A Smart Speed ​​Control Method for Conveyors Based on Sensor Data

Abstract

This invention belongs to the field of intelligent speed control technology for conveyors, specifically disclosing an intelligent speed control method for conveyors based on sensor data. This method includes: real-time acquisition of current speed, load, material flow, and operating status data of the conveyor; load trend analysis based on load and material flow data, combined with historical operating data, to predict future load change curves; determining the health status of the equipment based on key component status parameters; if healthy, generating a first smooth speed command based on the load curve and current speed; if unhealthy, determining a safe speed threshold and generating a second smooth speed command; and finally controlling the drive motor to perform speed regulation. This invention predicts the load change curve for a future set time period by real-time acquisition of material flow data and historical operating data, and generates smooth speed commands based on the prediction results using an S-curve planning algorithm, achieving forward-looking and smooth speed control.

Description

Technical Field

[0001] This invention belongs to the field of intelligent speed control technology for conveyors, and relates to an intelligent speed control method for conveyors based on sensor data. Background Technology

[0002] As a key continuous conveying equipment, the core task of conveyors is to achieve efficient and stable material transport. Traditional conveyor control systems generally adopt constant speed operation or speed regulation strategies based on simple thresholds. Constant speed operation ignores actual load conditions, maintaining the rated speed even under light load or no load, resulting in significant energy waste and high operating costs. At the same time, mechanical components are subjected to constant stress for a long time and cannot effectively buffer the impact of sudden load changes, which aggravates equipment wear, shortens service life, and increases maintenance costs.

[0003] For example, Chinese invention patent CN110967974A discloses an adaptive control method for coal flow balance based on rough set theory. This method collects coal flow state information, performs attribute reduction using rough set theory, and extracts control rules using a random forest algorithm. Finally, it achieves adaptive speed regulation of the scraper conveyor based on the rule base. This realizes response to the current operating conditions and adaptive control of coal flow balance.

[0004] The existing technologies mentioned above have the following shortcomings: 1. Currently, they mainly rely on the collection and rule matching of recent coal flow status information, lacking the ability to predict load trends in advance. Consequently, they cannot predict the continuous load curve for a future set time period based on material flow data and historical operating data, resulting in speed control commands lagging behind actual working condition changes. This makes it difficult to achieve advance smooth planning of speed, which not only limits the energy-saving effect but also easily causes mechanical and electrical shocks to the equipment.

[0005] 2. The current control strategy adjusts the speed based solely on the external material flow status, ignoring the operational health status of key components of the conveyor. When the equipment experiences sub-health or early-stage failure states such as excessive vibration or abnormal temperature, the system still outputs inappropriate high-speed commands based on efficiency targets, thereby exacerbating equipment wear and tear, accumulating failure risks, and potentially leading to unplanned shutdowns. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, a smart speed control method for conveyors based on sensor data is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides an intelligent speed control method for a conveyor based on sensor data, including: S1, real-time acquisition of the conveyor's current speed, load data, material flow data and operating status data.

[0008] S2. Based on the load data and material flow data, combined with the historical operating data of the conveyor, load trend analysis is performed, and the load change curve for a future set time period is predicted after calibration by correction coefficient.

[0009] S3. Based on the status parameters of each key component in the operating status data, determine whether the conveyor is operating healthily.

[0010] S4. If the conveyor is operating healthily, then based on the load change curve and the current speed, a first smooth speed command is generated through a speed planning algorithm.

[0011] S5. If the conveyor is not operating healthily, then the safe speed threshold of the conveyor is determined based on the operating status parameters that caused the unhealthy judgment, combined with the health decay coefficient and the preset speed constraint function. Based on the load change curve, the current speed and the safe speed threshold, a second smooth speed command is generated.

[0012] S6. Based on the smooth speed command, control the conveyor drive motor to perform speed adjustment operation.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention predicts the load change curve for a future set time period by collecting material flow data and historical operation data in real time, and generates a smooth speed command by using the S-curve planning algorithm based on the prediction results, thereby realizing the forward-looking and smooth control of speed, effectively avoiding speed regulation lag and speed change, and protecting mechanical equipment while improving energy saving effect.

[0014] (2) The present invention determines the health status of the equipment by measuring the operating status parameters of each key component of the conveyor, and determines the safe speed threshold based on the specific parameters when the equipment is unhealthy to limit the maximum operating speed. This enables the conveyor to reduce its speed when an abnormality occurs, avoiding the risk of forced high-speed operation under fault conditions, thereby enhancing the safety of the conveyor operation.

[0015] (3) By retrieving historical load sequences that match the current production rhythm, calculating correction coefficients and dynamically calibrating the basic load change curve, this invention effectively compensates for the deviation between theory and actual operation, enabling the load prediction results to adapt to different working conditions and equipment characteristics, thereby improving the prediction accuracy and the applicability of speed regulation strategies.

[0016] (4) By dynamically coordinating the starting point of load change with the theoretical shortest time required for smooth speed transition when generating speed commands, this invention determines the optimal start and end times of speed regulation, thus avoiding energy waste caused by rapid speed regulation due to insufficient remaining time or excessive time.

[0017] (5) By introducing load trend prediction based on historical and real-time data and safe speed closed-loop control based on equipment health status, this invention achieves a fundamental shift from passive response to forward-looking intelligent regulation, improves the timeliness and smoothness of speed regulation and the safety of equipment operation, and overcomes the current defects of speed regulation lag and neglect of equipment health status. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram showing the connections between the steps of the method of the present invention.

[0020] Figure 2 This is a schematic diagram illustrating the steps involved in predicting the load change curve over a predetermined time period according to the present invention.

[0021] Figure 3 This is a schematic diagram of the connection of the first smooth speed command generation step of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 As shown, the present invention provides an intelligent speed control method for a conveyor based on sensor data. The method includes: S1, real-time acquisition of the conveyor's current speed, load data, material flow data and operating status data.

[0024] The material flow data includes, but is not limited to: material batch information, rated weight of a single batch of material, and material arrival interval.

[0025] S2. Based on the load data and material flow data, combined with the historical operating data of the conveyor, load trend analysis is performed, and the load change curve for a future set time period is predicted after calibration by correction coefficient.

[0026] Please see Figure 2As shown, for example, the predicted load change curve for a future set time period includes: S2-1, obtaining the material arrival interval and batch information from the material flow data, and calculating the expected arrival time of each batch of materials within the future set time period.

[0027] Specifically, calculating the expected arrival time of each batch of materials within a future set time period includes: using the current time as the calculation time base, obtaining the arrival time of the current batch of materials from the material flow data, and reading the planned arrival intervals of subsequent batches. Then, starting from the current batch, the expected arrival times of each batch of materials are sequentially accumulated to obtain the future times when each batch of materials arrives at the conveyor inlet in sequence. This transforms the disordered material flow information into a time-ordered sequence of future events.

[0028] S2-2. Calculate the theoretical load rate curve of a single batch of material on the conveyor based on the rated weight of the material and the effective length of the conveyor. The effective length of the conveyor refers to the length of the section between the inlet and outlet of the conveyor used to carry the material.

[0029] Furthermore, the calculation of the theoretical load rate curve formed by a single batch of materials on the conveyor includes: S2-2-1, using the ratio of the effective length of the conveyor to the current speed as the passage time of a single batch of materials.

[0030] Preferably, to improve the accuracy of the theoretical load rate curve, the effective length of the conveyor can be dynamically compensated when calculating the material throughput time. Specifically, based on historical operating data, the system statistically analyzes the average deviation between the actual throughput distance of the material from the feed inlet to its uniform spread on the bearing section and the theoretical effective length, obtaining an effective throughput length compensation value. This compensation value is added to the theoretical effective length and used together in the calculation of the material throughput time, thereby compensating for the throughput time deviation caused by actual factors such as feed inlet congestion and material acceleration, making the generated theoretical load rate curve closer to the actual operating conditions.

[0031] S2-2-2, The ratio of the rated weight of a single batch of material to its effective length is taken as the theoretical load rate of the single batch of material on the conveyor.

[0032] S2-2-3. Construct a theoretical load rate curve with the theoretical load rate as the amplitude, the transit time of a single batch of materials as the duration, and the expected arrival time of a single batch of materials as the starting point.

[0033] S2-3. Based on the expected arrival time of each batch of materials, the theoretical load rate curves of each batch of materials are superimposed on the time axis to generate the basic load change curve.

[0034] S2-4. Retrieve the actual historical load sequence and its corresponding historical theoretical load curve that match the current production rhythm from the historical operating data of the conveyor, and calculate the correction coefficient between the historical load sequence and the corresponding historical theoretical load curve.

[0035] Furthermore, the correction coefficient between the calculated historical load sequence and the corresponding historical theoretical load curve includes: S2-4-1, obtaining the material flow rate within the current set time period from the material flow data and using it as the benchmark value for the current production rhythm.

[0036] S2-4-2. Traverse the historical operation data and calculate the average material flow rate of each historical cycle within the set time. If the deviation between the average material flow rate of the historical cycle and the current production rhythm benchmark value is within the preset tolerance range, then the historical cycle is determined to be a valid historical cycle.

[0037] To ensure data matching accuracy, the preset tolerance range is derived from statistical analysis of historical operating data. Specifically, it involves calculating the standard deviation of the average material flow rate for all historical periods in the historical database. Setting the preset tolerance range to ±k times the standard deviation means that historical periods whose differences from the current production rhythm benchmark are within the standard deviation are considered valid.

[0038] S2-4-3. Obtain the historical actual load sequence and its corresponding historical theoretical load curve within each effective historical period from the historical operating data of the conveyor, and then obtain the actual load value and theoretical load value at each sampling time.

[0039] S2-4-4. Based on the sampling time when the historical theoretical load curve value is not zero, calculate the ratio of the actual load value to the theoretical load value at the sampling time to obtain the instantaneous ratio.

[0040] S2-4-5. Calculate the mean of each instantaneous ratio and use the result of the mean calculation as a correction coefficient.

[0041] S2-5. Use the correction coefficient to calibrate the basic load change curve, output the calibrated continuous load prediction value, and form a load change curve for predicting the future set time period.

[0042] The steps for obtaining the load variation curve include: first, multiplying the theoretical load prediction value at each moment on the basic load variation curve by the correction coefficient to obtain the corrected load prediction value at each calibration moment; and finally connecting the corrected load prediction values ​​at all calibration moments to form a calibrated continuous load prediction curve.

[0043] S3. Based on the status parameters of each key component in the operating status data, determine whether the conveyor is operating healthily.

[0044] For example, the key components include, but are not limited to: drive motor, gearbox, transmission roller, load-bearing idler group and conveyor belt. The state parameters of the key components are illustrated as follows: The state parameters of the drive motor include the operating temperature of the three-phase winding, the input current and the vibration acceleration of the bearing.

[0045] For example, determining whether the conveyor is operating healthily includes: obtaining the status parameters of each key component of the conveyor from the operating status data, and comparing them with the corresponding preset status thresholds.

[0046] It should be noted that obtaining the preset state threshold is a process that combines standards and data: First, the rated parameters provided by the equipment manufacturer and industry safety standards are used as the initial benchmark for threshold setting. Then, based on the historical operating data of the conveyor, the initial threshold is optimized, and the normal fluctuation range of each state parameter during the fault-free operation period of the system is statistically analyzed. For example, the average value plus or minus three times the standard deviation is taken as the normal range boundary. Finally, the smaller value between the initial threshold and the upper limit of the statistical boundary is taken as the preset state threshold. The upper limit of the statistical boundary can be calculated based on historical fault-free data, such as the average value plus three times the standard deviation.

[0047] Based on the characteristics of normal distribution, the mean plus three times the standard deviation is used as the upper limit of the statistical boundary. Its statistical significance lies in that it can cover the vast majority of fault-free historical operating data, thereby ensuring that the preset state threshold can effectively distinguish between normal fluctuations and real anomalies. In this way, while ensuring equipment safety, it can minimize misjudgments caused by normal data fluctuations, prevent unnecessary system slowdowns or shutdowns, and maintain the continuity of operation.

[0048] If the status parameters of each key component do not exceed the corresponding preset status threshold, the conveyor is deemed to be operating healthily.

[0049] If any of the status parameters of the key components exceed the corresponding preset status threshold, the conveyor is deemed to be operating unhealthily.

[0050] Based on engineering safety principles, the logic for determining the health of the conveyor's operation follows the engineering principles of fault and safety. Any abnormality in a single critical component is considered a potential threat to the safety of the entire system, thus triggering protection mechanisms. This minimizes the risk of escalating faults or safety accidents due to missed detections. For example, even if the motor temperature and belt tension are normal, if the roller bearing temperature exceeds the standard, the system will determine it as unhealthy and adopt a conservative operating strategy, preventing potential serious consequences such as bearing seizure or belt tearing.

[0051] S4. If the conveyor is operating healthily, then based on the load change curve and the current speed, a first smooth speed command is generated through a speed planning algorithm.

[0052] Please see Figure 3 As shown, for example, the generation of the first smooth speed command includes: S4-1, taking the starting time of a future set time period as the planning reference time for speed control.

[0053] S4-2. Based on the current speed, the target speed, and the preset maximum allowable acceleration and maximum allowable rate of change of acceleration, calculate the theoretical shortest time for a smooth transition from the current speed to the target speed.

[0054] It should be added that the preset maximum allowable acceleration refers to the maximum allowable acceleration value of the conveyor system during speed regulation. Its value is based on the conveyor's mechanical design parameters, drive motor performance, historical operating data, and safety regulations. Specifically, the theoretical value can be calculated using the maximum motor torque, gearbox transmission ratio, and drum radius provided by the equipment manufacturer, and calibrated by combining the statistical range of acceleration from historical operating data to ensure that it does not exceed the system's mechanical strength and safety limits.

[0055] The preset maximum allowable rate of change of acceleration refers to the rate of change of acceleration, used to control the smooth change of acceleration and avoid mechanical impact. Its value is based on the dynamic response characteristics of the conveyor system and can be obtained through experimental testing or statistical analysis of historical data.

[0056] Furthermore, the calculation of the theoretical shortest time for a smooth transition from the current speed to the target speed includes: S4-2-1, using the absolute value of the difference between the current speed and the target speed as the speed change.

[0057] S4-2-2 Calculate the critical velocity change based on the preset maximum allowable acceleration and the maximum allowable rate of change of acceleration.

[0058] It should be added that the critical velocity change is calculated using the following formula: In the formula This is the critical velocity change. For the maximum permissible acceleration, This represents the maximum permissible rate of change of acceleration.

[0059] The formula defines a critical state for system speed planning. When the speed change is not greater than the critical speed change, it indicates that the distance available for acceleration is short. The system must begin to decelerate before the acceleration reaches the maximum permissible acceleration. Therefore, the entire speed regulation process is dominated by the maximum permissible rate of change of acceleration, and the full acceleration capacity cannot be utilized.

[0060] When the change in velocity exceeds the critical change in velocity, it indicates that there is sufficient acceleration time. The system has enough time to first increase the acceleration to the maximum permissible acceleration at the maximum permissible rate of change, then maintain that maximum acceleration for a period of time, and finally reduce the acceleration to zero at the maximum permissible rate of change. At this point, the system's acceleration capability is fully utilized.

[0061] S4-2-3. If the change in velocity does not exceed the critical change in velocity, then multiply the value 2 by the square root of the ratio of the change in velocity to the maximum allowable rate of change of acceleration to obtain the theoretical shortest time.

[0062] Considering the constraint of the rate of change of acceleration, when the change in velocity does not exceed the critical change in velocity, the formula for calculating the theoretical shortest time is as follows: In the formula Theoretically, the shortest time is when the change in velocity does not exceed the critical change in velocity, the system does not have enough distance to increase the acceleration to its maximum. The entire acceleration and deceleration process forms a symmetrical triangular acceleration curve. During acceleration, the acceleration is smoothly increased from 0 to a peak value at the maximum permissible rate of change of acceleration, and then immediately and smoothly decreased to 0 at the same rate. The deceleration process is a perfect mirror image of the acceleration process. The system smoothly changes the acceleration from 0 to a negative peak value at the maximum permissible rate of change of acceleration, and then smoothly returns to 0 at the same rate.

[0063] S4-2-4. If the change in velocity is greater than the critical change in velocity, then add the quotient of the change in velocity and the maximum allowable acceleration to the quotient of the maximum allowable acceleration and the maximum allowable rate of change of acceleration to obtain the theoretical shortest time.

[0064] Based on the characteristics of the trapezoidal acceleration curve, when the change in velocity exceeds the critical change in velocity, the formula for calculating the theoretical shortest time is as follows: , It represents the shortest time required to complete a change in speed under ideal conditions. This is the additional transition time necessary to satisfy the acceleration rate of change constraint and achieve a smooth increase and decrease in acceleration.

[0065] The system has sufficient velocity variation range to execute complete trapezoidal acceleration curve planning, and its motion process includes two completely symmetrical phases: acceleration and deceleration. The complete motion process is analyzed as follows: First, the acceleration is smoothly increased from 0 to the maximum permissible acceleration using the maximum permissible rate of change of acceleration. This phase takes [time missing]. Subsequently, the vehicle maintains the maximum permissible acceleration for uniform acceleration; this phase constitutes the main part of the velocity change. Upon approaching the target velocity, it enters the deceleration phase. This phase is completely symmetrical to the acceleration phase and takes the same amount of time. First, the acceleration is smoothly reduced from the maximum permissible acceleration to 0 at the maximum permissible rate of change, also taking the same amount of time. .

[0066] S4-3. Based on the planning reference time and the theoretical shortest time, dynamically determine the start and end times of speed control.

[0067] Furthermore, determining the start and end times of speed regulation includes: S4-3-1, calculating the initial remaining time from the current time to the planning reference time, and comparing it with the theoretical shortest time.

[0068] S4-3-2. If the initial remaining time is greater than or equal to the theoretical shortest time, calculate the difference between the initial remaining time and the theoretical shortest time to obtain the speed regulation delay margin.

[0069] S4-3-3. Add the current time to the speed control delay margin to obtain the starting time of speed control, and use the planned reference time as the ending time of speed control.

[0070] S4-3-4. If the initial remaining time is less than the theoretical minimum time, then the current time is taken as the starting time of speed regulation, and the current time is added to the theoretical minimum time. The result is taken as the ending time of speed regulation.

[0071] S4-4. Based on the start time, end time, current speed and target speed of speed regulation, a smooth speed curve is generated through the S-curve planning algorithm and used as the first smooth speed command.

[0072] Preferably, generating the smooth velocity curve includes: comparing the velocity change with the critical velocity change, and adaptively constructing the curve using two modes based on the comparison result: when the velocity change is greater than the critical velocity change, the smooth velocity curve is constructed using an acceleration-limited mode. In this mode, the system has sufficient space to reach the maximum allowable acceleration, and its acceleration profile is trapezoidal. The steps for constructing the smooth velocity curve are as follows: first, the ratio of the preset maximum allowable acceleration to the maximum allowable rate of change of acceleration is used as the acceleration period. .

[0073] Subtract the acceleration period from the quotient of the change in velocity and the maximum permissible acceleration to obtain the time of uniform acceleration. That is, the stage in which the acceleration remains constant at the maximum permissible acceleration. .

[0074] The starting moment of speed regulation Based on this, the end times of each stage are determined sequentially: End time of the acceleration phase: The end of the uniform acceleration phase: The end time of the uniform deceleration segment: The uniform deceleration phase reduces acceleration from its maximum permissible rate of change. It drops to 0, consistent with the time taken for the acceleration phase.

[0075] Finally, based on the above timeline sequence Current speed, target speed, and adherence to... and The constraints are used to generate a complete S-curve velocity profile through integration, which serves as the first smooth velocity command. The S-curve velocity profile encompasses the entire process of smooth acceleration rise, constant acceleration, and smooth acceleration fall.

[0076] When the velocity change is less than or equal to the critical velocity change, a smooth velocity curve is constructed using a rate-limited acceleration mode. In this mode, the system does not have sufficient space to reach... Its acceleration profile is triangular. The steps for constructing a smooth velocity curve are as follows: First, calculate the peak acceleration that the system can actually achieve during this speed adjustment according to the formula. , .

[0077] The ratio of peak acceleration to the maximum permissible rate of change of acceleration is taken as the acceleration period. ,Right now .

[0078] Then, based on the starting moment of speed regulation Based on this, determine the key time points and the end time of the acceleration phase: Speed ​​regulation termination time: .

[0079] Finally, based on time nodes Current speed, target speed, and adherence to... and The constraints generate S-curve speed commands.

[0080] S5. If the conveyor is not operating healthily, then the safe speed threshold of the conveyor is determined based on the operating status parameters that caused the unhealthy judgment, combined with the health decay coefficient and the preset speed constraint function. Based on the load change curve, the current speed and the safe speed threshold, a second smooth speed command is generated.

[0081] For example, determining the safe speed threshold of the conveyor includes: counting the number of unhealthy parameters and the total number of state parameters, and using the ratio of the two as the health decay coefficient.

[0082] To further optimize, different weights can be assigned to the criticality of equipment safe operation based on each state parameter, and the health decay coefficient can be obtained based on weighted calculation.

[0083] The health decay coefficient is input into a preset speed constraint function to obtain the safe speed threshold of the conveyor.

[0084] Considering the equipment safety protection requirements, the preset speed constraint function is a mathematical formula or lookup table function that maps the health decay coefficient to a safe speed threshold. When the equipment health decreases, the system can automatically and reasonably limit its maximum operating speed to achieve safety protection.

[0085] The expression for the velocity constraint function is: .

[0086] In the formula For the safe speed threshold, The health decay coefficient, The rated maximum speed designed for the conveyor. The minimum safe operating speed designed for the conveyor.

[0087] This function establishes a linear speed limit: when the equipment is in perfect health, the safe speed threshold equals the conveyor's designed maximum rated speed. As health gradually deteriorates, the safe speed threshold decreases linearly. When all parameters are abnormal, the speed is limited to the conveyor's designed minimum safe operating speed.

[0088] For example, generating the second smooth speed instruction includes: querying a preset load-speed mapping relationship based on the load change curve to determine the desired target speed.

[0089] Specifically, the preset load-speed mapping relationship defines the correspondence between the conveyor load rate and the optimal operating speed. Its core is to minimize the system's unit energy consumption while ensuring conveying efficiency through dynamic matching of speed and load.

[0090] The establishment and optimization of the mapping relationship combines theoretical analysis, experimental verification, and data-driven optimization. First, based on the mechanical and energy consumption model of the conveyor system, a theoretical relationship between load, speed, and energy efficiency is constructed. In the low-load range, system power consumption mainly consists of the inherent frictional resistance of components such as the conveyor belt and idlers; reducing speed can significantly reduce this type of power consumption. However, in the high-load range, material conveying and lifting power consumption becomes dominant, requiring a higher speed to ensure throughput capacity.

[0091] The theoretical relationship is then calibrated using experimental data or high-fidelity system simulation to fit a continuous target speed curve that characterizes the system’s optimal energy efficiency across the full load range, i.e., the optimal energy efficiency benchmark curve.

[0092] To achieve rapid response in the control system, the optimal energy efficiency baseline curve is discretized into several continuous intervals based on load rate, and a desired target speed is assigned to each interval, thereby generating a load-speed mapping lookup table. The system queries this table to map the predicted load value to the specific desired target speed.

[0093] The mapping relationship is configurable and adaptive. Its mapping strategy can be initially calibrated based on the specific conveyor's design parameters. For example, for long-distance conveyors, due to their higher proportion of inherent frictional resistance, the target speed setting in the light-load range will be adjusted accordingly.

[0094] In addition, after the system is put into operation, it enters the online self-optimization stage. By continuously collecting load, speed and actual energy consumption data during operation, and based on the preset energy efficiency evaluation index, it uses the optimization algorithm to periodically or trigger the interval threshold or target speed value in the mapping lookup table to make dynamic corrections, so that the system's energy efficiency control strategy can continuously evolve and optimize with the changes in actual operating conditions.

[0095] The target speed is compared with the safe speed threshold. If the target speed is less than or equal to the safe speed threshold, the restricted target speed is set as the target speed.

[0096] If the target speed is greater than the safe speed threshold, then the restricted target speed is set to the safe speed threshold.

[0097] Based on the current speed and the restricted target speed, a second smooth speed command is generated according to the method for generating the first smooth speed command.

[0098] S6. Based on the smooth speed command, control the conveyor drive motor to perform speed adjustment operation.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0100] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0102] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0103] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A sensor data based conveyor intelligent speed control method, characterized by: The method comprises: S1, real-time acquisition of current speed, load data, material flow data and running state data of the conveyor; S2, based on the load data and material flow data, combined with historical running data of the conveyor, load trend analysis is carried out, and after calibration by a correction coefficient, the load change curve of a future set time period is predicted; S3, based on the state parameters of each key component in the running state data, it is determined whether the conveyor is running healthily; S4, if the conveyor is running healthily, based on the load change curve and the current speed, a first smooth speed instruction is generated by a speed planning algorithm; S5, if the conveyor is not running healthily, according to the running state parameters that lead to the unhealthy determination, combined with a health attenuation coefficient and a preset speed constraint function, a safe speed threshold of the conveyor is determined, and based on the load change curve, the current speed and the safe speed threshold, a second smooth speed instruction is generated; S6, based on the smooth speed instruction, the conveyor drive motor is controlled to perform speed regulation operation.

2. The sensor data based intelligent speed control method of conveyors as claimed in claim 1 wherein: The prediction of the load change curve of the future set time period comprises: Obtain the material arrival interval length and batch information from the material flow data, calculate the expected arrival time of each batch of materials in the future set time period; According to the rated weight of a single batch of materials and the effective length of the conveyor, the theoretical load rate curve formed by a single batch of materials on the conveyor is calculated; According to the expected arrival time of each batch of materials, the theoretical load rate curves of each batch of materials are superimposed on the time axis to generate a basic load change curve; From the historical running data of the conveyor, retrieve the actual historical load sequence and the corresponding historical theoretical load curve that match the current production rhythm, and calculate the correction coefficient between the historical load sequence and the corresponding historical theoretical load curve; The basic load change curve is calibrated by using the correction coefficient, and the calibrated continuous load prediction value is output to form the load change curve of the future set time period.

3. The sensor data based intelligent speed control method of conveyors as claimed in claim 2, wherein: The calculation of the theoretical load rate curve formed by a single batch of materials on the conveyor comprises: The ratio of the effective length of the conveyor to the current speed is taken as the passing time of a single batch of materials; The ratio of the rated weight of a single batch of materials to the effective length is taken as the theoretical load rate of a single batch of materials on the conveyor; The theoretical load rate curve is constructed with the theoretical load rate as the amplitude, the passing time of a single batch of materials as the duration, and the expected arrival time of a single batch of materials as the starting point.

4. The sensor data based intelligent speed control method of conveyors as claimed in claim 2, wherein: The calculation of the correction coefficient between the historical load sequence and the corresponding historical theoretical load curve comprises: Obtain the material flow in the current set time period from the material flow data and take it as the current production rhythm reference value; Traverse the historical running data, calculate the average material flow in the set time of each historical period, and if the deviation of the average material flow of the historical period from the current production rhythm reference value is within the preset tolerance range, the historical period is determined as an effective historical period; From the historical running data of the conveyor, obtain the historical actual load sequence and the corresponding historical theoretical load curve in each effective historical period, and then obtain the actual load value and the theoretical load value at each sampling time; Based on the sampling time when the historical theoretical load curve value is not zero, a ratio of an actual load value to a theoretical load value at the sampling time is calculated to obtain each instantaneous ratio value; Mean value calculation is performed on each instantaneous ratio value, and the mean value calculation result is taken as a correction coefficient.

5. The sensor data based intelligent speed control method of conveyors as claimed in claim 1, wherein: The determination of whether the conveyor operation is healthy includes: State parameters of each key component of the conveyor are obtained from the operation state data, and are compared with corresponding preset state thresholds respectively; If the state parameters of each key component do not exceed the corresponding preset state thresholds, it is determined that the conveyor operation is healthy; If there is a state parameter of each key component that exceeds the corresponding preset state threshold, it is determined that the conveyor operation is unhealthy.

6. The sensor data based intelligent speed control method of conveyors as claimed in claim 1, wherein: The generation of the first smooth speed instruction includes: The starting time of the future set time period is taken as a planning reference time of speed regulation; Based on the current speed, the target speed, and the preset maximum allowed acceleration and the maximum allowed acceleration change rate, a theoretical shortest time for smooth transition from the current speed to the target speed is calculated; Based on the planning reference time and the theoretical shortest time, a speed regulation starting time and a speed regulation ending time are dynamically determined; Based on the speed regulation starting time, the speed regulation ending time, the current speed, and the target speed, a smooth speed curve is generated through an S-curve planning algorithm, and is taken as the first smooth speed instruction.

7. The sensor data based intelligent speed control method of conveyors as claimed in claim 6, wherein: The calculation of the theoretical shortest time for smooth transition from the current speed to the target speed includes: The absolute value of the difference between the current speed and the target speed is taken as a speed change amount; A critical speed change amount is calculated based on the preset maximum allowed acceleration and the maximum allowed acceleration change rate; If the speed change amount does not exceed the critical speed change amount, the square root of the ratio of twice the speed change amount to the maximum allowed acceleration change rate is taken as the theoretical shortest time; If the speed change amount is greater than the critical speed change amount, the ratio of the speed change amount to the maximum allowed acceleration, and the ratio of the maximum allowed acceleration to the maximum allowed acceleration change rate are calculated respectively, and the sum of the two ratios is taken as the theoretical shortest time.

8. The sensor data based intelligent speed control method of conveyors as claimed in claim 6, wherein: The determination of the speed regulation starting time and the speed regulation ending time includes: An initial remaining time length from the current time to the planning reference time is calculated, and is compared with the theoretical shortest time; If the initial remaining time length is greater than or equal to the theoretical shortest time, a difference between the initial remaining time length and the theoretical shortest time is calculated to obtain a speed regulation delay allowance; The current time and the speed regulation delay allowance are added to obtain the speed regulation starting time, and the planning reference time is taken as the speed regulation ending time; If the initial remaining time length is less than the theoretical shortest time, the current time is taken as the speed regulation starting time, and the current time and the theoretical shortest time are added to obtain the speed regulation ending time.

9. The sensor data based intelligent speed control method of conveyors as claimed in claim 1, wherein: The determination of the safe speed threshold of the conveyor includes: The number of unhealthy parameters and the total number of state parameters are counted, and the ratio of the two is taken as a health decay coefficient; The health decay coefficient is input into a preset speed constraint function to map a safe speed threshold of the conveyor.

10. The sensor data based intelligent speed control method of conveyors as claimed in claim 6, wherein: The generation of the second smooth speed instruction includes: An expected target speed is determined based on the load change curve and a preset load-speed mapping relationship; comparing the target speed with the safety speed threshold, if the target speed is less than or equal to the safety speed threshold, setting a limited target speed as the target speed; if the target speed is greater than the safety speed threshold, setting the limited target speed as the safety speed threshold; generating a second smooth speed instruction based on the current speed and the limited target speed according to a first smooth speed instruction generation method.

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