Online intelligent monitoring method and system for rail-mounted continuous transportation machinery
Through online intelligent monitoring methods, the sound and image data of orbital continuous transportation machinery are collected and analyzed, and abnormal noises and abnormalities in mechanical transportation are identified and evaluated, which solves the problem of normal operation of machinery under unmanned patrols, and realizes automated intelligent monitoring, improving transportation safety and efficiency.
Patent Information
- Application Number
- CN202411737528.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-03-25
AI Technical Summary
How to realize automated intelligent monitoring of track-type continuous transportation machinery without manned inspection to ensure the normal operation of the machinery.
Through online intelligent monitoring methods, the sound data of the machinery during material transportation and the image data of the transportation track are collected, abnormal noise events and abnormal situations are identified, their impact on mechanical transportation is evaluated, and a reminder or control the machinery to stop running based on the evaluation results.
It realizes fully automated intelligent monitoring of track-type continuous transportation machinery, reduces the need for manual inspection, improves monitoring effect, and ensures safe transportation of machinery.
Smart Images
Figure CN119691499A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of transport machinery monitoring, and in particular to an online intelligent monitoring method and system for track-type continuous transport machinery. Background Art
[0002] Rail-mounted continuous transport machinery, such as bucket wheel stackers and ship loaders, is an important equipment used to transport materials in ports. As remote control technology becomes increasingly mature, various companies have followed up with remote control modifications to transport machinery in order to achieve unmanned on-site operations. However, remote control of these transport machinery only transfers manual operations from the site to the office, and the operation of the machinery on site still requires manual monitoring by patrol personnel.
[0003] Therefore, how to realize automated intelligent monitoring of machinery and ensure its normal operation without human inspection is an urgent problem to be solved. Summary of the invention
[0004] In order to realize automated intelligent monitoring of machinery and ensure that the machinery can still operate normally without human inspection, the present application provides an online intelligent monitoring method and system for rail-type continuous transport machinery.
[0005] In a first aspect, the present application provides an online intelligent monitoring method for a track-type continuous transport machine, which adopts the following technical solution: The online intelligent monitoring method for a track-type continuous transport machine comprises the following steps: Initiate monitoring tasks based on mechanical material transport operations; Collecting actual sound data of the machine during the material transportation process, and based on the actual sound data, identifying abnormal sound events of the machine during the material transportation process and mechanical failures causing the abnormal sound events, and evaluating the impact of the mechanical failures on the normal transportation of the machine to obtain a first evaluation result; Collecting actual image data on the mechanical transport track, and based on the actual image data, identifying abnormal events on the mechanical transport track, and evaluating the impact of the abnormal events on normal mechanical transport, to obtain a second evaluation result; Based on the first evaluation result and the second evaluation result, a corresponding reminder is generated or the machine is directly controlled to stop running.
[0006] By adopting the above technical solutions, on the one hand, based on audio processing technology, the sound data in the mechanical transportation process is monitored in real time to ensure the normal transportation of the machinery; on the other hand, based on image processing technology, the image data of the transportation track is monitored in real time to ensure the smooth flow of the track; when there is an abnormal sound from the machine or an abnormality in the track, a reminder is generated in time or the machine is controlled to stop running, thereby ensuring the safe transportation of the machinery in all aspects, realizing fully automated intelligent monitoring of the machinery, eliminating the need for manual inspections, reducing waste of manpower, and achieving better monitoring effects.
[0007] In a specific implementation scheme, based on the actual image data, identifying abnormal events on the mechanical transportation track and evaluating the impact of the abnormal events on the normal transportation of the machinery specifically includes: Based on the actual image data, identifying surface obstacles on the mechanical transport track and extracting specific features of the surface obstacles; Obtaining the machine running speed and the type of material transported by the machine, and judging whether the surface obstacle will cause the machine to have a first dangerous event based on the specific characteristics of the surface obstacle, the machine running speed and the type of material transported by the machine; if so, determining that the surface obstacle will affect the normal transportation of the machine; The specific characteristics of the surface obstacles include one or more of type, shape, height, and size; and the first dangerous event includes one or more of damage, tilting, derailment, and material spillage.
[0008] By adopting the above technical solution, by accurately identifying the surface obstacles on the mechanical transportation track, according to the specific characteristics of the surface obstacles, combined with the mechanical operation speed and the type of transported materials, it is determined whether the surface obstacles will cause damage, tilting, derailment, material spillage, etc. to the machinery, thereby ensuring the safe transportation operation of the machinery.
[0009] In a specific implementation manner, after extracting the specific features of the surface obstacles, the method further comprises: Based on the specific features of the surface obstacle, determine whether the surface obstacle is a staff member; if so, issue an alarm; The position of the worker is continuously monitored, and when the worker is on the track and the distance between the worker and the machine is less than a preset safety distance, the machine is controlled to stop running.
[0010] By adopting the above technical solution, when the surface obstacle on the track is a staff member, an alarm is used to remind the staff member to leave, and the normal operation of the machinery is prioritized to avoid affecting the transportation efficiency; when the staff member has not evacuated to a safe area, the personnel should be guaranteed to safely control the machinery to stop, thereby improving the safety of the machinery operation.
[0011] In a specific implementation scheme, based on the actual image data, identifying abnormal events on the mechanical transportation track and evaluating the impact of the abnormal events on the normal transportation of the machinery, specifically also includes: Based on the actual image data, identifying defects on the mechanical transport track; the defects include cracks and breaks; If the defect on the mechanical transport track is a break, it is determined that the defect will affect the normal transport of the machine; If the defect on the mechanical transport track is a crack, then based on the crack position and crack degree, it is determined whether the crack will cause a second dangerous event to occur in the machine; if so, it is determined that the defect will affect the normal transportation of the machine; The second dangerous event includes reducing the material taking accuracy and / or deepening the cracking degree.
[0012] By adopting the above technical solution, defects on the mechanical transportation track are accurately identified. When the track is broken, the normal transportation of the machine will be affected, so the machine is directly controlled to stop running; when the track is cracked, according to the specific cracking position and degree, for example, when the cracking position is a key position of the track, the cracking here will cause the machine to deviate from the track and reduce the material picking accuracy, or when the cracking degree is large, the machine will continue to travel on it, which will deepen the cracking degree and cause greater maintenance costs. At this time, it is necessary to control the machine to stop running to ensure the safe transportation operation of the machinery and avoid causing greater losses.
[0013] In a specific implementation scheme, actual sound data of the machine during material transportation is collected, and based on the actual sound data, abnormal sound events of the machine during material transportation and mechanical failures causing the abnormal sound events are identified, specifically including: Collecting actual sound data of key parts of the machine during the material transportation process, and preprocessing the actual sound data; the preprocessing includes filtering out irregular sound data from the actual sound data and retaining regular sound data; Constructing a mechanical actual sound set based on the actual sound data preprocessed for each of the key parts; Based on the actual mechanical sound set and the pre-stored mechanical sample sound set, determine whether there is an abnormal sound event in each of the key parts, and analyze the mechanical fault that caused the abnormal sound event; wherein, the mechanical sample sound set includes sample sound data of each of the key parts when the machine is operating normally.
[0014] By adopting the above technical solution, through real-time monitoring of key parts of the machinery and then pre-processing the actual sound data collected, the accuracy of subsequent judgment of abnormal noise and analysis of mechanical failures is improved.
[0015] In a specific possible implementation scheme, judging whether there is an abnormal sound event at each of the key parts based on the actual mechanical sound set and the pre-stored mechanical sample sound set, and analyzing the mechanical fault causing the abnormal sound event, specifically includes: For each of the key parts, the actual sound data corresponding to the key part in the mechanical actual sound set and the sample sound data corresponding to the key part in the mechanical sample sound set are compared from the sound feature dimension to determine whether there is an abnormal sound event at the key part, and analyze the mechanical fault causing the abnormal sound event; The sound feature dimensions include one or more of frequency, volume and sound density.
[0016] By adopting the above technical solution, abnormal noises and mechanical failures can be comprehensively judged and analyzed from multiple dimensions such as frequency, volume and sound density, which greatly improves the judgment accuracy.
[0017] In a specific implementation scheme, the key parts include one or more of the mechanical travel drive mechanism, the mechanical belt drive mechanism, the mechanical rotation mechanism, the mechanical bucket wheel mechanism, and the mechanical tail car mechanism.
[0018] In a second aspect, the present application provides an online intelligent monitoring system for a track-type continuous transport machinery, which adopts the following technical solution: the system applies the online intelligent monitoring method for a track-type continuous transport machinery in the above-mentioned first aspect or any one of the possible implementation schemes of the first aspect; the system includes a starting module, a sound acquisition and recognition module, an image acquisition and recognition module, and a control module; The start module is used to start the monitoring task based on the mechanical material transportation operation; The sound collection and recognition module is used to collect actual sound data of the machine during the material transportation process, and based on the actual sound data, identify abnormal sound events of the machine during the material transportation process and mechanical failures causing the abnormal sound events, and evaluate the impact of the mechanical failures on the normal transportation of the machine to obtain a first evaluation result; The image acquisition and recognition module is used to acquire actual image data on the mechanical transportation track, and based on the actual image data, identify abnormal events on the mechanical transportation track, and evaluate the impact of the abnormal events on the normal transportation of the machine to obtain a second evaluation result; The control module is used to generate a corresponding reminder or directly control the machine to stop running based on the first evaluation result and the second evaluation result.
[0019] In the third aspect, the present application provides a terminal, adopting the following technical solution: the terminal includes a processor, a memory and a communication bus; the communication bus is used to realize the connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to realize the online intelligent monitoring method of the rail-type continuous transportation machinery as described in the first aspect or any one of the implementation plans of the first aspect.
[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: the computer-readable storage medium stores instructions, and when the instructions are executed, the online intelligent monitoring method of the rail-type continuous transportation machinery as described in the above-mentioned first aspect or any one of the possible implementation schemes of the first aspect is executed.
[0021] In summary, the technical solution of this application includes at least the following beneficial technical effects: 1. On the one hand, based on audio processing technology, the sound data of the mechanical transportation process is monitored in real time to ensure the normal transportation of the machinery; on the other hand, based on image processing technology, the image data of the transportation track is monitored in real time to ensure the smooth flow of the track; when there is an abnormal sound from the machine or an abnormality in the track, a reminder is generated in time or the machine is controlled to stop running, which fully guarantees the safe transportation of the machinery and realizes the fully automated intelligent monitoring of the machinery. There is no need for manual inspection, which reduces the waste of manpower and has better monitoring effect; 2. By accurately identifying the surface obstacles or defects on the mechanical transport track, and further judging the dangerous events that may be caused by the surface obstacles or defects, the safe transportation operation of the machinery can be ensured from the perspective of the transport track, and greater losses can be avoided; 3. Through real-time monitoring of key parts of the machinery and pre-processing of the actual sound data collected, the abnormal sound and mechanical failure are comprehensively judged from multiple dimensions such as frequency, volume and sound density, thereby improving the accuracy of abnormal sound judgment and mechanical failure analysis. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 It is a schematic diagram of the main flow of the monitoring method in the embodiment of the present application; Figure 2 This is a schematic diagram of the main process of monitoring the sound of mechanical operation in an embodiment of the present application; Figure 3 It is a schematic diagram of the main flow chart for the transport track monitoring process in the embodiment of the present application. DETAILED DESCRIPTION
[0023] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.
[0024] The present application embodiment provides an online intelligent monitoring method for a track-type continuous transport machinery, such as Figure 1 As shown, the method includes steps S1-S4.
[0025] S1, starts the monitoring task based on the mechanical material transportation operation.
[0026] S2, collecting actual sound data of the machinery during the material transportation process, and based on the actual sound data, identifying abnormal sound events of the machinery during the material transportation process and mechanical failures that cause the abnormal sound events, and evaluating the impact of the mechanical failures on the normal transportation of the machinery to obtain a first evaluation result.
[0027] In one possible implementation, refer to Figure 2 Step S2, collecting actual sound data of the machine during the material transportation process, and identifying abnormal sound events of the machine during the material transportation process and mechanical failures causing the abnormal sound events based on the actual sound data, specifically including the following steps: A10, collecting actual sound data of key parts of the machine during the material transportation process, and preprocessing the actual sound data; the preprocessing includes filtering out irregular sound data from the actual sound data and retaining regular sound data.
[0028] The key parts include one or more of the mechanical travel drive mechanism, the mechanical belt drive mechanism, the mechanical slewing mechanism, the mechanical bucket wheel mechanism, and the mechanical tail car mechanism; for these drive mechanisms, further refinement can be performed to collect one or more of the motor, reducer, coupling, and wheel in the drive mechanism. Of course, those skilled in the art can also flexibly arrange the locations where sound data needs to be collected according to actual conditions, and this application does not limit this.
[0029] A20, constructing a mechanical actual sound set based on the actual sound data of each key part after preprocessing; the mechanical actual sound set includes the actual sound data of each key part after preprocessing; A30, based on the actual sound set of the machine and the pre-stored sample sound set of the machine, determine whether there is an abnormal sound event in each of the key parts, and analyze the mechanical fault that caused the abnormal sound event; wherein, the sample sound set of the machine includes sample sound data of each of the key parts when the machine is operating normally.
[0030] Through the above steps A10-A30, by real-time monitoring of key parts of the machine and then pre-processing the collected actual sound data, the subsequent judgment accuracy of abnormal noise and the analysis accuracy of mechanical failure are improved.
[0031] In a possible implementation, in step A30, based on the actual mechanical sound set and the pre-stored mechanical sample sound set, it is determined whether there is an abnormal sound event at each of the key parts, and the mechanical fault causing the abnormal sound event is analyzed, which specifically includes the following steps: B10, for each of the key parts, comparing the actual sound data corresponding to the key part in the mechanical actual sound set and the sample sound data corresponding to the key part in the mechanical sample sound set from the sound feature dimension, determining whether there is an abnormal sound event at the key part, and analyzing the mechanical fault causing the abnormal sound event; The sound feature dimensions include one or more of frequency, volume and sound density.
[0032] Through step B10, the abnormal noise and mechanical failure are comprehensively judged and analyzed from multiple dimensions such as frequency, volume and sound density, which greatly improves the judgment accuracy.
[0033] S3, collecting actual image data on the mechanical transportation track, and based on the actual image data, identifying abnormal events on the mechanical transportation track, and evaluating the impact of the abnormal events on the normal transportation of the machine, to obtain a second evaluation result.
[0034] In one possible implementation, refer to Figure 3 In step S3, based on the actual image data, abnormal events of the mechanical transportation track are identified, and the impact of the abnormal events on the normal transportation of the machinery is evaluated, which specifically includes the following steps: C10, based on the actual image data, identifying surface obstacles on the mechanical transport track and extracting specific features of the surface obstacles.
[0035] C40, obtaining a machine operating speed and a type of material transported by the machine, and judging whether the surface obstacle will cause a first dangerous event to occur in the machine based on specific characteristics of the surface obstacle, the machine operating speed and the type of material transported by the machine; If so, it is determined that the surface obstacle will affect the normal transportation of the machine, and the machine is controlled to stop running; if not, it is determined that the surface obstacle will not affect the normal transportation of the machine, and a corresponding reminder can be generated to facilitate the staff to check and handle it after the mechanical transportation is completed.
[0036] The specific characteristics of the surface obstacles include one or more of type, shape, height, and size; and the first dangerous event includes one or more of damage, tilting, derailment, and material spillage.
[0037] Therefore, the above steps C10 and C40 accurately identify the surface obstacles on the mechanical transport track, and based on the specific characteristics of the surface obstacles, combined with the mechanical operating speed and the type of transported materials, determine whether the surface obstacles will cause damage, tilting, derailment, material spillage, etc. to the machinery, thereby ensuring the safe transportation operation of the machinery.
[0038] In a possible implementation manner, after extracting the specific features of the surface obstacles in step C10, the following steps are further included: C20, based on the specific features of the surface obstacle, determining whether the surface obstacle is a worker; if so, issuing an alarm; the alarm is used to remind the worker to stay away from the track; C30, continuously monitor the position of the worker, and when the worker is on the track and the distance between the worker and the machine is less than a preset safety distance, control the machine to stop running.
[0039] Through the above steps C20-C30, when the surface obstacle on the track is a staff member, priority is given to using an alarm to remind the staff to leave, giving priority to ensuring the normal operation of the machinery to avoid affecting the transportation efficiency; when the staff has not evacuated to a safe area, the personnel should be guaranteed to safely control the machinery to stop, thereby improving the safety of the machinery operation.
[0040] In one possible implementation, continue to refer to Figure 3 In step S3, based on the actual image data, abnormal events on the mechanical transportation track are identified, and the impact of the abnormal events on the normal transportation of the machinery is evaluated, which specifically includes the following steps: D10, based on the actual image data, identifying defects on the mechanical transport track; the defects include cracks and breaks.
[0041] D20, if the defect on the mechanical transport track is a break, it is determined that the defect will affect the normal transport of the machine, and the machine is controlled to stop running; If the defect on the mechanical transport track is a crack, then based on the crack position and crack degree, determine whether the crack will cause a second dangerous event to occur in the machine; if so, it is determined that the defect will affect the normal transportation of the machine, and the machine is controlled to stop running; if not, it is determined that the defect will not affect the normal transportation of the machine, and a corresponding reminder is generated at this time, so that the staff can check and handle it after the mechanical transportation is completed.
[0042] The second dangerous event includes reducing the material taking accuracy and / or deepening the cracking degree.
[0043] Through the above steps D10-D20, by accurately identifying the defects on the mechanical transportation track, when the track is broken, the track break will affect the normal transportation of the machine, and the machine is directly controlled to stop running; when the track is cracked, according to the specific cracking position and degree, for example, when the cracking position is a key position of the track, the cracking here will cause the machine to deviate from the track and reduce the material picking accuracy, or when the cracking degree is large, the machine will continue to travel on it and deepen the cracking degree, resulting in greater maintenance costs. At this time, it is necessary to control the machine to stop running to ensure the safe transportation operation of the machinery and avoid causing greater losses.
[0044] S4: Based on the first evaluation result and the second evaluation result, a corresponding reminder is generated or the machine is directly controlled to stop running.
[0045] In the above steps S1-S4, on the one hand, based on audio processing technology, the sound data in the mechanical transportation process is monitored in real time to ensure the normal transportation of the machinery; on the other hand, based on image processing technology, the image data of the transportation track is monitored in real time to ensure the smooth flow of the track; when there is an abnormal sound from the machine or an abnormality in the track, a reminder is generated in time or the machine is controlled to stop running, so as to comprehensively ensure the safe transportation of the machinery, realize fully automated intelligent monitoring of the machinery, eliminate the need for manual inspections, reduce waste of manpower, and achieve better monitoring effects.
[0046] In a possible implementation, the method further includes the following steps: Communication is established with the machine to identify electrical faults in the machine, thereby further improving the monitoring effect on the machine.
[0047] In a possible implementation, the method further includes the following steps: The hydraulic system of the machine is monitored to identify the faults of the hydraulic system; specifically, the hydraulic station, pipelines and cylinders in the hydraulic system can be monitored in all directions.
[0048] The embodiment of the present application provides an online intelligent monitoring system for a track-type continuous transport machinery, and applies the above-mentioned online intelligent monitoring method for a track-type continuous transport machinery. Specifically, the intelligent monitoring system includes a starting module, a sound acquisition and recognition module, an image acquisition and recognition module, and a control module; The start module is used to start the monitoring task based on the mechanical material transportation operation; The sound collection and recognition module is used to collect actual sound data of the machine during the material transportation process, and based on the actual sound data, identify abnormal sound events of the machine during the material transportation process and mechanical failures causing the abnormal sound events, and evaluate the impact of the mechanical failures on the normal transportation of the machine to obtain a first evaluation result; The image acquisition and recognition module is used to acquire actual image data on the mechanical transportation track, and based on the actual image data, identify abnormal events on the mechanical transportation track, and evaluate the impact of the abnormal events on the normal transportation of the machine to obtain a second evaluation result; The control module is used to generate a corresponding reminder or directly control the machine to stop running based on the first evaluation result and the second evaluation result.
[0049] An embodiment of the present application provides a terminal, including: a processor, a memory and a communication bus; the communication bus is used to realize the connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to realize the above-mentioned online intelligent monitoring method of rail-type continuous transportation machinery.
[0050] An embodiment of the present application provides a computer-readable storage medium, which stores instructions. When the instructions are executed, the above-mentioned online intelligent monitoring method for rail-type continuous transportation machinery is executed.
[0051] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.
Claims
1. A method for online intelligent monitoring of a track-type continuous transport machinery, characterized in that: The following steps are involved: Initiate monitoring tasks based on mechanical material transport operations; Collecting actual sound data of the machine during the material transportation process, and based on the actual sound data, identifying abnormal sound events of the machine during the material transportation process and mechanical failures causing the abnormal sound events, and evaluating the impact of the mechanical failures on the normal transportation of the machine to obtain a first evaluation result; Collecting actual image data on the mechanical transport track, and based on the actual image data, identifying abnormal events on the mechanical transport track, and evaluating the impact of the abnormal events on normal mechanical transport, to obtain a second evaluation result; Based on the first evaluation result and the second evaluation result, a corresponding reminder is generated or the machine is directly controlled to stop running.
2. The online intelligent monitoring method for rail-type continuous transport machinery according to claim 1 is characterized in that: Based on the actual image data, identifying abnormal events on the mechanical transportation track and evaluating the impact of the abnormal events on the normal transportation of the machinery, specifically including: Based on the actual image data, identifying surface obstacles on the mechanical transport track and extracting specific features of the surface obstacles; Obtaining the machine running speed and the type of material transported by the machine, and judging whether the surface obstacle will cause the machine to have a first dangerous event based on the specific characteristics of the surface obstacle, the machine running speed and the type of material transported by the machine; if so, determining that the surface obstacle will affect the normal transportation of the machine; The specific characteristics of the surface obstacles include one or more of type, shape, height, and size; and the first dangerous event includes one or more of damage, tilting, derailment, and material spillage.
3. The online intelligent monitoring method for rail-type continuous transport machinery according to claim 2 is characterized in that: After extracting the specific features of the surface obstacles, the method further includes: Based on the specific features of the surface obstacle, determine whether the surface obstacle is a staff member; if so, issue an alarm; The position of the worker is continuously monitored, and when the worker is on the track and the distance between the worker and the machine is less than a preset safety distance, the machine is controlled to stop running.
4. The online intelligent monitoring method for rail-type continuous transport machinery according to claim 1, characterized in that: Based on the actual image data, identifying abnormal events on the mechanical transportation track, and evaluating the impact of the abnormal events on the normal transportation of the machinery, specifically also includes: Based on the actual image data, identifying defects on the mechanical transport track; the defects include cracks and breaks; If the defect on the mechanical transport track is a break, it is determined that the defect will affect the normal transport of the machine; If the defect on the mechanical transport track is a crack, then based on the crack position and crack degree, it is determined whether the crack will cause a second dangerous event to occur in the machine; if so, it is determined that the defect will affect the normal transportation of the machine; The second dangerous event includes reducing the material taking accuracy and / or deepening the cracking degree.
5. The online intelligent monitoring method for rail-type continuous transport machinery according to claim 1, characterized in that: Collecting actual sound data of the machine during the material transportation process, and identifying abnormal sound events of the machine during the material transportation process and mechanical failures causing the abnormal sound events based on the actual sound data, specifically including: Collecting actual sound data of key parts of the machine during the material transportation process, and preprocessing the actual sound data; the preprocessing includes filtering out irregular sound data from the actual sound data and retaining regular sound data; Constructing a mechanical actual sound set based on the actual sound data preprocessed for each of the key parts; Based on the actual mechanical sound set and the pre-stored mechanical sample sound set, determine whether there is an abnormal sound event in each of the key parts, and analyze the mechanical fault that caused the abnormal sound event; wherein, the mechanical sample sound set includes sample sound data of each of the key parts when the machine is operating normally.
6. The online intelligent monitoring method for rail-type continuous transport machinery according to claim 5, characterized in that: The determining whether there is an abnormal sound event at each of the key parts based on the actual machine sound set and the pre-stored machine sample sound set, and analyzing the mechanical fault causing the abnormal sound event, specifically includes: For each of the key parts, the actual sound data corresponding to the key part in the mechanical actual sound set and the sample sound data corresponding to the key part in the mechanical sample sound set are compared from the sound feature dimension to determine whether there is an abnormal sound event at the key part, and analyze the mechanical fault causing the abnormal sound event; The sound feature dimensions include one or more of frequency, volume and sound density.
7. The online intelligent monitoring method for rail-type continuous transport machinery according to claim 6, characterized in that: The key parts include one or more of the mechanical travel drive mechanism, the mechanical belt conveyor drive mechanism, the mechanical rotation mechanism, the mechanical bucket wheel mechanism, and the mechanical tail car mechanism.
8. An online intelligent monitoring system for track-type continuous transport machinery, characterized in that: It includes a start-up module, a sound acquisition and recognition module, an image acquisition and recognition module and a control module; The start module is used to start the monitoring task based on the mechanical material transportation operation; The sound collection and recognition module is used to collect actual sound data of the machine during the material transportation process, and based on the actual sound data, identify abnormal sound events of the machine during the material transportation process and mechanical failures causing the abnormal sound events, and evaluate the impact of the mechanical failures on the normal transportation of the machine to obtain a first evaluation result; The image acquisition and recognition module is used to acquire actual image data on the mechanical transportation track, and based on the actual image data, identify abnormal events on the mechanical transportation track, and evaluate the impact of the abnormal events on the normal transportation of the machine to obtain a second evaluation result; The control module is used to generate a corresponding reminder or directly control the machine to stop running based on the first evaluation result and the second evaluation result.
9. A terminal, characterized in that: include: A processor, a memory and a communication bus; the communication bus is used to realize the connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to realize the online intelligent monitoring method of the rail-type continuous transportation machinery as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the online intelligent monitoring method for a rail-type continuous transport machinery as described in any one of claims 1-7 is executed.