Ore flow visual inspection and belt motor cooperative intelligent control method and system

Abstract

The invention discloses an ore flow visual detection and belt motor cooperative intelligent control method and system, and relates to the technical field of ore conveying, and the method comprises the steps: obtaining a real-time visual image of an ore flow in a belt conveying process, and carrying out the feature extraction; based on a feature extraction result, constructing an ore flow operation state quantity and carrying out quantitative calculation; the quantitative calculation result is compared with a preset control threshold value to judge the ore flow load state; generating a cooperative control instruction of the belt motor according to the judgment result; and the control instruction is issued to a belt motor execution mechanism to achieve self-adaptive operation control. The operation state quantity is constructed based on the ore flow visual information, the belt load state is quantitatively judged and cooperatively linked with the belt motor control, dynamic matching of the conveying load and the operation capacity is achieved, the overload risk can be recognized in advance, the operation parameters can be adaptively adjusted, and the operation efficiency is improved. And the operation stability, the safety and the overall conveying efficiency of the belt conveying system are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of ore conveying technology, and in particular to a method and system for visual inspection of ore flow and intelligent control of belt motor. Background Technology

[0002] In industrial settings such as mining, mineral processing, and material handling, belt conveyors are widely used for the continuous transport of bulk materials such as ores. The operating efficiency and safety of belt conveyor systems are directly affected by the load conditions of the ore flow. When the ore flow load is too high or unevenly distributed, it can easily lead to belt slippage, motor overload, accelerated equipment wear, or even shutdown failures, affecting the continuity of production.

[0003] In existing technologies, the control of belt conveyor loads largely relies on indirect judgments based on motor current, torque, or weighing devices. These methods typically only reflect the overall load situation and struggle to promptly detect the distribution and accumulation changes of ore flow on the belt surface. Furthermore, their control accuracy and response speed are limited when material properties change or operating conditions fluctuate. In addition, traditional control methods often employ fixed thresholds or empirical parameters, lacking the ability to adapt to dynamic changes in ore flow, which can easily lead to over-adjustment or response lag.

[0004] With the development of industrial vision technology, it has become possible to obtain the status of ore flow using visual information. However, existing solutions are mostly focused on the detection or monitoring level and have failed to form an effective synergy with belt motor control, making it difficult to achieve closed-loop linkage between ore flow status perception and execution control. To address this, we propose an intelligent control method and system for ore flow visual detection and belt motor synergy. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for visual inspection of ore flow and intelligent control of belt motor in coordination, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent control of ore flow visual inspection and belt motor coordination, comprising the following steps:

[0007] Step 1: Acquire real-time visual images of the ore flow during the belt conveyor process and extract features;

[0008] Step 2: Construct the ore flow operation state variables based on the feature extraction results and perform quantitative calculations;

[0009] Step 3: Compare the quantitative calculation results with the preset control threshold to determine the ore flow load status;

[0010] Step 4: Generate coordinated control commands for the belt motor based on the judgment results;

[0011] Step 5: Send the control command to the belt motor actuator to achieve adaptive operation control.

[0012] Preferably, step 1 includes:

[0013] Step 1.1: Arrange at least one set of industrial vision acquisition devices above or to the side of the belt conveyor along the running direction to continuously image the ore flow conveying area;

[0014] Step 1.2: Perform time synchronization processing on the acquired images based on the belt running speed to eliminate the distortion caused by the change in image acquisition frequency and belt running speed;

[0015] Step 1.3: Perform noise suppression, adaptive illumination compensation, and background separation processing on the acquired images in sequence to reduce the interference of dust, illumination changes, and belt texture on ore identification;

[0016] Step 1.4: Extract the effective contour region of the ore flow from the preprocessed image to provide stable input for subsequent state variable calculations.

[0017] Preferably, step 2 includes:

[0018] Step 2.1: Based on the ratio of the projected area of ​​the ore flow in the image to the effective conveying area of ​​the belt, calculate the ore flow coverage parameter α, which is used to characterize the occupancy of the belt surface.

[0019] Step 2.2: Based on the height change of the ore flow profile relative to the belt reference plane, calculate the ore flow accumulation height parameter β, which is used to reflect the accumulation trend of ore in the conveying direction;

[0020] Step 2.3: Based on the grayscale change rate, particle distribution density, or edge complexity of the ore flow surface texture, calculate the ore flow density parameter γ, which is used to characterize the material density per unit volume.

[0021] Step 2.4: Use the coverage parameter α, the stacking height parameter β, and the density parameter γ as the basic feature set to describe the instantaneous operating state of the ore flow.

[0022] Preferably, the formula for calculating the ore flow operating state quantity F in step 2.4 is:

[0023] ;

[0024] Among them, w1 is used to characterize the influence weight of the ore flow coverage degree on the belt load, w2 is used to characterize the influence weight of the ore flow accumulation height on the belt load, and w3 is used to characterize the influence weight of the ore flow compaction degree on the belt load, and satisfies w1+w2+w3=1.

[0025] Preferably, step 3 includes:

[0026] The calculated ore flow operating state quantity F is compared with the preset load threshold T;

[0027] When F≥T, the ore flow is determined to be in a state of high load or overload risk.

[0028] When F < T, the ore flow is determined to be under normal load.

[0029] Preferably, the load threshold T is a dynamically adjusted threshold, and the adjustment method includes:

[0030] The load threshold T is updated based on the statistical distribution of the belt conveyor's rated conveying capacity, belt width, operating speed, and historical ore flow operating status.

[0031] When a sustained increase in the amplitude of ore flow fluctuations is detected, the threshold sensitivity is increased to identify overload risks in advance.

[0032] When the ore flow is in a stable operating state for a long period of time, the threshold range can be relaxed to improve the overall conveying efficiency.

[0033] Preferably, step 4 includes:

[0034] Step 4.1: When it is determined that the ore flow is under high load, generate a control command to limit the speed of the belt motor to increase or decrease the current speed.

[0035] Step 4.2: When it is determined that the ore flow is in a safe load state, generate control instructions to maintain the current operating parameters or appropriately increase the operating efficiency;

[0036] Step 4.3: Convert the control command into control parameters that match the belt motor driver, including speed, current, or output torque adjustment parameters;

[0037] Step 4.4: By controlling the operating status of the belt motor, the ore flow load and belt conveying capacity are dynamically matched.

[0038] Preferably, the generation of motor control commands in step 4 further includes:

[0039] Based on the deviation ΔF between the ore flow operating state quantity F and the threshold T;

[0040] When the deviation value ΔF increases, a greater reduction or load limiting control is applied to the belt motor;

[0041] When the deviation value ΔF decreases, progressive recovery control is performed on the belt motor to avoid system oscillations caused by frequent adjustments.

[0042] Preferably, the method further includes:

[0043] After the belt motor executes the control command, the belt running current, speed and ore flow images are collected as feedback data;

[0044] The deviation between the calculated results of the ore flow operation status and the actual operation status is analyzed based on feedback data.

[0045] Based on the analysis results, the weighting coefficients w1, w2, w3 and the load threshold T are corrected to enable the ore flow visual inspection and belt motor collaborative control strategy to have optimization capabilities.

[0046] A visual inspection and belt motor coordinated intelligent control system for ore flow, implementing the visual inspection and belt motor coordinated intelligent control method for ore flow as described above, includes:

[0047] A visual acquisition module, which is used to acquire real-time image data of the ore flow;

[0048] An image processing and feature extraction module is used to extract features of ore flow coverage, accumulation height, and density.

[0049] A state quantity calculation module, which is used to calculate the operating state quantities of the ore flow;

[0050] A threshold determination module is used to compare the running state quantity with a preset threshold.

[0051] The control decision module is used to generate belt motor coordinated control commands based on the comparison results;

[0052] The motor execution module is used to receive and execute the control commands to realize intelligent control of the belt conveyor.

[0053] The technical effects and advantages of this invention are as follows:

[0054] This invention acquires real-time visual images of ore flow and extracts features to construct ore flow operating state variables, quantitatively describing the ore flow load state. This transforms belt load determination from traditional indirect electrical parameter judgment to a comprehensive judgment based on the actual material state. By comparing the operating state variables with dynamically adjusted control thresholds, it can accurately distinguish between normal and high load states. Based on the judgment results, it generates belt motor coordinated control commands that match the operating conditions, achieving dynamic matching between ore flow load and belt conveying capacity. By sending control commands to the belt motor actuator, it achieves adaptive adjustment of belt operating state, effectively reducing the risk of motor overload and abnormal belt operation, improving the stability and safety of the conveying process, and enhancing the overall system operating efficiency and intelligence level. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention, but do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0057] 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.

[0058] This invention provides, for example Figure 1 The method for visual inspection of ore flow and intelligent control of belt motors, as shown, includes the following steps:

[0059] Step 1: Acquire real-time visual images of the ore flow during the belt conveyor process and extract features;

[0060] Step 1 includes:

[0061] Step 1.1: Arrange at least one set of industrial vision acquisition devices above or to the side of the belt conveyor along the running direction to continuously image the ore flow conveying area;

[0062] Step 1.2: Perform time synchronization processing on the acquired images based on the belt running speed to eliminate the distortion caused by the change in image acquisition frequency and belt running speed;

[0063] Step 1.3: Perform noise suppression, adaptive illumination compensation, and background separation processing on the acquired images in sequence to reduce the interference of dust, illumination changes, and belt texture on ore identification;

[0064] Step 1.4: Extract the effective contour region of the ore flow from the preprocessed image to provide stable input for subsequent state variable calculations;

[0065] In this embodiment, step 1 is used to achieve stable and continuous acquisition of the ore flow state during belt conveying, which is the basis for subsequent ore flow state quantity calculation and motor coordinated control.

[0066] Specifically, in step 1.1, by arranging at least one set of industrial vision acquisition devices above or to the side above the belt conveyor along the running direction, the vision acquisition field of view covers the effective conveying area of ​​the belt, thereby enabling complete acquisition of the surface morphology, accumulation state and distribution of the ore flow during the conveying process. The industrial vision acquisition devices can be industrial cameras or line scan cameras to meet the requirements of continuous and high-frequency acquisition.

[0067] In step 1.2, considering the fluctuation of belt speed, the system performs time synchronization processing on the acquired images based on the belt speed and corrects the image acquisition time axis to keep the belt displacement between adjacent images consistent, thereby eliminating the problems of image stretching, compression or resampling caused by changes in running speed and improving the temporal consistency of visual data.

[0068] In step 1.3, in order to reduce the interference of dust, light changes and belt surface texture on ore identification at the mine site, noise suppression, adaptive lighting compensation and background separation are performed on the acquired image in sequence. Noise suppression is used to weaken random interference points, adaptive lighting compensation is used to reduce the impact of uneven brightness on image features, and background separation is used to separate the ore flow area from the belt background, thereby highlighting the effective information of the ore flow.

[0069] In step 1.4, based on the image preprocessed above, the contour of the ore flow region is extracted to obtain the effective contour region of the ore flow, providing stable and reliable input data for subsequent calculation of state quantities such as ore flow coverage, accumulation height and density;

[0070] Through the implementation of step 1 above, the present invention can stably acquire high-quality visual information of ore flow in complex mining transportation environments, effectively reducing the impact of dust, lighting changes and belt texture on the recognition results; at the same time, by introducing time synchronization processing based on belt running speed, the consistency between visual data and actual transportation process is ensured, providing a reliable data foundation for the accurate calculation of subsequent ore flow state quantities and belt motor coordinated control, thereby improving the overall detection accuracy and control stability of the system.

[0071] Step 2: Construct the ore flow operation state variables based on the feature extraction results and perform quantitative calculations;

[0072] Step 2 includes:

[0073] Step 2.1: Based on the ratio of the projected area of ​​the ore flow in the image to the effective conveying area of ​​the belt, calculate the ore flow coverage parameter α, which is used to characterize the occupancy of the belt surface.

[0074] Step 2.2: Based on the height change of the ore flow profile relative to the belt reference plane, calculate the ore flow accumulation height parameter β, which is used to reflect the accumulation trend of ore in the conveying direction;

[0075] Step 2.3: Based on the grayscale change rate, particle distribution density, or edge complexity of the ore flow surface texture, calculate the ore flow density parameter γ, which is used to characterize the material density per unit volume.

[0076] Step 2.4: Use the coverage parameter α, the stacking height parameter β, and the density parameter γ as the basic feature set to describe the instantaneous operating state of the ore flow.

[0077] The formula for calculating the ore flow operating state quantity F in step 2.4 is as follows:

[0078] ;

[0079] Where w1 is used to characterize the influence weight of ore flow coverage on belt load, w2 is used to characterize the influence weight of ore flow accumulation height on belt load, and w3 is used to characterize the influence weight of ore flow compaction on belt load, and satisfies w1+w2+w3=1.

[0080] In this embodiment, step 2 is used to uniformly quantify the operating status of the ore flow based on the completion of visual feature extraction of the ore flow, so as to provide comparable and calculable status indicators for subsequent load determination and motor coordinated control.

[0081] Specifically, in step 2.1, based on the effective contour area of ​​the ore flow obtained in step 1, the projected area of ​​the ore flow in the current image is calculated and normalized with the effective conveying area of ​​the belt to obtain the ore flow coverage parameter α. This parameter can reflect the degree of ore occupancy on the belt surface. The higher the coverage, the greater the amount of material carried by the belt per unit time.

[0082] In this embodiment, the ore flow coverage parameter α is calculated as follows:

[0083] First, after image preprocessing and background separation, the effective projection area of ​​the ore flow in the image is determined, and the pixel area S of this projection area is calculated. o ;

[0084] Simultaneously, the effective conveying area of ​​the belt in the same image is determined, and its corresponding pixel area S is calculated. b ;

[0085] By normalizing the above area, the ore flow coverage parameter α is obtained, and its calculation formula is as follows:

[0086] ;

[0087] Using the above calculation method, the coverage parameter α can intuitively reflect the proportion of ore on the conveyor belt surface. The calculation process is simple, stable, and unaffected by changes in ore color or local accumulation morphology.

[0088] In step 2.2, the height variation of the ore flow profile in the vertical direction is analyzed with the belt reference plane as a reference, and the ore flow accumulation height parameter β is calculated. The accumulation height parameter is used to characterize the accumulation trend of the ore flow during the transportation process and can reflect whether the ore flow has local accumulation or uneven distribution.

[0089] In this embodiment, the ore flow accumulation height parameter β is calculated as follows:

[0090] Based on the ore flow profile region, multiple sampling positions are selected in the direction perpendicular to the belt running direction to calculate the vertical height value of the ore flow profile points relative to the belt reference plane.

[0091] Statistical processing of the height values ​​yields the average accumulation height H of the ore flow at the current moment. avg ;

[0092] Compare the average stacking height with the allowable reference height H of the belt. ref After normalization, the stacking height parameter β is obtained, and its calculation formula is as follows:

[0093] ;

[0094] By normalizing the stacking height, the stacking height parameter β has a unified dimension, which facilitates its integration with other parameters and can effectively reflect the impact of the local stacking height of the ore flow on the belt load.

[0095] In step 2.3, based on the texture features of the ore flow surface, the gray scale change rate, particle distribution density or edge complexity of the ore flow are analyzed, and the ore flow density parameter γ is calculated. The density parameter is used to characterize the compactness of the ore in a unit volume, thereby indirectly reflecting the influence of material density change on belt load.

[0096] In this embodiment, the ore flow density parameter γ is calculated as follows:

[0097] Within the effective contour area of ​​the ore flow, extract image grayscale information and calculate grayscale change rate or edge pixel count;

[0098] Based on the grayscale change rate or edge complexity, the distribution density of ore particles within a unit area is determined, resulting in the texture density value D. o ;

[0099] Compare the texture density value with the preset reference density Dref After normalization, the density parameter γ is obtained, and its calculation formula is as follows:

[0100] ;

[0101] By introducing the density parameter γ, it is possible to distinguish between different ore flow states such as "high-density but loose" and "low-density but dense," making the ore flow operation state parameters more sensitive to changes in material density.

[0102] In step 2.4, the coverage parameter α, the stacking height parameter β, and the density parameter γ are used as the basic feature set to describe the instantaneous operating state of the ore flow. Based on this, the ore flow operating state quantity F is constructed. Through calculation, the multidimensional ore flow features are uniformly mapped into a single operating state quantity, thereby realizing a comprehensive quantitative description of the ore flow operating state.

[0103] Through the implementation method in step 2, this invention fuses and quantifies multi-dimensional visual features such as the coverage, accumulation height, and density of the ore flow to form an ore flow operating state quantity with clear physical meaning. This transforms complex and discrete visual information into comparable and identifiable numerical indicators. This state quantity can comprehensively reflect the combined impact of the ore flow on the belt load, providing a unified and stable calculation basis for subsequent load threshold determination and belt motor coordinated control, thereby improving the accuracy of the control strategy and the reliability of system operation.

[0104] Step 3: Compare the quantitative calculation results with the preset control threshold to determine the ore flow load status;

[0105] Step 3 includes:

[0106] The calculated ore flow operating state quantity F is compared with the preset load threshold T;

[0107] When F≥T, the ore flow is determined to be in a state of high load or overload risk.

[0108] When F < T, the ore flow is determined to be under normal load.

[0109] The load threshold T is a dynamically adjusted threshold, and the adjustment methods include:

[0110] The load threshold T is updated based on the statistical distribution of the belt conveyor's rated conveying capacity, belt width, operating speed, and historical ore flow operating status.

[0111] When a sustained increase in the amplitude of ore flow fluctuations is detected, the threshold sensitivity is increased to identify overload risks in advance.

[0112] When the ore flow is in a stable operating state for a long period of time, the threshold range can be relaxed to improve the overall conveying efficiency.

[0113] In this embodiment, step 3 is used to determine the current load state of the ore flow based on the calculation of the ore flow operating state, thereby providing a clear decision basis for the subsequent belt motor coordinated control.

[0114] Specifically, the ore flow operating status quantity F calculated in step 2 is first input into the load determination module and compared with the preset load threshold T. When the ore flow operating status quantity F is greater than or equal to the load threshold T, it indicates that the current ore flow's comprehensive load on the belt conveyor system is close to or exceeds the safe operating range. At this time, the ore flow is determined to be in a high load or overload risk state. When the ore flow operating status quantity F is less than the load threshold T, it indicates that the ore flow load is within the acceptable range of the belt conveyor system. At this time, the ore flow is determined to be in a normal load state.

[0115] In this embodiment, the load threshold T is not a fixed value, but is dynamically adjusted according to the operating conditions of the belt conveyor system. Specifically, the system comprehensively considers the rated conveying capacity of the belt conveyor, the belt width and the current operating speed, and combines the historical statistical distribution of the ore flow operating status over a period of time to periodically update the load threshold T so that the threshold can adapt to changes in conveying capacity under different operating conditions.

[0116] In addition, when the fluctuation range of the ore flow operation status is detected to be continuously increasing over multiple consecutive sampling periods, the system increases the sensitivity of the load threshold judgment, making the load status judgment more conservative, thereby identifying potential overload risks in advance; when the ore flow operation status remains stable for a long period of time, the system appropriately relaxes the load threshold range to reduce unnecessary control intervention and improve overall conveying efficiency.

[0117] Through the implementation of step 3, the present invention can accurately determine the load status based on the quantified ore flow operating status, and by introducing a dynamically adjusted load threshold, the load determination result can adapt to different conveying capacities and operating conditions; it can identify overload risks in advance when ore flow fluctuations intensify, and reduce redundant control when operation is stable, thereby ensuring the safety of belt conveying while taking into account conveying efficiency, and improving the reliability and intelligence level of system operation.

[0118] Step 4: Generate coordinated control commands for the belt motor based on the judgment results;

[0119] Step 4 includes:

[0120] Step 4.1: When it is determined that the ore flow is under high load, generate a control command to limit the speed of the belt motor to increase or decrease the current speed.

[0121] Step 4.2: When it is determined that the ore flow is in a safe load state, generate control instructions to maintain the current operating parameters or appropriately increase the operating efficiency;

[0122] Step 4.3: Convert the control commands into control parameters that match the belt motor driver, including speed, current, or output torque adjustment parameters;

[0123] Step 4.4: By controlling the operating status of the belt motor, the ore flow load and belt conveying capacity are dynamically matched.

[0124] Step 4, which involves generating motor control commands, also includes:

[0125] Based on the deviation ΔF between the ore flow operating state quantity F and the threshold T;

[0126] When the deviation value ΔF increases, the belt motor is subjected to a greater reduction in speed or load limiting control.

[0127] When the deviation value ΔF decreases, a gradual recovery control is implemented on the belt motor to avoid system oscillations caused by frequent adjustments;

[0128] In this embodiment, step 4 is used to generate a belt motor coordinated control command that matches the current operating condition based on the completion of the ore flow load status determination, so as to realize the dynamic coordination between the ore flow load and the belt conveying capacity.

[0129] Specifically, in step 4.1, when step 3 determines that the ore flow is in a high load or overload risk state, the control module generates a control command to limit the speed of the belt motor to increase or decrease the current speed according to the load determination result, so as to reduce the amount of material entering the belt system per unit time, thereby reducing the instantaneous load on the belt and motor and avoiding abnormal working conditions such as slippage, overflow or mechanical impact.

[0130] In step 4.2, when the ore flow is determined to be in a safe load state, the control module generates control instructions to maintain the existing operating parameters or moderately increase the operating efficiency within a safe range based on the current operating status, so as to make full use of the belt conveyor capacity and improve the overall conveying efficiency of the system.

[0131] In step 4.3, the above control commands are further converted into control parameters that match the belt motor driver, including adjustment parameters for motor speed, current or output torque, and sent to the belt motor actuator through the motor drive interface to achieve precise control of the belt motor's operating status.

[0132] In step 4.4, by continuously adjusting the operating status of the belt motor, the actual load level of the ore flow is dynamically matched with the conveying capacity of the belt conveyor system, thereby achieving stable and continuous conveying operation under different ore flow conditions.

[0133] Furthermore, in this embodiment, the generation of motor control commands also introduces a collaborative control mechanism based on deviation values. Specifically, the motor control strategy is refined according to the deviation value △F between the ore flow operating state quantity F and the load threshold T. When the deviation value △F increases, it indicates that the current load deviates more from the safe range, and the control module performs a larger deceleration or load limiting control on the belt motor to quickly suppress the upward trend of the load. When the deviation value △F decreases, the control module performs a gradual recovery control on the belt motor, so that the motor operating parameters gradually return to the target range, thereby avoiding system oscillation or operational instability caused by frequent adjustments.

[0134] Through the implementation of step 4, the present invention can generate differentiated belt motor control commands based on the ore flow load state and its deviation degree, realizing the transformation from "simple threshold control" to "continuous collaborative control"; it can quickly suppress risks when the load increases and smoothly restore the operating state when the load decreases, thereby effectively reducing the impact load and frequent start-stop phenomenon of the belt motor, and improving the stability, reliability and service life of the conveying system.

[0135] Step 5: Send control commands to the belt motor actuator to achieve adaptive operation control;

[0136] In this embodiment, step 5 is used to send the belt motor cooperative control command generated in step 4 to the belt motor actuator, and realize the adaptive operation control of the belt conveyor system through the execution result;

[0137] Specifically, the control module sends the converted motor control parameters to the belt motor actuator through a communication interface established with the belt motor driver. These control parameters include, but are not limited to, motor speed, current limit values, or output torque setpoints. Upon receiving the control commands, the belt motor actuator adjusts the motor's operating status in real time according to the control parameters, thereby changing the belt's conveying speed and load-bearing capacity.

[0138] During operation, changes in the belt motor's operating status directly affect the ore flow's transport, ensuring that the material flow entering the belt system matches the current permissible transport capacity. When the ore flow load is high, the motor actuator reduces its operating speed or limits its output capacity according to control commands to prevent the load from rising further; when the ore flow load is within a safe range, the motor actuator maintains or gradually restores the target operating state according to control commands.

[0139] In addition, in this embodiment, while executing control commands, the belt motor actuator monitors the motor's operating status in real time and sends the motor speed, current and other operating parameters back to the control module as feedback information, providing a basis for the subsequent update and optimization of control commands, thereby forming an adaptive operation control process that combines control execution and status feedback.

[0140] Through the implementation of step 5, the present invention can effectively implement the control strategy based on visual detection of ore flow and load determination to the belt motor execution layer, realize the real-time linkage between ore flow load and belt running status; automatically adjust belt running parameters under different working conditions, making the conveying process more stable and reliable, thereby reducing the risk of motor overload, belt slippage and mechanical impact, and improving the operational safety, stability and overall service life of the conveying system.

[0141] The methods also include:

[0142] After the belt motor executes the control command, the belt running current, speed and ore flow images are collected as feedback data;

[0143] The deviation between the calculated results of the ore flow operation status and the actual operation status is analyzed based on feedback data.

[0144] Based on the analysis results, the weight coefficients w1, w2, w3 and the load threshold T are corrected to enable the ore flow visual inspection and belt motor collaborative control strategy to have optimization capabilities.

[0145] In this embodiment, after the method completes the execution of the belt motor control command, it also includes feedback collection and analysis of the execution results to achieve continuous optimization of the ore flow visual detection and belt motor collaborative control strategy.

[0146] Specifically, after the belt motor executes the control command, the system synchronously collects the operating parameters of the belt motor, such as the operating current and speed, and re-acquires the real-time visual image of the ore flow. The operating parameters and the visual image together constitute the feedback dataset. By monitoring the belt operating current and speed, the actual load status of the belt motor under the current control strategy can be reflected. By re-acquiring the ore flow image, the actual impact of the control command on the ore flow conveying status can be reflected.

[0147] After obtaining the feedback data, the system analyzes the deviation between the calculation results of the aforementioned ore flow operating status and the actual operating status of the belt conveyor system. The deviation analysis is used to determine the consistency between the current ore flow operating status and load determination results and the actual working conditions, thereby identifying whether there is a deviation in the weight configuration or threshold setting.

[0148] Based on the deviation analysis results, the system corrects the weight system w1, w2, w3 and load threshold T used in the calculation of ore flow operating status, so that each characteristic parameter is more in line with the actual operating characteristics in subsequent calculations. Through the above correction process, the ore flow visual detection and belt motor collaborative control strategy can be adaptively adjusted according to changes in the operating environment, material characteristics and equipment status, thus having continuous optimization capabilities.

[0149] By introducing a feedback acquisition and parameter correction mechanism, this invention can continuously correct the calculation results of the ore flow operating state during the actual operation of the belt motor, so that the visual detection results are consistent with the actual load state. This self-optimization process avoids the problem of fixed parameters gradually becoming ineffective in long-term operation, improves the accuracy and adaptability of ore flow load determination and motor control strategy, thereby enhancing the system's stable operation capability and long-term application value under complex working conditions.

[0150] A visual inspection and belt motor coordinated intelligent control system for ore flow, comprising a visual inspection and belt motor coordinated intelligent control method for ore flow implementing any one of the above-mentioned methods, including:

[0151] The visual acquisition module is used to acquire real-time image data of the ore flow.

[0152] The image processing and feature extraction module is used to extract features such as ore flow coverage, accumulation height, and density.

[0153] The state variable calculation module is used to calculate the operating state variables of the ore flow.

[0154] The threshold determination module is used to compare the running status quantity with the preset threshold.

[0155] The control decision module is used to generate belt motor coordinated control commands based on the comparison results.

[0156] The motor actuator module is used to receive and execute control commands to achieve intelligent control of the belt conveyor.

[0157] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for visual inspection of ore flow and coordinated intelligent control of belt motor, characterized in that, Includes the following steps: Step 1: Acquire real-time visual images of the ore flow during the belt conveyor process and extract features; Step 2: Construct the ore flow operation state variables based on the feature extraction results and perform quantitative calculations; Step 3: Compare the quantitative calculation results with the preset control threshold to determine the ore flow load status; Step 4: Generate coordinated control commands for the belt motor based on the judgment results; Step 5: Send the control command to the belt motor actuator to achieve adaptive operation control.

2. The method for visual inspection of ore flow and intelligent control of belt motor in coordination according to claim 1, characterized in that, Step 1 includes: Step 1.1: Arrange at least one set of industrial vision acquisition devices above or to the side of the belt conveyor along the running direction to continuously image the ore flow conveying area; Step 1.2: Perform time synchronization processing on the acquired images based on the belt running speed to eliminate the distortion caused by the change in image acquisition frequency and belt running speed; Step 1.3: Perform noise suppression, adaptive illumination compensation, and background separation processing on the acquired images in sequence to reduce the interference of dust, illumination changes, and belt texture on ore identification; Step 1.4: Extract the effective contour region of the ore flow from the preprocessed image to provide stable input for subsequent state variable calculations.

3. The method for visual inspection of ore flow and intelligent control of belt motor in coordination according to claim 1, characterized in that, Step 2 includes: Step 2.1: Based on the ratio of the projected area of ​​the ore flow in the image to the effective conveying area of ​​the belt, calculate the ore flow coverage parameter α, which is used to characterize the occupancy of the belt surface. Step 2.2: Based on the height change of the ore flow profile relative to the belt reference plane, calculate the ore flow accumulation height parameter β, which is used to reflect the accumulation trend of ore in the conveying direction; Step 2.3: Based on the grayscale change rate, particle distribution density, or edge complexity of the ore flow surface texture, calculate the ore flow density parameter γ, which is used to characterize the material density per unit volume. Step 2.4: Use the coverage parameter α, the stacking height parameter β, and the density parameter γ as the basic feature set to describe the instantaneous operating state of the ore flow.

4. The method for visual inspection of ore flow and intelligent control of belt motor in coordination according to claim 3, characterized in that, The formula for calculating the ore flow operating state quantity F in step 2.4 is as follows: ； Among them, w1 is used to characterize the influence weight of the ore flow coverage degree on the belt load, w2 is used to characterize the influence weight of the ore flow accumulation height on the belt load, and w3 is used to characterize the influence weight of the ore flow compaction degree on the belt load, and satisfies w1+w2+w3=1.

5. The method for visual inspection of ore flow and intelligent control of belt motor in coordination according to claim 1, characterized in that, Step 3 includes: The calculated ore flow operating state quantity F is compared with the preset load threshold T; When F≥T, the ore flow is determined to be in a state of high load or overload risk. When F < T, the ore flow is determined to be under normal load.

6. The method for visual inspection of ore flow and intelligent control of belt motor in coordination according to claim 5, characterized in that, The load threshold T is a dynamically adjustable threshold, and the adjustment methods include: The load threshold T is updated based on the statistical distribution of the belt conveyor's rated conveying capacity, belt width, operating speed, and historical ore flow operating status. When a sustained increase in the fluctuation range of ore flow is detected, the threshold sensitivity is increased to identify overload risks in advance. When the ore flow is in a stable operating state for a long period of time, the threshold range can be relaxed to improve the overall conveying efficiency.

7. The method for visual inspection of ore flow and intelligent control of belt motor in coordination according to claim 1, characterized in that, Step 4 includes: Step 4.1: When it is determined that the ore flow is under high load, generate a control command to limit the speed of the belt motor to increase or decrease the current speed. Step 4.2: When it is determined that the ore flow is in a safe load state, generate control instructions to maintain the current operating parameters or appropriately increase the operating efficiency; Step 4.3: Convert the control command into control parameters that match the belt motor driver, including speed, current, or output torque adjustment parameters; Step 4.4: By controlling the operating status of the belt motor, the ore flow load and belt conveying capacity are dynamically matched.

8. The method for visual inspection of ore flow and intelligent control of belt motor in coordination according to claim 1, characterized in that, The generation of motor control commands in step 4 also includes: Based on the deviation ΔF between the ore flow operating state quantity F and the threshold T; When the deviation value ΔF increases, a greater reduction or load limiting control is applied to the belt motor; When the deviation value ΔF decreases, progressive recovery control is performed on the belt motor to avoid system oscillations caused by frequent adjustments.

9. The method for visual inspection of ore flow and intelligent control of belt motor in coordination according to claim 1, characterized in that, The method further includes: After the belt motor executes the control command, the belt running current, speed and ore flow images are collected as feedback data; The deviation between the calculated results of the ore flow operation status and the actual operation status is analyzed based on feedback data. Based on the analysis results, the weighting coefficients w1, w2, w3 and the load threshold T are corrected to enable the ore flow visual inspection and belt motor collaborative control strategy to have optimization capabilities.

10. A visual inspection and belt motor coordinated intelligent control system for ore flow, implementing the visual inspection and belt motor coordinated intelligent control method for ore flow as described in any one of claims 1-9, characterized in that, include: A visual acquisition module, which is used to acquire real-time image data of the ore flow; An image processing and feature extraction module is used to extract features of ore flow coverage, accumulation height, and density. A state quantity calculation module is used to calculate the operating state quantities of the ore flow. A threshold determination module is used to compare the running state quantity with a preset threshold. The control decision module is used to generate belt motor coordinated control commands based on the comparison results; The motor execution module is used to receive and execute the control commands to realize intelligent control of the belt conveyor.

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