling model. Through the calculation and analysis of the model, the module can obtain the optimal opening and closing time information of the plug valve. The precise control of these time points is crucial to ensure the continuity and stability of material transportation.

[0115] Real-time data collection: Based on the opening and closing time information of the plug valve, the module further collects key parameters of the material during pipeline transportation, including air flow velocity, pressure, and material flow rate. These real-time data reflect the immediate status of material transportation and provide a basis for subsequent adjustment and optimization.

[0116] Data preprocessing: preprocess the collected pipeline transportation monitoring data to eliminate noise, correct deviations, etc., to ensure the accuracy and reliability of the data. The preprocessed data is closer to the actual situation, laying a solid foundation for subsequent analysis and processing.

[0117] Transportation status judgment: compare the pre-processed pipeline transportation monitoring data with the preset material pipeline transmission detection threshold to determine the current transportation status. This step helps to quickly identify abnormal conditions that may occur during the transportation process, such as blockage, unstable airflow, etc.

[0118] Airflow control algorithm matching: According to the current delivery state, the corresponding airflow control algorithm is matched in the preset airflow control algorithm knowledge base. Each delivery state corresponds to a set of optimized airflow control strategies, which aims to optimize the delivery effect by adjusting the airflow parameters.

[0119] Data processing and parameter generation, using the airflow control algorithm that matches the current delivery status to deeply process the pre-processed pipeline delivery monitoring data. Through complex calculation and analysis processes, the pilot airflow delivery adjustment parameters are finally generated. These parameters will be directly used to adjust the speed and pressure of the pilot airflow to achieve the best delivery effect.

[0120] In summary, the pilot air flow conveying adjustment module realizes the dynamic optimization adjustment of the pilot conveying system through a series of sophisticated data processing and control strategy generation processes. This not only improves the operating efficiency and stability of the conveying system, but also effectively reduces energy consumption and failure rate, providing strong support for the intelligent management of power plants and energy conservation and emission reduction goals.

[0121] Specifically, the pilot conveying system provided by the present invention, the control and adjustment optimization module is also used for:

[0122] receiving a pilot air flow delivery adjustment parameter, modifying a sensor configuration according to the pilot air flow delivery adjustment parameter to obtain a modified sensor configuration, and loading the modified sensor configuration into the sensor;

[0123] receiving data collected by the sensor loaded with the modified sensor configuration to obtain real-time sensor collected data, and preprocessing the real-time sensor collected data to obtain preprocessed real-time sensor collected data;

[0124] The preprocessed real-time sensor collected data is substituted into the preset pilot conveying system equipment scheduling model to obtain the real-time conveying equipment scheduling result, and the real-time conveying equipment scheduling result is compared with the pilot conveying equipment scheduling result. If the comparison result of the real-time conveying equipment scheduling result with the pilot conveying equipment scheduling result is greater than the preset error range value, the pilot conveying system equipment scheduling model is optimized to obtain the optimized pilot conveying system equipment scheduling model, and the optimized pilot conveying system equipment scheduling model is used to replace the preset pilot conveying system equipment scheduling model.

[0125] The control and regulation optimization module plays a core role in optimization and regulation in the pilot conveying system. It realizes dynamic optimization of the conveying process by integrating the functions of the sensor module, data analysis and processing module, and intelligent control module. The following is a detailed analysis of the specific functions of this module:

[0126] Sensor module: responsible for real-time collection of various physical quantity data in the conveying system, such as air pressure, flow, speed, etc. These data are the basis for subsequent analysis and processing.

[0127] Data analysis and processing module: Real-time analysis and processing of data collected by the sensor module, extraction of key information, and identification of state changes during the transportation process. The processed data provides precise control basis for the intelligent control module.

[0128] Control, adjust and optimize processes;

[0129] Receiving pilot air flow regulation parameters: Based on the regulation parameters generated by the pilot air flow regulation module, these parameters are directly related to the optimal settings of the air flow velocity and pressure.

[0130] Modify sensor configuration: According to the received pilot air flow adjustment parameters, the sensor configuration is adjusted accordingly. This helps the sensor to more accurately capture subtle changes in the delivery process.

[0131] Real-time data collection: Use sensors loaded with new configurations to collect data in real time to ensure data accuracy and timeliness.

[0132] Data preprocessing: Perform necessary preprocessing on the real-time collected data, such as filtering and denoising, to improve the reliability and accuracy of the data.

[0133] Real-time transportation equipment scheduling: Substitute the pre-processed data into the preset pilot transportation system equipment scheduling model to calculate the real-time transportation equipment scheduling results.

[0134] Comparison and optimization of scheduling results: Compare the real-time calculated dispatch results of the conveying equipment with the previous prediction results. If the difference between the two exceeds the preset error range, it means that the current dispatch model may need further optimization. If optimization is required, the control and adjustment optimization module will iteratively adjust the dispatch model based on real-time data and historical data until a better dispatch strategy is found.

[0135] Model replacement: Replace the preset model with the optimized pilot conveying system equipment scheduling model to ensure that the system always operates in the optimal state.

[0136] Through the above process, the control and optimization module can not only control the transportation process in real time, but also continuously optimize the scheduling model based on feedback data to improve the overall efficiency and stability of the transportation system. This function is particularly important for places such as power plants that require continuous and stable operation, which can effectively reduce energy consumption, reduce failure rates, and improve overall operational benefits.

[0137] In a second aspect, the present invention provides a pilot conveying method, which is applied to the pilot conveying system, and is characterized by comprising:

[0138] Step S101, obtaining pilot conveying system operation data and pilot conveying demand information, the pilot conveying system operation data including material level signal, air pressure data, material flow data, blockage location information, conveying equipment status and environmental parameters;

[0139] Step S102, classifying the pilot conveying system operation data according to the pilot conveying demand to obtain the pilot conveying demand classification result, matching the pilot conveying demand information in a preset model library to obtain a simulation prediction model corresponding to the pilot conveying demand information, substituting the pilot conveying demand classification result into the corresponding simulation prediction model to obtain a pilot conveying demand simulation result data set;

[0140] Step S103, retrieve the error data and abnormal data in the pilot transport demand simulation result data set, collect the associated devices corresponding to the error data and abnormal data, detect the operating status of the associated devices corresponding to the error data and abnormal data, and if the operating status of the associated devices corresponding to the error data and abnormal data is normal, detect the data collection accuracy of the associated devices corresponding to the error data and abnormal data, and obtain the data collection accuracy result of the associated devices corresponding to the error data and abnormal data;

[0141] Step S104, according to the equipment data collection standard accuracy corresponding to the pilot conveying demand information in the database, the equipment data collection standard accuracy is compared with the associated equipment data collection accuracy results corresponding to the error data and the abnormal data to obtain the equipment data collection accuracy comparison result, and the equipment data collection accuracy comparison result includes the equipment data collection accuracy being qualified and the equipment data collection accuracy being unqualified;

[0142] Step S105, if the equipment data collection accuracy is unqualified, retrieve the historical operation data of the equipment with unqualified equipment data collection accuracy, establish an equipment data collection virtual model based on the historical operation data of the equipment with unqualified equipment data collection accuracy, use the equipment data collection virtual model to generate equipment data collection simulation data, replace the unqualified data in the equipment data collection accuracy comparison result with the equipment data collection simulation data, obtain the corrected pilot conveying system operation data, substitute the pilot conveying system operation data into the preset pilot conveying system equipment scheduling model, obtain the pilot conveying equipment scheduling result, and send the pilot conveying equipment scheduling result to the corresponding equipment in the pilot conveying system.

[0143] Specifically, the pilot conveying control method provided by the present invention, step S105, comprises:

[0144] Step 501, obtaining the pilot conveying system operation data including the speed and pressure of the pilot airflow, and performing data processing on the obtained pilot conveying system operation data including the speed and pressure of the pilot airflow to obtain the pilot airflow conveying adjustment parameters;

[0145] Step 502, using the pilot airflow transport adjustment parameters to perform data processing and analysis to obtain an optimized pilot transport system equipment scheduling model, and using the optimized pilot transport system equipment scheduling model to replace the preset pilot transport system equipment scheduling model.

[0146] Specifically, the pilot conveying control method provided by the present invention, step 501, comprises:

[0147] The pilot conveying system operation data also includes the speed and pressure of the pilot airflow, and the speed and pressure of the pilot airflow are substituted into the preset pilot conveying system equipment scheduling model to obtain the opening and closing time information of the plug valve;

[0148] According to the opening and closing time information of the control plug valve, the air flow velocity, pressure and material flow rate of the material in the pipeline transportation process are collected;

[0149] The airflow velocity, pressure and material flow rate of the material in the pipeline transportation process are preprocessed to obtain the preprocessed pipeline transportation monitoring data, and the current transportation state is determined according to the preset material pipeline transmission detection threshold. The corresponding airflow control algorithm is matched in the preset airflow control algorithm knowledge base according to the current transportation state, and the preprocessed pipeline transportation monitoring data is processed using the airflow control algorithm corresponding to the current transportation state to obtain the pilot airflow transportation adjustment parameters.

[0150] Specifically, the pilot conveying control method provided by the present invention, step 502 includes:

[0151] receiving a pilot air flow delivery adjustment parameter, modifying a sensor configuration according to the pilot air flow delivery adjustment parameter to obtain a modified sensor configuration, and loading the modified sensor configuration into the sensor;

[0152] receiving data collected by the sensor loaded with the modified sensor configuration to obtain real-time sensor collected data, and preprocessing the real-time sensor collected data to obtain preprocessed real-time sensor collected data;

[0153] The preprocessed real-time sensor collected data is substituted into the preset pilot conveying system equipment scheduling model to obtain the real-time conveying equipment scheduling result, and the real-time conveying equipment scheduling result is compared with the pilot conveying equipment scheduling result. If the comparison result of the real-time conveying equipment scheduling result with the pilot conveying equipment scheduling result is greater than the preset error range value, the pilot conveying system equipment scheduling model is optimized to obtain the optimized pilot conveying system equipment scheduling model, and the optimized pilot conveying system equipment scheduling model is used to replace the preset pilot conveying system equipment scheduling model.

[0154] The present invention aims at the problem that when the data processed and analyzed by the pilot conveying system is wrong, the control and adjustment of the equipment in the system will be wrong. The following technical solutions are proposed to solve this problem:

[0155] The data acquisition unit obtains the operation data and demand information of the pilot conveying system, including material level signals, air pressure data, material flow data, blockage location information, conveying equipment status and environmental parameters, etc. The simulation prediction unit classifies the data according to the demand, matches the corresponding simulation prediction model, and predicts the results of the operation demand.

[0156] The data acquisition accuracy detection unit retrieves error and abnormal data from the simulation prediction results and associates them with specific equipment. First, the operating status of these associated equipment is detected. If the equipment status is normal, the data acquisition accuracy is further tested. The data acquisition accuracy comparison unit compares the data acquisition accuracy of the collected equipment with the standard accuracy to identify equipment with unqualified accuracy.

[0157] For equipment with unqualified accuracy, the data correction unit establishes a virtual model through its historical operation data, generates simulation data to replace the unqualified data, and corrects the system operation data. The data correction unit substitutes the corrected data into the preset equipment scheduling model to obtain more accurate equipment scheduling results and send them to the corresponding equipment. Furthermore, the pilot airflow delivery adjustment module obtains and processes the speed and pressure data of the pilot airflow, optimizes the equipment scheduling model, and improves the accuracy and efficiency of control.

[0158] The control and regulation optimization module analyzes and processes real-time sensor data to compare the difference between the real-time scheduling results and the preset scheduling results. If the difference exceeds the preset range, the equipment scheduling model is optimized and replaced with a more accurate model to ensure the continuous optimization and adaptability of system control.

[0159] The present invention not only solves the problem of control adjustment errors caused by data processing errors, but also improves the stability and reliability of the pilot conveying system through real-time data analysis and model optimization, ensuring the automation and intelligence level of the system.

[0160] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A pilot conveying system, characterized in that: include: A data acquisition unit, used to acquire the operation data of the pilot conveying system and the pilot conveying demand information. The operation data of the pilot conveying system includes material level signals, air pressure data, material flow data, blockage location information, conveying equipment status and environmental parameters; A simulation prediction unit is used to classify the operation data of the pilot conveying system according to the pilot conveying demand, obtain the pilot conveying demand classification result, match the pilot conveying demand information in a preset model library, obtain the simulation prediction model corresponding to the pilot conveying demand information, substitute the pilot conveying demand classification result into the corresponding simulation prediction model, and obtain the pilot conveying demand simulation result data set; A data acquisition accuracy detection unit is used to retrieve error data and abnormal data in the pilot transport demand simulation result data set, collect associated devices corresponding to the error data and abnormal data, detect the operating status of the associated devices corresponding to the error data and abnormal data, and if the operating status of the associated devices corresponding to the error data and abnormal data is normal, detect the data acquisition accuracy of the associated devices corresponding to the error data and abnormal data to obtain the data acquisition accuracy results of the associated devices corresponding to the error data and abnormal data; The data collection accuracy comparison unit is used to compare the equipment data collection standard accuracy with the associated equipment data collection accuracy results corresponding to the error data and the abnormal data according to the equipment data collection standard accuracy corresponding to the pilot conveying demand information in the database, and obtain the equipment data collection accuracy comparison result, and the equipment data collection accuracy comparison result includes the equipment data collection accuracy being qualified and the equipment data collection accuracy being unqualified; A data correction unit is used for, if the equipment data collection accuracy is unqualified, retrieving the historical operation data of the equipment with unqualified equipment data collection accuracy, establishing an equipment data collection virtual model according to the historical operation data of the equipment with unqualified equipment data collection accuracy, using the equipment data collection virtual model to generate equipment data collection simulation data, replacing the unqualified data in the equipment data collection accuracy comparison result with the equipment data collection simulation data, obtaining the corrected pilot conveying system operation data, substituting the pilot conveying system operation data into the preset pilot conveying system equipment scheduling model, obtaining the pilot conveying equipment scheduling result, and sending the pilot conveying equipment scheduling result to the corresponding equipment in the pilot conveying system.

2. The pilot conveying system according to claim 1, characterized in that: The data correction unit comprises: A pilot air flow conveying adjustment module is used to obtain the pilot conveying system operation data including the speed and pressure of the pilot air flow, and to process the obtained pilot conveying system operation data including the speed and pressure of the pilot air flow to obtain the pilot air flow conveying adjustment parameters; The control and adjustment optimization module is used to use the pilot airflow conveying adjustment parameters to perform data processing and analysis to obtain an optimized pilot conveying system equipment scheduling model, and use the optimized pilot conveying system equipment scheduling model to replace the preset pilot conveying system equipment scheduling model.

3. The pilot conveying system according to claim 2, characterized in that: The pilot air flow conveying and regulating module is also used for: The pilot conveying system operation data also includes the speed and pressure of the pilot airflow, and the speed and pressure of the pilot airflow are substituted into the preset pilot conveying system equipment scheduling model to obtain the opening and closing time information of the plug valve; According to the opening and closing time information of the control plug valve, the air flow velocity, pressure and material flow rate of the material in the pipeline transportation process are collected; The airflow velocity, pressure and material flow rate of the material in the pipeline transportation process are preprocessed to obtain the preprocessed pipeline transportation monitoring data, and the current transportation state is determined according to the preset material pipeline transmission detection threshold. The corresponding airflow control algorithm is matched in the preset airflow control algorithm knowledge base according to the current transportation state, and the preprocessed pipeline transportation monitoring data is processed using the airflow control algorithm corresponding to the current transportation state to obtain the pilot airflow transportation adjustment parameters.

4. The pilot conveying system according to claim 2, characterized in that: The control adjustment optimization module is also used for: receiving a pilot air flow delivery adjustment parameter, modifying a sensor configuration according to the pilot air flow delivery adjustment parameter to obtain a modified sensor configuration, and loading the modified sensor configuration into the sensor; receiving data collected by the sensor loaded with the modified sensor configuration to obtain real-time sensor collected data, and preprocessing the real-time sensor collected data to obtain preprocessed real-time sensor collected data; The preprocessed real-time sensor collected data is substituted into the preset pilot conveying system equipment scheduling model to obtain the real-time conveying equipment scheduling result, and the real-time conveying equipment scheduling result is compared with the pilot conveying equipment scheduling result. If the comparison result of the real-time conveying equipment scheduling result with the pilot conveying equipment scheduling result is greater than the preset error range value, the pilot conveying system equipment scheduling model is optimized to obtain the optimized pilot conveying system equipment scheduling model, and the optimized pilot conveying system equipment scheduling model is used to replace the preset pilot conveying system equipment scheduling model.

5. A pilot conveying method, applied to the pilot conveying system according to any one of claims 1 to 4, characterized in that: include: Step S101, obtaining pilot conveying system operation data and pilot conveying demand information, the pilot conveying system operation data including material level signal, air pressure data, material flow data, blockage location information, conveying equipment status and environmental parameters; Step S102, classifying the pilot conveying system operation data according to the pilot conveying demand to obtain the pilot conveying demand classification result, matching the pilot conveying demand information in a preset model library to obtain a simulation prediction model corresponding to the pilot conveying demand information, substituting the pilot conveying demand classification result into the corresponding simulation prediction model to obtain a pilot conveying demand simulation result data set; Step S103, retrieve the error data and abnormal data in the pilot transport demand simulation result data set, collect the associated devices corresponding to the error data and abnormal data, detect the operating status of the associated devices corresponding to the error data and abnormal data, and if the operating status of the associated devices corresponding to the error data and abnormal data is normal, detect the data collection accuracy of the associated devices corresponding to the error data and abnormal data, and obtain the data collection accuracy result of the associated devices corresponding to the error data and abnormal data; Step S104, according to the equipment data collection standard accuracy corresponding to the pilot conveying demand information in the database, the equipment data collection standard accuracy is compared with the associated equipment data collection accuracy results corresponding to the error data and the abnormal data to obtain the equipment data collection accuracy comparison result, and the equipment data collection accuracy comparison result includes the equipment data collection accuracy being qualified and the equipment data collection accuracy being unqualified; Step S105, if the equipment data collection accuracy is unqualified, retrieve the historical operation data of the equipment with unqualified equipment data collection accuracy, establish an equipment data collection virtual model based on the historical operation data of the equipment with unqualified equipment data collection accuracy, use the equipment data collection virtual model to generate equipment data collection simulation data, replace the unqualified data in the equipment data collection accuracy comparison result with the equipment data collection simulation data, obtain the corrected pilot conveying system operation data, substitute the pilot conveying system operation data into the preset pilot conveying system equipment scheduling model, obtain the pilot conveying equipment scheduling result, and send the pilot conveying equipment scheduling result to the corresponding equipment in the pilot conveying system.

6. The pilot conveying control method according to claim 5, characterized in that: The step S105 includes: Step 501, obtaining the pilot conveying system operation data including the speed and pressure of the pilot airflow, and performing data processing on the obtained pilot conveying system operation data including the speed and pressure of the pilot airflow to obtain the pilot airflow conveying adjustment parameters; Step 502, using the pilot airflow transport adjustment parameters to perform data processing and analysis to obtain an optimized pilot transport system equipment scheduling model, and using the optimized pilot transport system equipment scheduling model to replace the preset pilot transport system equipment scheduling model.

7. The pilot conveying system and control method according to claim 6, characterized in that: The step 501 includes: The pilot conveying system operation data also includes the speed and pressure of the pilot airflow, and the speed and pressure of the pilot airflow are substituted into the preset pilot conveying system equipment scheduling model to obtain the opening and closing time information of the plug valve; According to the opening and closing time information of the control plug valve, the air flow velocity, pressure and material flow rate of the material in the pipeline transportation process are collected; The airflow velocity, pressure and material flow rate of the material in the pipeline transportation process are preprocessed to obtain the preprocessed pipeline transportation monitoring data, and the current transportation state is determined according to the preset material pipeline transmission detection threshold. The corresponding airflow control algorithm is matched in the preset airflow control algorithm knowledge base according to the current transportation state, and the preprocessed pipeline transportation monitoring data is processed using the airflow control algorithm corresponding to the current transportation state to obtain the pilot airflow transportation adjustment parameters.

8. The pilot conveying system and control method according to claim 6, characterized in that: The step 502 includes: receiving a pilot air flow delivery adjustment parameter, modifying a sensor configuration according to the pilot air flow delivery adjustment parameter to obtain a modified sensor configuration, and loading the modified sensor configuration into the sensor; receiving data collected by the sensor loaded with the modified sensor configuration to obtain real-time sensor collected data, and preprocessing the real-time sensor collected data to obtain preprocessed real-time sensor collected data; The preprocessed real-time sensor collected data is substituted into the preset pilot conveying system equipment scheduling model to obtain the real-time conveying equipment scheduling result, and the real-time conveying equipment scheduling result is compared with the pilot conveying equipment scheduling result. If the comparison result of the real-time conveying equipment scheduling result with the pilot conveying equipment scheduling result is greater than the preset error range value, the pilot conveying system equipment scheduling model is optimized to obtain the optimized pilot conveying system equipment scheduling model, and the optimized pilot conveying system equipment scheduling model is used to replace the preset pilot conveying system equipment scheduling model.

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