Horizontal belt conveyor operation monitoring system and method based on data analysis

Abstract

The present application discloses a horizontal belt conveyor operation monitoring system and method based on data analysis, which belongs to the field of state prediction. The present application imports operation abnormality assessment results, belt operation abnormality assessment results and transportation mineral status data into the belt conveyor life prediction strategy to estimate the belt conveyor life. By comprehensively analyzing the belt conveyor drive operation data and belt operation data during the operation process, the abnormal characteristics of the belt conveyor life implied in the analysis data are analyzed, and then the abnormal characteristics and the damage rate of the mineral transportation to the belt conveyor life are comprehensively evaluated to accurately estimate the belt conveyor life, thereby improving the accuracy of the belt conveyor life prediction.

Description

Technical Field

[0001] The present application belongs to the field of state prediction, and specifically relates to a horizontal belt conveyor operation monitoring system and method based on data analysis. Background Art

[0002] Mining belt conveyor, also known as mining belt conveyor or coal mine belt conveyor, is a continuous transportation equipment widely used in mining, coal, metallurgy, chemical industry, port, building materials and other industries. It is mainly composed of a drive device, a transmission roller, a conveyor belt, a roller, a tensioning device, a redirecting roller and a sweeper. The drive device drives the transmission roller to rotate, so that friction is generated between the conveyor belt and the transmission roller, and the conveyor belt is driven to transport materials. The advantages of mining belt conveyors include high efficiency and energy saving, simple structure, strong adaptability and good automatic control performance. It can greatly improve production efficiency, reduce energy consumption, reduce equipment maintenance costs, and is suitable for the transportation of a variety of materials to meet the needs of different industries and different environments. In addition, modern mining belt conveyors generally adopt computer automatic control systems, which can realize remote control and data monitoring, and improve the intelligence level of the equipment. Since most existing horizontal belt conveyors require continuous operation, the minerals transported on them are prone to fall off when a fault occurs. When estimating the life of the belt conveyor, the existing technology can usually only monitor some abnormal characteristics that exceed the normal value and issue an alarm. It is unable to comprehensively analyze the belt conveyor drive operation characteristics and belt operation characteristics during the operation process, and thus it is impossible to comprehensively evaluate the damage rate of the changing abnormal characteristics and the transportation of minerals to the life of the belt conveyor, resulting in a low accuracy rate in estimating the life of the belt conveyor. Most of the existing technologies have the above problems.

[0003] In order to solve the problems raised in this background technology, the present application designs a horizontal belt conveyor operation monitoring system and method based on data analysis. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, this application proposes a horizontal belt conveyor operation monitoring system and method based on data analysis. This application imports the operation abnormality assessment results, belt operation abnormality assessment results and transportation mineral situation data into the belt conveyor life prediction strategy to estimate the belt conveyor life. By comprehensively analyzing the belt conveyor drive operation data and belt operation data during the operation process, the abnormal characteristics of the belt conveyor life implied in the data are analyzed, and then the abnormal characteristics and the damage rate of the mineral transportation to the belt conveyor life are comprehensively evaluated to accurately estimate the belt conveyor life, thereby improving the accuracy of the belt conveyor life prediction.

[0005] To achieve the above objectives, the present application provides the following technical solutions: In a first aspect, the present application provides a method for monitoring the operation of a horizontal belt conveyor based on data analysis, which includes the following specific steps:

[0006] S1. Acquire the horizontal belt conveyor driving operation data and belt operation data during operation, and simultaneously acquire the mineral transportation condition data during operation;

[0007] S2. Importing the horizontal belt conveyor drive operation data into the drive operation abnormality assessment model to perform drive operation abnormality assessment;

[0008] S3, importing the acquired belt operation data into a belt abnormality assessment model to perform belt operation abnormality assessment;

[0009] S4. Importing the abnormal operation evaluation results, the belt operation abnormality evaluation results, and the transported mineral situation data into the belt conveyor life estimation strategy to estimate the belt conveyor life;

[0010] S5. Perform life comparison warning based on the estimated results of the belt conveyor life.

[0011] As a preferred technical solution of the horizontal belt conveyor operation monitoring method based on data analysis, the specific content of obtaining the horizontal belt conveyor drive operation data and belt operation data during operation, and simultaneously obtaining the transport mineral situation data during operation is:

[0012] S11. Using an image acquisition terminal installed below the horizontal belt conveyor, collect surface crack data of the horizontal belt and slip data of the horizontal belt during operation. The surface crack data includes crack length and crack width data, and the slip data on both sides of the belt is edge displacement data relative to a normal belt trajectory when each point on the belt edge passes directly above the image acquisition terminal during operation.

[0013] S12. Collecting driving device operation data through the driving device data collection terminal, wherein the driving device operation data includes driving device temperature, driving device speed, and driving device vibration frequency and amplitude data;

[0014] S13. Acquire real-time quality data of transported minerals and simultaneously acquire transport plan data.

[0015] As a preferred technical solution of the horizontal belt conveyor operation monitoring method based on data analysis, the method of importing the horizontal belt conveyor drive operation data into the drive operation abnormality evaluation model to perform drive operation abnormality evaluation includes the following specific steps:

[0016] S21. Test the drive device at set intervals to obtain the drive device temperature, drive device speed, and drive device vibration frequency and amplitude data during the operation of the drive device. The test duration is preferably the duration of two rotations of the horizontal belt.

[0017] S22, importing the acquired driving device temperature, driving device speed, driving device vibration frequency, and amplitude data into a driving device abnormal value calculation formula to calculate the driving device abnormal value, wherein the driving device abnormal value calculation formula is: , where Tc is the test duration, dt is the time integral, Tt is the temperature at test time t, Tmc is the median of the temperature safety range of the drive device, Tm is the difference between the maximum and minimum values ​​of the temperature safety range of the drive device. Since the temperature changes during the operation of the drive device, Vt is the speed data at test time t, Vm is the speed data to be output, Vt-Vm is used to evaluate the speed difference, m is the number of vibrations of the drive device during the test at time t, rit is the i-th vibration amplitude of the drive device during the test at time t, and rm is the maximum value of the vibration safety range. is the proportion of abnormal vibration, is the abnormal speed ratio, is the temperature anomaly ratio. In this formula, the vibration, temperature and speed anomalies of the drive equipment are comprehensively analyzed to determine the anomaly of the drive equipment.

[0018] S23, obtaining the calculated abnormal value of the driving device, and substituting it into the abnormal change speed calculation formula of the driving device to calculate the abnormal change speed of the driving device, wherein the abnormal change speed calculation formula of the driving device is: , where Xc is the abnormal change speed of the driving device, Xt is the abnormal value of the driving device calculated in this test cycle, X(t-1) is the abnormal value of the driving device calculated in the previous test cycle, Mt is the mass of the transported minerals between the end of the previous test cycle and the end of this test cycle, and M is the set safety value of the transported mineral mass, that is, the maximum mass of the minerals that can be placed on the conveyor belt;

[0019] In this step, the vibration, temperature and speed of the driving equipment are tested, and then a comprehensive abnormal analysis is performed on the vibration, temperature and speed of the driving equipment to obtain the abnormal change speed, which is conducive to further predicting the abnormal change trend of the driving equipment.

[0020] As a preferred technical solution of the horizontal belt conveyor operation monitoring method based on data analysis, the step of importing the acquired belt operation data into the belt anomaly assessment model to perform belt operation anomaly assessment includes the following specific steps:

[0021] S31, obtaining surface crack data of the horizontal belt and sliding data on both sides of the horizontal belt during the test period;

[0022] S32. Obtain the surface crack data of the horizontal belt during the test period and import it into the crack abnormal value calculation formula to calculate the crack abnormal value, wherein the crack abnormal value calculation formula is: , where M is the number of cracks, sj is the length of the j-th crack, zj is the width of the j-th crack, sc is the safety value of the surface crack length, and zc is the safety value of the surface crack width;

[0023] S33, obtaining the sliding data on both sides of the horizontal belt and importing it into the sliding displacement abnormal value calculation formula to calculate the sliding displacement abnormal value, wherein the sliding displacement abnormal value calculation formula is: , where J is the transmission displacement of the horizontal belt during the test period, xc is the edge displacement data of the c-th point on the horizontal belt edge relative to the normal belt trajectory when it passes directly above the image acquisition terminal, Xm is the width of the horizontal belt, and dc is the integral of the points on the horizontal belt edge;

[0024] S34. Obtain the calculated crack abnormal value and sliding displacement abnormal value and import them into the belt operation abnormal value calculation formula to calculate the belt operation abnormal value, wherein the belt operation abnormal value calculation formula is: ,in, is the coefficient of crack anomaly ratio.

[0025] As a preferred technical solution for the horizontal belt conveyor operation monitoring method based on data analysis, the method of importing the operation abnormality assessment results, the belt operation abnormality assessment results and the transported mineral situation data into the belt conveyor life estimation strategy to estimate the belt conveyor life includes the following specific contents:

[0026] S41. Obtain the calculated belt operation abnormality value and import it into the belt abnormal change speed calculation formula to calculate the belt abnormal change speed, wherein the belt abnormal change speed calculation formula is: , where Spt is the belt operation abnormality value calculated in this test cycle, and Sp(t-1) is the belt operation abnormality value calculated in the previous test cycle;

[0027] S42. Obtain the calculated belt operation abnormality value, drive device abnormality value, drive device abnormal change speed, and belt abnormal change speed, and substitute them into the life estimation value calculation formula to calculate the life estimation value. The life estimation value calculation formula is: , where Xr is the set abnormal threshold, Vr is the mineral transportation speed of the horizontal belt conveyor in the next stage, that is, how much mass of minerals is required to be transported in the next stage in average time. is the driving anomaly ratio coefficient.

[0028] As a preferred technical solution for the horizontal belt conveyor operation monitoring method based on data analysis, the life comparison warning based on the obtained belt conveyor life estimation results includes the following specific contents:

[0029] The calculated life estimate is compared with the set belt life threshold. If the life estimate is greater than or equal to the set belt life threshold, no belt life warning is issued. If the life estimate is less than the set belt life threshold, a belt life warning is issued. It should be noted that since horizontal belt conveyors usually operate continuously when transporting minerals, belt life warnings are required in advance. The belt life threshold here can be flexibly set according to needs. If the maintenance preparation cycle is 1 hour, the belt life threshold here can be set to 1.5 hours to 2 hours.

[0030] In a second aspect, the present application provides a horizontal belt conveyor operation monitoring system based on data analysis, which is implemented based on the above-mentioned horizontal belt conveyor operation monitoring method based on data analysis, and specifically includes a data acquisition module, a drive operation abnormality evaluation module, a belt operation abnormality evaluation module, a belt conveyor life estimation module and a comparison and early warning module;

[0031] The data acquisition module is used to acquire the horizontal belt conveyor driving operation data and belt operation data during operation, and simultaneously acquire the mineral transportation condition data during operation;

[0032] The drive operation abnormality evaluation module is used to import the drive operation data of the horizontal belt conveyor into the drive operation abnormality evaluation model to perform drive operation abnormality evaluation;

[0033] The belt operation abnormality assessment module is used to import the acquired belt operation data into the belt abnormality assessment model to perform belt operation abnormality assessment;

[0034] The belt conveyor life estimation module is used to import the operation abnormality assessment results, the belt operation abnormality assessment results and the transport mineral situation data into the belt conveyor life estimation strategy to estimate the belt conveyor life;

[0035] The comparison and warning module is used to perform life comparison and warning based on the obtained belt conveyor life estimation result;

[0036] It also includes a control module, which is used to control the operation of the data acquisition module, the drive operation abnormality evaluation module, the belt operation abnormality evaluation module, the belt conveyor life estimation module and the comparison and early warning module.

[0037] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0038] The processor executes the above-mentioned horizontal belt conveyor operation monitoring method based on data analysis by calling the computer program stored in the memory.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute the above-mentioned method for monitoring the operation of a horizontal belt conveyor based on data analysis.

[0040] Compared with the prior art, the beneficial effects of the present application are as follows: the present application first obtains the horizontal belt conveyor drive operation data and belt operation data during the operation process, and simultaneously obtains the mineral transportation condition data during the operation process, and then imports the horizontal belt conveyor drive operation data into the drive operation abnormality evaluation model to perform drive operation abnormality evaluation, and imports the acquired belt operation data into the belt abnormality evaluation model to perform belt operation abnormality evaluation, and finally imports the operation abnormality evaluation results, the belt operation abnormality evaluation results and the mineral transportation condition data into the belt conveyor life prediction strategy to perform belt conveyor life prediction, by comprehensively analyzing the belt conveyor drive operation data and belt operation data during the operation process, analyzing the abnormal characteristics of the belt conveyor life implied in the data, and then comprehensively evaluating the abnormal characteristics and the damage rate of the mineral transportation to the belt conveyor life, so as to accurately estimate the belt conveyor life, thereby improving the accuracy of the belt conveyor life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Other features, objects and advantages of the present application will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings.

[0042] Figure 1 This is a schematic diagram of the overall process of the horizontal belt conveyor operation monitoring method based on data analysis in this application;

[0043] Figure 2 This is a schematic diagram of step S2 of the horizontal belt conveyor operation monitoring method based on data analysis in this application;

[0044] Figure 3 This is a schematic diagram of step S3 of the horizontal belt conveyor operation monitoring method based on data analysis in this application;

[0045] Figure 4 This is a schematic diagram of the overall framework of the horizontal belt conveyor operation monitoring system based on data analysis in this application;

[0046] Figure 5 This is a schematic diagram of the crack feature extraction process for this application;

[0047] Figure 6 Schematic diagram of a horizontal belt conveyor applicable to this application. DETAILED DESCRIPTION

[0048] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only some embodiments of the present application, rather than all embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present application, its application, or use.

[0049] Example 1. To solve the technical problems raised in the background technology, this application provides a preferred embodiment: Figure 1 - Figure 3 As shown, the horizontal belt conveyor operation monitoring method based on data analysis includes the following specific steps:

[0050] S1. Acquire the horizontal belt conveyor driving operation data and belt operation data during operation, and simultaneously acquire the mineral transportation condition data during operation;

[0051] In this embodiment, the specific contents of obtaining the horizontal belt conveyor driving operation data and belt operation data during operation, and simultaneously obtaining the mineral transportation condition data during operation are:

[0052] S11, such as Figure 6 As shown, an image acquisition terminal installed below the horizontal belt conveyor is used to collect surface crack data of the horizontal belt and sliding data on both sides of the horizontal belt during operation. The surface crack data includes crack length and crack width data, and the sliding data on both sides of the belt is the edge displacement data of each point on the edge of the belt relative to the normal belt trajectory when it passes directly above the image acquisition terminal during operation.

[0053] It should be noted that the specific steps for obtaining the crack length and width data on the belt conveyor belt can be as follows: Figure 5 As shown,

[0054] S111. Image acquisition: First, use an image acquisition terminal (such as a high-definition camera) installed below the conveyor belt to capture images of the belt surface. Ensure that the camera can clearly capture details of the belt surface, including any cracks that may exist.

[0055] S112. Image processing: The collected images are transferred to a computer and processed using image processing software. The image processing software can perform operations such as image enhancement, filtering, and noise reduction to improve image quality and make cracks more visible.

[0056] S113, Crack Identification: In image processing software, cracks on the belt are identified using algorithms such as image segmentation and edge detection. These algorithms can separate cracks from the belt background based on pixel characteristics such as color, brightness, and texture.

[0057] S114. Crack measurement: For identified cracks, use the measurement tools in the image processing software to measure the length and width of the crack. The length can be determined by setting marking points at both ends of the crack and measuring the distance between the two points. The width can be determined by setting a vertical line at the widest point of the crack and measuring the distance between the vertical line and the edges of the crack.

[0058] S12. Collecting driving device operation data through the driving device data collection terminal, wherein the driving device operation data includes driving device temperature, driving device speed, and driving device vibration frequency and amplitude data;

[0059] S13. Acquire real-time quality data of transported minerals and simultaneously acquire transport plan data;

[0060] S2. Importing the horizontal belt conveyor drive operation data into the drive operation abnormality assessment model to perform drive operation abnormality assessment;

[0061] In this embodiment, the process of importing the horizontal belt conveyor drive operation data into the drive operation abnormality assessment model to perform drive operation abnormality assessment includes the following specific steps:

[0062] S21. Test the drive device at set intervals to obtain the drive device temperature, drive device speed, and drive device vibration frequency and amplitude data during the operation of the drive device. The test duration is preferably the duration of two rotations of the horizontal belt.

[0063] S22, importing the acquired driving device temperature, driving device speed, driving device vibration frequency, and amplitude data into a driving device abnormal value calculation formula to calculate the driving device abnormal value, wherein the driving device abnormal value calculation formula is: , where Tc is the test duration, dt is the time integral, Tt is the temperature at test time t, Tmc is the median of the temperature safety range of the drive device, Tm is the difference between the maximum and minimum values ​​of the temperature safety range of the drive device. Since the temperature changes during the operation of the drive device, Vt is the speed data at test time t, Vm is the speed data to be output, Vt-Vm is used to evaluate the speed difference, m is the number of vibrations of the drive device during the test at time t, rit is the i-th vibration amplitude of the drive device during the test at time t, and rm is the maximum value of the vibration safety range. is the proportion of abnormal vibration, is the abnormal speed ratio, is the temperature anomaly ratio. In this formula, the vibration, temperature and speed anomalies of the drive equipment are comprehensively analyzed to determine the anomaly of the drive equipment.

[0064] S23, obtaining the calculated abnormal value of the driving device, and substituting it into the abnormal change speed calculation formula of the driving device to calculate the abnormal change speed of the driving device, wherein the abnormal change speed calculation formula of the driving device is: , where Xc is the abnormal change speed of the driving device, Xt is the abnormal value of the driving device calculated in this test cycle, X(t-1) is the abnormal value of the driving device calculated in the previous test cycle, Mt is the mass of the transported minerals between the end of the previous test cycle and the end of this test cycle, and M is the set safety value of the transported mineral mass, that is, the maximum mass of the minerals that can be placed on the conveyor belt;

[0065] In this step, the vibration, temperature and speed of the driving equipment are tested, and then a comprehensive abnormal analysis is performed on the vibration, temperature and speed of the driving equipment to obtain the abnormal change speed, which is conducive to further predicting the abnormal change trend of the driving equipment;

[0066] S3, importing the acquired belt operation data into a belt abnormality assessment model to perform belt operation abnormality assessment;

[0067] In this embodiment, importing the acquired belt operation data into the belt abnormality assessment model to perform belt operation abnormality assessment includes the following specific steps:

[0068] S31, obtaining surface crack data of the horizontal belt and sliding data on both sides of the horizontal belt during the test period;

[0069] S32. Obtain the surface crack data of the horizontal belt during the test period and import it into the crack abnormal value calculation formula to calculate the crack abnormal value, wherein the crack abnormal value calculation formula is: , where M is the number of cracks, sj is the length of the j-th crack, zj is the width of the j-th crack, sc is the surface crack length safety value, and zc is the surface crack width safety value; crack analysis helps prevent potential safety risks. When cracks appear on the belt, its strength and durability will drop significantly, which may cause the belt to break unexpectedly during equipment operation. A broken belt will not only cause equipment downtime, but may also trigger a chain reaction, causing damage to other components and even causing safety accidents. Therefore, timely detection and treatment of belt cracks can effectively avoid these potential risks; crack analysis helps optimize equipment maintenance and management. By analyzing belt cracks, we can understand the belt's usage status and predict its remaining life, thereby formulating a more scientific and reasonable maintenance plan;

[0070] S33, obtaining the sliding data on both sides of the horizontal belt and importing it into the sliding displacement abnormal value calculation formula to calculate the sliding displacement abnormal value, wherein the sliding displacement abnormal value calculation formula is: , where J is the transmission displacement of the horizontal belt during the test cycle, xc is the edge displacement data of the horizontal belt edge when the c-th point passes directly above the image acquisition terminal relative to the normal belt trajectory, Xm is the width of the horizontal belt, and dc is the integral of the points on the horizontal belt edge. Here, the sliding displacement is analyzed to determine whether there is a risk of derailment of the horizontal belt.

[0071] S34. Obtain the calculated crack abnormal value and sliding displacement abnormal value and import them into the belt operation abnormal value calculation formula to calculate the belt operation abnormal value, wherein the belt operation abnormal value calculation formula is: ,in, is the coefficient of crack anomaly proportion;

[0072] S4. Importing the abnormal operation evaluation results, the belt operation abnormality evaluation results, and the transported mineral situation data into the belt conveyor life estimation strategy to estimate the belt conveyor life;

[0073] In this embodiment, the operation abnormality assessment results, belt operation abnormality assessment results, and transported mineral condition data are imported into the belt conveyor life estimation strategy to estimate the belt conveyor life, including the following specific contents:

[0074] S41. Obtain the calculated belt operation abnormality value and import it into the belt abnormal change speed calculation formula to calculate the belt abnormal change speed, wherein the belt abnormal change speed calculation formula is: , where Spt is the belt operation abnormality value calculated in this test cycle, and Sp(t-1) is the belt operation abnormality value calculated in the previous test cycle;

[0075] S42. Obtain the calculated belt operation abnormality value, drive device abnormality value, drive device abnormal change speed, and belt abnormal change speed, and substitute them into the life estimation value calculation formula to calculate the life estimation value. The life estimation value calculation formula is: , where Xr is the set abnormal threshold, Vr is the mineral transportation speed of the horizontal belt conveyor in the next stage, that is, how much mass of minerals is required to be transported in the next stage in average time. is the driving abnormality ratio coefficient;

[0076] S5. Perform life comparison warning based on the estimated results of the belt conveyor life;

[0077] In this embodiment, the life comparison warning based on the obtained belt conveyor life estimation result includes the following specific contents:

[0078] The calculated life estimate is compared with the set belt life threshold. If the life estimate is greater than or equal to the set belt life threshold, no belt life warning is issued. If the life estimate is less than the set belt life threshold, a belt life warning is issued. It should be noted that since horizontal belt conveyors usually operate continuously when transporting minerals, belt life warnings are required in advance. The belt life threshold here can be flexibly set according to needs. If the maintenance preparation cycle is 1 hour, the belt life threshold here can be set to 1.5 hours to 2 hours.

[0079] It should be specifically explained that the method for obtaining the values ​​of the vibration abnormality ratio, speed abnormality ratio, temperature abnormality ratio, crack abnormality ratio coefficient, drive abnormality ratio coefficient and the set abnormality threshold in this embodiment is preferably: obtaining 500 sets of horizontal belt conveyor drive operation data, belt operation data and mineral transportation situation data during operation, substituting them into the life estimation value calculation formula to calculate the life estimation value, importing the calculated life estimation value and the actual service life of the horizontal belt conveyor into the fitting software, and outputting the values ​​of the vibration abnormality ratio, speed abnormality ratio, temperature abnormality ratio, crack abnormality ratio coefficient, drive abnormality ratio coefficient and the set abnormality threshold that meet the maximum life judgment accuracy.

[0080] The advantages of this embodiment over the prior art are: the operation abnormality assessment results, belt operation abnormality assessment results and mineral transportation condition data are imported into the belt conveyor life prediction strategy to perform belt conveyor life prediction, and by comprehensively analyzing the belt conveyor drive operation data and belt operation data during the operation process, the abnormal characteristics of the belt conveyor life implied in the analysis data are analyzed, and then the abnormal characteristics and the damage rate of the mineral transportation to the belt conveyor life are comprehensively evaluated to accurately estimate the belt conveyor life, thereby improving the accuracy of the belt conveyor life prediction.

[0081] Example 2. Figure 4As shown, a horizontal belt conveyor operation monitoring system based on data analysis is implemented based on the above-mentioned horizontal belt conveyor operation monitoring method based on data analysis, and specifically includes a data acquisition module, a drive operation abnormality evaluation module, a belt operation abnormality evaluation module, a belt conveyor life prediction module and a comparison and early warning module, wherein the data acquisition module is used to obtain the drive operation data and belt operation data of the horizontal belt conveyor during operation, and at the same time obtain the transportation mineral condition data during operation, the drive operation abnormality evaluation module is used to import the drive operation data of the horizontal belt conveyor into the drive operation abnormality evaluation model to perform drive operation abnormality evaluation, the belt operation abnormality evaluation module is used to import the acquired belt operation data into the belt abnormality evaluation model to perform belt operation abnormality evaluation, the belt conveyor life prediction module is used to import the operation abnormality evaluation result, the belt operation abnormality evaluation result and the transportation mineral condition data into the belt conveyor life prediction strategy to perform belt conveyor life estimation, the comparison and early warning module is used to perform life comparison early warning according to the obtained belt conveyor life estimation result, and also includes a control module, the control module is used to control the operation of the data acquisition module, the drive operation abnormality evaluation module, the belt operation abnormality evaluation module, the belt conveyor life prediction module and the comparison and early warning module.

[0082] The system can execute the method in any of the aforementioned embodiments and can achieve the same or similar technical effects, which will not be described in detail here.

[0083] Example 3. This embodiment provides an electronic device. At the hardware level, the electronic device includes a processor and optionally an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0084] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0085] The electronic device may vary significantly due to configuration or performance, and may include one or more processors and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the data analysis-based horizontal belt conveyor operation monitoring method provided in the above-mentioned method embodiment. The electronic device may also include other components for implementing the device's functions. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface to facilitate data input and output. This embodiment will not be described in detail here.

[0086] Example 4. This example provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0087] When the computer program is run on a computer device, the computer device is caused to execute the above-mentioned method for monitoring the operation of a horizontal belt conveyor based on data analysis.

[0088] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0089] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0090] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0091] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application of this application is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned application concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for monitoring the operation of a horizontal belt conveyor based on data analysis, characterized in that: It includes the following specific steps: S1. Acquire the horizontal belt conveyor driving operation data and belt operation data during operation, and simultaneously acquire the mineral transportation condition data during operation; S2. Importing the horizontal belt conveyor drive operation data into the drive operation abnormality assessment model to perform drive operation abnormality assessment; The specific steps include: S21. Test the drive device at set intervals to obtain the drive device temperature, drive device speed, and drive device vibration frequency and amplitude data during the operation of the drive device; S22, importing the acquired driving device temperature, driving device speed, driving device vibration frequency, and amplitude data into a driving device abnormal value calculation formula to calculate the driving device abnormal value, wherein the driving device abnormal value calculation formula is: , where Tc is the test duration, dt is the time integral, Tt is the temperature at test time t, Tmc is the median of the temperature safety range of the drive device, Tm is the difference between the maximum and minimum values ​​of the temperature safety range of the drive device, Vt is the speed data at test time t, Vm is the speed data to be output, Vt-Vm is used to evaluate the speed difference, m is the number of vibrations of the drive device during the test at time t, rit is the i-th vibration amplitude of the drive device during the test at time t, and rm is the maximum value of the vibration safety range. is the proportion of abnormal vibration, is the abnormal speed ratio, is the proportion of temperature anomaly; S23, obtaining the calculated abnormal value of the driving device, and substituting it into the abnormal change speed calculation formula of the driving device to calculate the abnormal change speed of the driving device, wherein the abnormal change speed calculation formula of the driving device is: , where Xc is the abnormal change speed of the driving equipment, Xt is the abnormal value of the driving equipment calculated in this test cycle, X(t-1) is the abnormal value of the driving equipment calculated in the previous test cycle, Mt is the mass of the transported minerals between the end of the previous test cycle and the end of this test cycle, and M is the set safety value of the transported mineral mass; S3, importing the acquired belt operation data into the belt abnormality assessment model to perform belt operation abnormality assessment; including the following specific steps: S31, obtaining surface crack data of the horizontal belt and sliding data on both sides of the horizontal belt during the test period; S32. Obtain the surface crack data of the horizontal belt during the test period and import it into the crack abnormal value calculation formula to calculate the crack abnormal value, wherein the crack abnormal value calculation formula is: , where M is the number of cracks, sj is the length of the j-th crack, zj is the width of the j-th crack, sc is the safety value of the surface crack length, and zc is the safety value of the surface crack width; S33, obtaining the sliding data on both sides of the horizontal belt and importing it into the sliding displacement abnormal value calculation formula to calculate the sliding displacement abnormal value, wherein the sliding displacement abnormal value calculation formula is: , where J is the transmission displacement of the horizontal belt during the test period, xc is the edge displacement data of the c-th point on the horizontal belt edge relative to the normal belt trajectory when it passes directly above the image acquisition terminal, Xm is the width of the horizontal belt, and dc is the integral of the points on the horizontal belt edge; S34. Obtain the calculated crack abnormal value and sliding displacement abnormal value and import them into the belt operation abnormal value calculation formula to calculate the belt operation abnormal value, wherein the belt operation abnormal value calculation formula is: ,in, is the coefficient of crack anomaly proportion; S4. Import the abnormal operation assessment results, the belt operation abnormality assessment results, and the transported mineral situation data into the belt conveyor life estimation strategy to estimate the belt conveyor life; including the following specific contents: S41. Obtain the calculated belt operation abnormality value and import it into the belt abnormal change speed calculation formula to calculate the belt abnormal change speed, wherein the belt abnormal change speed calculation formula is: , where Spt is the belt operation abnormality value calculated in this test cycle, and Sp(t-1) is the belt operation abnormality value calculated in the previous test cycle; S42. Obtain the calculated belt operation abnormality value, drive device abnormality value, drive device abnormal change speed, and belt abnormal change speed, and substitute them into the life estimation value calculation formula to calculate the life estimation value. The life estimation value calculation formula is: , where Xr is the set abnormal threshold, Vr is the mineral transportation speed of the horizontal belt conveyor in the next stage, that is, how much mass of minerals is required to be transported in the next stage in average time. is the driving abnormality ratio coefficient; S5. Perform life comparison warning based on the estimated results of the belt conveyor life.

2. The method for monitoring the operation of a horizontal belt conveyor based on data analysis according to claim 1, characterized in that: The specific contents of obtaining the horizontal belt conveyor driving operation data and belt operation data during operation, and simultaneously obtaining the mineral transportation situation data during operation are: S11, collecting surface crack data of the horizontal belt and sliding data on both sides of the horizontal belt during operation of the horizontal belt conveyor through an image acquisition terminal installed below the horizontal belt conveyor; S12. Collecting driving device operation data through the driving device data collection terminal, wherein the driving device operation data includes driving device temperature, driving device speed, and driving device vibration frequency and amplitude data; S13. Acquire real-time quality data of transported minerals and simultaneously acquire transport plan data.

3. The method for monitoring the operation of a horizontal belt conveyor based on data analysis according to claim 1, characterized in that: The life comparison warning based on the obtained belt conveyor life estimation result includes the following specific contents: comparing the calculated life estimation value with the set belt life threshold value. If the life estimation value is greater than or equal to the set belt life threshold value, no belt life warning is issued. If the life estimation value is less than the set belt life threshold value, a belt life warning is issued.

4. A horizontal belt conveyor operation monitoring system based on data analysis, which is implemented based on the horizontal belt conveyor operation monitoring method based on data analysis according to any one of claims 1 to 3, characterized in that: It specifically includes a data acquisition module, a drive operation abnormality assessment module, a belt operation abnormality assessment module, a belt conveyor life estimation module and a comparison and early warning module; The data acquisition module is used to acquire the horizontal belt conveyor driving operation data and belt operation data during operation, and simultaneously acquire the mineral transportation condition data during operation; The drive operation abnormality evaluation module is used to import the drive operation data of the horizontal belt conveyor into the drive operation abnormality evaluation model to perform drive operation abnormality evaluation; The belt operation abnormality assessment module is used to import the acquired belt operation data into the belt abnormality assessment model to perform belt operation abnormality assessment; The belt conveyor life estimation module is used to import the operation abnormality assessment results, the belt operation abnormality assessment results and the transport mineral situation data into the belt conveyor life estimation strategy to estimate the belt conveyor life; The comparison and warning module is used to perform life comparison and warning based on the obtained belt conveyor life estimation result.

5. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the horizontal belt conveyor operation monitoring method based on data analysis as described in any one of claims 1 to 3 by calling the computer program stored in the memory.

6. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the horizontal belt conveyor operation monitoring method based on data analysis as described in any one of claims 1 to 3.

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