An intelligent coal mine operation collaborative system and method

Abstract

This invention provides an intelligent coal mine operation collaboration system and method, relating to the field of intelligent coal mine operation technology. It includes: an auxiliary operation module that uses preset intelligent tools to guide target operators in performing coal mine operations; an operation monitoring module that monitors the entire process of target operators performing coal mine operations in real time and issues warnings and abnormality responses when anomalies are detected; and a predictive maintenance module that takes corresponding equipment maintenance strategies based on periodically predicted future equipment failures. By using intelligent tools to guide target operators in performing coal mine operations and monitoring the entire process in real time, issuing warnings and abnormality responses when anomalies are detected, and periodically predicting future equipment failures and taking equipment maintenance strategies, the system effectively improves coal mine operation efficiency, human-machine collaboration efficiency, and equipment reliability and stability.

Description

Technical Field

[0001] This invention relates to the field of intelligent coal mine operation technology, and in particular to an intelligent coal mine operation collaborative system and method. Background Technology

[0002] In recent years, with the rapid development of technology, the level of intelligence of electromechanical equipment in the coal mining industry has continued to improve. However, in stark contrast, the level of digitalization in human operations and human-machine collaborative operations remains low. This disparity results in a high proportion of maintenance time, long non-production operation time, insufficient human-machine collaborative management in about two-thirds of production support positions, and low human-machine collaborative efficiency. Therefore, how to reduce the proportion of maintenance time and improve human-machine collaborative efficiency and production efficiency has become one of the current research focuses.

[0003] Therefore, the present invention provides an intelligent coal mine operation collaboration system and method. Summary of the Invention

[0004] This invention provides an intelligent coal mine operation collaboration system and method, which uses intelligent tools to guide target operators in performing coal mine operations and monitors the entire process of target operators performing coal mine operations in real time. When an anomaly is detected, warnings and anomaly responses are issued. The system also predicts the future failure conditions of operating equipment and takes equipment maintenance strategies to maintain the operating equipment, thereby effectively improving the efficiency of coal mine operations, the efficiency of human-machine collaboration, and the reliability and stability of equipment.

[0005] This invention provides an intelligent coal mine operation collaboration system, comprising:

[0006] Assisted Operations Module: Used to guide target workers in performing coal mine operations using preset intelligent tools;

[0007] Monitoring module: Used to monitor the entire process of coal mine operations performed by target workers in real time, and to issue warnings and take corresponding abnormality responses when an anomaly is detected;

[0008] Predictive maintenance module: It is used to periodically predict the future failure conditions of the operating equipment, obtain the predicted failure results, and then take corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

[0009] Preferably, the auxiliary operation module includes:

[0010] Login Unit: Used by the target operator to obtain the tool status of the preset smart tool in the display area of ​​the preset smart tool;

[0011] If the current preset smart tool is in an available state, the target operator will perform user authentication and login according to the user login operation of the current preset smart tool;

[0012] If the current preset smart tool is unavailable, the target operator generates a status anomaly report based on the current preset smart tool's number, tool name, and tool status, and sends it to the tool maintenance personnel. Then, the preset smart tool is re-adapted until it is adapted to a preset smart tool with an available status. Finally, the user authentication and login are performed according to the user login operation of the newly adapted preset smart tool.

[0013] Project selection unit: After the target operator successfully logs into the preset smart tool, they can select the target coal mine operation project from the list of designated coal mine operation projects of the target operator according to the project selection operation of the current preset smart tool, and obtain the target operation guidance data.

[0014] Preferably, the monitoring module includes:

[0015] Status monitoring unit: used to monitor in real time the data of the target operator performing coal mine operations according to the target operation guidance data using preset monitoring tools, and obtain the first status data;

[0016] Status Analysis Unit: Used to analyze the current operation behavior of the target operator based on the equipment status data of the operating equipment in the first status data, and obtain adjustment suggestions;

[0017] The adjustment suggestions and equipment status data are displayed in the preset intelligent tool display area to promptly remind the target operators to operate according to the adjustment suggestions and record the equipment status improvement effect data;

[0018] Behavior analysis unit: used to identify dangerous behaviors of the target worker based on the first video data in the first state data, and to issue a behavior warning to the current target worker when dangerous behaviors are identified;

[0019] Recording unit: Used to automatically record key data and data stamps of target workers during coal mine operations.

[0020] Preferably, the state analysis unit includes:

[0021] Status determination block: Used to preprocess the device status data of the currently operating device to obtain the target status data;

[0022] The target state data is input into a pre-established device state recognition model, and the state recognition result is output.

[0023] Behavior Analysis Block: Based on the status recognition result, when the operating equipment is in an abnormal state, it identifies whether there is a correlation between the current target operator's operation behavior and the abnormal state of the operating equipment based on a preset correlation recognition model, and obtains the correlation recognition result;

[0024] Based on the association identification results, if an association exists, it is determined that the current operation behavior needs to be adjusted, and this is output as a status analysis result.

[0025] If there is no correlation, the current operation behavior is determined to be unnecessary to adjust;

[0026] The operating equipment is subjected to fault identification and judgment according to the equipment fault detection scheme. When a fault is detected, it is determined that the current operating equipment is faulty. The fault type is then combined with the fault type to output the status analysis result.

[0027] It is recommended to generate a block; this block is used to verbally notify the target operator to stop the operation when the status analysis result indicates that the operating equipment is faulty.

[0028] A fault notification report generated based on the equipment fault type notifies equipment maintenance personnel to perform equipment maintenance.

[0029] When the status analysis result indicates that the current operation behavior needs to be adjusted, the current operation behavior is regarded as the first behavior to be adjusted;

[0030] Obtain the list of replacement operations for the first behavior to be adjusted;

[0031] If there is only a single replacement operation in the list of replacement operations, then the current first behavior to be adjusted will be replaced by the replacement operation as an adjustment suggestion output.

[0032] If there are multiple replacement operations in the list of replacement operations, the suitability of each replacement operation is evaluated to obtain the first suitability coefficient.

[0033] The first behavior to be adjusted will be replaced by the replacement operation with the one that has the largest first fit coefficient, and this will be output as an adjustment suggestion.

[0034] Preferably, the formula for calculating the first adaptation coefficient is as follows:

[0035] In the formula, It is represented as the first adaptation coefficient for the i-th replacement operation. This represents the average device status improvement effect value of the i-th replacement operation within a preset time period; This represents the j-th historical replacement parameter of the current first behavior to be adjusted, which is replaced by the i-th replacement operation within a preset time period, where j=1, 2, ... ,m; This represents the total value of the j-th historical replacement parameter that replaces the current first behavior to be adjusted within a preset time period using all replacement operations. This represents the influence weight of the j-th historical replacement parameter on the first fit coefficient used to calculate the replacement operation behavior; Let represent the satisfaction coefficient of the replacement skill for the i-th replacement operation.

[0036] Preferably, the behavior analysis unit includes:

[0037] Feature extraction block: used to preprocess the acquired first video data to obtain target video frame data;

[0038] The first behavioral features of the target workers are extracted from the target video frame data based on deep learning algorithms.

[0039] Hazard Analysis Block: Used to compare the first behavioral feature with the set hazardous behavior pattern library to obtain the first comparison result;

[0040] Based on the first comparison result, if it is determined that the current target worker is engaging in dangerous behavior by not wearing safety equipment, a voice warning will be triggered to the current target worker for not wearing safety equipment.

[0041] Upon receiving a warning that the target worker was not wearing safety equipment, the worker must immediately stop the current coal mine operation and then put on the safety equipment.

[0042] If it is determined that the current target worker has engaged in dangerous or irregular work operations, the dangerous characteristics in the current first comparison result will be used as the matching condition to obtain the correct work operation guidance from the set work operation database.

[0043] At a set frequency, repeatedly issue warnings and corresponding instructions on the correct operation to the current target operator regarding unauthorized or dangerous operations until the target operator corrects the dangerous behavior and completes the operation.

[0044] Upon receiving a warning of a violation or dangerous operation and corresponding instructions for correct operation, the target operator shall immediately correct the current dangerous operation in accordance with the instructions for correct operation.

[0045] Preferably, the predictive maintenance module includes:

[0046] Fault prediction unit: used to periodically collect historical operating data of the operating equipment within a preset time period, perform data preprocessing and feature extraction to obtain the first training data;

[0047] The first training data is used to train the neural network to obtain a fault prediction model;

[0048] Determine the prediction time according to the set prediction time period;

[0049] Using the equipment operation data at the predicted time of operation as input, the fault prediction model is input to obtain the predicted fault occurrence time and predicted fault type, which are then output as the predicted fault result.

[0050] Maintenance unit: Used to determine the maintenance sequence of operating equipment based on the predicted fault occurrence time in the predicted fault results and the importance level of the operating equipment;

[0051] The equipment maintenance content is obtained by filtering the equipment maintenance database using the operating equipment number and the predicted fault type in the predicted fault results as filtering criteria.

[0052] Develop an equipment maintenance plan based on the maintenance sequence and content of the equipment operation;

[0053] Equipment maintenance personnel perform maintenance tasks according to the equipment maintenance plan and generate equipment maintenance records.

[0054] This invention provides an intelligent coal mine operation collaboration method, comprising:

[0055] Step 1: Use pre-set intelligent tools to guide the target workers in performing coal mine operations;

[0056] Step 2: Monitor the entire process of the target workers performing coal mine operations in real time, and issue warnings and take corresponding abnormal response measures when an anomaly is detected;

[0057] Step 3: Periodically predict future failures of the operating equipment to obtain predicted failure results, and then take corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

[0058] Compared with the prior art, the beneficial effects of this application are as follows:

[0059] By using intelligent tools to guide target workers in performing coal mine operations and monitoring the entire process of these operations in real time, issuing warnings and responses when anomalies are detected, and periodically predicting future equipment failures and implementing equipment maintenance strategies, it is possible to effectively improve coal mine operation efficiency, human-machine collaboration efficiency, and equipment reliability and stability.

[0060] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0061] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0062] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0063] Figure 1 This is a structural diagram of an intelligent coal mine operation collaborative system according to an embodiment of the present invention;

[0064] Figure 2 This is a flowchart of an intelligent coal mine operation collaboration method according to an embodiment of the present invention. Detailed Implementation

[0065] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0066] This invention provides an intelligent coal mine operation collaboration system, such as... Figure 1 As shown, it includes:

[0067] Assisted Operations Module: Used to guide target workers in performing coal mine operations using preset intelligent tools;

[0068] Monitoring module: Used to monitor the entire process of coal mine operations performed by target workers in real time, and to issue warnings and take corresponding abnormality responses when an anomaly is detected;

[0069] Predictive maintenance module: It is used to periodically predict the future failure conditions of the operating equipment, obtain the predicted failure results, and then take corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

[0070] In this embodiment, the target worker refers to the staff currently performing coal mine operations; the preset intelligent tools refer to intelligent tools used to assist the target worker in accurately completing coal mine operations, such as intelligent handheld devices and smart glasses; the preset monitoring tools for monitoring the entire process of the target worker performing coal mine operations include cameras and various sensors, such as temperature sensors; warnings refer to danger warnings for not wearing safety equipment, danger warnings for violations of operating procedures, and danger warnings for dangerous operating procedures; the predicted fault results include the predicted fault occurrence time and predicted fault type, which are obtained by inputting the equipment operation data of the operating equipment at the predicted time into the fault prediction model, where the predicted time refers to the time when the operating equipment is predicted to fail; the fault prediction model is a model obtained by training a neural network using the first training data, used to predict the future fault occurrence time and fault type of the operating equipment, where the first training data is obtained by preprocessing and feature extraction of the historical operating data of the operating equipment, and then labeling it with fault tags; the equipment maintenance plan consists of the predicted fault results, operating equipment number, maintenance sequence, maintenance time, maintenance personnel, and maintenance content.

[0071] The beneficial effects of the above technical solution are: by using intelligent tools to guide the target operators in performing coal mine operations and to monitor the entire process of the target operators in performing coal mine operations in real time, warnings and abnormal responses are issued when abnormalities are detected; by regularly predicting the future failure of operating equipment and taking equipment maintenance strategies to maintain the operating equipment, the efficiency of coal mine operations, the efficiency of human-machine collaboration, and the reliability and stability of equipment can be effectively improved.

[0072] This invention provides an intelligent coal mine operation collaboration system, wherein the auxiliary operation module includes:

[0073] Login Unit: Used by the target operator to obtain the tool status of the preset smart tool in the display area of ​​the preset smart tool;

[0074] If the current preset smart tool is in an available state, the target operator will perform user authentication and login according to the user login operation of the current preset smart tool;

[0075] If the current preset smart tool is unavailable, the target operator generates a status anomaly report based on the current preset smart tool's number, tool name, and tool status, and sends it to the tool maintenance personnel. Then, the preset smart tool is re-adapted until it is adapted to a preset smart tool with an available status. Finally, the user authentication and login are performed according to the user login operation of the newly adapted preset smart tool.

[0076] Project selection unit: After the target operator successfully logs into the preset smart tool, they can select the target coal mine operation project from the list of designated coal mine operation projects of the target operator according to the project selection operation of the current preset smart tool, and obtain the target operation guidance data.

[0077] In this embodiment, the target operator refers to the worker who needs to perform coal mine operations; the preset smart tool refers to the intelligent tool used to assist the target operator in accurately completing the coal mine operation, such as a smart handheld device or smart glasses; the display area refers to the set area of ​​the preset smart tool used to display tool status, work guidance data, adjustment suggestions, and equipment status data; the tool status includes two types: available and unavailable.

[0078] In this embodiment, the user login operation refers to the user login operation guide, including authentication methods and login operation steps, used to assist the target operator in completing authentication and user login. Authentication methods include fingerprint, facial recognition, etc. The status anomaly report consists of the number, name, status, report generation time, and personnel number of the preset smart tool whose current status is unavailable. Tool maintenance personnel use this information to repair and maintain the preset smart tool based on the received status anomaly report. The project selection operation refers to the operation guide for the user to select a coal mine operation project. The designated coal mine operation project list consists of all coal mine operation projects that the current target operator is allowed to perform. The target coal mine operation project refers to the coal mine operation project that the current target operator needs to perform. The target operation guidance data refers to the operation guidance data for the target coal mine operation project, which includes operation procedures, precautions, operation guidelines, etc., and data types include text descriptions, videos, and images, etc.

[0079] The beneficial effects of the above technical solution are: by having the target operators check the usage status of the intelligent tools they use before starting coal mine operations, and by using preset intelligent tools for identity verification and user login to obtain the corresponding operation guidance data, it is beneficial to effectively improve operation efficiency and safety.

[0080] This invention provides an intelligent coal mine operation collaborative system, wherein the monitoring operation module includes:

[0081] Status monitoring unit: used to monitor in real time the data of the target operator performing coal mine operations according to the target operation guidance data using preset monitoring tools, and obtain the first status data;

[0082] Status Analysis Unit: Used to analyze the current operation behavior of the target operator based on the equipment status data of the operating equipment in the first status data, and obtain adjustment suggestions;

[0083] The adjustment suggestions and equipment status data are displayed in the preset intelligent tool display area to promptly remind the target operators to operate according to the adjustment suggestions and record the equipment status improvement effect data;

[0084] Behavior analysis unit: used to identify dangerous behaviors of the target worker based on the first video data in the first state data, and to issue a behavior warning to the current target worker when dangerous behaviors are identified;

[0085] Recording unit: Used to automatically record key data and data stamps of target workers during coal mine operations.

[0086] In this embodiment, the pre-set monitoring tools include cameras and various sensors, such as temperature sensors; the work guidance data includes work procedures, precautions, operation guidelines, etc., and the data types include text descriptions, videos, and pictures, etc.; the first status data consists of equipment status data and first video data, wherein the equipment status data consists of the equipment number and corresponding status data of the operating equipment operated by the target operator during the coal mine operation, such as machine operating parameters, temperature, pressure, etc.

[0087] In this embodiment, the adjustment suggestion refers to the adjustment strategy of replacing the operation of the target worker; the equipment status improvement effect data refers to the change value of the status data of the corresponding operating equipment after the operation of the current target worker is adjusted according to the adjustment suggestion; the dangerous behavior of the personnel refers to the dangerous operation behavior of the target worker in the process of coal mine operation, such as not wearing safety equipment, violation of regulations, etc.; the key data refers to all the data generated by the target worker in the process of coal mine operation, including the behavioral data of the target worker, equipment status change data, environmental parameter data, behavioral warnings, adjustment suggestions, and operation adjustment data, etc.

[0088] The beneficial effects of the above technical solution are: by monitoring the process of target workers carrying out coal mine operations in real time, based on the acquired first state data, it realizes functions such as identification of dangerous behaviors of personnel, operation adjustment and automatic recording, providing comprehensive safety protection and management support for coal mine operations, which helps to improve work efficiency, reduce accident risks and improve human-machine collaboration efficiency.

[0089] This invention provides an intelligent coal mine operation collaboration system, wherein the status analysis unit includes:

[0090] Status determination block: Used to preprocess the device status data of the currently operating device to obtain the target status data;

[0091] The target state data is input into a pre-established device state recognition model, and the state recognition result is output.

[0092] Behavior Analysis Block: Based on the status recognition result, when the operating equipment is in an abnormal state, it identifies whether there is a correlation between the current target operator's operation behavior and the abnormal state of the operating equipment based on a preset correlation recognition model, and obtains the correlation recognition result;

[0093] Based on the association identification results, if an association exists, it is determined that the current operation behavior needs to be adjusted, and this is output as a status analysis result.

[0094] If there is no correlation, the current operation behavior is determined to be unnecessary to adjust;

[0095] The operating equipment is subjected to fault identification and judgment according to the equipment fault detection scheme. When a fault is detected, it is determined that the current operating equipment is faulty. The fault type is then combined with the fault type to output the status analysis result.

[0096] It is recommended to generate a block; this block is used to verbally notify the target operator to stop the operation when the status analysis result indicates that the operating equipment is faulty.

[0097] A fault notification report generated based on the equipment fault type notifies equipment maintenance personnel to perform equipment maintenance.

[0098] When the status analysis result indicates that the current operation behavior needs to be adjusted, the current operation behavior is regarded as the first behavior to be adjusted;

[0099] Obtain the list of replacement operations for the first behavior to be adjusted;

[0100] If there is only a single replacement operation in the list of replacement operations, then the current first behavior to be adjusted will be replaced by the replacement operation as an adjustment suggestion output.

[0101] If there are multiple replacement operations in the list of replacement operations, the suitability of each replacement operation is evaluated to obtain the first suitability coefficient.

[0102] The first behavior to be adjusted will be replaced by the replacement operation with the one that has the largest first fit coefficient, and this will be output as an adjustment suggestion.

[0103] In this embodiment, the target state data is data obtained by cleaning, denoising, and formatting preprocessing of equipment state data; the equipment state recognition model is a model trained on a random forest using training data obtained after preprocessing and feature extraction of historical operating state data of the operating equipment, used to identify and judge the current state of the operating equipment, including two judgment results: normal and abnormal; the association recognition model is a model obtained by training a neural network using the acquired association training data, used to identify the association between the current operation behavior and the abnormal state of the operating equipment, wherein the association training data is obtained by preprocessing, feature extraction, and equipment state labeling of the corresponding operator operation behavior data during the time period when the operating equipment state is identified as abnormal; the association recognition result includes two results: there is an association and there is no association; the equipment fault detection scheme consists of equipment fault detection items and fault detection steps, wherein the fault detection items include hardware detection, software detection, and communication status detection; equipment fault types include hardware faults, software faults, and communication faults, etc.; the state analysis result includes two results: the current operation behavior needs to be adjusted and the operating equipment has a fault; the operation behavior includes operation time, operation action, operation parameters, etc.

[0104] In this embodiment, the fault notification report consists of the equipment number of the operating equipment currently in an abnormal state, the equipment fault type, the current equipment status data, the target operator number, and the fault identification time; the first behavior to be adjusted refers to the corresponding operation behavior that the status analysis result indicates needs to be adjusted for the current operation behavior; the list of alternative operation behaviors consists of alternative operation behaviors, operating equipment numbers, and replaceable operation behaviors; the alternative operation behavior refers to a pre-determined operation behavior that can replace the current operation behavior to optimize the abnormal equipment status caused by the operation behavior; the adjustment suggestion refers to replacing the current first behavior to be adjusted with the alternative operation behavior; the first adaptation coefficient is used to describe the degree of adaptation of the alternative operation behavior to the current operation behavior.

[0105] The beneficial effects of the above technical solution are: by intelligently analyzing the data generated by real-time monitoring of equipment status and operational behavior during coal mine operations, the operational behavior of operators can be optimized and equipment faults can be identified and handled, significantly improving the efficiency of human-machine collaborative operations and increasing overall production efficiency.

[0106] This invention provides an intelligent coal mine operation collaboration system, wherein the calculation formula for the first adaptation coefficient is as follows:

[0107] In the formula, It is represented as the first adaptation coefficient for the i-th replacement operation. This represents the average device status improvement effect value of the i-th replacement operation within a preset time period; This represents the j-th historical replacement parameter of the current first behavior to be adjusted, which is replaced by the i-th replacement operation within a preset time period, where j=1, 2, ... ,m; This represents the total value of the j-th historical replacement parameter that replaces the current first behavior to be adjusted within a preset time period using all replacement operations. This represents the influence weight of the j-th historical replacement parameter on the first fit coefficient used to calculate the replacement operation behavior; Let represent the satisfaction coefficient of the replacement skill for the i-th replacement operation.

[0108] In this embodiment, the first adaptation coefficient is used to describe the degree of adaptation of the replacement operation behavior to the current operation behavior; the preset time period is predetermined; the average equipment status improvement effect value is obtained by averaging all equipment status improvement effect values ​​obtained by replacing the first behavior to be adjusted with the current replacement operation behavior within the preset time period; the equipment status improvement effect value is obtained by weighted averaging the data change difference of the corresponding operation equipment status data within a set time period after each replacement operation behavior to the first behavior to be adjusted, wherein the weighting of the data change difference of the status data is obtained by solving the matrix constructed by pairwise comparison and relative importance scoring using the analytic hierarchy process.

[0109] In this embodiment, the historical replacement parameters include the historical number of times the replacement operation has been used, the total duration of the historical operation, and the total number of actions included in the replacement operation. The influence weight of the historical replacement parameters on the calculation of the first fit coefficient of the replacement operation is obtained by solving a matrix constructed by using the analytic hierarchy process (AHP) to perform pairwise comparisons and relative importance scoring of the historical replacement parameters.

[0110] In this embodiment, the steps for obtaining the replacement skill satisfaction coefficient are as follows:

[0111] 1. Obtain all operators whose equipment status improvement effect value is greater than the average equipment status improvement effect value when replacing the first adjustment behavior with the current replacement operation behavior in the past within the preset time period, and mark them as screening personnel;

[0112] 2. The screening personnel with the lowest equipment status improvement effect value among the screening personnel shall be regarded as the first personnel;

[0113] 3. The difference between the skill professional score of the current target worker and the skill professional score of the first worker is used as the output of the replacement skill satisfaction coefficient;

[0114] The skill and professional ratings for the operators are predetermined, with a range of values ​​ranging from [value missing]. .

[0115] The beneficial effects of the above technical solution are: by calculating the first adaptation coefficient, data support is provided for selecting the most suitable replacement operation behavior to replace the current first behavior to be adjusted, which helps to ensure the accuracy and reliability of the obtained adjustment suggestions, and thus helps to improve production efficiency.

[0116] This invention provides an intelligent coal mine operation collaboration system, wherein the behavior analysis unit includes:

[0117] Feature extraction block: used to preprocess the acquired first video data to obtain target video frame data;

[0118] The first behavioral features of the target workers are extracted from the target video frame data based on deep learning algorithms.

[0119] Hazard Analysis Block: Used to compare the first behavioral feature with the set hazardous behavior pattern library to obtain the first comparison result;

[0120] Based on the first comparison result, if it is determined that the current target worker is engaging in dangerous behavior by not wearing safety equipment, a voice warning will be triggered to the current target worker for not wearing safety equipment.

[0121] Upon receiving a warning that the target worker was not wearing safety equipment, the worker must immediately stop the current coal mine operation and then put on the safety equipment.

[0122] If it is determined that the current target worker has engaged in dangerous or irregular work operations, the dangerous characteristics in the current first comparison result will be used as the matching condition to obtain the correct work operation guidance from the set work operation database.

[0123] At a set frequency, repeatedly issue warnings and corresponding instructions on the correct operation to the current target operator regarding unauthorized or dangerous operations until the target operator corrects the dangerous behavior and completes the operation.

[0124] Upon receiving a warning of a violation or dangerous operation and corresponding instructions for correct operation, the target operator shall immediately correct the current dangerous operation in accordance with the instructions for correct operation.

[0125] In this embodiment, the target video frame data is obtained by decoding and converting the first video data, denoising, enhancing contrast, and cropping useless areas, and then extracting keyframes containing the behavior of the target worker; the first behavioral feature refers to the behavioral features of the target worker detected from the target video frame data using a deep learning algorithm, including posture, action, positional relationship, etc.

[0126] In this embodiment, the dangerous behavior pattern library is pre-set and consists of dangerous features extracted from the operator's illegal and dangerous operation behaviors, as well as the corresponding dangerous behavior labels; the first comparison result refers to the judgment result made on whether the first behavior feature of the target operator belongs to dangerous behavior and the corresponding dangerous feature when it belongs to dangerous behavior after comparing the first behavior feature with the dangerous features in the pre-set dangerous behavior pattern library using the cosine similarity algorithm.

[0127] In this embodiment, for example, there are dangerous features q1, q2, q3 with similarity values ​​of 0.5, 0.75, and 0.85 with the first behavioral feature z1, respectively. Among them, the similarity values ​​of dangerous features q2 and q3 with the first behavioral feature z1 are greater than the set similarity threshold of 0.7. At this time, it is determined that the behavior of the current target worker is a dangerous behavior, and the dangerous behavior type is the corresponding dangerous behavior type of dangerous features q2 and q3.

[0128] In this embodiment, the set frequency is preset, generally repeating once 10 seconds after the last voice warning ends; the correct operation guidance refers to the correct operation content obtained by matching the dangerous characteristics in the current first comparison result from the set operation database. The set operation database is a database formed by classifying and organizing all correct operation content of coal mine operations performed by operators based on existing safety specifications and operation manuals according to the dangerous characteristics of dangerous behaviors. The correct operation content includes operation steps, safety specifications, correct operation methods, etc.

[0129] The beneficial effects of the above technical solution are: by processing and analyzing video data in real time, it is possible to promptly detect and warn workers of behaviors such as not wearing safety equipment, violating regulations, or engaging in dangerous operations, thereby responding in a timely manner, effectively avoiding potential safety accidents, and improving the safety and efficiency of coal mine operations.

[0130] This invention provides an intelligent coal mine operation collaborative system, wherein the predictive maintenance module includes:

[0131] Fault prediction unit: used to periodically collect historical operating data of the operating equipment within a preset time period, perform data preprocessing and feature extraction to obtain the first training data;

[0132] The first training data is used to train the neural network to obtain a fault prediction model;

[0133] Determine the prediction time according to the set prediction time period;

[0134] Using the equipment operation data at the predicted time of operation as input, the fault prediction model is input to obtain the predicted fault occurrence time and predicted fault type, which are then output as the predicted fault result.

[0135] Maintenance unit: Used to determine the maintenance sequence of operating equipment based on the predicted fault occurrence time in the predicted fault results and the importance level of the operating equipment;

[0136] The equipment maintenance content is obtained by filtering the equipment maintenance database using the operating equipment number and the predicted fault type in the predicted fault results as filtering criteria.

[0137] Develop an equipment maintenance plan based on the maintenance sequence and content of the equipment operation;

[0138] Equipment maintenance personnel perform maintenance tasks according to the equipment maintenance plan and generate equipment maintenance records.

[0139] In this embodiment, the preset time period is predetermined; historical operating data refers to the historical operating status data and historical operating parameters of the operating equipment, such as temperature, pressure, vibration, current, etc.; the first training data is obtained by preprocessing and extracting features from the historical operating data of the operating equipment, and then labeling them with fault tags; the fault prediction model is a model obtained by training a neural network using the first training data, used to predict the future fault time and fault type of the operating equipment; the set prediction time period is a predetermined equipment fault prediction period; the prediction time refers to the time when the operating equipment fault is predicted; the predicted fault result includes the predicted fault occurrence time and the predicted fault type.

[0140] In this embodiment, the importance level includes two levels: critical and non-critical, which are predetermined; the equipment maintenance database consists of the equipment maintenance content of all operating equipment and historical equipment maintenance records; the equipment maintenance content includes maintenance steps, required tools, spare parts list, etc.; the equipment maintenance plan consists of predicted failure results, operating equipment number, maintenance sequence, maintenance time, maintenance personnel, and maintenance content, wherein the maintenance time is obtained by the equipment maintenance personnel after sorting the maintenance tasks according to the current maintenance sequence of the operating equipment; the equipment maintenance record consists of the equipment maintenance plan, equipment maintenance start and end time, equipment maintenance effect, and the number of spare parts consumed during maintenance, wherein the equipment maintenance effect includes two types: success and failure.

[0141] The beneficial effects of the above technical solution are: by regularly predicting the future failures of operating equipment and generating corresponding equipment maintenance plans for early maintenance, the proportion of maintenance time can be effectively reduced, and the reliability and stability of the equipment can be improved.

[0142] This invention provides an intelligent coal mine operation collaboration method, such as... Figure 2 As shown, it includes:

[0143] Step 1: Use pre-set intelligent tools to guide the target workers in performing coal mine operations;

[0144] Step 2: Monitor the entire process of the target workers performing coal mine operations in real time, and issue warnings and take corresponding abnormal response measures when an anomaly is detected;

[0145] Step 3: Periodically predict future failures of the operating equipment to obtain predicted failure results, and then take corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

[0146] The beneficial effects of the above technical solution are: by using intelligent tools to guide the target operators in performing coal mine operations and to monitor the entire process of the target operators in performing coal mine operations in real time, warnings and abnormal responses are issued when abnormalities are detected; by regularly predicting the future failure of operating equipment and taking equipment maintenance strategies to maintain the operating equipment, the efficiency of coal mine operations, the efficiency of human-machine collaboration, and the reliability and stability of equipment can be effectively improved.

[0147] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent coal mine operation collaborative system, characterized in that, include: Assisted Operations Module: Used to guide target workers in performing coal mine operations using preset intelligent tools; Monitoring module: Used to monitor the entire process of coal mine operations performed by target workers in real time, and to issue warnings and take corresponding abnormality responses when an anomaly is detected; It includes a status analysis unit, which analyzes the current operational behavior of the target operator based on the equipment status data of the operating equipment in the first status data, and obtains adjustment suggestions; Predictive maintenance module: used to periodically predict future failures of operating equipment, obtain predicted failure results, and then take corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results; The state analysis unit includes: Status determination block: Used to preprocess the device status data of the currently operating device to obtain the target status data; The target state data is input into a pre-established device state recognition model, and the state recognition result is output. Behavior Analysis Block: Based on the status recognition result, when the operating equipment is in an abnormal state, it identifies whether there is a correlation between the current target operator's operation behavior and the abnormal state of the operating equipment based on a preset correlation recognition model, and obtains the correlation recognition result; Based on the association identification results, if an association exists, it is determined that the current operation behavior needs to be adjusted, and this is output as a status analysis result. If there is no correlation, the current operation behavior is determined to be unnecessary to adjust; The operating equipment is subjected to fault identification and judgment according to the equipment fault detection scheme. When a fault is detected, it is determined that the current operating equipment is faulty. The fault type is then combined with the fault type to output the status analysis result. It is recommended to generate a block; this block is used to verbally notify the target operator to stop the operation when the status analysis result indicates that the operating equipment is faulty. A fault notification report generated based on the equipment fault type notifies equipment maintenance personnel to perform equipment maintenance. When the status analysis result indicates that the current operation behavior needs to be adjusted, the current operation behavior is regarded as the first behavior to be adjusted; Obtain the list of replacement operations for the first behavior to be adjusted; If there is only a single replacement operation in the list of replacement operations, then the current first behavior to be adjusted will be replaced by the replacement operation as an adjustment suggestion output. If there are multiple replacement operations in the list of replacement operations, the suitability of each replacement operation is evaluated to obtain the first suitability coefficient. The adjustment suggestion will be output by replacing the current first behavior to be adjusted with the replacement operation behavior that has the largest first adaptation coefficient; the formula for calculating the first adaptation coefficient is as follows: In the formula, It is represented as the first adaptation coefficient for the i-th replacement operation. This represents the average device status improvement effect value of the i-th replacement operation within a preset time period; This represents the j-th historical replacement parameter of the current first behavior to be adjusted, which is replaced by the i-th replacement operation within a preset time period, where j=1, 2, ... ,m; This represents the total value of the j-th historical replacement parameter that replaces the current first behavior to be adjusted within a preset time period using all replacement operations. This represents the influence weight of the j-th historical replacement parameter on the first fit coefficient used to calculate the replacement operation behavior; Let represent the satisfaction coefficient of the replacement skill for the i-th replacement operation.

2. The intelligent coal mine operation collaborative system according to claim 1, characterized in that, The auxiliary operation module includes: Login Unit: Used by the target operator to obtain the tool status of the preset smart tool in the display area of ​​the preset smart tool; If the current preset smart tool is in an available state, the target operator will perform user authentication and login according to the user login operation of the current preset smart tool; If the current preset smart tool is unavailable, the target operator generates a status anomaly report based on the current preset smart tool's number, tool name, and tool status, and sends it to the tool maintenance personnel. Then, the preset smart tool is re-adapted until it is adapted to a preset smart tool with an available status. Finally, the user authentication and login are performed according to the user login operation of the newly adapted preset smart tool. Project selection unit: After the target operator successfully logs into the preset smart tool, they can select the target coal mine operation project from the list of designated coal mine operation projects of the target operator according to the project selection operation of the current preset smart tool, and obtain the target operation guidance data.

3. The intelligent coal mine operation collaborative system according to claim 1, characterized in that, The monitoring module includes: Status monitoring unit: used to monitor in real time the data of the target operator performing coal mine operations according to the target operation guidance data using preset monitoring tools, and obtain the first status data; The adjustment suggestions and equipment status data are displayed in the preset intelligent tool display area to promptly remind the target operators to operate according to the adjustment suggestions and record the equipment status improvement effect data; Behavior analysis unit: used to identify dangerous behaviors of the target worker based on the first video data in the first state data, and to issue a behavior warning to the current target worker when dangerous behaviors are identified; Recording unit: Used to automatically record key data and data stamps of target workers during coal mine operations.

4. The intelligent coal mine operation collaborative system according to claim 3, characterized in that, The behavior analysis unit includes: Feature extraction block: used to preprocess the acquired first video data to obtain target video frame data; The first behavioral features of the target workers are extracted from the target video frame data based on deep learning algorithms. Hazard Analysis Block: Used to compare the first behavioral feature with the set hazardous behavior pattern library to obtain the first comparison result; Based on the first comparison result, if it is determined that the current target worker is engaging in dangerous behavior by not wearing safety equipment, a voice warning will be triggered to the current target worker for not wearing safety equipment. Upon receiving a warning that the target worker was not wearing safety equipment, the worker must immediately stop the current coal mine operation and then put on the safety equipment. If it is determined that the current target worker has engaged in dangerous or irregular work operations, the dangerous characteristics in the current first comparison result will be used as the matching condition to obtain the correct work operation guidance from the set work operation database. At a set frequency, repeatedly issue warnings and corresponding instructions on the correct operation to the current target operator regarding unauthorized or dangerous operations until the target operator corrects the dangerous behavior and completes the operation. Upon receiving a warning of a violation or dangerous operation and corresponding instructions for correct operation, the target operator shall immediately correct the current dangerous operation in accordance with the instructions for correct operation.

5. The intelligent coal mine operation collaborative system according to claim 1, characterized in that, The predictive maintenance module includes: Fault prediction unit: used to periodically collect historical operating data of the operating equipment within a preset time period, perform data preprocessing and feature extraction to obtain the first training data; The first training data is used to train the neural network to obtain a fault prediction model; Determine the prediction time according to the set prediction time period; Using the equipment operation data at the predicted time of operation as input, the fault prediction model is input to obtain the predicted fault occurrence time and predicted fault type, which are then output as the predicted fault result. Maintenance unit: Used to determine the maintenance sequence of operating equipment based on the predicted fault occurrence time in the predicted fault results and the importance level of the operating equipment; The equipment maintenance content is obtained by filtering the equipment maintenance database using the operating equipment number and the predicted fault type in the predicted fault results as filtering criteria. Develop an equipment maintenance plan based on the maintenance sequence and content of the equipment operation; Equipment maintenance personnel perform maintenance tasks according to the equipment maintenance plan and generate equipment maintenance records.

6. An intelligent coal mine operation collaboration method, applied to the intelligent coal mine operation collaboration system according to any one of claims 1-5, characterized in that, include: Step 1: Use pre-set intelligent tools to guide the target workers in performing coal mine operations; Step 2: Monitor the entire process of the target workers performing coal mine operations in real time, and issue warnings and take corresponding abnormal response measures when an anomaly is detected; This includes: analyzing the current operational behavior of the target operator based on the equipment status data of the operating equipment in the first state data, and obtaining adjustment suggestions; Preprocess the current operating equipment status data to obtain target status data; input the target status data into a pre-established equipment status recognition model, and output the status recognition result; Based on the status recognition results, when the operating equipment is in an abnormal state, the system identifies whether there is a correlation between the current target operator's operation behavior and the abnormal state of the operating equipment based on a preset association recognition model, and obtains the association recognition results. Based on the association identification results, if an association exists, it is determined that the current operation behavior needs to be adjusted, and this is output as a status analysis result. If there is no correlation, the current operation behavior is determined to be unnecessary to adjust; The operating equipment is subjected to fault identification and judgment according to the equipment fault detection scheme. When a fault is detected, it is determined that the current operating equipment is faulty. The fault type is then combined with the fault type to output the status analysis result. When the status analysis result indicates that the operating equipment is faulty, the target operator will be notified via voice to stop the operation. A fault notification report generated based on the equipment fault type notifies equipment maintenance personnel to perform equipment maintenance. When the status analysis result indicates that the current operation behavior needs to be adjusted, the current operation behavior is regarded as the first behavior to be adjusted; Obtain the list of replacement operations for the first behavior to be adjusted; If there is only a single replacement operation in the list of replacement operations, then the current first behavior to be adjusted will be replaced by the replacement operation as an adjustment suggestion output. If there are multiple replacement operations in the list of replacement operations, the suitability of each replacement operation is evaluated to obtain the first suitability coefficient. The first behavior to be adjusted will be replaced by the replacement operation behavior with the largest first fit coefficient as the adjustment suggestion output. The formula for calculating the first adaptation coefficient is as follows: In the formula, It is represented as the first adaptation coefficient for the i-th replacement operation. This represents the average device status improvement effect value of the i-th replacement operation within a preset time period; This represents the j-th historical replacement parameter of the current first behavior to be adjusted, which is replaced by the i-th replacement operation within a preset time period, where j=1, 2, ... ,m; This represents the total value of the j-th historical replacement parameter that replaces the current first behavior to be adjusted within a preset time period using all replacement operations. This represents the influence weight of the j-th historical replacement parameter on the first fit coefficient used to calculate the replacement operation behavior; Let be the satisfaction coefficient of the replacement skill for the i-th replacement operation; Step 3: Periodically predict future failures of the operating equipment to obtain predicted failure results, and then take corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

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