Distributed coal feeder intelligent control system

Abstract

The invention provides an intelligent control system for a distributed coal feeder, and the system comprises a distributed sensing module which is used for obtaining the weight distribution data and speed data of a material; the edge calculation module is used for carrying out signal conditioning and synchronous processing on the data; the core operation module is used for optimizing control parameters, generating a control instruction according to the control parameters and outputting the control instruction; and the control execution module is used for adjusting the running state of the conveying equipment according to the control instruction. According to the method, the control precision and stability of the coal feeding process are integrally improved, energy waste and equipment loss caused by measurement errors or improper control are reduced, and meanwhile, a reliable data basis is provided for subsequent fault diagnosis and safety protection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal feeder control, and particularly relates to a distributed coal feeder intelligent control system. BACKGROUND

[0002] The conventional coal feeder control system has inherent defects such as low measurement accuracy, poor working condition adaptability, insufficient safety protection and the like. In the prior art, the weighing sensor is susceptible to temperature drift, which leads to measurement error, the single speed sensor has insufficient reliability in harsh working conditions, and the control algorithm mostly uses fixed parameter PID, which is difficult to cope with sudden flow conditions. The existing coal quantity accumulation algorithm is significantly affected by mechanical vibration, the coal feeding rate statistics has single dimension, and the statistical accuracy is poor, which seriously reduces the operation efficiency and safety level of the coal feeding system in the industrial field such as thermal power generation. SUMMARY

[0003] Therefore, the present application provides a distributed coal feeder intelligent control system to solve the technical defects in the prior art.

[0004] Specifically, the present application provides a distributed coal feeder intelligent control system, comprising: a distributed sensing module for acquiring material weight distribution data and speed data; an edge computing module for signal conditioning and synchronous processing of the data; a core operation module for optimizing control parameters, generating control instructions according to the control parameters and outputting; a control execution module for adjusting the running state of the conveying equipment according to the control instructions.

[0005] In some embodiments, the distributed sensing module comprises an array type weighing sensor group and a double-channel speed sensor, the weighing sensor group is equipped with a temperature compensation unit to process and generate temperature-compensated weight data; the speed sensor adopts a photoelectric encoder and a Hall element double-redundancy design to process and generate a speed signal.

[0006] In some embodiments, the edge computing module is configured with a hardware timestamp synchronization module to process and generate microsecond-level synchronization data, and an anti-interference filter to process and generate a filtered sensor signal.

[0007] In some embodiments, the dynamic load modeling of the core operation module comprises: calculating an equivalent load center position to process and generate a correction coefficient, and a vibration analysis module to separate and process to generate an effective weight signal.

[0008] In some embodiments, a predictive compensation mechanism is enabled for sudden flow conditions, and a predictive correction amount is calculated based on historical data. The predictive correction amount is then processed by CRC check to generate verified control parameters.

[0009] In some embodiments, the calculation step of the prediction correction includes: Based on the historical data and a preset correction formula, the predicted correction amount is calculated, wherein the correction formula includes:

[0010] in, To predict the correction amount, Let be the spatial weighting coefficient of the i-th sensor. As a speed-affecting factor, This is the error correction factor. This represents the weight change of the i-th sensor in the weighing sensor group. Indicates the previous period in historical data The speed is measured in sampling cycles, where each cycle represents a fixed time interval for the speed sensor to collect data. Given the prediction data from the previous k predictions, It represents the current time reference point during calculation. The total number of sensors, For the length of the speed history window, This represents the total number of predictions.

[0011] In some embodiments, the control execution module includes an intelligent frequency converter and a fault isolation device, wherein, The intelligent frequency converter is used to precisely adjust the motor speed according to instructions; The fault isolation device is used to continuously monitor the operating status and quickly cut off the power supply to protect the equipment when an abnormality occurs.

[0012] In some embodiments, the system further includes: The mode control module is used to switch to degraded control mode when belt slippage or sensor abnormality is detected, trigger the self-diagnostic process to generate fault codes, write all operating parameters to the preset memory, and mark abnormal data.

[0013] In some embodiments, the system further includes: The coal quantity accumulation module is used to generate the cumulative coal quantity transmission volume based on the integral algorithm; The coal feeding rate module is used to realize real-time coal feeding rate statistics at both minute and hourly time scales, and to display the information representing the real-time coal feeding rate on the target screen.

[0014] In some embodiments, a multi-stage safety interlocking module is also provided, which is used to generate a safety control signal by sequentially performing speed limiting, slow stop and emergency braking processes when overload, speed out of control or communication interruption is detected, and all protection actions have a delay determination function.

[0015] At least one embodiment of the present application distributes a sensing module for obtaining material weight distribution data and speed data, an edge computing module for signal conditioning and synchronization processing of the data, a core operation module for optimizing control parameters, generating control instructions and outputting according to the control parameters, and a control execution module for adjusting the running state of the conveying equipment according to the control instructions. The present application improves the control accuracy and stability of the coal feeding process as a whole, reduces energy waste and equipment loss caused by measurement errors or improper control, and provides a reliable data basis for subsequent fault diagnosis and safety protection. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a structural block diagram of a distributed coal feeder intelligent control system provided by the present application. DETAILED DESCRIPTION

[0017] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present specification. However, the present specification can be implemented in many different ways than those described herein, and those skilled in the art can make similar extensions without departing from the connotation of the present specification, so the present specification is not limited by the specific implementation disclosed below.

[0018] The terms used in one or more embodiments of the present specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of the present specification. The singular forms "a" and "the" used in one or more embodiments of the present specification 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" used in one or more embodiments of the present specification means and includes any or all possible combinations of one or more associated listed items. The modification of "one" and "multiple" mentioned in the present disclosure is illustrative and not limiting, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0019] It should be understood that, although the terms first, second, etc. can be employed in describing various information in one or more embodiments of the present specification, such information should not be limited to these terms. These terms are only used to differentiate one piece of information from another piece of information of the same type. For example, without departing from the scope of one or more embodiments of the present specification, first can also be referred to as second, and similarly, second can also be referred to as first. Depending on the context, the word "if' as used herein can be interpreted as "when" or "upon" or "in response to determining".

[0020] Referring to Figure 1 , Figure 1 A structural block diagram of a distributed coal feeder intelligent control system is shown, which is provided according to some embodiments of the present specification, and the distributed coal feeder intelligent control system comprises: a distributed sensing module for acquiring material weight distribution data and speed data; an edge computing module for signal conditioning and synchronous processing of the data; a core operation module for optimizing control parameters, generating control instructions according to the control parameters and outputting; a control execution module for adjusting the running state of the conveying equipment according to the control instructions.

[0021] The distributed coal feeder intelligent control system can refer to a material conveying control system designed with a modular architecture, which realizes precise adjustment and intelligent management of the coal feeding process through multi-module cooperation. The distributed sensing module can refer to a hardware unit for real-time acquisition of material weight distribution and speed data, including array type weighing sensors and speed sensors. The material weight distribution data can refer to the weight information of the materials carried by the belt at different positions obtained by the weighing sensor group. The speed data can refer to the running speed information of the conveying belt detected by the speed sensor. The edge computing module can refer to a computing unit deployed at the data acquisition end, responsible for preprocessing and synchronization of the original sensor signals. Signal conditioning can refer to amplification, filtering, linearization and other processing of the sensor output signals to improve data quality. Synchronous processing can refer to time domain alignment of multi-source sensor data through hardware timestamps. The core operation module can refer to the central processing unit of the system, used to execute control algorithms and parameter optimization. The control parameters can refer to the set of running parameters required by the system to adjust the conveying equipment. The control instructions can refer to the device adjustment commands output by the core operation module. The control execution module can refer to the terminal execution mechanism for driving the conveying equipment to act according to the control instructions. The running state of the conveying equipment can refer to the real-time working condition parameters such as the speed and load of the belt conveyor.

[0022] The at least one embodiment of the present application comprises a distributed sensing module for obtaining material weight distribution data and speed data, an edge computing module for signal conditioning and synchronous processing of the data, a core operation module for optimizing control parameters, generating control instructions according to the control parameters and outputting, and a control execution module for adjusting the running state of the conveying equipment according to the control instructions. The present application improves the control accuracy and stability of the coal feeding process as a whole, reduces energy waste and equipment loss caused by measurement errors or improper control, and provides a reliable data basis for subsequent fault diagnosis and safety protection.

[0023] In some optional implementations, the distributed sensing module comprises an array type weighing sensor group and a double-channel speed sensor, the weighing sensor group is equipped with a temperature compensation unit to process and generate temperature-compensated weight data, and the speed sensor adopts a photoelectric encoder and a Hall element double-redundancy design to process and generate a speed signal.

[0024] The array type weighing sensor can refer to a plurality of weighing units arranged transversely along the conveying belt for synchronous detection of the weight distribution of the material in the width direction of the belt. The temperature drift compensation can refer to eliminating the influence of environmental temperature changes on the weighing accuracy through a built-in temperature sensor and a compensation algorithm. The dynamic calibration can refer to a sensor calibration process automatically performed during system operation. The speed sensor redundancy configuration can refer to using multiple sets of speed sensors to work in parallel to improve the reliability of speed detection. The vibration interference can refer to high-frequency noise caused by mechanical vibration of the conveying equipment to the sensor signal. The adaptive filtering can refer to a signal filtering algorithm that automatically adjusts parameters according to real-time working conditions. The effective signal can refer to sensor data reflecting the true material state after filtering.

[0025] The spatial distribution detection of the array type weighing sensor significantly improves the comprehensiveness and accuracy of material weight measurement, and the temperature drift compensation mechanism effectively overcomes the adverse effects of environmental factors on measurement accuracy. The dynamic calibration function ensures that the sensor remains in the best working state during long-term operation, and the redundancy design of the speed sensor greatly improves the reliability of key data acquisition. The adaptive filtering technology can intelligently identify and eliminate various vibration interferences, ensuring that the system extracts effective signals that truly reflect the material state. The synergistic effect of these technical features enables the system to maintain stable measurement performance in complex industrial environments, providing a solid data foundation for subsequent precise control, while reducing the risk of system failure caused by sensor failure.

[0026] In some optional implementations, the edge computing module is configured with a hardware timestamp synchronization module to process and generate microsecond-level synchronous data, and an anti-interference filter to process and generate filtered sensor signals.

[0027] The hardware timestamp synchronization module can refer to a device that uses a dedicated clock circuit to add precise time labels to sensor data. Microsecond-level synchronized data can refer to multi-source sensor data with a time synchronization accuracy of one millionth of a second. The anti-interference filter can refer to a digital signal processing unit used to eliminate electromagnetic noise and mechanical vibration interference. The filtered sensor signal can refer to the sensor output data that retains valid information after noise suppression processing.

[0028] The hardware timestamp synchronization module achieves precise time alignment of multi-sensor data, and the microsecond-level synchronization accuracy effectively solves the timing error problem of traditional software synchronization. The anti-interference filter specifically suppresses the electromagnetic noise and mechanical vibration interference specific to the industrial site, significantly improving the signal-to-noise ratio of the sensor signal. The combination of these two technologies enables the system to obtain high-precision time-synchronized data while ensuring that the signal quality meets the strict requirements of the control system, providing a reliable data input basis for the subsequent core operation module, thereby improving the response speed and regulation accuracy of the control system as a whole.

[0029] In some optional implementations, the dynamic load modeling of the core operation module includes: calculating an equivalent load center position to generate a correction coefficient, and a vibration analysis module to separate to generate an effective weight signal.

[0030] Dynamic load modeling can refer to a mathematical representation method of the time-varying weight distribution of materials during the conveying process. The equivalent load center position can refer to the equivalent action point coordinates of the weight distribution of materials on the cross section of the belt. The correction coefficient can refer to an adjustment parameter used to compensate for the influence of uneven belt tension and other factors on weight measurement. The vibration analysis module can refer to a signal processing unit that separates mechanical vibration from material weight through frequency domain analysis. The effective weight signal can refer to a pure signal that reflects the true weight of the material after removing vibration interference.

[0031] As an example, when the conveying belt carries irregularly distributed coal powder, the system first calculates the equivalent load center position of the current cross section (e.g., 650 mm from the left edge of the belt) based on the arrayed load cell data, generates a correction coefficient of 0.95 in combination with the belt tension distribution characteristics. The vibration analysis module synchronously collects acceleration sensor signals, identifies the main mechanical vibration frequency component of 12 Hz through fast Fourier transform, and outputs the effective weight signal after filtering out the interference in this frequency band from the original signal.

[0032] The dynamic load modeling realizes the accurate mathematical description of irregular material distribution, and the equivalent load center position calculation can accurately reflect the actual spatial distribution characteristics of the material. The introduction of the correction coefficient effectively compensates the systematic error caused by uneven belt tension, and the innovative frequency domain processing technology of the vibration analysis module successfully separates the mechanical vibration noise and the effective signal. These technologies work together to accurately obtain the real material weight information under the complex working conditions of mechanical vibration and uneven material distribution, providing key data support for subsequent accurate coal supply control, while significantly reducing the measurement error caused by vibration interference.

[0033] In some optional implementations, for the sudden flow condition, a prediction compensation mechanism is enabled, a prediction correction amount is calculated based on historical data, and the prediction correction amount generates a control parameter after CRC check processing.

[0034] The sudden flow condition can refer to a running state in which the flow of the conveyed material suddenly changes. The prediction compensation mechanism can refer to an algorithm module that adjusts the control output in advance according to the trend of the working condition. The historical data can refer to a set of time series parameters such as flow and speed recorded during system operation. The prediction correction amount can refer to a control amount adjustment value calculated to compensate for the impact of flow mutation. CRC check can refer to a method of verifying data integrity using a cyclic redundancy check code. The control parameter after check can refer to valid control instruction data that passes integrity verification.

[0035] As an example, when it is detected that the flow at the coal feeder discharge port increases by 30% within 3 seconds, the system immediately retrieves the historical data of similar working conditions in the last 5 minutes, and calculates the prediction correction amount (such as increasing the frequency converter frequency by 8%) through a sliding window algorithm. The correction amount is checked by CRC-16 to generate a control parameter package containing a check code, ensuring that there is no bit error when the data is transmitted to the actuator.

[0036] The prediction compensation mechanism significantly improves the response speed of the system to sudden conditions, and the intelligent prediction based on historical data effectively reduces the control lag caused by flow mutation. The introduction of CRC check technology ensures the transmission reliability of key control parameters, preventing misoperation due to communication interference. The combination of this forward-looking control strategy and data security guarantee makes the system maintain stable conveying while controlling the material measurement error under sudden conditions to the minimum range, which is particularly suitable for industrial scenarios such as coal feeding systems in thermal power plants that are sensitive to flow mutation.

[0037] In some optional implementations, the calculation step of the prediction correction amount includes: According to the historical data and a preset correction amount calculation formula, the prediction correction amount is calculated, wherein the correction amount calculation formula includes:

[0038] wherein, is a prediction correction amount, is a spatial weight coefficient of the i th sensor, is a speed influence factor, is an error correction coefficient, represents a weight change amount of the i th sensor of the group of load cells, represents the speed of the previous sampling periods in the historical data, each period representing a fixed time interval for the speed sensor to collect data, is prediction data of the previous k times before the current time, is a current time reference point when calculating, is the total number of sensors, is the length of the speed history window, is the total number of predictions.

[0039] The spatial weight coefficient can refer to a distribution parameter reflecting the importance of the load cell in the transverse position of the conveyor belt. The speed influence factor can refer to a correction proportion parameter of the running speed of the belt on the measurement error of the material weight. The error correction coefficient can refer to a calibration parameter for eliminating the inherent deviation of the system. The weight change amount can refer to the weight difference of the material detected by a single sensor in the adjacent sampling period. The sampling period can refer to a fixed time unit for the speed sensor to collect the running speed of the belt. The prediction data can refer to the estimated value of the control amount generated in the previous calculation period without implementation. The current time reference point can refer to the real-time time marker as a reference in the calculation process. The total number of sensors can refer to the number of load cells participating in the calculation. The length of the speed history window can refer to the number of historical speed data used to calculate the speed influence factor. The total number of predictions can refer to the frequency statistics value of the cumulative execution of the prediction correction by the system.

[0040] The control accuracy in dynamic working conditions is significantly improved by the fusion of multi-dimensional parameters. The introduction of the spatial weight coefficient realizes accurate compensation for the differences in sensor layout. The speed influence factor effectively eliminates the measurement error caused by the change in belt speed. The historical data window mechanism guarantees the time sequence continuity of the correction amount, and the error correction coefficient fundamentally suppresses the cumulative effect of the inherent deviation of the system. This comprehensive correction strategy enables the system to maintain stable measurement accuracy and control response in complex working conditions such as uneven material distribution and belt speed fluctuations, and is particularly suitable for continuous conveying scenarios in industries such as power and metallurgy that require strict control of the feed accuracy.

[0041] In some optional implementations, the control execution module includes an intelligent frequency converter and a fault isolation device. The intelligent frequency converter is used to accurately adjust the motor speed according to the instructions. The fault isolation device is used to continuously monitor the operating state and quickly cut off the power supply to protect the equipment when an abnormality occurs.

[0042] The control execution module can refer to a terminal functional unit that receives control instructions and drives the device to run. The intelligent frequency converter can refer to a motor speed control device with adaptive adjustment capability. The fault isolation device can refer to a protection mechanism that detects electrical abnormalities in real time and performs safety shutdown.

[0043] The intelligent frequency converter realizes precise dynamic adjustment of motor speed, significantly improving the stability and energy efficiency ratio of material conveying. The dual protection mechanism of the fault isolation device effectively prevents device overload damage, and its rapid response characteristics significantly reduce the safety risk under abnormal working conditions. This integrated control and protection design is particularly suitable for harsh industrial environments such as high dust and strong vibration, ensuring continuous production while extending the service life of the device.

[0044] In some optional implementations, the system further includes a mode control module for switching to a degraded control mode when belt slip or sensor abnormalities are detected, triggering a self-diagnosis process to generate fault codes, writing all running parameters to a preset memory, and marking abnormal data.

[0045] The mode control module can refer to a logic unit responsible for system running state switching and abnormality management. The degraded control mode can refer to a simplified safety operation strategy enabled when the device is abnormal. The self-diagnosis process can refer to a program sequence that automatically detects fault sources and generates analysis results. The fault code can refer to a standardized numerical code that identifies a specific abnormal type. The preset memory can refer to a non-volatile storage area specifically allocated for saving abnormal data. The abnormal data can refer to a set of monitoring values that deviate significantly from normal running parameters.

[0046] As an example, when the photoelectric sensor detects that the belt speed is lower than the motor speed and the vibration value exceeds the standard, the mode control module immediately switches to the degraded mode: limits the conveying capacity to 60% of the rated value, while starting the self-diagnosis process to detect encoder signal interruption faults (generate E207 code), encrypt the current motor current, belt tension, and other parameters to FLASH memory, and add a red label to the abnormal period data.

[0047] The intelligent mode switching ensures basic running capability under abnormal working conditions, and the degraded control mode effectively prevents the occurrence of secondary faults. The self-diagnosis function significantly shortens the fault positioning time, and the standardized fault code system facilitates maintenance personnel to quickly understand the nature of the problem. The data storage and labeling mechanism provides a complete evidence chain for subsequent fault backtracking and preventive maintenance, which is particularly suitable for high-load conveying scenarios such as mines and ports that require uninterrupted operation.

[0048] In some optional implementations, the system further comprises: a coal quantity accumulation module configured to generate a coal quantity accumulation transmission quantity according to an integral algorithm; and a coal feeding rate module configured to implement minute-level and hour-level double-time-scale statistical real-time coal feeding rate and display information representing the real-time coal feeding rate to a target screen.

[0049] The coal quantity accumulation module can refer to a functional unit for calculating total material transmission quantity through continuous integral operation. The integral algorithm can refer to a mathematical calculation method based on time series data accumulation summation. The coal quantity accumulation transmission quantity can refer to total weight of material passing through a conveying system within a specific time period. The coal feeding rate module can refer to a monitoring unit for statistics and display of material transmission quantity per unit time. The double-time-scale can refer to a mode of simultaneously using two different periods for data processing. The real-time coal feeding rate can refer to actual material weight conveyed in the current unit time. The target screen can refer to a human-computer interaction interface terminal for displaying monitoring data.

[0050] As an example, in a coal-fired power plant coal conveying system, the coal quantity accumulation module collects weighing sensor data every 5 seconds, and generates a shift coal conveying quantity (current display value 2586 tons) through trapezoidal integral method accumulation. The coal feeding rate module synchronously calculates the coal conveying quantity per minute (current value 42.3 tons / minute) and the coal conveying quantity per hour (current average value 2540 tons / hour), and the data is displayed in the form of dynamic curve in the special monitoring area of the control room touch screen after smoothing processing.

[0051] Through the double metering mechanism, full-process monitoring of material conveying is realized. The integral algorithm ensures high-precision calculation of the accumulated quantity. Double-time-scale statistics meet the real-time requirements of production scheduling, and provide macro data support for energy efficiency analysis. The visual interface enables the operating personnel to quickly master the system state, and is particularly suitable for the fields of power and chemical industry which require accurate metering and process control, and effectively improves the data level of production management.

[0052] In some optional implementations, a multi-stage safety interlocking module is further provided, configured to, when overload, speed out of control or communication interruption is detected, sequentially execute speed limiting, slow stop and emergency braking processing to generate a safety control signal, and all protection actions have a time delay determination function processing to generate an interlocking trigger flag.

[0053] The multi-stage safety interlocking module can refer to a protection control unit triggered in layers according to risk levels. The safety control signal can refer to a level or digital quantity output representing the protection state of the system. The time delay determination function can refer to a logic judgment mechanism introducing a time window to verify the abnormal persistence. The interlocking trigger flag can refer to a state register bit identifying that the protection action has been activated.

[0054] For example, when the weighing sensor detects 120% overload and lasts for 500 ms, the first interlock starts the speed limit to 80% of the rated value; if the speed sensor shows 10% over-difference for 1 second, the second interlock triggers the slow-stop command and releases the hydraulic brake; when the communication module fails to handshake for 3 times in succession, the third interlock directly activates the emergency brake valve, and at the same time sends a safety control signal through the CAN bus, and all actions generate corresponding interlock trigger flags and are stored in the PLC state register.

[0055] The risk progressive control is realized through the hierarchical response mechanism, the speed limit stage provides the system with self-recovery opportunity, the slow-stop function effectively avoids the mechanical impact caused by the emergency stop, and the emergency brake as the final guarantee ensures the rapid risk avoidance under extreme working conditions. The delay determination function significantly reduces the probability of false triggering, and the standardized record of interlock flags facilitates fault tracing and system diagnosis, and is especially suitable for the heavy equipment field such as mines and metallurgy.

[0056] The preferred embodiments disclosed in the specification are only used to help explain the specification. The alternative embodiments do not describe all the details and do not limit the invention to the specific implementation. Obviously, according to the content of the invention, many modifications and changes can be made. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can well understand and utilize the specification. The specification is limited by the claims and their entire scope and equivalents.

Claims

1. A distributed coal feeder intelligent control system, characterized in that, The system comprises: a distributed sensing module for acquiring material weight distribution data and speed data; an edge computing module for signal conditioning and synchronous processing of the data; a core operation module for optimizing control parameters, generating control instructions according to the control parameters, and outputting the control instructions; a control execution module for adjusting the running state of the conveying equipment according to the control instructions.

2. The system of claim 1, wherein, The distributed sensing module comprises an array type weighing sensor group and a double-channel speed sensor, The weighing sensor group is equipped with a temperature compensation unit to process and generate temperature-compensated weight data. The speed sensor adopts a dual-redundancy design of photoelectric encoder and Hall element to process and generate speed signals.

3. The system of claim 2, wherein, The edge computing module is configured with a hardware timestamp synchronization module to process and generate microsecond-level synchronization data, and an anti-interference filter to process and generate filtered sensor signals.

4. The system of claim 2, wherein, The dynamic load modeling of the core operation module includes: calculating the equivalent load center position to process and generate correction coefficients, and a vibration analysis module to separate and process to generate effective weight signals.

5. The system of claim 4, wherein, For sudden flow conditions, a prediction compensation mechanism is enabled, and a prediction correction amount is calculated based on historical data. The prediction correction amount is processed by CRC check to generate checked control parameters.

6. The system of claim 5, wherein, The calculation steps of the prediction correction amount include: calculating the prediction correction amount according to the historical data and a preset correction amount calculation formula, wherein the correction amount calculation formula includes: wherein, is a prediction correction amount, is a spatial weight coefficient of the i th sensor, is a speed influence factor, is an error correction coefficient, represents a weight change amount of the i th sensor of the group of load cells, represents the speed of the previous sampling periods in the historical data, each period representing a fixed time interval in which the speed sensor collects data, is prediction data of the previous k times before the current time, is a current time reference point when calculating, is the total number of sensors, is the length of the speed history window, is the total number of predictions.

7. The system of claim 1, wherein, The control execution module includes an intelligent frequency converter and a fault isolation device, wherein, The intelligent frequency converter is used to accurately adjust the motor speed according to the instructions; The fault isolation device is used to continuously monitor the running state and quickly cut off the power supply to protect the equipment when an abnormality occurs.

8. The system of claim 1, wherein, Further comprising: a mode control module for switching to a degraded control mode when belt slip or sensor abnormality is detected, triggering a self-diagnosis process to generate fault codes, writing all running parameters to a preset memory, and marking abnormal data.

9. The system of claim 1, wherein, The system further comprises: a coal quantity accumulation module for generating coal quantity cumulative transmission quantity according to an integral algorithm; a coal feeding rate module for realizing minute-level and hour-level double-time scale statistics of real-time coal feeding rate, and displaying information representing the real-time coal feeding rate to a target screen.

10. The system of claim 1, wherein, A multi-stage safety interlocking module is also provided for sequentially executing speed limiting, slow stopping, and emergency braking to generate safety control signals when overload, speed out of control, or communication interruption is detected. All protection actions have a time delay determination function to generate interlocking trigger flags.

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