Intelligent coal mine operation coordination system and method

Abstract

The invention provides an intelligent coal mine operation cooperation system and method, and relates to the technical field of intelligent coal mine operation, and the system comprises an auxiliary operation module which uses a preset intelligent tool to guide a target operator to execute coal mine operation; the monitoring operation module is used for monitoring the whole coal mine operation execution process of the target operation personnel in real time, and giving out an alarm and an abnormal response when an abnormal condition is detected; and the prediction maintenance module is used for carrying out equipment maintenance by adopting a corresponding equipment maintenance strategy based on a predicted fault result obtained by periodically predicting the future fault condition of the equipment. A target operator is guided to execute coal mine operation by using an intelligent tool, the whole process of executing the coal mine operation by the target operator is monitored in real time, and warning and abnormity response are given out when abnormity is detected; and the future fault condition of the operation equipment is periodically predicted, and the operation equipment is maintained by adopting an equipment maintenance strategy, so that the coal mine operation efficiency, the man-machine cooperation efficiency and the reliability and stability of the equipment can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent coal mine operation, and in particular to an intelligent coal mine operation coordination system and method. Background Art

[0002] In recent years, with the rapid development of science and technology, the intelligence level of electromechanical equipment in the coal mining industry has continued to improve. However, in sharp contrast, the digital level of human operations and human-machine collaborative operations is low. This contrast leads to a high proportion of maintenance time, long non-production operation time, insufficient human-machine collaborative management in about 2 / 3 of production auxiliary 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 coordination system and method. Summary of the invention

[0004] The present invention provides an intelligent coal mine operation collaboration system and method, which are used to guide target operators to perform coal mine operations by utilizing intelligent tools, and to monitor the entire process of target operators performing coal mine operations in real time, and to issue warnings and abnormal responses when abnormalities are detected; regularly predict future failure conditions of operating equipment and adopt equipment maintenance strategies to maintain operating equipment, which can effectively improve coal mine operation efficiency, human-machine collaboration efficiency, and equipment reliability and stability.

[0005] The present invention provides an intelligent coal mine operation coordination system, comprising: Auxiliary operation module: used to guide target operators to perform coal mine operations using preset intelligent tools; Monitoring operation module: used to monitor the entire process of coal mining operations performed by target operators in real time, and issue warnings and take corresponding abnormal responses when abnormalities are detected; Predictive maintenance module: used to regularly predict future failure conditions of operating equipment to obtain predicted failure results, and then adopt corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

[0006] Preferably, the auxiliary operation module includes: Login unit: used for the target operator to obtain the tool status of the preset intelligent tool in the display area of ​​the preset intelligent tool; If the tool status of the current preset intelligent tool is available, the target operator performs user identity authentication and user login according to the user login operation of the current preset intelligent tool; If the tool status of the current preset smart tool is unavailable, the target operator generates a status abnormality report based on the number, tool name and tool status of the current preset smart tool and transmits it to the tool maintenance personnel, and then re-adapts the preset smart tool until it is adapted to a preset smart tool with an available tool status, and then performs user identity authentication and user login 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 intelligent tool, according to the project selection operation of the current preset intelligent tool, the target coal mine operation project is selected from the designated coal mine operation project list of the target operator to obtain the target operation guidance data.

[0007] Preferably, the monitoring operation module includes: A state 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 a preset monitoring tool, and obtain first state data; A status analysis unit is 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 to obtain adjustment suggestions; Display the adjustment suggestions and equipment status data in the display area of ​​the preset intelligent tool, promptly remind the target operator to operate according to the adjustment suggestions, and record the equipment status improvement effect data; A behavior analysis unit is used to identify dangerous behaviors of the target operator according to the first video data in the first state data, and issue a behavior warning to the current target operator when dangerous behaviors are identified; Recording unit: used to automatically record the key data and data stamps of the target operator during the coal mine operation process.

[0008] Preferably, the state analysis unit comprises: Status judgment block: used to pre-process the device status data of the currently operated device to obtain the target status data; Inputting the target state data into a pre-established equipment state recognition model and outputting a state recognition result; Behavior analysis block: used to identify whether there is a correlation between the operation behavior of the current target operator and the abnormal state of the operation device based on the preset association recognition model according to the state recognition result, when the operation device is in an abnormal state, and obtain the association recognition result; According to the association identification result, if there is an association relationship, it is determined that the current operation behavior needs to be adjusted and output as a state analysis result; If there is no association relationship, it is determined that the current operation behavior does not need to be adjusted; Performing fault identification and judgment on the operating device according to the device fault detection scheme, and when a fault is identified, determining that the current operating device has a fault, and then outputting it as a state analysis result in combination with the device fault type; A suggestion generation block is used to notify the target operator to stop the operation by voice when the status analysis result shows that the operating device has a fault; The fault notification report generated based on the equipment fault type notifies the equipment maintenance personnel to carry out equipment maintenance; When the status analysis result shows that the current operation behavior needs to be adjusted, the current operation behavior is regarded as the first behavior to be adjusted; Obtaining a list of replacement operation behaviors of the first behavior to be adjusted; If there is only a single replacement operation behavior in the replacement operation behavior list, the replacement operation behavior is used to replace the current first behavior to be adjusted as an adjustment suggestion output; If there are multiple replacement operation behaviors in the replacement operation behavior list, then evaluating the adaptation degree of each replacement operation behavior to obtain a first adaptation coefficient; The replacement operation behavior with the largest first adaptation coefficient is used to replace the current first behavior to be adjusted as an adjustment suggestion output.

[0009] Preferably, the calculation formula of the first adaptation coefficient is as follows: ; In the formula, It is expressed as the first adaptation coefficient of the i-th replacement operation behavior; It is represented as the average equipment status improvement effect value of the i-th replacement operation behavior within a preset time period; It is represented as the jth historical replacement parameter of replacing the current first behavior to be adjusted with the ith replacement operation behavior within a preset time period, where j=1, 2, , m; It is represented as the total value of the j-th historical replacement parameter of replacing the current first behavior to be adjusted by all replacement operation behaviors within a preset time period; It is represented as the influence weight of the j-th historical replacement parameter on the first adaptation coefficient of the calculation replacement operation behavior; Expressed as the replacement skill satisfaction coefficient of the i-th replacement operation behavior.

[0010] Preferably, the behavior analysis unit comprises: Feature extraction block: used to preprocess the acquired first video data to obtain target video frame data; Extracting a first behavior feature of a target operator from the target video frame data based on a deep learning algorithm; Danger analysis block: used to compare the first behavior feature with the set dangerous behavior pattern library to obtain a first comparison result; According to the first comparison result, if it is determined that the current target operator has dangerous behavior of not wearing safety equipment, triggering voice to issue a dangerous warning of not wearing safety equipment to the current target operator; The target operator receives a danger warning of not wearing safety equipment, immediately stops the current coal mine operation, and then puts on safety equipment; If it is determined that the current target operator has engaged in illegal operations or dangerous operations, the dangerous features in the current first comparison result are used as matching conditions to obtain correct operation guidance from the set operation database; Repeatedly issue warnings of illegal or dangerous operations and corresponding correct operation instructions to the current target operator at a set frequency until the target operator corrects the dangerous behavior and completes the operation; After receiving the danger warning of illegal operation or dangerous operation and the corresponding correct operation instruction, the target operator immediately corrects the current dangerous operation according to the correct operation instruction.

[0011] Preferably, the predictive maintenance module includes: Fault prediction unit: used for regularly collecting historical operation data of the operating equipment within a preset time period, performing data preprocessing and feature extraction to obtain first training data; Using the first training data to train a neural network to obtain a fault prediction model; Determine the prediction time according to the set prediction time period; Using the equipment operation data of the equipment in operation at the prediction time as input, inputting it into the fault prediction model, obtaining the predicted fault occurrence time and the predicted fault type, and outputting them as the predicted fault result; Maintenance unit: used to determine the maintenance sequence of the operating equipment according to the predicted fault occurrence time in the predicted fault result and the importance level of the operating equipment; The operating equipment number and the predicted fault type in the predicted fault result are used as screening conditions to filter and obtain equipment maintenance content from the equipment maintenance database; Develop equipment maintenance plans based on the maintenance sequence and equipment maintenance content of the operating equipment; The equipment maintenance personnel perform maintenance tasks according to the equipment maintenance plan and generate equipment maintenance records.

[0012] The present invention provides an intelligent coal mine operation coordination method, comprising: Step 1: Use preset intelligent tools to guide target operators to perform coal mine operations; Step 2: Real-time monitoring of the entire process of the target operator performing coal mining operations, and when an abnormality is detected, a warning is issued and a corresponding abnormal response is taken; Step 3: Regularly predict future failure conditions of the operating equipment to obtain predicted failure results, and then adopt corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

[0013] Compared with the prior art, the present invention has the following beneficial effects: By using intelligent tools to guide target operators to perform coal mine operations and monitoring the entire process of target operators performing coal mine operations in real time, warnings and abnormal responses are issued when abnormalities are detected; future failure conditions of operating equipment are regularly predicted and equipment maintenance strategies are adopted to maintain operating equipment. This can effectively improve coal mine operating efficiency, human-machine collaboration efficiency, and equipment reliability and stability.

[0014] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0015] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a structural diagram of an intelligent coal mine operation coordination system in an embodiment of the present invention; Figure 2 The present invention is a flowchart of an intelligent coal mine operation coordination method in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0018] The embodiment of the present invention provides an intelligent coal mine operation coordination system, such as Figure 1 As shown, including: Auxiliary operation module: used to guide target operators to perform coal mine operations using preset intelligent tools; Monitoring operation module: used to monitor the entire process of coal mining operations performed by target operators in real time, and issue warnings and take corresponding abnormal responses when abnormalities are detected; Predictive maintenance module: used to regularly predict future failure conditions of operating equipment to obtain predicted failure results, and then adopt corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

[0019] In this embodiment, the target operator refers to the staff who currently needs to perform coal mine operations; the preset intelligent tool refers to an intelligent tool used to assist the target operator to accurately complete the coal mine operation, such as a smart handheld device and smart glasses; the preset monitoring tools for monitoring the entire process of the target operator 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 illegal operations, and danger warnings for dangerous operations; the predicted fault result includes the predicted time of fault occurrence and the predicted fault type, which is obtained by taking the equipment operation data of the operating equipment at the predicted time as the input and inputting it into the fault prediction model, wherein the predicted time refers to the time when the operating equipment failure is predicted; the fault prediction model is a model obtained by training the neural network with the first training data, which is used to predict the future time and type of fault of the operating equipment, wherein the first training data is obtained by preprocessing and extracting the historical operation data of the operating equipment, and then annotating the fault label; the equipment maintenance plan consists of the predicted fault result, the operating equipment number, the maintenance sequence, the maintenance time, the maintenance personnel and the maintenance content.

[0020] The beneficial effects of the above technical solution are: by using intelligent tools to guide the target operators to perform coal mine operations, and monitoring the entire process of the target operators performing coal mine operations in real time, issuing warnings and abnormal responses when abnormalities are detected; regularly predicting future failure conditions of operating equipment and adopting equipment maintenance strategies to maintain operating equipment, it can achieve effective improvement in coal mine operation efficiency, human-machine collaboration efficiency, and equipment reliability and stability.

[0021] The embodiment of the present invention provides an intelligent coal mine operation coordination system, wherein the auxiliary operation module comprises: Login unit: used for the target operator to obtain the tool status of the preset intelligent tool in the display area of ​​the preset intelligent tool; If the tool status of the current preset intelligent tool is available, the target operator performs user identity authentication and user login according to the user login operation of the current preset intelligent tool; If the tool status of the current preset smart tool is unavailable, the target operator generates a status abnormality report based on the number, tool name and tool status of the current preset smart tool and transmits it to the tool maintenance personnel, and then re-adapts the preset smart tool until it is adapted to a preset smart tool with an available tool status, and then performs user identity authentication and user login 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 intelligent tool, according to the project selection operation of the current preset intelligent tool, the target coal mine operation project is selected from the designated coal mine operation project list of the target operator to obtain the target operation guidance data.

[0022] In this embodiment, the target operator refers to the staff who currently needs to perform coal mine operations; the preset smart tool refers to an intelligent tool used to assist the target operator to accurately complete coal mine operations, such as smart handheld devices and smart glasses; the display area refers to the set area of ​​the preset smart tool used to display tool status, operation guidance data, adjustment suggestions and equipment status data; the tool status includes available and unavailable.

[0023] In this embodiment, the user login operation refers to the user login operation guide, including the identity authentication method and the login operation steps, which are used to assist the target operator to complete the identity authentication and user login, wherein the identity authentication method includes fingerprint, facial recognition, etc.; the status abnormality report is composed of the number of the preset smart tool whose current tool status is unavailable, the tool name, the tool status, the report generation time, and the personnel number of the target operator; the tool maintenance personnel are used to repair and maintain the preset smart tool based on the received status abnormality report; the project selection operation refers to the operation guide for the user to select the coal mine operation project; the designated coal mine operation project list is composed of all coal mine operation projects allowed to be executed by the current target operator; the target coal mine operation project refers to the coal mine operation project that the current target operator needs to execute; the target operation guidance data refers to the operation guidance data of the target coal mine operation project, wherein the operation guidance data includes the operation process, precautions, operation guide, etc., and the data type includes text description, video, and picture, etc.

[0024] The beneficial effect of the above technical solution is: before starting coal mine operations, the target operators check the usage status of the smart tools used, and use the preset smart tools to perform identity authentication and user login to obtain corresponding operation guidance data, which is conducive to effectively improving operation efficiency and safety.

[0025] The embodiment of the present invention provides an intelligent coal mine operation coordination system, wherein the monitoring operation module comprises: A state 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 a preset monitoring tool, and obtain first state data; A status analysis unit is 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 to obtain adjustment suggestions; Display the adjustment suggestions and equipment status data in the display area of ​​the preset intelligent tool, promptly remind the target operator to operate according to the adjustment suggestions, and record the equipment status improvement effect data; A behavior analysis unit is used to identify dangerous behaviors of the target operator according to the first video data in the first state data, and issue a behavior warning to the current target operator when dangerous behaviors are identified; Recording unit: used to automatically record the key data and data stamps of the target operator during the coal mine operation process.

[0026] In this embodiment, the preset monitoring tools include cameras and various sensors, such as temperature sensors; the operation instruction data include operation procedures, precautions, operation guides, etc., and the data types include text descriptions, videos, and pictures, etc.; the first status data is composed of equipment status data and first video data, wherein the equipment status data is composed 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.

[0027] In this embodiment, adjustment suggestions refer to adjustment strategies for replacing the target operator's operating operations; equipment status improvement effect data refers to the status data change value of the corresponding operating equipment after the current target operator's operation is adjusted using the adjustment suggestions; personnel dangerous behavior refers to dangerous operating behaviors of the target operator during coal mine operations, such as not wearing safety equipment, illegal operations, etc.; key data refers to all data generated by the target operator during coal mine operations, including target operator behavior data, equipment status change data, environmental parameter data, behavior warnings, adjustment suggestions and operation adjustment data, etc.

[0028] The beneficial effects of the above technical solution are: by real-time monitoring of the process of target operators performing coal mine operations, functions such as personnel dangerous behavior identification, operation adjustment and automatic recording are realized based on the acquired first state data, providing all-round safety protection and management support for coal mine operations, which helps to improve operating efficiency, reduce accident risks and improve human-machine collaboration efficiency.

[0029] An embodiment of the present invention provides an intelligent coal mine operation coordination system, wherein the state analysis unit comprises: Status judgment block: used to pre-process the device status data of the currently operated device to obtain the target status data; Inputting the target state data into a pre-established equipment state recognition model and outputting a state recognition result; Behavior analysis block: used to identify whether there is a correlation between the operation behavior of the current target operator and the abnormal state of the operation device based on the preset association recognition model according to the state recognition result, when the operation device is in an abnormal state, and obtain the association recognition result; According to the association identification result, if there is an association relationship, it is determined that the current operation behavior needs to be adjusted and output as a state analysis result; If there is no association relationship, it is determined that the current operation behavior does not need to be adjusted; Performing fault identification and judgment on the operating device according to the device fault detection scheme, and when a fault is identified, determining that the current operating device has a fault, and then outputting it as a state analysis result in combination with the device fault type; A suggestion generation block is used to notify the target operator to stop the operation by voice when the status analysis result shows that the operating device has a fault; The fault notification report generated based on the equipment fault type notifies the equipment maintenance personnel to carry out equipment maintenance; When the status analysis result shows that the current operation behavior needs to be adjusted, the current operation behavior is regarded as the first behavior to be adjusted; Obtaining a list of replacement operation behaviors of the first behavior to be adjusted; If there is only a single replacement operation behavior in the replacement operation behavior list, the replacement operation behavior is used to replace the current first behavior to be adjusted as an adjustment suggestion output; If there are multiple replacement operation behaviors in the replacement operation behavior list, then evaluating the adaptation degree of each replacement operation behavior to obtain a first adaptation coefficient; The replacement operation behavior with the largest first adaptation coefficient is used to replace the current first behavior to be adjusted as an adjustment suggestion output.

[0030] In this embodiment, the target state data is data obtained by performing cleaning, denoising and formatting preprocessing operations on the device state data; the device state recognition model is a model obtained by training the random forest using the training data obtained by preprocessing and feature extracting the historical operating state data of the operating device, and is used to identify and judge the state of the current operating device, including two judgment results: normal and abnormal; the association recognition model is a model obtained by training the neural network using the acquired association training data, and is used to identify the association between the current operation behavior and the abnormal state of the operating device, wherein the association training data is obtained by performing data preprocessing, feature extraction and device state labeling on the corresponding operation behavior data of the operator during the time period when the operating device state is identified as abnormal; the association recognition result includes two results: the existence of the association relationship and the non-existence of the association relationship; the equipment fault detection scheme is composed of equipment fault detection items and fault detection steps, wherein the fault detection items include hardware detection, software detection and communication state detection; the equipment fault types include hardware fault, software fault and communication fault, etc.; the state analysis result includes two results: the current operation behavior needs to be adjusted and the operation device has a fault; the operation behavior includes operation time, operation action, operation parameters, etc.

[0031] In this embodiment, the fault notification report is composed of the equipment number, equipment fault type and current equipment status data of the operating equipment with abnormal current status, as well as the target operator number and fault identification time; the first behavior to be adjusted refers to the corresponding operation behavior for which the status analysis result shows that the current operation behavior needs to be adjusted; the replacement operation behavior list is composed of replacement operation behaviors, operating equipment numbers and replaceable operation operations; replacement operation behaviors refer to predetermined operation behaviors that can replace the current operation operation behaviors to optimize the abnormal equipment status caused by the operation operation behaviors; adjustment suggestions refer to replacing the current first behavior to be adjusted with replacement operation behaviors; the first adaptation coefficient is used to express the degree of adaptability of the replacement operation behaviors to replace the current operation operation behaviors.

[0032] The beneficial effect of the above technical solution is: through intelligent analysis of data generated by real-time monitoring of equipment status and operating behavior during coal mine operations, the operating behavior of operators is optimized and equipment faults are identified and processed, which significantly improves the efficiency of human-machine collaborative operations and improves overall production efficiency.

[0033] An embodiment of the present invention provides an intelligent coal mine operation coordination system, and the calculation formula of the first adaptation coefficient is as follows: ; In the formula, It is expressed as the first adaptation coefficient of the i-th replacement operation behavior; It is represented as the average equipment status improvement effect value of the i-th replacement operation behavior within a preset time period; It is represented as the jth historical replacement parameter of replacing the current first behavior to be adjusted with the ith replacement operation behavior within a preset time period, where j=1, 2, , m; It is represented as the total value of the j-th historical replacement parameter of replacing the current first behavior to be adjusted by all replacement operation behaviors within a preset time period; It is represented as the influence weight of the j-th historical replacement parameter on the first adaptation coefficient of the calculation replacement operation behavior; Expressed as the replacement skill satisfaction coefficient of the i-th replacement operation behavior.

[0034] In this embodiment, the first adaptation coefficient is used to express the degree of adaptation of the replacement operation behavior to replace the current operation behavior; the preset time period is predetermined; the average equipment state improvement effect value is obtained by averaging all equipment state 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 state improvement effect value is obtained by weighted averaging the data change difference of the state data of the corresponding operating equipment within the set time period after each replacement of the first behavior to be adjusted with the replacement operation behavior, wherein the weighting of the data change difference of the state data is obtained by solving the matrix constructed after pairwise comparison and relative importance scoring using the hierarchical analysis method.

[0035] In this embodiment, the historical replacement parameters include the historical number of times the replacement operation behavior is used, the total duration of the historical operation behavior, and the total number of actions included in the replacement operation behavior; the influence weight of the historical replacement parameters on the calculation of the first adaptation coefficient of the replacement operation behavior is obtained by solving the matrix constructed after pairwise comparison and relative importance scoring of the historical replacement parameters using the hierarchical analysis method.

[0036] In this embodiment, the steps for obtaining the replacement skill satisfaction coefficient are as follows: 1. Obtain all operators whose equipment status improvement effect value of replacing the first behavior to be adjusted by using the current replacement operation behavior history within a preset time period is greater than the average equipment status improvement effect value, and mark them as screening personnel; 2. The screening personnel with the lowest equipment status improvement effect value among the screening personnel are regarded as the first person; 3. The difference between the skill professional score of the current target operator and the skill professional score of the first operator is output as the replacement skill satisfaction coefficient; Among them, the skill professional score of the operator is predetermined and the value range is .

[0037] The beneficial effect of the above technical solution is: by calculating the first adaptation coefficient, data support is provided for selecting the replacement operation behavior that is most suitable for replacing the current first behavior to be adjusted, which is conducive to ensuring the accuracy and reliability of the obtained adjustment suggestions, and further helps to improve production efficiency.

[0038] An embodiment of the present invention provides an intelligent coal mine operation coordination system, wherein the behavior analysis unit comprises: Feature extraction block: used to preprocess the acquired first video data to obtain target video frame data; Extracting a first behavior feature of a target operator from the target video frame data based on a deep learning algorithm; Danger analysis block: used to compare the first behavior feature with the set dangerous behavior pattern library to obtain a first comparison result; According to the first comparison result, if it is determined that the current target operator has dangerous behavior of not wearing safety equipment, triggering voice to issue a dangerous warning of not wearing safety equipment to the current target operator; The target operator receives a danger warning of not wearing safety equipment, immediately stops the current coal mine operation, and then puts on safety equipment; If it is determined that the current target operator has engaged in illegal operations or dangerous operations, the dangerous features in the current first comparison result are used as matching conditions to obtain correct operation guidance from the set operation database; Repeatedly issue warnings of illegal or dangerous operations and corresponding correct operation instructions to the current target operator at a set frequency until the target operator corrects the dangerous behavior and completes the operation; After receiving the danger warning of illegal operation or dangerous operation and the corresponding correct operation instruction, the target operator immediately corrects the current dangerous operation according to the correct operation instruction.

[0039] In this embodiment, the target video frame data is obtained by decoding and converting the first video data, denoising, enhancing the contrast, and cropping useless areas, and then extracting the key frames containing the behavior of the target operator; the first behavior feature refers to the behavior feature of the target operator detected from the target video frame data using a deep learning algorithm, including posture, movement, positional relationship, etc.

[0040] In this embodiment, the dangerous behavior pattern library is pre-set and consists of dangerous features extracted from the operators' illegal operating behaviors and dangerous operating behaviors, and corresponding dangerous behavior labels; the first comparison result refers to the judgment result of whether the first behavior feature of the target operator belongs to a dangerous behavior and the corresponding dangerous feature when it belongs to a dangerous behavior after similarity comparison between the first behavior feature and the dangerous feature in the set dangerous behavior pattern library using the cosine similarity algorithm.

[0041] In this embodiment, for example, the similarity values ​​of the dangerous features q1, q2, q3 and the first behavior feature z1 are 0.5, 0.75, and 0.85 respectively, among which the similarity values ​​of the dangerous features q2 and q3 and the first behavior 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 operator is a dangerous behavior, and the dangerous behavior type is the corresponding dangerous behavior type of the dangerous features q2 and q3.

[0042] In this embodiment, the set frequency is pre-set and is generally repeated once 10 seconds after the last voice warning ends; the correct operation guidance refers to the correct operation content matched from the set operation database with the dangerous characteristics in the current first comparison result as the matching condition, wherein the set operation database refers to a database formed by classifying and arranging all correct operation contents of coal mine operations performed by operators based on existing safety specifications and operation manuals according to the dangerous characteristics of dangerous behaviors, wherein the correct operation content includes operation steps, safety specifications, correct operation methods, etc.

[0043] The beneficial effect of the above technical solution is that through real-time processing and analysis of video data, it is possible to promptly detect and warn operators of not wearing safety equipment, illegal operations or dangerous operations, so as to make timely responses, effectively avoid potential safety accidents, and improve the safety and efficiency of coal mine operations.

[0044] An embodiment of the present invention provides an intelligent coal mine operation coordination system, wherein the predictive maintenance module comprises: Fault prediction unit: used for regularly collecting historical operation data of the operating equipment within a preset time period, performing data preprocessing and feature extraction to obtain first training data; Using the first training data to train a neural network to obtain a fault prediction model; Determine the prediction time according to the set prediction time period; Using the equipment operation data of the equipment in operation at the prediction time as input, inputting it into the fault prediction model, obtaining the predicted fault occurrence time and the predicted fault type, and outputting them as the predicted fault result; Maintenance unit: used to determine the maintenance sequence of the operating equipment according to the predicted fault occurrence time in the predicted fault result and the importance level of the operating equipment; The operating equipment number and the predicted fault type in the predicted fault result are used as screening conditions to filter and obtain equipment maintenance content from the equipment maintenance database; Develop equipment maintenance plans based on the maintenance sequence and equipment maintenance content of the operating equipment; The equipment maintenance personnel perform maintenance tasks according to the equipment maintenance plan and generate equipment maintenance records.

[0045] In this embodiment, the preset time period is predetermined; the historical operation data refers to the historical operation status data and historical operation parameters of the operating device, such as temperature, pressure, vibration, current, etc.; the first training data is obtained by preprocessing and extracting the historical operation data of the operating device, and then obtaining the features after fault labeling; the fault prediction model is a model obtained by training the neural network using the first training data, and is used to predict the future fault time and fault type of the operating device; the set prediction time period is a predetermined equipment fault prediction period; the prediction time refers to the time when the operating device fault is predicted; the predicted fault result includes the predicted fault occurrence time and the predicted fault type.

[0046] 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, among which the maintenance time is obtained by the equipment maintenance personnel after sorting the maintenance tasks according to the maintenance sequence of the current 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 for maintenance, among which the equipment maintenance effect includes success and failure.

[0047] The beneficial effect of the above technical solution is: by regularly predicting future failure conditions of operating equipment and generating corresponding equipment maintenance plans for early maintenance, the proportion of maintenance time can be effectively reduced and equipment reliability and stability can be improved.

[0048] The embodiment of the present invention provides an intelligent coal mine operation coordination method, such as Figure 2 As shown, including: Step 1: Use preset intelligent tools to guide target operators to perform coal mine operations; Step 2: Real-time monitoring of the entire process of the target operator performing coal mining operations, and when an abnormality is detected, a warning is issued and a corresponding abnormal response is taken; Step 3: Regularly predict future failure conditions of the operating equipment to obtain predicted failure results, and then adopt corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

[0049] The beneficial effects of the above technical solution are: by using intelligent tools to guide the target operators to perform coal mine operations, and monitoring the entire process of the target operators performing coal mine operations in real time, issuing warnings and abnormal responses when abnormalities are detected; regularly predicting future failure conditions of operating equipment and adopting equipment maintenance strategies to maintain operating equipment, it can achieve effective improvement in coal mine operation efficiency, human-machine collaboration efficiency, and equipment reliability and stability.

[0050] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent coal mine operation coordination system, characterized in that: include: Auxiliary operation module: used to guide target operators to perform coal mine operations using preset intelligent tools; Monitoring operation module: used to monitor the entire process of coal mining operations performed by target operators in real time, and issue warnings and take corresponding abnormal responses when abnormalities are detected; Predictive maintenance module: used to regularly predict future failure conditions of operating equipment to obtain predicted failure results, and then adopt corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

2. The intelligent coal mine operation coordination system according to claim 1 is characterized in that: The auxiliary operation module includes: Login unit: used for the target operator to obtain the tool status of the preset intelligent tool in the display area of ​​the preset intelligent tool; If the tool status of the current preset intelligent tool is available, the target operator performs user identity authentication and user login according to the user login operation of the current preset intelligent tool; If the tool status of the current preset smart tool is unavailable, the target operator generates a status abnormality report based on the number, tool name and tool status of the current preset smart tool and transmits it to the tool maintenance personnel, and then re-adapts the preset smart tool until it is adapted to a preset smart tool with an available tool status, and then performs user identity authentication and user login 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 intelligent tool, according to the project selection operation of the current preset intelligent tool, the target coal mine operation project is selected from the designated coal mine operation project list of the target operator to obtain the target operation guidance data.

3. The intelligent coal mine operation coordination system according to claim 1 is characterized in that: The monitoring operation module includes: A state 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 a preset monitoring tool, and obtain first state data; A status analysis unit is 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 to obtain adjustment suggestions; Display the adjustment suggestions and equipment status data in the display area of ​​the preset intelligent tool, promptly remind the target operator to operate according to the adjustment suggestions, and record the equipment status improvement effect data; A behavior analysis unit is used to identify dangerous behaviors of the target operator according to the first video data in the first state data, and issue a behavior warning to the current target operator when dangerous behaviors are identified; Recording unit: used to automatically record the key data and data stamps of the target operator during the coal mine operation process.

4. The intelligent coal mine operation coordination system according to claim 3 is characterized in that: The state analysis unit comprises: Status judgment block: used to pre-process the device status data of the currently operated device to obtain the target status data; Inputting the target state data into a pre-established equipment state recognition model and outputting a state recognition result; Behavior analysis block: used to identify whether there is a correlation between the operation behavior of the current target operator and the abnormal state of the operation device based on the preset association recognition model according to the state recognition result, when the operation device is in an abnormal state, and obtain the association recognition result; According to the association identification result, if there is an association relationship, it is determined that the current operation behavior needs to be adjusted and output as a state analysis result; If there is no association relationship, it is determined that the current operation behavior does not need to be adjusted; Performing fault identification and judgment on the operating device according to the device fault detection scheme, and when a fault is identified, determining that the current operating device has a fault, and then outputting it as a state analysis result in combination with the device fault type; A suggestion generation block is used to notify the target operator to stop the operation by voice when the status analysis result shows that the operating device has a fault; The fault notification report generated based on the equipment fault type notifies the equipment maintenance personnel to carry out equipment maintenance; When the status analysis result shows that the current operation behavior needs to be adjusted, the current operation behavior is regarded as the first behavior to be adjusted; Obtaining a list of replacement operation behaviors of the first behavior to be adjusted; If there is only a single replacement operation behavior in the replacement operation behavior list, the replacement operation behavior is used to replace the current first behavior to be adjusted as an adjustment suggestion output; If there are multiple replacement operation behaviors in the replacement operation behavior list, then evaluating the adaptation degree of each replacement operation behavior to obtain a first adaptation coefficient; The replacement operation behavior with the largest first adaptation coefficient is used to replace the current first behavior to be adjusted as an adjustment suggestion output.

5. The intelligent coal mine operation coordination system according to claim 4 is characterized in that: The calculation formula of the first adaptation coefficient is as follows: ; In the formula, It is expressed as the first adaptation coefficient of the i-th replacement operation behavior; It is represented as the average equipment status improvement effect value of the i-th replacement operation behavior within a preset time period; It is represented as the jth historical replacement parameter of replacing the current first behavior to be adjusted with the ith replacement operation behavior within a preset time period, where j=1, 2, , m; It is represented as the total value of the j-th historical replacement parameter of replacing the current first behavior to be adjusted by all replacement operation behaviors within a preset time period; It is represented as the influence weight of the j-th historical replacement parameter on the first adaptation coefficient of the calculation replacement operation behavior; Expressed as the replacement skill satisfaction coefficient of the i-th replacement operation behavior.

6. The intelligent coal mine operation coordination system according to claim 3 is characterized in that: The behavior analysis unit comprises: Feature extraction block: used to preprocess the acquired first video data to obtain target video frame data; Extracting a first behavior feature of a target operator from the target video frame data based on a deep learning algorithm; Danger analysis block: used to compare the first behavior feature with the set dangerous behavior pattern library to obtain a first comparison result; According to the first comparison result, if it is determined that the current target operator has dangerous behavior of not wearing safety equipment, triggering voice to issue a dangerous warning of not wearing safety equipment to the current target operator; The target operator receives a warning of danger of not wearing safety equipment, immediately stops the current coal mine operation, and then puts on safety equipment; If it is determined that the current target operator has engaged in illegal operations or dangerous operations, the dangerous features in the current first comparison result are used as matching conditions to obtain correct operation guidance from the set operation database; Repeatedly issue warnings of illegal or dangerous operations and corresponding correct operation instructions to the current target operator at a set frequency until the target operator corrects the dangerous behavior and completes the operation; After receiving the danger warning of illegal operation or dangerous operation and the corresponding correct operation instruction, the target operator immediately corrects the current dangerous operation according to the correct operation instruction.

7. The intelligent coal mine operation coordination system according to claim 1, characterized in that: The predictive maintenance module includes: Fault prediction unit: used for regularly collecting historical operation data of the operating equipment within a preset time period, performing data preprocessing and feature extraction to obtain first training data; Using the first training data to train a neural network to obtain a fault prediction model; Determine the prediction time according to the set prediction time period; Using the equipment operation data of the equipment in operation at the prediction time as input, inputting it into the fault prediction model, obtaining the predicted fault occurrence time and the predicted fault type, and outputting them as the predicted fault result; Maintenance unit: used to determine the maintenance sequence of the operating equipment according to the predicted fault occurrence time in the predicted fault result and the importance level of the operating equipment; The operating equipment number and the predicted fault type in the predicted fault result are used as screening conditions to filter and obtain equipment maintenance content from the equipment maintenance database; Develop equipment maintenance plans based on the maintenance sequence and equipment maintenance content of the operating equipment; The equipment maintenance personnel perform maintenance tasks according to the equipment maintenance plan and generate equipment maintenance records.

8. An intelligent coal mine operation coordination method, characterized in that: include: Step 1: Use preset intelligent tools to guide target operators to perform coal mine operations; Step 2: Real-time monitoring of the entire process of the target operator performing coal mining operations, and when an abnormality is detected, a warning is issued and a corresponding abnormal response is taken; Step 3: Regularly predict future failure conditions of the operating equipment to obtain predicted failure results, and then adopt corresponding equipment maintenance plans to maintain the operating equipment based on the predicted failure results.

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