An intelligent mine belt conveyor unattended system based on automatic control technology

Abstract

The present invention relates to the technical field of conveying equipment, and more specifically, to an intelligent unmanned mine belt conveyor system based on automatic control technology. The system comprises: a data acquisition module for collecting tension data and pressure data of each roller; a local mutation acquisition module for acquiring local differences in pressure data collected from each roller and local differences in tension data collected from each roller; acquiring the confidence level of the local mutation of each roller; acquiring the local mutation of each roller based on the confidence level of the local mutation of each roller and the local differences in tension data and pressure data collected from each roller; an abnormality acquisition module for acquiring the abnormality level of each roller based on the local mutation of each roller; an abnormality warning module for making abnormality judgments on each roller based on the abnormality level of each roller. The present invention can accurately judge the abnormality of the roller.

Description

Technical Field

[0001] The present invention generally relates to the technical field of conveying equipment. More specifically, the present invention relates to an intelligent mine belt conveyor unmanned system based on automatic control technology. Background Art

[0002] As an important material conveying equipment in mines, the stability of belt conveyors directly affects the production efficiency and safety of mines. However, traditional belt conveyor monitoring systems generally rely on manual supervision. Manual inspections not only increase labor costs, but are also prone to human errors and fail to promptly detect potential equipment failures, which in turn lead to safety accidents or production interruptions. Abnormal conditions of belt conveyors, such as excessive or insufficient belt tension, uneven roller loads, belt deviation, material accumulation, etc., are often the main causes of belt conveyor failures. Traditional belt conveyor monitoring methods mainly rely on single mechanical detection equipment, such as temperature sensors, vibration sensors, etc., which make it difficult to accurately identify various complex fault types. Therefore, there is an urgent need for a system that collects multi-dimensional data in real time and performs intelligent analysis to comprehensively improve the operation monitoring capabilities of belt conveyors.

[0003] Traditional anomaly detection algorithms can only detect data with a high degree of outliers in the acquired data. During the normal operation of the belt conveyor, the presence of ore on the belt conveyor will cause local changes in the collected data; if the belt conveyor has an abnormality (such as roller jamming), it will also cause local changes in the collected data. Therefore, traditional anomaly detection algorithms cannot effectively distinguish between the two, resulting in inaccurate anomaly monitoring results. Summary of the Invention

[0004] To address the technical problem that traditional anomaly detection algorithms cannot effectively distinguish local changes in data collected when ore is present on a conveyor belt, or local changes in data collected when an anomaly occurs (e.g., roller jams), resulting in inaccurate anomaly monitoring results, this paper proposes an intelligent unmanned mine conveyor belt system based on automated control technology. The system includes the following modules:

[0005] Data acquisition module, used to collect tension data and pressure data of each roller;

[0006] A local mutation acquisition module is used to obtain local differences in pressure data collected from each roller and local differences in tension data collected from each roller; obtain a confidence level of the local mutation of each roller based on the consistency of the pressure data and tension data collected from each roller; and obtain the local mutation of each roller based on the local differences and the confidence level;

[0007] Abnormality degree acquisition module, used to obtain the abnormality degree of each roller , Represents the abnormality degree of the i-th roller; Represents the local mutation of the i-th roller; Represents the mean of the local mutation of all the surrounding rollers of the i-th roller; represents the standard deviation of the local mutation of all the surrounding rollers of the i-th roller; norm() represents the normalization function; exp() represents the exponential function with a natural constant as the base;

[0008] The abnormal warning module is used to judge the abnormality of each roller according to the degree of abnormality of each roller.

[0009] Preferably, obtaining the local difference of the pressure data collected by each roller includes:

[0010] Get the neighboring rollers of each roller;

[0011] ;

[0012] Where, represents the local difference of the pressure data collected by the i-th roller; Represents the number of neighboring rollers of the i-th roller; Represents the pressure data value collected by the i-th roller; represents the pressure data value collected by the a-th neighboring roller of the ith roller; || represents the absolute value symbol; norm() represents the normalization function; according to the method for obtaining the local difference of the pressure data collected by the ith roller, the local difference of the tension data collected by the ith roller is obtained.

[0013] The greater the local difference in the pressure data and tension data collected from each roller, the greater the local mutation of each roller.

[0014] Preferably, obtaining the neighboring rollers of each roller includes:

[0015] The number of adjacent rollers N is preset, and the N rollers adjacent to the left side of the i-th roller and the N rollers adjacent to the right side are used as neighboring rollers of the i-th roller.

[0016] This facilitates subsequent analysis of the pressure and tension data collected from each roller and its neighboring rollers.

[0017] Preferably, obtaining the confidence level of the local mutation of each roller includes:

[0018] ;

[0019] Where, Represents the confidence level of the local mutation of the i-th roller; Represents the pressure data value collected by the i-th roller; Represents the tension data value collected by the i-th roller; Represents the standard deviation of the pressure data values collected from all rollers; represents the standard deviation of the tension data values collected from all rollers; || represents the absolute value symbol; exp() represents an exponential function with a natural constant as the base.

[0020] Preferably, obtaining the local mutation of each roller includes:

[0021] ;

[0022] Where, Represents the local mutation of the i-th roller; Represents the confidence level of the local mutation of the i-th roller; represents the local difference of the pressure data collected by the i-th roller; Indicates the The local differences in the tension data collected from each roller.

[0023] The greater the local mutation of each roller, the greater the degree of abnormality.

[0024] Preferably, the acquisition of the surrounding rollers includes:

[0025] The number of rollers is preset as M, and the M rollers adjacent to the left side of the i-th roller and the M rollers adjacent to the right side are used as the surrounding rollers of the i-th roller.

[0026] Preferably, the abnormality determination of each roller according to the degree of abnormality of each roller includes:

[0027] A warning threshold T1 is preset. If the abnormality of any roller is greater than the abnormality threshold T1, the staff will be notified to inspect and repair the roller on the belt conveyor.

[0028] Make the early warning results more accurate.

[0029] Preferably, the collecting of tension data and pressure data of each roller includes:

[0030] A pressure sensor is installed near any roller bearing of the belt conveyor or on the roller support frame, and a tension sensor is arranged at both ends of the roller in contact with the belt. At any moment during the operation of the belt conveyor, the pressure data and tension data of the roller are collected to obtain the pressure data and tension data of each roller.

[0031] The present invention has the following technical effects: the present invention analyzes the local mutation difference between any roller and the rollers around it and the mean of the local mutation of the rollers around it to obtain the abnormality degree of the roller, effectively distinguishes the normal operation state and the abnormal operation state of the belt conveyor, and thus makes the early warning result more accurate; further, the tension data difference and pressure data difference collected from each roller and its neighboring rollers are analyzed to obtain the local difference of the pressure data collected from each roller and the local difference of the tension data collected from each roller, and analyzes the changes in the pressure data and tension data of each roller to calculate the confidence level, and then obtains the local mutation of each roller based on the mean of the local differences in the pressure data and tension data collected from each roller and the confidence level, thereby improving the accuracy of the local mutation and making the subsequent calculation results of the abnormality level more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0033] Figure 1 This is a system block diagram of an unmanned intelligent mine belt conveyor system based on automatic control technology in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0035] It should be understood that when the terms "first," "second," and the like are used in the claims, description, and drawings of the present invention, they are merely used to distinguish between different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the description and claims of the present invention indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0036] The present invention provides an intelligent mine belt conveyor unmanned system based on automatic control technology. Figure 1 As shown, a smart mine belt conveyor unmanned system based on automatic control technology includes modules 101 to 104, which are described in detail below.

[0037] The data acquisition module 101 is used to collect the tension data and pressure data of each roller.

[0038] It should be noted that rollers are key components used to support the conveyor belt in a belt conveyor, bearing the weight of the belt and material. During the operation of the belt conveyor, the rollers are affected by various factors such as the material weight, conveying speed, and belt tension, which may lead to uneven load changes, or even wear, damage, or jamming, thereby affecting the normal operation of the belt conveyor. Belt tension is also one of the key factors affecting the normal operation of the belt conveyor. Excessive belt tension can lead to excessive stretching and damage to the belt, while too little tension can cause the belt to slip, unstable material conveying, and even cause the belt to deviate or fall off. Therefore, the present invention requires abnormal monitoring of the roller pressure and belt tension of the belt conveyor.

[0039] Since the rollers bear the weight of the belt and materials during the operation of the belt conveyor, the pressure sensor will sense the pressure borne by the rollers. When the belt conveyor is unbalanced or abnormal, the pressure data collected by the sensor will change. The tension sensor can obtain the belt tension data by measuring the elongation, deformation or tension of the belt. The belt tension data changes with the different forces on the belt.

[0040] In an embodiment of the present invention, a pressure sensor is installed near any roller bearing of the belt conveyor or on the roller support frame, and tension sensors are arranged at both ends of the roller in contact with the belt. At any moment during the operation of the belt conveyor, the pressure data and tension data of the roller are collected. Similarly, the pressure data and tension data of each roller are collected.

[0041] It should be noted that a tension sensor and a pressure sensor are arranged on each roller of the belt conveyor.

[0042] The local mutation acquisition module 102 is used to obtain the local difference of the pressure data collected by each roller and the local difference of the tension data collected by each roller; obtain the confidence level of the local mutation of each roller based on the consistency of the pressure data and tension data collected by each roller; obtain the local mutation of each roller based on the confidence level of the local mutation of each roller and the local difference of the tension data and pressure data collected by each roller.

[0043] It should be noted that the traditional anomaly detection algorithm can only detect data with a high degree of outliers in the acquired data. During the normal operation of the belt conveyor, the presence of ore on the belt conveyor will cause local changes in the collected data. If the belt conveyor has an abnormality (such as roller jamming), it will also cause local changes in the collected data. Therefore, the traditional anomaly detection algorithm cannot effectively distinguish between the two, resulting in inaccurate abnormal monitoring results. It is known that during the operation of the belt conveyor, the pressure on the roller and the belt tension are key factors affecting the stability of the belt conveyor. Changes in the size of the ore material carried on the belt conveyor or the cause of roller jamming will cause local changes in the pressure data and tension data collected from the roller. Therefore, the present invention first combines the difference in pressure data collected from each roller and its neighboring rollers to obtain the local difference in pressure data collected from each roller; similarly, the local difference in tension data collected from each roller is obtained.

[0044] In an embodiment of the present invention, the local difference of the pressure data collected by each roller is obtained:

[0045] The N rollers adjacent to the left side of the i-th roller and the N rollers adjacent to the right side are used as neighboring rollers of the i-th roller. In the embodiment of the present invention, the number of adjacent rollers N is preset to be 2. In other embodiments, the implementer may preset the value of N according to specific implementation conditions.

[0046] ;

[0047] Where, represents the local difference of the pressure data collected by the i-th roller; Represents the number of neighboring rollers of the i-th roller; Represents the pressure data value collected by the i-th roller; represents the pressure data value collected by the ath neighboring roller of the ith roller; || represents the absolute value sign; norm() represents the normalization function; if the difference between the pressure data values collected by the ith roller and its neighboring rollers is smaller, the local difference of the pressure data collected by the ith roller is smaller, and the local mutation of the ith roller is smaller.

[0048] According to the method for obtaining the local difference of the pressure data collected by the i-th roller, the local difference of the tension data collected by the i-th roller is obtained.

[0049] It should be noted that, it is known that the local differences in the pressure data collected by each roller and the local differences in the tension data collected by each roller are obtained. If the mean value of the local differences between the pressure data and the tension data collected by any roller is larger, it means that the local mutation of the roller is larger. However, the pressure data and tension data collected by the pressure sensor and the tension sensor are likely to be affected by noise. Therefore, in order to reduce the influence of noise data, the present invention obtains the confidence level of the local mutation of each roller by analyzing the consistency of the pressure data value and the tension data value collected by each roller, thereby further optimizing the accuracy of the local mutation of each roller.

[0050] In this embodiment of the present invention, the confidence level of the local mutation of the i-th roller is obtained:

[0051] ;

[0052] Where, Represents the confidence level of the local mutation of the i-th roller; Represents the pressure data value collected by the i-th roller; Represents the tension data value collected by the i-th roller; Represents the standard deviation of the pressure data values collected from all rollers; Represents the standard deviation of the tension data values collected from all rollers; || represents the absolute value symbol; exp() represents the exponential function with a natural constant as the base; The smaller the difference, the more consistent the performance of the i-th roller in the pressure data and tension data, and the higher the confidence level of the local mutation degree of the roller;

[0053] It should be noted that the reason for obtaining the ratio of the tension data value collected by the i-th roller to the standard deviation of the tension data values collected by all rollers, as well as the ratio of the pressure data value collected by the i-th roller to the standard deviation of the pressure data values collected by all rollers, is to eliminate the influence of the different dimensions of the pressure data and the tension data on the calculation results, thereby making the difference calculation result between the two more accurate, and further making the confidence level of the local mutation of the i-th roller more accurate.

[0054] Get the local mutation of the i-th roller:

[0055] ;

[0056] Where, Represents the local mutation of the i-th roller; Represents the confidence level of the local mutation of the i-th roller; represents the local difference of the pressure data collected by the i-th roller; Indicates the The local difference of the tension data collected by each roller is the same; if the mean of the local differences between the pressure data and the tension data collected by the roller is larger, and the performance of the i-th roller in the pressure data and the tension data is more consistent, that is, the confidence level of the local mutation of the i-th roller is greater, then the local mutation of the roller is higher.

[0057] The abnormality degree acquisition module 103 is used to acquire the abnormality degree of each roller according to the local mutation of each roller.

[0058] It should be noted that when any roller is stuck, the sudden change in the tension data and pressure data collected by the roller itself causes the roller to have a higher local mutation, but the surrounding rollers are not directly affected, so the local mutation of the surrounding rollers will not increase. At this time, if the difference in the local mutation of the roller is large compared with the local rollers around it, and the average mutation of the rollers around the roller is small, it means that the abnormality is caused by the abnormality of the roller itself, so the abnormality of the roller is relatively large.

[0059] In the process of normal ore transportation by the belt conveyor, if the ore distribution is too small, the pressure data and tension data of any roller will change, that is, the local mutation of the roller will increase. In this case, the rollers around it will also be affected similarly, resulting in a corresponding increase in local mutation. Therefore, if the difference in local mutation between the roller and the rollers around it is small, and the average mutation of the rollers around the roller is large, it means that this may be a normal and small-scale fluctuation, not caused by a serious failure of a single roller. Therefore, the abnormality of the roller is relatively small.

[0060] In an embodiment of the present invention, the surrounding rollers of each roller are obtained: the number of rollers M is preset, and the M rollers adjacent to the left side of the i-th roller and the M rollers adjacent to the right side are used as the surrounding rollers of the i-th roller; in an embodiment of the present invention, the preset number of rollers M=10, and in other embodiments, the implementer can preset the value of M according to the specific implementation method.

[0061] Get the abnormality degree of the i-th roller:

[0062] ;

[0063] Where, Represents the abnormality degree of the i-th roller; Represents the local mutation of the i-th roller; Represents the mean of the local mutation of all the surrounding rollers of the i-th roller; represents the standard deviation of the local mutation of all the surrounding rollers of the i-th roller; norm() represents the normalization function; exp() represents the exponential function with a natural constant as the base;

[0064] Represents the difference between the local mutation of the i-th roller and the mean of the local mutation of all its surrounding rollers. The larger the value, the higher the mutation of the pressure data and tension data collected by the i-th roller, and the higher the abnormality of the i-th roller; The smaller the value of , the smaller the fluctuation of the local mutation of all the surrounding rollers of the i-th roller. The more accurate the value.

[0065] If the local mutation of the i-th roller is larger than that of the surrounding rollers, and the average mutation of all the surrounding rollers is smaller, it means that an abnormality may occur at the i-th roller (such as roller jamming);

[0066] If the local mutation of the i-th roller is larger than that of the surrounding rollers, and the average mutation of all the surrounding rollers is larger, it means that when the belt conveyor is transporting ore normally, the local mutation of the i-th roller becomes larger, and the mutation of the surrounding rollers also increases accordingly. This is a normal phenomenon, and the degree of abnormality of the i-th roller is relatively small.

[0067] The abnormality warning module 104 is used to determine the abnormality of each roller according to the abnormality degree of each roller.

[0068] In an embodiment of the present invention, a warning threshold value T1 is preset. If the abnormality degree of any roller is greater than the abnormality threshold value T1, the system will issue a warning to the staff and the roller on the belt conveyor needs to be repaired. In an embodiment of the present invention, the preset warning threshold value T1=0.75. In other embodiments, the implementer can preset the value of the warning threshold value T1 according to the specific implementation situation.

[0069] While this specification has shown and described several embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0070] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A smart mine belt conveyor unattended system based on automatic control technology, characterized in that: Includes the following modules: Data acquisition module, used to collect tension data and pressure data of each roller; The local mutation acquisition module is used to obtain the local difference of the pressure data collected by each roller, including: obtaining the neighboring rollers of each roller; ； Where, represents the local difference of the pressure data collected by the i-th roller; Represents the number of neighboring rollers of the i-th roller; Represents the pressure data value collected by the i-th roller; represents the pressure data value collected by the ath neighboring roller of the i-th roller; || represents the absolute value symbol; norm() represents the normalization function; according to the method for obtaining the local difference of the pressure data collected by the i-th roller, the local difference of the tension data collected by the i-th roller is obtained; Obtain the local difference of the tension data collected from each roller; according to the consistency of the pressure data and tension data collected from each roller, obtain the confidence level of the local mutation of each roller, including: ； Where, Represents the confidence level of the local mutation of the i-th roller; Represents the pressure data value collected by the i-th roller; Represents the tension data value collected by the i-th roller; Represents the standard deviation of the pressure data values collected from all rollers; represents the standard deviation of the tension data values collected from all rollers; || represents the absolute value symbol; exp() represents an exponential function with a natural constant as the base; based on the local difference and confidence level, the local mutation of each roller is obtained, including: ； Where, Represents the local mutation of the i-th roller; Represents the confidence level of the local mutation of the i-th roller; represents the local difference of the pressure data collected by the i-th roller; Indicates the Local differences in tension data collected from each roller; Abnormality degree acquisition module, used to obtain the abnormality degree of each roller , Represents the abnormality degree of the i-th roller; Represents the local mutation of the i-th roller; Represents the mean of the local mutation of all the surrounding rollers of the i-th roller; represents the standard deviation of the local mutation of all the surrounding rollers of the i-th roller; norm() represents the normalization function; exp() represents the exponential function with a natural constant as the base; The abnormal warning module is used to judge the abnormality of each roller according to the degree of abnormality of each roller.

2. The intelligent mine belt conveyor unattended system based on automatic control technology according to claim 1 is characterized in that: The step of obtaining neighboring rollers of each roller includes: The number of adjacent rollers N is preset, and the N rollers adjacent to the left side of the i-th roller and the N rollers adjacent to the right side are used as neighboring rollers of the i-th roller.

3. The intelligent mine belt conveyor unattended system based on automatic control technology according to claim 1 is characterized in that: The acquisition of the surrounding rollers includes: The number of rollers is preset as M, and the M rollers adjacent to the left side of the i-th roller and the M rollers adjacent to the right side are used as the surrounding rollers of the i-th roller.

4. The intelligent mine belt conveyor unattended system based on automatic control technology according to claim 1 is characterized in that: The abnormality determination of each roller according to the abnormality degree of each roller includes: A warning threshold T1 is preset. If the abnormality of any roller is greater than the abnormality threshold T1, the staff will be notified to inspect and repair the roller on the belt conveyor.

5. The intelligent mine belt conveyor unattended system based on automatic control technology according to claim 1 is characterized in that: The collecting of tension data and pressure data of each roller includes: A pressure sensor is installed near any roller bearing of the belt conveyor or on the roller support frame, and a tension sensor is arranged at both ends of the roller in contact with the belt. At any moment during the operation of the belt conveyor, the pressure data and tension data of the roller are collected to obtain the pressure data and tension data of each roller.

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