Intelligent coal conveying safety detection method

Abstract

The invention belongs to the technical field of coal conveying safety detection, and particularly relates to an intelligent coal conveying safety detection method, which comprises the following specific steps of: acquiring a belt image of equipment through a belt image acquisition module; if yes, an image collected by the belt image collection module is compared with data stored in the belt state storage module through a belt comparison module, after comparison, whether the image collected by the belt image collection module is similar in the belt state storage module or not is judged through a belt judgment module, and if not, the belt state storage module is started; if yes, the belt state is abnormal. According to the invention, through the equipment detection module and the personnel detection module, the safety detection of the equipment state and the personnel behavior can be realized, and the equipment fault and the personnel behavior can be predicted, so that the potential safety hazard to occur can be predicted in advance; therefore, dangers can be avoided to a certain extent, and the safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal conveying safety detection, and specifically provides an intelligent coal conveying safety detection method. Background Art

[0002] The coal conveying site refers to the place where relevant equipment and operators are located during the process of transporting coal from the mining site or coal processing plant through conveying equipment (such as conveyor belts, railways, ships, etc.). At present, in order to improve the safety of the coal conveying site, a safety monitoring system is usually set up at the coal conveying site. However, in the existing safety monitoring systems, usually only reminders can be given for the safety hazards that have occurred, and it is impossible to predict the upcoming safety hazards, which has limitations. Therefore, an intelligent coal conveying safety detection method is invented. Summary of the Invention

[0003] To solve the above technical problems, according to one aspect of the present invention, the following technical solutions are provided:

[0004] An intelligent coal conveying safety detection method, which includes the following specific steps:

[0005] Step 1: The belt image acquisition module collects the belt image of the equipment. After the collection, the belt comparison module compares the image collected by the belt image acquisition module with the data stored in the belt status storage module. After the comparison, the belt judgment module judges whether the image collected by the belt image acquisition module is similar in the belt status storage module. If not, it means that the belt status is abnormal. At this time, the reminder module will remind the personnel. After the reminder, the belt acquisition module will find the corresponding solution in the belt solution database according to the data judged by the belt judgment module and implement it;

[0006] Step 2: The equipment operation parameter acquisition module collects the operation parameters of the equipment. After the collection, the equipment operation comparison module compares the data collected by the equipment operation parameter acquisition module with the data stored in the equipment operation storage module. After the comparison, the equipment operation judgment module judges whether the data collected by the equipment operation parameter acquisition module is similar in the equipment operation storage module. If not, it means that the equipment status is abnormal. At this time, the reminder module will remind the personnel. After the reminder, the equipment operation acquisition module will find the corresponding solution in the equipment solution database according to the data judged by the equipment operation judgment module and implement it;

[0007] Step 3: The fault data storage module stores the data from the belt status detection module and the equipment status detection module. After storage, the equipment fault prediction module analyzes and predicts the data stored in the fault data storage module to be able to detect potential equipment fault hazards in advance, providing a basis for equipment maintenance and repair, realizing preventive maintenance of the equipment. After prediction, the reminder module can remind the personnel;

[0008] Step 4: The personnel image acquisition module acquires the image of the personnel. After acquisition, the personnel position detection module determines the position of the personnel based on the image acquired by the personnel image acquisition module;

[0009] Step 5: The personnel action acquisition module acquires the personnel action based on the image acquired by the personnel image acquisition module. After acquisition, the personnel action comparison module is used to compare the action acquired by the personnel action acquisition module with the actions stored in the personnel action storage module. After comparison, the personnel action judgment module judges whether there is a similarity between the action acquired by the personnel action acquisition module and the actions stored in the personnel action storage module. If there is, it means that the personnel's action belongs to a dangerous action. At this time, the reminder module can remind the personnel;

[0010] Step 6: The face recognition module recognizes the face of the personnel based on the image acquired by the personnel image acquisition module. After recognition, the identity acquisition module finds the corresponding job type in the identity storage module according to the data recognized by the face recognition module;

[0011] Step 7: The data statistics module stores and statistics the data from the personnel action analysis module, the personnel position detection module and the identity recognition module. After that, the personnel action prediction module analyzes and predicts the data statistics by the data statistics module to be able to predict the next action of the personnel. After prediction, the reminder module can remind the personnel;

[0012] Step 8: The central processing unit acquires the predicted data from the equipment detection module and the personnel detection module. After acquisition, the reminder module notifies the personnel according to the data acquired by the central processing unit.

[0013] As a preferred solution of an intelligent coal conveying safety detection method according to the present invention, further comprising an intelligent coal conveying safety detection system, the intelligent coal conveying safety detection system includes:

[0014] An equipment detection module, which is used to detect the operating state of the equipment and the belt state in the equipment, and can predict the faults of the equipment according to the detected data;

[0015] A personnel detection module, which is used to detect the actions, positions, and identities of personnel, and can predict the actions of personnel based on the detected data;

[0016] A central processing unit, which is used to collect the predicted data of the equipment detection module and the personnel detection module;

[0017] A reminder module, which is used to notify personnel according to the data collected by the central processing unit.

[0018] As a preferred solution of an intelligent coal conveying safety detection method described in the present invention, wherein: the equipment detection module includes:

[0019] A belt status detection module, which is used to detect the status of the belt, and can implement corresponding solutions for the belt according to the detected data;

[0020] An equipment status detection module, which is used to detect the status of the equipment, and can implement corresponding solutions for the equipment according to the detected data;

[0021] A fault data storage module, which is used to store the data of the belt status detection module and the equipment status detection module;

[0022] An equipment fault prediction module, which is used to analyze and predict the data stored in the fault data storage module, so as to be able to discover potential equipment fault hidden dangers in advance, provide a basis for equipment maintenance and repair, realize preventive maintenance of the equipment, and after prediction, can remind personnel through the reminder module.

[0023] As a preferred solution of an intelligent coal conveying safety detection method described in the present invention, wherein: the belt status detection module includes:

[0024] A belt image acquisition module, which is used to acquire the belt image of the equipment;

[0025] A belt status storage module, which is used to store the normal status of the belt;

[0026] A belt comparison module, which is used to compare the image acquired by the belt image acquisition module with the data stored in the belt status storage module;

[0027] A belt judgment module, which is used to judge whether there is a similarity between the image acquired by the belt image acquisition module and the data stored in the belt status storage module. If not, it means that the belt status is abnormal. At this time, personnel will be reminded through the reminder module;

[0028] A belt solution database, which is used to store various belt abnormalities and their corresponding solutions;

[0029] The belt acquisition module is used to find the corresponding solution in the belt solution database according to the data judged by the belt judgment module and implement it.

[0030] As a preferred solution of an intelligent coal conveying safety detection method according to the present invention, wherein: the equipment status detection module includes:

[0031] The equipment operation parameter acquisition module is used to acquire the operation parameters of the equipment;

[0032] The equipment operation storage module is used to store the normal operation parameters of the equipment;

[0033] The equipment operation comparison module is used to compare the data collected by the equipment operation parameter acquisition module with the data stored in the equipment operation storage module;

[0034] The equipment operation judgment module is used to judge whether there is similarity in the data collected by the equipment operation parameter acquisition module in the equipment operation storage module. If not, it means that the equipment status is abnormal. At this time, the reminder module will remind the personnel;

[0035] The equipment solution database is used to store various equipment anomalies and their corresponding solutions;

[0036] The equipment operation acquisition module is used to find the corresponding solution in the equipment solution database according to the data judged by the equipment operation judgment module and implement it.

[0037] As a preferred solution of an intelligent coal conveying safety detection method according to the present invention, wherein: the personnel detection module includes:

[0038] The personnel image acquisition module is used to acquire the images of personnel;

[0039] The personnel action analysis module is used to analyze whether the actions of personnel belong to dangerous actions according to the images acquired by the personnel image acquisition module;

[0040] The personnel position detection module is used to determine the position of personnel according to the images acquired by the personnel image acquisition module;

[0041] The identity recognition module is used to determine the identity of personnel according to the images acquired by the personnel image acquisition module;

[0042] The data statistics module is used to store and statistically analyze the data of the personnel action analysis module, the personnel position detection module and the identity recognition module;

[0043] The personnel action prediction module is used to analyze and predict the data statistically analyzed by the data statistics module, so as to be able to predict the next action of the personnel. After the prediction, the reminder module can be used to remind the personnel.

[0044] As a preferred solution of the intelligent coal conveying safety detection method described in the present invention, wherein: the personnel action analysis module includes:

[0045] The personnel action acquisition module is used to acquire the personnel action according to the image collected by the personnel image acquisition module;

[0046] The personnel action storage module is used to store various dangerous actions;

[0047] The personnel action comparison module is used to compare the action acquired by the personnel action acquisition module with the action stored in the personnel action storage module;

[0048] The personnel action judgment module is used to judge whether there is a similarity between the action acquired by the personnel action acquisition module and the action stored in the personnel action storage module. If there is a similarity, it means that the action of the personnel belongs to a dangerous action. At this time, the reminder module will be used to remind the personnel.

[0049] As a preferred solution of the intelligent coal conveying safety detection method described in the present invention, wherein: the identity recognition module includes:

[0050] The face recognition module is used to recognize the face of the personnel according to the image collected by the personnel image acquisition module;

[0051] The identity storage module is used to store the employee information and the corresponding job types;

[0052] The identity acquisition module is used to find the corresponding job type in the identity storage module according to the data recognized by the face recognition module.

[0053] Compared with the prior art:

[0054] Through the provided equipment detection module and personnel detection module, the present invention can realize the safety detection of the equipment status and personnel behavior, and can also predict the equipment failure and personnel behavior, so as to be able to predict the potential safety hazards in advance, thereby avoiding the occurrence of danger to a certain extent and improving the safety; in addition, in the provided equipment detection module, after detecting the equipment failure, it can provide corresponding solutions according to the equipment failure and implement them, thereby improving the maintenance efficiency of the equipment failure to a certain extent; in addition, in the provided personnel detection module, it can predict the personnel action by combining the action, position and job type of the personnel, thereby improving the prediction accuracy to a certain extent. Brief Description of the Drawings

[0055] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0056] Figure 2 This is a schematic diagram of the process of the belt status detection module of the present invention;

[0057] Figure 3 This is a schematic diagram of the process of the equipment status detection module of the present invention. Detailed Embodiment

[0058] To make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0059] The present invention provides an intelligent coal conveying safety detection method. Please refer to Figures 1 - 3 ;

[0060] The specific steps are as follows:

[0061] Step 1: The belt image acquisition module collects the belt images of the equipment. After the collection, the belt comparison module compares the images collected by the belt image acquisition module with the data stored in the belt status storage module. After the comparison, the belt judgment module judges whether the images collected by the belt image acquisition module are similar in the belt status storage module. If not, it indicates that the belt status is abnormal. At this time, the reminder module will remind the personnel. After the reminder, the belt acquisition module will find the corresponding solution in the belt solution database according to the data judged by the belt judgment module and implement it;

[0062] Step 2: The equipment operation parameter acquisition module collects the operation parameters of the equipment. After the collection, the equipment operation comparison module compares the data collected by the equipment operation parameter acquisition module with the data stored in the equipment operation storage module. After the comparison, the equipment operation judgment module judges whether the data collected by the equipment operation parameter acquisition module is similar in the equipment operation storage module. If not, it indicates that the equipment status is abnormal. At this time, the reminder module will remind the personnel. After the reminder, the equipment operation acquisition module will find the corresponding solution in the equipment solution database according to the data judged by the equipment operation judgment module and implement it;

[0063] Step 3: The fault data storage module stores the data from the belt status detection module and the equipment status detection module. After storage, the equipment fault prediction module analyzes and predicts the data stored in the fault data storage module to be able to detect potential equipment fault hazards in advance, providing a basis for equipment maintenance and repair, realizing preventive maintenance of the equipment. And after prediction, the reminder module can remind the personnel;

[0064] Step 4: The personnel image acquisition module acquires the image of the personnel. After acquisition, the personnel position detection module determines the position of the personnel based on the image acquired by the personnel image acquisition module;

[0065] Step 5: The personnel action acquisition module acquires the personnel action based on the image acquired by the personnel image acquisition module. After acquisition, the personnel action comparison module is used to compare the action acquired by the personnel action acquisition module with the actions stored in the personnel action storage module. After comparison, the personnel action judgment module judges whether there is a similarity between the action acquired by the personnel action acquisition module and the actions stored in the personnel action storage module. If there is, it means that the personnel's action is a dangerous action. At this time, the reminder module can remind the personnel;

[0066] Step 6: The face recognition module recognizes the face of the personnel based on the image acquired by the personnel image acquisition module. After recognition, the identity acquisition module finds the corresponding job type in the identity storage module according to the data recognized by the face recognition module;

[0067] Step 7: The data statistics module stores and statistics the data from the personnel action analysis module, the personnel position detection module, and the identity recognition module. After that, the personnel action prediction module analyzes and predicts the data statistically processed by the data statistics module to be able to predict the next action of the personnel. And after prediction, the reminder module can remind the personnel;

[0068] Step 8: The central processing unit acquires the predicted data from the equipment detection module and the personnel detection module. After acquisition, the reminder module notifies the personnel according to the data acquired by the central processing unit.

[0069] It further includes an intelligent coal conveying safety detection system, and the intelligent coal conveying safety detection system includes: a device detection module for detecting the operating state of the device and the state of the belt in the device, and capable of predicting the faults of the device according to the detected data; a personnel detection module for detecting the actions, positions and identities of personnel, and capable of predicting the actions of personnel according to the detected data; a central processing unit for collecting the predicted data of the device detection module and the personnel detection module; and a reminder module for notifying personnel according to the data collected by the central processing unit.

[0070] The device detection module includes: a belt state detection module for detecting the state of the belt and capable of implementing corresponding solutions for the belt according to the detected data; a device state detection module for detecting the state of the device and capable of implementing corresponding solutions for the device according to the detected data; a fault data storage module for storing the data of the belt state detection module and the device state detection module; and a device fault prediction module for analyzing and predicting the data stored in the fault data storage module, so as to be able to discover potential device fault hidden dangers in advance, provide a basis for device maintenance and repair, realize preventive maintenance of the device, and after prediction, be able to remind personnel through the reminder module.

[0071] The belt state detection module includes: a belt image acquisition module for acquiring the belt image of the device; a belt state storage module for storing the normal state of the belt; a belt comparison module for comparing the image acquired by the belt image acquisition module with the data stored in the belt state storage module; a belt judgment module for judging whether there is similarity between the image acquired by the belt image acquisition module and the data stored in the belt state storage module. If not, it means that the belt state is abnormal. At this time, personnel will be reminded through the reminder module; a belt solution database for storing various belt anomalies and their corresponding solutions; and a belt acquisition module for finding and implementing the corresponding solution in the belt solution database according to the data judged by the belt judgment module.

[0072] The device status detection module includes: a device operation parameter acquisition module for acquiring the operation parameters of the device; a device operation storage module for storing the normal operation parameters of the device; a device operation comparison module for comparing the data acquired by the device operation parameter acquisition module with the data stored in the device operation storage module; a device operation judgment module for judging whether there is similarity between the data acquired by the device operation parameter acquisition module and the data stored in the device operation storage module. If not, it indicates that the device status is abnormal. At this time, the reminder module will remind the personnel; a device solution database for storing various device anomalies and their corresponding solutions; a device operation acquisition module for finding and implementing the corresponding solution in the device solution database according to the data judged by the device operation judgment module.

[0073] The personnel detection module includes: a personnel image acquisition module for acquiring the images of personnel; a personnel action analysis module for analyzing whether the actions of personnel belong to dangerous actions according to the images acquired by the personnel image acquisition module; a personnel position detection module for determining the positions of personnel according to the images acquired by the personnel image acquisition module; an identity recognition module for determining the identities of personnel according to the images acquired by the personnel image acquisition module; a data statistics module for storing and statistically analyzing the data of the personnel action analysis module, the personnel position detection module, and the identity recognition module; a personnel action prediction module for analyzing and predicting the data statistically analyzed by the data statistics module to be able to predict the next actions of personnel, and after prediction, the reminder module will remind the personnel.

[0074] The personnel action analysis module includes: a personnel action acquisition module for acquiring personnel actions according to the images acquired by the personnel image acquisition module; a personnel action storage module for storing various dangerous actions; a personnel action comparison module for comparing the actions acquired by the personnel action acquisition module with the actions stored in the personnel action storage module; a personnel action judgment module for judging whether there is similarity between the actions acquired by the personnel action acquisition module and the actions stored in the personnel action storage module. If so, it indicates that the actions of personnel belong to dangerous actions. At this time, the reminder module will remind the personnel.

[0075] The identity recognition module includes: a face recognition module for recognizing the faces of personnel according to the images acquired by the personnel image acquisition module; an identity storage module for storing employee information and their corresponding job types; an identity acquisition module for finding the corresponding job types in the identity storage module according to the data recognized by the face recognition module.

[0076] Although the present invention has been described above with reference to the embodiments, various modifications thereof can be made and components thereof can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in the present invention can be combined with each other in any way, and the exhaustive description of these combinations is not given in this specification only for the consideration of saving space and resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An intelligent coal transportation safety detection method, characterized in that it includes the following specific steps: Step 1: The belt image of the equipment is collected through the belt image collection module. After the collection, the image collected by the belt image collection module is compared with the data stored in the belt status storage module through the belt comparison module. After the comparison, the belt judgment module is used to judge whether the image collected by the belt image collection module is similar to the image in the belt status storage module. If not, it means that the belt status is abnormal. At this time, the reminder module will remind the personnel. After the reminder, the belt acquisition module will find the corresponding solution in the belt solution database according to the data judged by the belt judgment module and implement it; Step 2: The equipment operation parameters are collected through the equipment operation parameter collection module. After the collection, the data collected by the equipment operation parameter collection module will be compared with the data stored in the equipment operation storage module through the equipment operation comparison module. After the comparison, the equipment operation judgment module will be used to judge whether the data collected by the equipment operation parameter collection module is similar to the data in the equipment operation storage module. If not, it means that the equipment state is abnormal. At this time, the reminder module will be used to remind the personnel. After the reminder, the equipment operation acquisition module will find the corresponding solution in the equipment solution database according to the data judged by the equipment operation judgment module, and implement it; Step 3: The data of the belt status detection module and the equipment status detection module are stored through the fault data storage module. After storage, the data stored in the fault data storage module will be analyzed and predicted through the equipment fault prediction module, so as to be able to discover potential equipment failure hazards in advance, provide a basis for equipment maintenance and overhaul, and realize preventive maintenance of equipment. After prediction, the reminder module can remind personnel; Step 4: The image of the person is collected by the person image collection module. After the collection, the person position detection module will determine the position of the person according to the image collected by the person image collection module; Step 5: The personnel action is acquired by the personnel action acquisition module according to the image acquired by the personnel image acquisition module. After acquisition, the personnel action comparison module is used to compare the action acquired by the personnel action acquisition module with the action stored in the personnel action storage module. After comparison, the personnel action judgment module is used to judge whether the action acquired by the personnel action acquisition module is similar to the action stored in the personnel action storage module. If so, it means that the action of the personnel is a dangerous action. At this time, the reminder module will remind the personnel; Step 6: The face recognition module recognizes the face of the person according to the image collected by the person image collection module. After the recognition, the identity acquisition module finds the corresponding job type in the identity storage module according to the data recognized by the face recognition module. Step 7: The data from the personnel action analysis module, the personnel location detection module and the identity recognition module are stored and counted through the data statistics module. After that, the personnel action prediction module will analyze and predict the data counted by the data statistics module to predict the next action of the personnel, and after the prediction, the reminder module can remind the personnel; Step 8: The data predicted by the equipment detection module and the personnel detection module are collected by the central processing unit. After the collection, the reminder module will notify the personnel according to the data collected by the central processing unit.

2. The intelligent coal transportation safety detection method according to claim 1 is characterized in that: It also includes an intelligent coal transportation safety detection system, which includes: The equipment detection module is used to detect the running status of the equipment and the status of the belt in the equipment, and can predict the failure of the equipment based on the detected data; The personnel detection module is used to detect the movements, positions and identities of personnel, and can predict the movements of personnel based on the detected data; A central processing unit, used to collect data predicted by the equipment detection module and the personnel detection module; The reminder module is used to notify personnel based on the data collected by the central processor.

3. The intelligent coal transportation safety detection method according to claim 2 is characterized in that: The device detection module comprises: The belt status detection module is used to detect the status of the belt and implement corresponding solutions for the belt according to the detected data; The device status detection module is used to detect the status of the device and implement corresponding solutions for the device according to the detected data; A fault data storage module is used to store data of the belt status detection module and the equipment status detection module; The equipment failure prediction module is used to analyze and predict the data stored in the fault data storage module so as to discover potential equipment failure hazards in advance, provide a basis for equipment maintenance and overhaul, realize preventive maintenance of equipment, and after the prediction, remind personnel through the reminder module.

4. The intelligent coal transportation safety detection method according to claim 3 is characterized in that: The belt status detection module comprises: A belt image acquisition module is used to acquire the belt image of the equipment; A belt status storage module is used to store the normal status of the belt; A belt comparison module, used to compare the image acquired by the belt image acquisition module with the data stored in the belt status storage module; The belt judgment module is used to judge whether the image collected by the belt image acquisition module is similar to the one in the belt status storage module. If not, it means that the belt status is abnormal. At this time, the reminder module will remind the personnel; Belt solution database, used to store various belt anomalies and their corresponding solutions; The belt acquisition module is used to find the corresponding solution in the belt solution database according to the data judged by the belt judgment module and implement it.

5. The intelligent coal transportation safety detection method according to claim 3 is characterized in that: The device status detection module includes: Equipment operation parameter collection module, used to collect equipment operation parameters; The equipment operation storage module is used to store the normal operating parameters of the equipment; The equipment operation comparison module is used to compare the data collected by the equipment operation parameter collection module with the data stored in the equipment operation storage module; The equipment operation judgment module is used to judge whether the data collected by the equipment operation parameter collection module is similar to that in the equipment operation storage module. If not, it means that the equipment status is abnormal. At this time, the reminder module will remind the personnel; Equipment solution database, used to store various equipment anomalies and their corresponding solutions; The equipment operation acquisition module is used to find the corresponding solution in the equipment solution database according to the data judged by the equipment operation judgment module, and implement it.

6. The intelligent coal transportation safety detection method according to claim 2 is characterized in that: The personnel detection module comprises: A personnel image acquisition module, used for acquiring images of personnel; A personnel action analysis module is used to analyze whether the personnel action is a dangerous action based on the image collected by the personnel image collection module; A personnel position detection module, used to determine the position of a person based on the image collected by the personnel image collection module; An identity recognition module, used to determine the identity of a person based on the image collected by the person image collection module; A data statistics module is used to store and count the data of the personnel action analysis module, the personnel location detection module and the identity recognition module; The personnel action prediction module is used to analyze and predict the data collected by the data statistics module so as to predict the next action of the personnel, and after the prediction, the personnel can be reminded through the reminder module.

7. An intelligent coal transportation safety detection method according to claim 6, characterized in that: The personnel action parsing module includes: A personnel action acquisition module, used to acquire personnel actions based on the images acquired by the personnel image acquisition module; Personnel action storage module, used to store various dangerous actions; A personnel action comparison module, used to compare the action acquired by the personnel action acquisition module with the action stored in the personnel action storage module; The personnel action judgment module is used to judge whether the action obtained by the personnel action acquisition module is similar to that in the personnel action storage module. If so, it means that the action of the personnel is a dangerous action. At this time, the personnel will be reminded through the reminder module.

8. An intelligent coal transportation safety detection method according to claim 6, characterized in that: The identity recognition module comprises: A face recognition module, used to recognize the face of a person based on the image collected by the person image collection module; Identity storage module, used to store employee information and their corresponding job types; The identity acquisition module is used to find the corresponding job type in the identity storage module according to the data recognized by the face recognition module.

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