Tension optimization control system and method for tensioning device of underground coal mine belt conveyor

Abstract

The invention discloses a tension optimization control system and method for a tensioning device of an underground coal mine belt conveyor, and relates to the technical field of tensioning device optimization control. The method comprises the steps that S1, multi-source tensioning data are collected in real time, and data preprocessing is conducted on the multi-source tensioning data; s2, predicting a coal flow load based on historical multi-source tensioning data, and taking a dynamic compensation measure for a tensioning device; s3, in combination with the multi-source tensioning data and the coal flow load prediction result, collaborative distribution is carried out on the hybrid execution mechanism, and automatic regulation and control are carried out based on the collaborative distribution result; s4, abnormity is detected in real time based on the multi-source tensioning data, and early warning is carried out; s5, performing safety risk assessment according to the real-time multi-source tensioning data, and taking adaptive regulation and control measures in combination with a safety risk assessment result and an anomaly detection result; the problem that faults are caused due to the fact that high-frequency pulsation and short-time impact of belt tension are difficult to adjust in time is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized control of tensioning devices, and specifically to a tension optimization control system and method for a belt conveyor tensioning device in underground coal mines. Background Art

[0002] As an important device for continuous coal transportation in underground coal mines, the belt conveyor is a key link connecting coal mining, transportation, and ground production. With the continuous improvement of the automation and intelligence levels of coal mines, the conveying system faces higher operating efficiency and safety requirements. Under the complex and variable working conditions in the mine, factors such as the coal flow load, drive start-stop, and environmental temperature change of the belt conveyor will cause frequent fluctuations in the belt tension, generating high-frequency pulsations and short-term impacts, which are extremely likely to cause faults such as belt jumping, slipping, and even breaking.

[0003] For example, the invention patent with the publication number CN106325149B discloses an intelligent control data acquisition and processing method for a belt conveyor during the tunneling process of an earth pressure balance shield. It is realized based on an acquisition and processing system, which includes a signal processing unit, a power supply module, a display unit, an interface unit, an A / D conversion module, and a CPU control module; the CPU control module includes a load mode judgment unit, a data optimization unit, and a data correction unit. The load mode judgment unit initializes the tension data C under the no-load state of the belt conveyor and judges its working mode according to the load tonnage; the data optimization unit obtains the optimal tension value D according to the sensor data; the data correction unit compares the tension data C with the optimal tension value D and performs PID adjustment processing. The present invention synchronously collects data in groups through tension sensors, uses fuzzy control algorithms and PID adjustment for data processing, adjusts its speed according to the real-time working load of the belt conveyor, has precise control, is convenient for operation at the construction site, and achieves the purpose of energy conservation and consumption reduction.

[0004] For example, the invention patent with the publication number CN104991532B discloses a centralized control automation device for an underground transportation system, including a track belt, a first coal feeder anti-collapse device, a second coal feeder anti-blocking and anti-collapse device, a control terminal, and a camera. The control terminal is used to control the track belt, the first coal feeder anti-collapse device, and the second coal feeder anti-blocking and anti-collapse device. The present invention combines the traditional underground transportation system with modern control systems and computer systems to realize remote monitoring, control of the transportation system, and the acquisition and processing of relevant audio and video data. The present invention has high automation efficiency and reliable control results. By installing cameras at each important section of each belt and coal feeder, the images of each video are concentrated and transmitted to the monitor, which is convenient for personnel to observe centrally, and the coal feeder is started according to the coal storage situation in the coal bunker to reduce the occurrence of bunker blocking.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of the present application, it is found that the above technology has at least the following technical problems: Factors such as coal flow, load, drive start-stop, etc. will cause high-frequency pulsation and short-term impact on the belt tension. The underground environment has many changes and large disturbances. The existing control system has a slow response speed, is difficult to adjust in time, and is prone to problems such as belt jumping, slipping, and even breakage, affecting transportation safety and efficiency.

[0006] Therefore, in view of the above problems, there is an urgent need for an optimized control system and method for the tension of the belt conveyor tensioning device in coal mines. Summary of the Invention

[0007] Technical problems to be solved In view of the deficiencies of the prior art, the present invention provides an optimized control system and method for the tension of the belt conveyor tensioning device in coal mines, which solves the problem of difficult adjustment in time due to high-frequency pulsation and short-term impact of the belt tension, thus causing failures.

[0008] Technical solution To achieve the above objectives, the present invention is realized through the following technical solutions: An optimized control method for the tension of the belt conveyor tensioning device in coal mines, including: S1, collecting multi-source tension data in real time, preprocessing the multi-source tension data, and establishing a multi-source tension database; S2, predicting the coal flow load based on historical multi-source tension data, and taking dynamic compensation measures for the tensioning device according to the prediction result of the coal flow load; S3, combining the multi-source tension data with the prediction result of the coal flow load, coordinately allocating the hybrid actuator, and performing automatic control based on the coordinated allocation result; S4, detecting anomalies in real time based on the multi-source tension data, and performing early warning according to the anomaly detection result; S5, performing safety risk assessment based on the real-time multi-source tension data, and taking adaptive control measures in combination with the safety risk assessment result and the anomaly detection result.

[0009] Further, the specific process of collecting multi-source tension data in real time, preprocessing the multi-source tension data, and establishing a multi-source tension database is as follows: Collect multi-source tension data in real time. The multi-source tension data includes: measuring the tension of the belt using an FBG fiber optic strain sensor; collecting tension data and vibration data using a vector tension scale and a MEMS triaxial vibration sensor; collecting coal flow load data using a coal flow sensor and a vision monitoring camera, including: instantaneous coal flow rate, coal flow pulsation spectrum, visual coal flow image; collecting current and torque data using a current sensor and a torque sensor; collecting the rotational speed data of the driving wheel using a rotational speed sensor; suppressing high-frequency noise through wavelet filtering, removing sudden out-of-range signals and unreasonable extreme multi-source tension data, filling accidental missing data points using the Kalman filter interpolation method, and performing standardization and normalization processing on the multi-source tension data; synchronously transmitting the multi-source tension data to the edge computing platform at the microsecond level and storing it in the multi-source tension database.

[0010] Further, the specific process of predicting the coal flow load based on historical multi-source tension data is as follows: Obtain the current coal flow load data, and process the visual coal flow image through an image processing algorithm to extract the mean value of the ROI region to obtain the characteristic mean value of the current coal flow monitoring image; Based on the sliding time window length, calculate the first derivative of the characteristic mean value of the current coal flow monitoring image with respect to time to obtain the change rate of the characteristic mean value of the coal flow monitoring image; Multiply the current coal flow load data by the time series weight factor to obtain the time series weighted value, and based on the sliding time window length, accumulate the time series weighted values of all sampling points and divide by the sliding time window length to obtain the time series weighted sliding mean value; Multiply the change rate of the characteristic mean value of the coal flow monitoring image by the visual mutation response weight factor to obtain the visual mutation response term; Add the time series weighted sliding mean value and the visual mutation response term to obtain the coal flow load prediction value.

[0011] Further, the specific process of taking dynamic compensation measures for the tensioning device according to the coal flow load prediction result is as follows: According to the coal flow load prediction value, identify sudden increases and decreases in the coal flow, and adjust the action amplitude and speed of the tensioning cylinder and the servo lead screw in advance; If it is predicted that the coal flow increase value within the next 200 milliseconds exceeds the sudden increase threshold, automatically increase the tension setting value and increase the torque and power of the drive motor in advance; When the coal flow load prediction value suddenly increases and the tension and vibration data respectively exceed the tension and vibration average values, issue emergency tensioning and speed limit instructions in advance and cooperate with the safety brake; If it is predicted that the coal flow reduction value exceeds the sudden decrease threshold, automatically reduce the tension; And when the real-time tension is lower than the safety threshold, issue early warnings of belt slipping and belt breakage, and automatically link the alarm and braking systems; Upload the coal flow load prediction value, the actual coal flow load data curve, and abnormal points to the cloud platform automatically to generate inspection suggestions and spare parts warnings.

[0012] Furthermore, the specific process of collaborative allocation for the hybrid actuator by combining multi-source tension data and the coal flow load prediction results is as follows: Select an electro-hydraulic servo cylinder as the coarse adjustment mechanism and a servo ball screw as the fine adjustment mechanism to construct a hybrid actuator; obtain the coal flow load prediction value, and calculate the expected tension value through a regression fitting algorithm to obtain the target tension value; subtract the currently actually measured tension from the target tension value to obtain the current tension compensation demand value; based on the sliding time window length, perform mean filtering on the current tension compensation demand value, extract the low-frequency component that is stable and slowly changing to obtain the low-frequency tension demand value, and multiply the low-frequency tension demand value by the electro-hydraulic servo cylinder gain weight factor to obtain the collaborative allocation value of the electro-hydraulic servo cylinder; subtract the low-frequency tension demand value from the current tension compensation demand value to obtain the high-frequency tension demand value, and multiply the high-frequency tension demand value by the servo ball screw gain weight factor to obtain the collaborative allocation value of the servo ball screw.

[0013] Furthermore, the specific process of automatic regulation based on the collaborative allocation result is as follows: Based on the collaborative allocation values of the electro-hydraulic servo cylinder and the servo ball screw, the edge controller issues the low-frequency and stable tension adjustment instructions to the electro-hydraulic servo cylinder in real time; issue the high-frequency and fast compensation instructions to the servo ball screw; collect the feedback signals of the electro-hydraulic servo cylinder and the servo ball screw, and the feedback signals include actual thrust, displacement, and response speed, and compare them with the corresponding collaborative allocation values respectively, and dynamically adjust the gain weight factors of the electro-hydraulic servo cylinder and the servo ball screw according to the comparison error; if there are phenomena such as slow execution, insufficient response, and overshoot, automatically recalculate the collaborative allocation value; when it is found that the actual tension effect after allocation is abnormal, immediately execute speed limit, shutdown operations and issue an alarm; archive all allocation instructions and feedback signals in real time, and comprehensively analyze to generate energy consumption analysis, life prediction, and maintenance suggestions.

[0014] Furthermore, the specific process of real-time detection of anomalies based on multi-source tension data is as follows: Obtain the current actual tension of the belt and the belt vibration data at the current moment. Based on the sliding time window, calculate the average value and standard deviation of the tension in the recent period to obtain the historical mean tension and the historical standard deviation of tension respectively; and calculate the average value and standard deviation of the vibration data in the recent period to obtain the historical mean vibration and the historical standard deviation of vibration respectively; subtract the historical mean tension from the current actual tension and take the absolute value to obtain the tension deviation value, divide the tension deviation value by the historical standard deviation of tension to obtain the tension deviation degree, and multiply the tension deviation degree by the tension anomaly weight factor to obtain the tension anomaly value; subtract the historical mean vibration from the belt vibration data at the current moment and take the absolute value to obtain the vibration deviation value, divide the vibration deviation value by the historical standard deviation of vibration to obtain the vibration deviation degree, and multiply the vibration deviation degree by the vibration anomaly weight factor to obtain the vibration anomaly value; add the tension anomaly value and the vibration anomaly value to obtain the anomaly monitoring value.

[0015] Furthermore, the specific process of early warning based on the anomaly detection results is as follows: when the anomaly monitoring value is less than or equal to the anomaly threshold, it is determined to be in a normal state, and routine monitoring is maintained. The multi-source tension data, anomaly monitoring values, and device status are regularly archived; if no anomaly is continuously determined for a period of time, the threshold optimization operation is performed according to the actual monitoring situation; when the anomaly monitoring value is greater than the anomaly threshold, it is determined to be in an abnormal state, the anomaly warning is triggered, the audible and visual alarm is activated, and the alarm information, including the anomaly monitoring value, timestamp, tension, and vibration data, is pushed to the operation and maintenance terminal; the emergency adjustment mode of the tensioning mechanism is started to quickly increase and relieve the tension; deceleration and shutdown are automatically performed; and the anomaly threshold, tension anomaly weight factor, and vibration anomaly weight factor are automatically adjusted according to the anomaly monitoring value and actual anomaly feedback information.

[0016] Furthermore, the specific process of performing a safety risk assessment based on real-time multi-source tension data, combining the safety risk assessment results with the anomaly detection results, and taking adaptive control measures is as follows: obtain the rotational speed data of the driving wheel and the driven wheel, calculate the ratio of the rotational speed difference between the driving wheel and the driven wheel to the rotational speed of the main wheel to obtain the current slip ratio, and screen the maximum slip ratio within the sliding time window length as the slip ratio limit safety value; obtain the current actual tension, and screen the maximum tension within the sliding time window length as the tension limit safety value; obtain the belt vibration data at the current moment, and screen the maximum vibration data within the sliding time window length as the vibration limit safety value; add the ratio of the current slip ratio to the slip ratio limit safety value, the ratio of the current actual tension to the tension limit safety value, and the ratio of the belt vibration data at the current moment to the vibration limit safety value, and then divide by the constant three to obtain the average comprehensive ratio as the comprehensive safety risk assessment value; continuously obtain the safety risk assessment value. If the comprehensive safety risk assessment values for two consecutive times do not exceed the risk threshold, maintain routine monitoring and the collection of multi-source tension data, and update the multi-source tension data and the comprehensive safety risk assessment value in real time. If the comprehensive safety risk assessment values for four consecutive times do not exceed the risk threshold, adaptively fine-tune the risk threshold; if the comprehensive safety risk assessment values for two consecutive times exceed the risk threshold, give an audible and visual alarm reminder, automatically issue a braking command to control the synchronous braking of the driving wheel and the driven wheel; and control the hybrid actuator to perform a quick action; push the alarm and action logs to the terminal in real time; automatically push the anomaly to the operation and maintenance personnel via text message according to the anomaly monitoring value and the comprehensive safety risk assessment value, and automatically generate operation and maintenance suggestions and anomaly risk reports.

[0017] The second aspect of the present invention provides a tension optimization control system for a belt conveyor tensioning device in a coal mine, including: a high-speed perception and real-time fusion module, a prediction compensation control module, a hybrid actuator module, an abnormal self-adaptation module, and a safety protection and cloud collaborative operation and maintenance module. Among them, the high-speed perception and real-time fusion module is used to collect multi-source tension data in real time, perform data preprocessing on the multi-source tension data, and establish a multi-source tension database; the prediction compensation control module is used to predict the coal flow load based on historical multi-source tension data, and take dynamic compensation measures for the tensioning device according to the prediction result of the coal flow load; the hybrid actuator module is used to combine the multi-source tension data and the prediction result of the coal flow load, perform collaborative allocation on the hybrid actuator, and perform automatic regulation based on the collaborative allocation result; the abnormal self-adaptation module is used to detect abnormalities in real time based on the multi-source tension data and give early warnings according to the abnormal detection result; the safety protection and cloud collaborative operation and maintenance module is used to perform safety risk assessment according to the real-time multi-source tension data, and take adaptive regulation measures in combination with the safety risk assessment result and the abnormal detection result.

[0018] Beneficial effects The present invention has the following beneficial effects: (1) In the present invention, by integrating multiple types of sensors and multiple data filtering methods, high-reliability, low-noise, and microsecond-level synchronous data acquisition is achieved, greatly improving the accuracy and sensitivity of tension state monitoring.

[0019] (2) In the present invention, by combining coal flow load prediction and an adaptive adjustment mechanism, an early response to sudden working conditions and precise control are achieved, effectively preventing risks such as slipping and belt breakage, and ensuring the safe operation of the system.

[0020] (3) In the present invention, through the hybrid execution of an electro-hydraulic servo cylinder and a servo ball screw, large-range rapid adjustment and high-precision compensation are achieved, improving the adjustment efficiency while optimizing energy consumption and equipment life.

[0021] (4) In the present invention, by constructing abnormal detection and risk assessment based on multiple indicators, real-time early warning and automatic protection are achieved, forming operation and maintenance suggestions and self-learning optimization, comprehensively improving the intelligence and safety of the system.

[0022] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. Description of the drawings

[0023] Figure 1 It is a flow chart of the tension optimization control method for a belt conveyor tensioning device in a coal mine; Figure 2 It is a structure diagram of the tension optimization control system for a belt conveyor tensioning device in a coal mine; Figure 3 It is a trend chart of abnormal monitoring values. Detailed implementation manners

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. As understood by those skilled in the art, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figures 1 - 3 , the embodiments of the present invention provide a technical solution: a tension optimization control system and method for a belt conveyor tensioning device in a coal mine, including: S1, collecting multi-source tension data in real time, performing data preprocessing on the multi-source tension data, and establishing a multi-source tension database; S2, predicting the coal flow load based on historical multi-source tension data, and taking dynamic compensation measures for the tensioning device according to the prediction result of the coal flow load; S3, combining the multi-source tension data with the prediction result of the coal flow load, performing collaborative allocation on the hybrid actuator, and performing automatic control based on the collaborative allocation result; S4, detecting anomalies in real time based on the multi-source tension data, and performing early warning according to the anomaly detection result; S5, performing safety risk assessment according to the real-time multi-source tension data, and taking adaptive control measures in combination with the safety risk assessment result and the anomaly detection result.

[0026] Specifically, the specific process of collecting multi-source tension data in real time, performing data preprocessing on the multi-source tension data, and establishing a multi-source tension database is as follows: collecting multi-source tension data in real time, and the multi-source tension data includes: measuring the tension of the belt by using an FBG optical fiber strain sensor; collecting tension data and vibration data by using a vector tension scale and a MEMS triaxial vibration sensor; collecting coal flow load data by using a coal flow sensor and a vision monitoring camera, including: instantaneous coal flow rate, coal flow pulsation spectrum, visual coal flow image; collecting current and torque data by using a current sensor and a torque sensor; collecting the rotational speed data of the driving wheel by using a rotational speed sensor. When performing data preprocessing on the multi-source tension data, first, suppress high-frequency noise through wavelet filtering, and eliminate sudden out-of-range signals and unreasonable extreme multi-source tension data; then, fill in occasional missing data points by using the Kalman filter interpolation method, and perform standardization and normalization processing on the multi-source tension data; synchronously transmit the multi-source tension data to the edge computing platform at the microsecond level, and store it in the multi-source tension database.

[0027] In this implementation scheme, multi-source tension data of the belt are collected in real time through multi-type high-precision sensors. After filtering, denoising, anomaly rejection, missing value interpolation, and normalization processing, they are synchronously transmitted at the microsecond level and stored in the database. This not only significantly improves the accuracy and integrity of the data but also provides a high-quality and all-round data foundation for subsequent intelligent analysis, fault prediction, and optimization control, supporting the system to achieve a higher level of intelligence and safe operation and maintenance.

[0028] Specifically, the specific process of predicting the coal flow load based on historical multi-source tension data is as follows: Obtain the current coal flow load data, and process the visual coal flow image through an image processing algorithm. The image processing algorithm specifically includes: grayscale conversion, ROI region segmentation, and calculation of the grayscale mean value of the ROI region. Extract the mean value of the ROI region to obtain the feature mean value of the current coal flow monitoring image; Based on the sliding time window length, calculate the first derivative of the feature mean value of the current coal flow monitoring image with respect to time to obtain the change rate of the feature mean value of the coal flow monitoring image, which reflects the mutation characteristics of the coal flow distribution and flow state; Multiply the current coal flow load data by the time series weight factor to obtain the time series weighted value, and based on the sliding time window length, accumulate the time series weighted values of all sampling points and divide by the sliding time window length to obtain the time series weighted sliding mean value, thereby smoothing the coal flow change and highlighting the trend characteristics; Multiply the change rate of the feature mean value of the coal flow monitoring image by the visual mutation response weight factor to obtain the visual mutation response term, further enhancing the system's perception ability of abnormal fluctuations in the coal flow; Considering both the time series and visual changes, add the time series weighted sliding mean value and the visual mutation response term to obtain the coal flow load prediction value.

[0029] Among them, the specific formula for the coal flow load prediction value is: ; In the formula, represents the predicted value of the coal flow load at the future moment, which is used to predict the coal flow load for subsequent tension and compensation control and respond to possible fluctuations in advance; represents the sliding time window length; represents the current coal flow load data; represents the feature mean value of the current coal flow monitoring image, which serves as a leading signal for judging the mutation of the coal flow state; represents the change rate of the feature mean value of the coal flow monitoring image, indicating the change speed of the image features over time. A rapid change means a sudden increase or decrease in the coal flow, which has an immediate impact on the load; represents the time series weight factor. Through sliding window prediction of historical coal flow load data and actual future coal flow load data and adjustment using the genetic algorithm, the time series weight factor with the smallest sliding window prediction error is the optimal time series weight factor, and its value range is between 0.2 and 0.6; It represents the visual mutation response weight factor, which is obtained by least squares fitting based on the change rate data of the characteristic mean of historical coal flow monitoring images, and its value range is between 0.5 and 2.0; It represents the time series weighted moving average, which reflects the short-term change trend of the actual coal flow load measured by the coal flow sensor and is the basic prediction of the upcoming load; It represents the visual mutation response term, which quantifies the first-order change rate of the characteristics of the coal flow monitoring image in time to capture the load jump caused by the coal flow mutation and improve the leading response ability to sudden coal flow states.

[0030] In this implementation scheme, by integrating the real-time coal flow load, image feature changes and weight allocation, and combining the sliding weighted mean and visual mutation response, the dynamic and accurate prediction of the coal flow load is realized. This method not only improves the system's perception and response ability to coal flow fluctuations, but also can identify sudden anomalies of coal flow in advance, timely drive the downstream tension control and protection mechanism, providing a solid data and algorithm basis for the active regulation and intelligent linkage of the underground conveying system.

[0031] Specifically, the specific process of taking dynamic compensation measures for the tensioning device according to the coal flow load prediction result is as follows: according to the predicted value of the coal flow load, identify the sudden increase and sudden decrease of the coal flow, and adjust the action amplitude and speed of the tensioning cylinder and servo lead screw in advance to achieve precise compensation for different disturbances; if it is predicted that the increased value of the coal flow exceeds the sudden increase threshold within the next 200 milliseconds, automatically increase the tension setting value, and increase the torque and power of the drive motor in advance, so as to effectively prevent the risks of belt slack and slippage caused by sudden increase of coal flow; when the predicted value of the coal flow load suddenly increases and the tension and vibration data respectively exceed the average values of tension and vibration, issue emergency tensioning and speed limit instructions in advance, and cooperate with the safety brake to ensure the safe operation of the conveying system to the greatest extent; if it is predicted that the decreased value of the coal flow exceeds the sudden decrease threshold, automatically reduce the tension to avoid belt damage caused by over-tension; and when the real-time tension is lower than the safety threshold, issue early warnings of belt slippage and belt breakage in advance, and automatically link the alarm and braking systems; upload the predicted value of the coal flow load, the actual coal flow load data curve and abnormal points to the cloud platform automatically to generate inspection suggestions and spare parts warnings, further improving the intelligence and initiative of operation and maintenance.

[0032] In this implementation scheme, by predicting the coal flow load in advance, identifying the mutation trend, and dynamically adjusting the tensioning and driving parameters, the rapid response and protection of abnormal working conditions are realized. This method can not only effectively prevent major faults such as belt jumping, slippage and breakage, but also support the real-time upload of key data and warning information to the cloud, realize the intelligent push of abnormal inspection suggestions and spare parts warnings, and significantly improve the intrinsic safety, remote management ability and intelligent operation and maintenance level of the system.

[0033] Specifically, the specific process of collaborative allocation of the hybrid actuator by combining multi-source tension data and the coal flow load prediction result is as follows: Select an electro-hydraulic servo cylinder as the coarse adjustment mechanism, which is responsible for large-amplitude and low-frequency tension adjustment; select a servo ball screw as the fine adjustment mechanism, which is specifically used to handle small-amplitude and high-frequency rapid tension compensation, and construct a hybrid actuator; obtain the coal flow load prediction value, convert the expected tension value into the target tension value through the regression fitting algorithm, so as to determine the target tension; subtract the currently actually measured current tension from the target tension value to obtain the current tension compensation demand value, which provides a quantitative basis for subsequent adjustment; based on the sliding time window length, perform average filtering on the current tension compensation demand value, extract the low-frequency component that is stable and slowly changing to obtain the low-frequency tension demand value, multiply the low-frequency tension demand value by the electro-hydraulic servo cylinder gain weight factor to obtain the collaborative allocation value of the electro-hydraulic servo cylinder, ensuring the stability and efficiency of the main adjustment direction; subtract the low-frequency tension demand value from the current tension compensation demand value to obtain the high-frequency tension demand value, multiply the high-frequency tension demand value by the servo ball screw gain weight factor to obtain the collaborative allocation value of the servo ball screw, realizing the precise and rapid response to high-frequency disturbances.

[0034] Among them, the specific formula for the collaborative allocation value is: ; In the formula, represents the current tension compensation demand value, representing the error between the expectation and the actual value, and is the direct driving signal for all control actions; represents the target tension value at the future time, reflecting the expected belt tension compensation demand; represents the current actual tension, representing the real-time stress state; represents the low-frequency tension demand value, providing a stable control command for the electro-hydraulic servo cylinder of the coarse adjustment mechanism; represents the high-frequency tension demand value, providing a stable control command for the servo ball screw of the fine adjustment mechanism; represents the electro-hydraulic servo cylinder gain weight factor, which is obtained by fitting the optimal gain between the actual tension output and the expectation through the linear regression algorithm based on the historical tension data of the belt and the collaborative allocation value of the electro-hydraulic servo cylinder, and the value range is between 0.5 and 2.0; represents the servo ball screw gain weight factor, which is obtained by taking the high-frequency segment response and fitting with the least squares method based on the high-frequency tension demand value and the collaborative allocation value of the servo ball screw, and the value range is between 0.8 and 3.0; represents the collaborative allocation value of the electro-hydraulic servo cylinder, which is used for large-amplitude and slow-changing tension adjustment; represents the collaborative allocation value of the servo ball screw, which is used for small-amplitude and rapid-changing dynamic compensation.

[0035] In this implementation scheme, based on the coal flow load prediction and multi-source tension data, a regression algorithm is used to dynamically allocate the adjustment tasks of the coarse adjustment and fine adjustment mechanisms, realizing the collaborative control of the hybrid actuator and the precise compensation of tension, improving the dynamic adaptability of the tensioning system and the overall regulation accuracy; through the frequency division decoupling strategy, the coarse adjustment is responsible for the low-frequency large-amplitude adjustment, and the fine adjustment is responsible for the high-frequency fast response, significantly improving the response speed, regulation accuracy and dynamic stability of the system, and effectively alleviating the risks such as tape skipping and slipping caused by tension fluctuations.

[0036] Specifically, the specific process of automatic regulation based on the collaborative allocation result is as follows: based on the collaborative allocation values of the electro-hydraulic servo cylinder and the servo ball screw, the edge controller issues the low-frequency and stable tension adjustment instructions to the electro-hydraulic servo cylinder in real time; the high-frequency and fast compensation instructions are issued to the servo ball screw to realize the division of labor and efficient cooperation between the coarse adjustment and fine adjustment mechanisms; the feedback signals of the electro-hydraulic servo cylinder and the servo ball screw are collected, and the feedback signals include the actual thrust, displacement and response speed, and are respectively compared with the corresponding collaborative allocation values. According to the comparison error, the gain weight factors of the electro-hydraulic servo cylinder and the servo ball screw are dynamically adjusted, where the error is strictly limited within five percent to ensure the control accuracy and response sensitivity; if there are phenomena such as slow execution, insufficient response and overshoot, the collaborative allocation value is automatically recalculated; when it is found that the actual tensioning effect after allocation is abnormal, the speed limit and shutdown operations are immediately executed and an alarm is issued to ensure the safety of the equipment; all allocation instructions and feedback signals are archived in real time, and comprehensive analysis is carried out to generate energy consumption analysis, life prediction and maintenance suggestions, providing data support for equipment health management and intelligent operation and maintenance decision-making.

[0037] In this implementation scheme, through the automatic regulation and feedback correction of the collaborative allocation value, the real-time cooperation and dynamic optimization of the coarse adjustment and fine adjustment mechanisms are realized. After obtaining the feedback signals such as the real-time thrust, displacement and response speed of the actuator, the gain parameters can be adaptively adjusted according to the actual error to ensure the tension adjustment accuracy and execution consistency. This mechanism not only enhances the response ability and stability of the actuator under complex working conditions, but also effectively avoids execution anomalies such as overshoot and hysteresis. At the same time, it provides high-value operation data support and decision-making basis for subsequent energy consumption analysis, fault identification, life prediction and intelligent operation and maintenance strategies.

[0038] Specifically, the real-time anomaly detection process based on multi-source tensioning data is as follows: The current actual belt tension and the current belt vibration data are obtained. Based on a sliding time window, the average and standard deviation of the tension over the recent period are calculated to obtain the historical tension mean and tension standard deviation, respectively. Furthermore, the average and standard deviation of the vibration data over the recent period are calculated to obtain the historical vibration mean and vibration standard deviation, respectively. This statistical feature extraction not only dynamically reflects the normal fluctuation range of tension and vibration, but also provides a reliable baseline for subsequent anomaly measurement. The tension deviation value is obtained by subtracting the historical tension mean from the current actual tension and taking its absolute value. The tension deviation value is divided by the historical tension standard deviation to obtain the tension deviation degree. The tension deviation degree is multiplied by the tension anomaly weight factor to obtain the tension anomaly value. The vibration deviation value is obtained by subtracting the historical vibration mean from the current belt vibration data and taking its absolute value. The vibration deviation degree is obtained by dividing the vibration deviation value by the historical vibration standard deviation to obtain the vibration deviation degree. The vibration anomaly value is multiplied by the vibration anomaly weight factor to obtain the vibration anomaly value. The tension anomaly value and the vibration anomaly value are added together to obtain the anomaly monitoring value. By integrating the two types of abnormal signals, abnormal operating conditions during operation can be sensitively identified, enabling real-time dynamic monitoring of equipment health status and early risk detection.

[0039] The specific formula for the abnormal monitoring value is: ; Where, Indicates the abnormal monitoring value, indicating the degree to which the current tension and vibration signals deviate from the normal working conditions. It is a quantitative value used by the system to determine whether there is an abnormality. It is used to detect short-term abnormalities and sudden trend changes, facilitating early warning. Indicates the current actual tension, used to detect tension fluctuations and anomalies; Indicates the historical average value of tension, reflecting the stable center value of recent belt tension, and is used to determine whether the current tension deviates from the normal value; Indicates the historical standard deviation of tension, which is used to measure the normal fluctuation range of tension. The larger the standard deviation, the more severe the tension fluctuation. Indicates the current belt vibration data, reflecting whether the belt has abnormal vibration, impact or failure; Indicates the historical mean value of vibration, which is used to determine whether the current vibration level deviates from the normal range; Indicates the historical standard deviation of vibration, which is used to measure the fluctuation amplitude of vibration under normal working conditions; The tension anomaly weight factor is obtained by fitting the historical tension data and the historical anomaly monitoring value dataset through the minimum error regression algorithm. It is used to adjust the influence of the tension term on the anomaly monitoring value and reflects the sensitivity and importance of tension changes to anomalies. The value range is between 0.3 and 0.8. It represents the vibration anomaly weight factor, which is obtained by fitting through the least error regression algorithm based on historical vibration data and historical anomaly monitoring value datasets. It is used to adjust the influence of the vibration term on the anomaly monitoring value, reflecting the sensitivity and importance of vibration changes to anomalies, and its value range is between 0.2 and 0.7.

[0040] Set the tension anomaly weight factor to 0.6 and the vibration anomaly weight factor to 0.4. When the weight factors are the same and the current actual tension of the belt and the vibration data are constantly changing over time, calculate the anomaly monitoring value at each moment. As shown in Table 1, the data table of anomaly monitoring values.

[0041] Table 1 Data table of anomaly monitoring values Sampling point Normalized Tm(t) Normalized a(t) μT σT μa σa Sa(t) 1 0.65 0.55 0.6 0.07 0.5 0.08 0.679 2 0.78 0.62 0.6 0.07 0.5 0.08 2.143 3 0.58 0.47 0.6 0.07 0.5 0.08 0.321 4 0.72 0.67 0.6 0.07 0.5 0.08 1.879 5 0.60 0.80 0.6 0.07 0.5 0.08 1.500 Such as Figure 3 shown, it is the anomaly monitoring value trend chart provided by the embodiment of the present application. The abscissa in the figure is the sampling time point, the ordinate is the anomaly monitoring value, and the anomaly warning threshold is set to 1. According to Table 1 and Figure 3 it can be seen that when the weight factors are the same and the current actual tension of the belt and the vibration data are constantly changing over time, the change trend of the anomaly monitoring value.

[0042] In this implementation scheme, through the sliding window statistics and standardized deviation analysis of tension and vibration data, not only the anomaly degree is quantified in real time, but also the historical baseline can be updated adaptively and the threshold can be optimized dynamically, so as to achieve higher sensitivity and lower false alarm rate anomaly detection and warning in the multi-source data fusion scenario; with this mechanism, faults can be accurately captured at the budding stage and automatically linked to protection actions, greatly improving the early protection ability and overall operation reliability of the underground conveying system.

[0043] Specifically, the specific process of early warning according to the anomaly detection result is as follows: when the anomaly monitoring value is less than or equal to the anomaly threshold, it is determined to be in a normal state, and routine monitoring is maintained. Archive multi-source tension data, anomaly monitoring values and device status regularly to provide historical data support for subsequent equipment health management; if it is continuously determined to be anomaly-free for a period of time, perform threshold optimization operations according to the actual monitoring situation, so as to adaptively improve the system sensitivity and robustness; when the anomaly monitoring value is greater than the anomaly threshold, it is determined to be in an abnormal state, trigger an anomaly warning, start an audible and visual alarm, and push the alarm information, including the anomaly monitoring value, timestamp, tension and vibration data, to the operation and maintenance terminal to realize real-time notification and traceability of fault events; start the emergency adjustment mode of the tensioning mechanism to quickly increase and relieve the tension to suppress potential risks; automatically decelerate and stop to prevent the fault from further expanding; and automatically adjust the anomaly threshold, tension anomaly weight factor and vibration anomaly weight factor according to the anomaly monitoring value and actual anomaly feedback information to continuously optimize the warning strategy and enhance the adaptive and intelligent protection ability.

[0044] In this implementation plan, the system status is dynamically judged according to the anomaly detection results, which can trigger early warnings and emergency regulations in a timely manner, support the push of alarm information, automatic adjustment of parameters, and intelligent optimization of thresholds. At the same time, the complete event link is uploaded to the cloud in real time and automatically archived, providing a basis for subsequent fault tracing, operation and maintenance decision-making, and model self-learning, further improving the system's fault protection and adaptive capabilities.

[0045] Specifically, the specific process of taking adaptive control measures according to the real-time multi-source tension data, combining the results of safety risk assessment and anomaly detection is as follows: Obtain the rotational speed data of the driving wheel and the driven wheel, calculate the ratio of the rotational speed difference between the driving wheel and the driven wheel to the rotational speed of the main wheel to obtain the current slip ratio, and select the maximum slip ratio within the sliding time window length as the slip ratio limit safety value to dynamically reflect the maximum slip risk of the equipment during operation; Obtain the current actual tension, and select the maximum tension within the sliding time window length as the tension limit safety value to ensure that the risk of excessive tension is monitored in a timely manner; Obtain the belt vibration data at the current moment, and select the maximum vibration data within the sliding time window length as the vibration limit safety value to comprehensively evaluate the dynamic operation state of the belt; Add the ratio of the current slip ratio to the slip ratio limit safety value, the ratio of the current actual tension to the tension limit safety value, and the ratio of the belt vibration data at the current moment to the vibration limit safety value, and then divide by the constant three to obtain the average comprehensive proportion as the comprehensive safety risk assessment value, realizing the unified risk quantification under multiple indicators; Continuously obtain the safety risk assessment value. If the comprehensive safety risk assessment values for two consecutive times do not exceed the risk threshold, maintain the conventional monitoring and the collection of multi-source tension data, and update the multi-source tension data and the comprehensive safety risk assessment value in real time. If the comprehensive safety risk assessment values for four consecutive times do not exceed the risk threshold, adaptively fine-tune the risk threshold; If the comprehensive safety risk assessment values for two consecutive times exceed the risk threshold and improve the system's adaptability to different working conditions, give an audible and visual alarm reminder, automatically issue a braking command, and control the driving wheel and the driven wheel to brake synchronously; And control the hybrid actuator to perform a quick action to ensure a quick response and equipment safety in case of anomalies; Push the alarm and action logs to the terminal in real time; According to the anomaly monitoring value and the comprehensive safety risk assessment value, automatically push the anomaly to the operation and maintenance personnel via text message, and automatically generate operation and maintenance suggestions and anomaly risk reports to realize intelligent operation and maintenance and scientific decision-making support.

[0046] Among them, the specific formula for the comprehensive safety risk assessment value is: ; In the formula, represents the comprehensive safety risk assessment value, reflecting the overall risk level of the current belt operation, and is used to control the limit safety and overall health; Represents the current slip ratio, which is a direct indicator reflecting slipping, jamming, and out-of-step conditions; Represents the limit safety value of the slip ratio; Represents the current actual tension, which reflects the tension borne by the belt in real time and is the main basis for judging overload and slack; Represents the limit safety value of the tension; Represents the belt vibration data at the current moment, which reflects problems such as belt jumping, impact, and abnormal vibration and is an important means for monitoring early faults; Represents the limit safety value of the vibration.

[0047] In this implementation plan, by calculating the safety ratios of the slip ratio, tension, and vibration in real time, comprehensively evaluating the integrated risks dynamically, and combining with the abnormal detection results to achieve hierarchical response and adaptive regulation. When the risk increases, it automatically links to braking, tensioning, and cloud alarm. When the risk subsides, it adaptively optimizes the thresholds and control parameters, and pushes the complete data, analysis, and suggestions to the operation and maintenance platform. It not only realizes accurate early warning and rapid protection but also provides decision-making support for long-term trend assessment, intelligent maintenance scheduling, and spare parts management, comprehensively improving the intrinsic safety and intelligent operation and maintenance capabilities of the system.

[0048] Referring to Figure 2 As shown, the second aspect of the present invention provides a tension optimization control system for a belt conveyor tensioning device in a coal mine underground, which is applied to the above-mentioned tension optimization control method for a belt conveyor tensioning device in a coal mine underground, and includes: a high-speed perception and real-time fusion module, a prediction compensation control module, a hybrid actuator module, an abnormal self-adaptation module, and a safety protection and cloud collaborative operation and maintenance module. Among them, the high-speed perception and real-time fusion module is used to collect multi-source tensioning data in real time, perform data preprocessing on the multi-source tensioning data, and establish a multi-source tensioning database; the prediction compensation control module is used to predict the coal flow load based on historical multi-source tensioning data and take dynamic compensation measures for the tensioning device according to the prediction result of the coal flow load; the hybrid actuator module is used to combine the multi-source tensioning data and the prediction result of the coal flow load, perform collaborative allocation on the hybrid actuator, and perform automatic regulation based on the collaborative allocation result; the abnormal self-adaptation module is used to detect abnormalities in real time based on the multi-source tensioning data and give early warnings according to the abnormal detection results; the safety protection and cloud collaborative operation and maintenance module is used to perform safety risk assessment according to the real-time multi-source tensioning data and take adaptive regulation measures in combination with the safety risk assessment result and the abnormal detection result.

[0049] In this implementation plan, integrating multi-source data acquisition, intelligent prediction compensation, hybrid actuator collaboration, real-time abnormal self-adaptation, and cloud security operation and maintenance can complete tensioning state perception, prediction, allocation, regulation, and cloud closed-loop feedback at the millisecond level. It not only realizes high-precision tension control and rapid risk response but also supports remote fault diagnosis, energy consumption analysis, and life prediction, comprehensively improving the intelligent level, operation reliability, and intrinsic safety of the underground conveying system.

[0050] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0051] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. As understood by those skilled in the art, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. Tension optimization control method for a belt conveyor tensioning device in a coal mine underground, characterized in that, include: S1, real-time collection of multi-source tensioning data, data preprocessing of the multi-source tensioning data, and establishment of a multi-source tensioning database; S2, predicting the coal flow load based on historical multi-source tensioning data, and taking dynamic compensation measures for the tensioning device according to the coal flow load prediction results; S3, combining multi-source tensioning data with coal flow load prediction results, coordinately allocates hybrid actuators, and performs automatic control based on the coordinated allocation results; S4, detects anomalies in real time based on multi-source tensioning data and issues early warnings based on the anomaly detection results; S5, conducts safety risk assessment based on real-time multi-source tensioning data, and takes adaptive control measures based on the safety risk assessment results and anomaly detection results.

2. The tension optimization control method for the belt conveyor tensioning device in the coal mine as claimed in claim 1, wherein The specific process of real-time acquisition of multi-source tensioning data, data preprocessing of the multi-source tensioning data, and establishment of a multi-source tensioning database is as follows: Real-time collection of multi-source tensioning data, including: measuring belt tension using FBG fiber strain sensors; collecting tension and vibration data using vector tension scales and MEMS triaxial vibration sensors; collecting coal flow load data using coal flow sensors and visual monitoring cameras, including: coal flow instantaneous flow rate, coal flow pulsation spectrum, and visual coal flow images; collecting current and torque data using current sensors and torque sensors; and collecting impeller speed data using speed sensors. Wavelet filtering is used to suppress high-frequency noise, eliminate sudden over-range signals and unreasonable extreme multi-source tension data, and use the Kalman filter interpolation method to fill in occasional missing data points, and standardize and normalize the multi-source tension data; The multi-source tensioning data is synchronously transmitted to the edge computing platform at the microsecond level and stored in the multi-source tensioning database.

3. The tension optimization control method for the belt conveyor tensioning device in the coal mine underground according to claim 1, characterized in that, The specific process of predicting coal flow load based on historical multi-source tension data is as follows: Obtain the current coal flow load data, process the visual coal flow image through the image processing algorithm, extract the mean value of the ROI area to obtain the feature mean of the current coal flow monitoring image; Based on the length of the sliding time window, the first-order derivative of the characteristic mean of the current coal flow monitoring image with respect to time is calculated to obtain the characteristic mean change rate of the coal flow monitoring image; The current coal flow load data is multiplied by the time series weight factor to obtain the time series weighted value. Based on the length of the sliding time window, the time series weighted values of all sampling points are accumulated and then divided by the length of the sliding time window to obtain the time series weighted sliding mean. The characteristic mean change rate of the coal flow monitoring image is multiplied by the visual mutation response weight factor to obtain the visual mutation response term. The coal flow load prediction value is obtained by adding the time series weighted sliding mean and the visual mutation response term.

4. The tension optimization control method for the belt conveyor tensioning device in the coal mine underground according to claim 1, characterized in that, The specific process of taking dynamic compensation measures for the tensioning device according to the coal flow load prediction result is as follows: According to the coal flow load prediction value, identify the sudden increase and decrease of coal flow, and adjust the movement amplitude and speed of the tensioning cylinder and servo screw in advance; If it is predicted that the coal flow increase value will exceed the sudden increase threshold within the next 200 milliseconds, the tension force setting value will be automatically increased, and the drive motor torque and power will be increased in advance; when the coal flow load prediction value suddenly increases and the tension and vibration data exceed the tension and vibration average values respectively, emergency tension and speed limit instructions will be issued in advance, and safety braking will be coordinated; If it is predicted that the coal flow reduction value exceeds the sudden reduction threshold, the tension is automatically reduced; and when the real-time tension is lower than the safety threshold, early warnings of belt slipping and belt breakage are issued, and the alarm and braking systems are automatically linked; The predicted value of the coal flow load, the actual coal flow load data curve, and the abnormal points are automatically uploaded to the cloud platform to generate inspection suggestions and spare part warnings.

5. The tension optimization control method for the belt conveyor tensioning device in the coal mine underground according to claim 1, characterized in that, The specific process of jointly allocating the hybrid actuator by combining multi-source tension data and the predicted result of the coal flow load is as follows: An electro-hydraulic servo cylinder is selected as the coarse adjustment mechanism, and a servo ball screw is selected as the fine adjustment mechanism to construct a hybrid actuator; Obtain the predicted value of the coal flow load, and calculate the expected tension value through a regression fitting algorithm to obtain the target tension value; Subtract the currently actually measured tension collected in real time from the target tension value to obtain the current tension compensation requirement value; Based on the sliding time window length, perform mean filtering on the current tension compensation requirement value, extract the low-frequency component that is stable and slowly changing to obtain the low-frequency tension requirement value, and multiply the low-frequency tension requirement value by the electro-hydraulic servo cylinder gain weight factor to obtain the collaborative allocation value of the electro-hydraulic servo cylinder; Subtract the low-frequency tension requirement value from the current tension compensation requirement value to obtain the high-frequency tension requirement value, and multiply the high-frequency tension requirement value by the servo ball screw gain weight factor to obtain the collaborative allocation value of the servo ball screw.

6. The tension optimization control method of the belt conveyor tensioning device in the coal mine shaft according to claim 1, characterized in that The specific process of automatic regulation based on the collaborative allocation result is as follows: Based on the collaborative allocation values of the electro-hydraulic servo cylinder and the servo ball screw, the edge controller issues the low-frequency and stable tension adjustment instructions to the electro-hydraulic servo cylinder in real time; the high-frequency and fast compensation instructions are issued to the servo ball screw; Collect the feedback signals of the electro-hydraulic servo cylinder and the servo ball screw. The feedback signals include actual thrust, displacement, and response speed, and compare them with the corresponding collaborative allocation values respectively. Dynamically adjust the gain weight factors of the electro-hydraulic servo cylinder and the servo ball screw according to the comparison error; if there are phenomena such as slow execution, insufficient response, and overshoot, automatically recalculate the collaborative allocation value; when it is found that the actual tensioning effect after allocation is abnormal, immediately execute speed limit and shutdown operations and issue an alarm; Archive all allocation instructions and feedback signals in real time, and comprehensively analyze to generate energy consumption analysis, life prediction, and maintenance suggestions.

7. The tension optimization control method for the belt conveyor tensioning device in the coal mine as claimed in claim 1, characterized in that, The specific process of real-time detection of abnormalities based on multi-source tension data is as follows: Obtain the current actual tension of the belt and the belt vibration data at the current moment. Based on the sliding time window, calculate the average value and standard deviation of the tension in the recent period to obtain the historical mean value and historical standard deviation of the tension respectively; and calculate the average value and standard deviation of the vibration data in the recent period to obtain the historical mean value and historical standard deviation of the vibration respectively; Subtract the average value of the tension history from the current actual tension and take the absolute value to obtain the tension deviation value. Divide the tension deviation value by the standard deviation of the tension history to obtain the degree of tension deviation. Multiply the degree of tension deviation by the tension anomaly weight factor to obtain the tension anomaly value. Subtract the average value of the vibration history from the current belt vibration data at the current moment and take the absolute value to obtain the vibration deviation value. Divide the vibration deviation value by the standard deviation of the vibration history to obtain the degree of vibration deviation. Multiply the degree of vibration deviation by the vibration anomaly weight factor to obtain the vibration anomaly value. Add the tension anomaly value and the vibration anomaly value to obtain the anomaly monitoring value.

8. The tension optimization control method for the belt conveyor tensioning device in the coal mine underground according to claim 1, characterized in that, The specific process of early warning based on the anomaly detection result is as follows: When the anomaly monitoring value is less than or equal to the anomaly threshold, it is determined to be in a normal state, and routine monitoring is maintained. Archive the multi-source tension data, anomaly monitoring value, and device status regularly. If it is continuously determined to be anomaly-free for a period of time, perform threshold optimization operations according to the actual monitoring situation. When the anomaly monitoring value is greater than the anomaly threshold, it is determined to be in an abnormal state, trigger an anomaly warning, activate the audible and visual alarm, and push the alarm information, including the anomaly monitoring value, timestamp, tension, and vibration data, to the operation and maintenance terminal. Start the emergency adjustment mode of the tensioning mechanism to quickly increase and relieve the tension. Automatically decelerate and stop. And automatically adjust the anomaly threshold, tension anomaly weight factor, and vibration anomaly weight factor according to the anomaly monitoring value and the actual anomaly feedback information.

9. The tension optimization control method for the belt conveyor tensioning device in the coal mine underground according to claim 1, characterized in that The specific process of performing a safety risk assessment based on the real-time multi-source tension data, combining the safety risk assessment result and the anomaly detection result, and taking adaptive control measures is as follows: Obtain the rotational speed data of the driving wheel and the driven wheel, calculate the ratio of the rotational speed difference between the driving wheel and the driven wheel to the rotational speed of the driving wheel to obtain the current slip ratio, and select the maximum slip ratio within the sliding time window length as the slip ratio limit safety value. Obtain the current actual tension, and select the maximum tension within the sliding time window length as the tension limit safety value. Obtain the current belt vibration data at the current moment, and select the maximum vibration data within the sliding time window length as the vibration limit safety value. Add the ratio of the current slip ratio to the slip ratio limit safety value, the ratio of the current actual tension to the tension limit safety value, and the ratio of the current belt vibration data at the current moment to the vibration limit safety value, and then divide by three constants to obtain the average comprehensive ratio as the comprehensive safety risk assessment value. Continuously obtain the safety risk assessment value. If the comprehensive safety risk assessment values for two consecutive times do not exceed the risk threshold, maintain routine monitoring and the collection of multi-source tension data, and update the multi-source tension data and the comprehensive safety risk assessment value in real time. If the comprehensive safety risk assessment values for four consecutive times do not exceed the risk threshold, adaptively fine-tune the risk threshold. If the comprehensive safety risk assessment values for two consecutive times exceed the risk threshold, give an audible and visual alarm reminder, automatically issue a braking command, and control the driving wheel and the driven wheel to brake synchronously. And control the hybrid actuator to perform a quick action. Push the alarm and action logs to the terminal in real time. Automatically push the anomaly to the operation and maintenance personnel via text message according to the anomaly monitoring value and the comprehensive safety risk assessment value, and automatically generate operation and maintenance suggestions and anomaly risk reports.

10. Belt conveyor tension optimization control system for underground coal mines, comprising: A high-speed perception and real-time fusion module, a prediction compensation control module, a hybrid actuator module, an anomaly self-adaptation module, and a safety protection and cloud collaborative operation and maintenance module, characterized in that: The high-speed perception and real-time fusion module is used to collect multi-source tension data in real time, perform data preprocessing on the multi-source tension data, and establish a multi-source tension database; The prediction compensation control module is used to predict the coal flow load based on historical multi-source tension data and take dynamic compensation measures for the tensioning device according to the prediction result of the coal flow load; The hybrid actuator module is used to combine the multi-source tension data and the prediction result of the coal flow load, perform collaborative allocation on the hybrid actuator, and perform automatic regulation based on the collaborative allocation result; The anomaly self-adaptation module is used to detect anomalies in real time based on multi-source tension data and give early warnings according to the anomaly detection results; The safety protection and cloud collaborative operation and maintenance module is used to perform safety risk assessment according to real-time multi-source tension data, and take adaptive regulation measures by combining the safety risk assessment result and the anomaly detection result.

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