Mining personnel and environment safety management monitoring system

Abstract

The invention relates to the technical field of mining monitoring, in particular to a mining personnel and environment safety management monitoring system. In a traditional monitoring system, environment monitoring is generally carried out depending on a wired sensor network or a single-type sensor, and complex and changeable environment conditions cannot be reflected accurately in real time; at the same time, an effective mining personnel positioning function is lacked, and an emergency plan cannot be quickly responded when an emergency occurs, so that evacuation and rescue work is delayed. In order to solve the technical problem, the invention provides the mining personnel and environment safety management monitoring system, the system comprises a mining personnel safety management subsystem, an environment safety management subsystem and a control center, the control center comprehensively manages the two subsystems, the safety of mining area workers is combined with the mining area environment safety, and the safety of the mining area workers is improved. The safety management level of a mining area can be remarkably improved, and the risk of accidents is reduced.

Description

Technical Field

[0001] The present invention relates to the field of mining monitoring technology, and in particular to a mining personnel and environmental safety management and monitoring system. Background Art

[0002] Mining areas are places where ore is mined or mineral raw materials are produced. Mining is a high-risk industry, and safety production must be taken seriously. The main factors affecting mine safety include landslides, collapses, blasting, mechanical injuries, falls from heights, occupational hazards, vehicle injuries, electrical injuries, etc. Mining areas should formulate corresponding safety precautions for various hazardous factors and establish corresponding accident emergency mechanisms to ensure that accidents can be handled in a timely manner to reduce personal and property losses. In traditional monitoring systems, they usually rely on wired sensor networks or single types of sensors for environmental monitoring, which cannot accurately reflect complex and changing environmental conditions in real time; at the same time, they lack effective mining personnel positioning functions, making it difficult to meet the needs of mining personnel for real-time location information in emergency situations; and in the event of an emergency, they cannot respond quickly to emergency plans, resulting in delayed evacuation and rescue work, increasing safety risks.

[0003] Therefore, there is an urgent need to develop a mining personnel and environmental safety management and monitoring system to overcome the shortcomings of the prior art. Summary of the invention

[0004] (1) Technical issues to be solved

[0005] The present invention aims to solve the problems that the environmental monitoring relies on a wired sensor network or a single type of sensor, which cannot accurately reflect the complex and changeable environmental conditions in real time; at the same time, it lacks an effective mining personnel positioning function, making it difficult to meet the demand for real-time location information of mining personnel in emergency situations; and in the event of an emergency, it is impossible to respond quickly to the emergency plan, resulting in delays in evacuation and rescue work, increasing safety risks.

[0006] (2) Technical solution

[0007] In order to solve the above technical problems, the present invention provides a mining personnel and environmental safety management and monitoring system, including a mining personnel safety management and monitoring subsystem, an environmental safety management and monitoring subsystem, and a control center.

[0008] The mine personnel safety management and monitoring subsystem is used to monitor the safety of mine workers in real time to ensure the safety of mine workers;

[0009] The environmental safety management and monitoring subsystem is used to conduct real-time monitoring and management of the environmental safety of the mining area, including comprehensive monitoring of the environmental parameter data within the mining area;

[0010] The control center is used to uniformly manage the mining personnel safety management and monitoring subsystem and the environmental safety management and monitoring subsystem, analyze the data information of the two subsystems, issue early warnings in a timely manner, direct the emergency response of the mining area, and provide decision support for management personnel.

[0011] Furthermore, the mine personnel safety management and monitoring subsystem includes: a positioning tracking unit, a behavior monitoring unit, a health monitoring unit, and an intelligent alarm unit;

[0012] The positioning and tracking unit is used to track the location information of mining personnel in real time;

[0013] The behavior monitoring unit is used to monitor the behavior of the mining staff in real time and determine whether the mining staff has any dangerous behavior;

[0014] The health monitoring unit is used to monitor the vital signs of the mining workers and to warn them of health risks;

[0015] The intelligent alarm unit assesses the location, dangerous behaviors, and health risks of mining workers, and issues early warnings to prevent accidents.

[0016] Furthermore, the specific steps of the positioning and tracking unit to track the location information of the mining area staff in real time are:

[0017] S1 first obtains the latest map of the mining area, divides different working areas according to the specific conditions of the mining area, marks the boundaries of safe areas and dangerous areas, sets corresponding safety rules for each area, and sets attributes for each area. Only mining area workers who meet the attributes can enter or enter within a limited time; safety rules include setting a stay time threshold in the area;

[0018] S2 distributes smart wearable devices to each mining worker. The devices have built-in personnel information and obtain the location of mining workers in real time through positioning base stations;

[0019] S3 receives data from the smart wearable device through the positioning base station, and transmits the data to the mining personnel safety management and monitoring subsystem to determine the location of the mining personnel;

[0020] S4 When mining workers enter unauthorized areas, the intelligent alarm unit notifies mining managers and sends out sound and light signal alarms through smart wearable devices to remind mining workers who enter unauthorized areas to evacuate the area as soon as possible.

[0021] Furthermore, the behavior monitoring unit is used to monitor whether the mining staff has dangerous behavior or a tendency to have dangerous behavior, including:

[0022] S1 installs a high-definition camera at a key position in the mining area to ensure that the camera can monitor the entire area without blind spots and capture the actions of the mining area workers; synchronizes the high-definition camera with the smart wearable device, and the high-definition camera can move with the movement of the smart wearable device to obtain multi-dimensional data;

[0023] S2 uses edge detection algorithms to preprocess video images and extract feature vectors, including contours, key points, and motion patterns, from processed images and data acquired from smart wearable devices;

[0024] S3 inputs the real-time collected and processed data into the deep learning model to perform behavior recognition, and analyzes the recognition results according to the abnormal behavior judgment rules to determine whether there is abnormal behavior;

[0025] S4 When abnormal behavior is detected, the corresponding emergency plan is activated according to different abnormal behaviors, such as evacuation, shutdown of mechanical equipment, and relevant personnel are notified through sound, light or electronic means.

[0026] Furthermore, the abnormal behavior of the mine staff deviates from the baseline behavior to a certain extent, including: failure to wear safety equipment, that is, failure to wear safety equipment as required by the mine regulations; activities in unauthorized areas, that is, entering restricted areas without permission; abnormal work events, that is, appearing in the work area or during working hours during non-working hours; abnormal behavior patterns, that is, abnormal stay time in the dangerous area or abnormal movement speed; when the abnormal behavior of the mine staff occurs twice or less, the mine only criticizes and educates the mine staff; when the abnormal behavior occurs more than twice, the mine takes measures to transfer or dismiss the mine staff.

[0027] Furthermore, the smart wearable device integrates a positioning device, a physiological monitoring device, a behavior monitoring device, a communication device, and an alarm device; the alarm device uses situational awareness for communication, and intelligently adjusts the way information is transmitted according to the current location and status of the mine workers, including noise level, light intensity, and personal health status, including vibration, light signals, sound or temperature changes. At the same time, the transmission method can adjust the reminder intensity according to different situations to alert the mine workers.

[0028] Furthermore, the environmental safety management and monitoring subsystem integrates multiple sensors to monitor environmental parameter data of safety hazards within the mining area, such as gas explosions and rock collapse, and environmental parameters such as temperature, humidity, concentration of harmful gases, and dust concentration; at the same time, it can monitor geological dynamics and vegetation coverage to provide more comprehensive data support for disaster warning; and the environmental safety management and monitoring subsystem can monitor the impact of the mining area on the surrounding environment, such as water pollution and air pollution, automatically generate environmental protection reports based on the monitoring results, and compare them with the environmental protection laws and regulations and standards in the area to evaluate the environmental compliance of the mining area; the environmental safety management and monitoring subsystem can also automatically adjust the layout of sensors and monitoring strategies according to monitoring objectives and environmental conditions.

[0029] Further, the control center includes a data integration and analysis unit, a decision support unit, a communication coordination unit, an information management unit, an emergency response unit, an emergency training unit, and a user visualization unit;

[0030] The data integration and analysis unit is used to collect the data provided by the subsystem, process the received data, and determine whether there are safety hazards through the mine safety judgment model deployed in the data integration and analysis unit;

[0031] The decision support unit is used to provide decision support, including emergency response plans and early warning information release, when the mine safety judgment model detects safety hazards; and uses augmented reality AR technology to build a real-time three-dimensional model of the mine area. The three-dimensional model integrates more types of data, including equipment operation status, ore quality and location distribution, and geological structure data. According to the data collected by the environmental safety management and monitoring subsystem, the environmental conditions in the three-dimensional model, such as ventilation, temperature, and water quality, are adjusted in real time to simulate the real environment. The three-dimensional model built by AR technology can be operated by custom gestures or voice commands, including zooming and rotating, to provide a visual display to help managers make decisions;

[0032] The emergency response unit is used to respond to the formulated emergency response plan when the mine safety judgment model detects potential safety hazards, help workers evacuate, and reduce losses in the mine;

[0033] The communication coordination unit is used to serve as a communication hub between the mining personnel safety management monitoring subsystem and the environmental safety monitoring subsystem to ensure information flow transmission and coordinate the execution of emergency response measures;

[0034] The information management unit is used to manage the mine staff information, equipment information, and safety records to ensure the accuracy and reliability of the information;

[0035] The emergency training unit is used to use virtual reality (VR) technology to construct a simulated environment when a danger occurs in a certain area of ​​the mining area, thereby enhancing the ability of mining staff to respond to emergencies.

[0036] Furthermore, the data integration and analysis unit judges safety hazards on the integrated subsystem data through a mine safety judgment model deployed internally, and the generation process of the mine safety judgment model includes:

[0037] S1 Data preparation: collect historical data, including environmental parameters, location of mining workers and related safety incident records; data cleaning, process missing values ​​and outliers in the data; feature engineering, extract features that are helpful for model prediction; data annotation, mark historical data with corresponding labels; data segmentation, divide the processed data set into training set and test set;

[0038] S2 model training: Use the training set data to train the gradient boosting decision tree model, where the calculation formula of the gradient boosting decision tree model is:

[0039] F(x)=h1(x)+h2(x)+…+h t (x),

[0040] Among them, F(x) is the final gradient boosting machine model, which accumulates the decision tree h learned at each step t (x) is the prediction of the security risk level; t is the total number of decision trees; h t (x) is the decision tree learned in step t. Each decision tree h t (x) is to reduce the previous step model F t-1 (x) is constructed based on the residual to correct the prediction error of the previous model on the safety risk level; the negative gradient, i.e. the residual, is calculated in each step of the calculation, where the residual calculation formula is:

[0041]

[0042] Among them, x i represents the characteristic vector of the i-th data point, i.e., the environmental monitoring parameters of the mining area, including temperature, humidity, concentration of harmful gases, location of mining area staff, and equipment status; y i represents the target variable of the i-th data point, that is, the label added to the data point, including the safety risk level of the mining area or the occurrence of a specific safety incident; r ti Represents the residual of the i-th data point at the t-th step, and the residual is the model F t-1 (x) The negative gradient at this data point is the error between the safety risk prediction and the actual safety status of the i-th data point in the current step in the mining personnel and environmental safety management monitoring system; L(yi , F(x i )) is the loss function, which is a function that measures the error between the predicted value F(x i ) and the true value y i ;

[0043] S3 Model Evaluation: Use the test set to test the trained model and evaluate the prediction performance of the model. The specific evaluation metrics are accuracy, recall rate, and F1 score;

[0044] Accuracy represents the ability of the mine safety judgment model to correctly identify the safety status, and the evaluation calculation of accuracy is:

[0045] Precision = Number of correctly predicted environmental parameters / Number of predicted environmental parameters * 100%,

[0046] The recall rate can reflect the proportion of actual safety problems captured by the mine safety judgment model, and the evaluation calculation of the recall rate is:

[0047] Recall = Number of correctly predicted environmental parameters / Total number of environmental parameters * 100%,

[0048] The F1 score can balance the accuracy and recall rate and can specifically measure the overall performance of the mine safety judgment model. The evaluation calculation of the F1 score is:

[0049] F1 = 2 * Precision * Recall / (Precision + Recall) * 100%;

[0050] S4 Model Deployment: Deploy the trained model to the control center to process the collected environmental parameter data and the location data of mine workers in real time;

[0051] S5 Real-time Prediction: Preprocess and extract features from the environmental parameter data and the location data of mine workers collected in real time by environmental sensors, and then input the processed data into the mine safety judgment model for prediction;

[0052] S6 Model Update and Maintenance: Monitor the prediction performance of the model. If the performance is found to decline, retrain and optimize the model.

[0053] Furthermore, when the mine safety judgment model judges potential safety hazards for the data integrated by the subsystem, the predicted value output is the probability score Score. The magnitude of the probability score Score represents different safety levels. The safety levels are preset and include normal S, warning W, and danger D. When the probability score Score > 0.7, it is safe S; when 0.3 < Score <= 0.7, it is warning W; when Score < 0.3, it is danger D.

[0054] (3) Beneficial effects

[0055] The mining personnel and environmental safety management and monitoring system is equipped with a mining personnel safety management and monitoring subsystem and an environmental safety management and monitoring subsystem. The environmental safety management and monitoring subsystem is solely responsible for all-round monitoring of environmental parameters in the mining area, while the mining personnel safety management and monitoring subsystem conducts real-time monitoring and tracking of mining area staff. A control center coordinates the mining personnel safety management and monitoring subsystem and the environmental safety management and monitoring subsystem. When an emergency occurs to mining area staff or environmental safety, it can respond quickly and achieve comprehensive monitoring and management in the mining area through the collaborative work of the two subsystems.

[0056] The positioning tracking unit and behavior monitoring unit set in the mine personnel safety management monitoring subsystem monitor the behavior and activity areas of mine workers, which can effectively improve the safety management level of the mine area, reduce the probability of accidents, protect the lives of mine workers, and also promote the safe production and sustainable development of the mine area.

[0057] The mine safety judgment model deployed in the mine personnel and environmental safety management monitoring system uses historical data for continuous learning and self-adjustment based on real-time feedback, which can continuously improve the accuracy and efficiency of the mine safety judgment model predictions; at the same time, using real-time data analysis, the mine personnel and environmental safety management monitoring system can quickly predict potential safety risks such as gas explosions and rock collapse, so as to take preventive measures in advance; and the probability scores output by the mine safety judgment model are divided into different safety levels through level thresholds. Once the threshold is exceeded, the emergency response plan is immediately activated. Such operations can significantly improve the safety management level of the mine area and provide quick and effective measures in emergency situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the present invention.

[0059] Figure 2 Schematic diagram of the generation process of the mining area safety judgment model of the present invention.

[0060] Figure 3 It is a schematic diagram of the process of the positioning and tracking unit in the present invention.

[0061] Figure 4 It is a process schematic diagram of the behavior monitoring unit in the present invention. DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0063] Example 1

[0064] Reference Figure 1 The mining personnel and environmental safety management and monitoring system includes the mining personnel safety management and monitoring subsystem, the environmental safety management and monitoring subsystem, and the control center.

[0065] The mine personnel safety management and monitoring subsystem is used to monitor the safety of mine workers in real time to ensure the safety of mine workers; the mine personnel safety management and monitoring subsystem includes: positioning tracking unit, behavior monitoring unit, health monitoring unit, and intelligent alarm unit;

[0066] The positioning and tracking unit is used to track the location information of mine workers in real time, demarcate various work areas within the mine, divide the boundaries between dangerous and safe areas, and limit the areas where mine workers are located, so as to protect the safety of mine workers.

[0067] The behavior monitoring unit obtains multi-dimensional behavior data of mine workers through synchronization with high-definition cameras and smart wearable devices to determine whether the mine workers have engaged in dangerous behavior.

[0068] The health monitoring unit is used to monitor the vital signs of workers in the mining area, including heart rate monitoring, fatigue monitoring and other vital signs monitoring, and to warn of health risks to workers in the mining area.

[0069] The intelligent alarm unit assesses the location, dangerous behaviors, and health risks of mine workers, and issues early warnings to prevent accidents when mine workers enter dangerous areas, engage in dangerous activities, or have health problems.

[0070] The environmental safety management and monitoring subsystem is used to monitor and manage the environmental safety of the mining area in real time, including comprehensive monitoring of the environmental parameter data inside the mining area. The environmental safety management and monitoring subsystem monitors the environmental parameter data of potential safety hazards inside the mining area by integrating multiple sensors, such as gas explosions, rock collapse, and environmental parameters such as temperature, humidity, concentration of harmful gases, and dust concentration; at the same time, it can monitor geological dynamics, including groundwater levels, seismic activities, etc., to provide more comprehensive data support for disaster warning; and the environmental safety management and monitoring subsystem can monitor the impact of the mining area on the surrounding environment, such as water pollution and air pollution, and automatically generate environmental protection reports based on the monitoring results, and compare them with the environmental protection laws and standards in the area to evaluate the environmental compliance of the mining area. At the same time, the environmental safety management and monitoring subsystem can combine sensor data with geographic information system GIS data to monitor geological dynamics and vegetation coverage, providing more comprehensive data support for disaster warning. The environmental safety management and monitoring subsystem can also automatically adjust the layout and monitoring strategy of sensors according to monitoring targets and environmental conditions. When the risk in a certain area increases, the environmental safety management and monitoring subsystem automatically increases the monitoring density in that area.

[0071] The control center is used to uniformly manage the mine personnel safety management and monitoring subsystem and the environmental safety management and monitoring subsystem, analyze the data information of the two subsystems, issue early warnings in a timely manner, command the emergency response of the mine area, and provide decision support for managers. The control center also includes data integration and analysis units, decision support units, communication coordination units, information management units, emergency response units, emergency training units, and user visualization units.

[0072] Among them, the data integration and analysis unit is used to collect data provided by the subsystem, process the received data, and determine whether there are safety hazards through the mine safety judgment model deployed in the data integration and analysis unit.

[0073] The decision support unit is used to provide decision support, including emergency response plans and early warning information release, when the mine safety judgment model detects safety hazards. It uses augmented reality AR technology to build a real-time three-dimensional model of the mine area. AR devices such as smart glasses and helmets are connected to the control center through a control interface to establish a real-time three-dimensional model of the mine area. The real-time three-dimensional model of the mine area integrates more types of data, including equipment operation status, ore quality and location distribution, geological structure data, etc., and the real-time three-dimensional model of the mine area superimposes the data of mine staff and mine environment, such as environmental parameter data, mine staff location information, etc., and adjusts the environmental conditions in the three-dimensional model in real time, such as ventilation, temperature, and water quality, according to the data collected by the environmental safety management and monitoring subsystem to simulate the real environment. After that, the management personnel drag and drop the real-time three-dimensional model through custom gestures or voice commands, such as zooming in, zooming out, and rotating, so as to have a clear understanding of the real-time situation of the mine area and the mine staff, and formulate emergency response strategies for the current situation of the mine area. Through AR technology, managers can intuitively view the real-time data of the mine in the form of a three-dimensional model. At the same time, using gestures, managers can interact with the system more naturally and intuitively, reducing the complexity of operation. The emergency response unit is used to respond to the emergency response plan when the mine safety judgment model detects safety hazards, help staff evacuate, and reduce losses in the mine.

[0074] The communication coordination unit is used as the communication hub between the mine personnel safety management monitoring subsystem and the environmental safety monitoring subsystem to ensure the transmission of information flow and coordinate the implementation of emergency response measures.

[0075] The information management unit is used to manage various information records within the mining area, including staff information, equipment information, and safety records, to ensure the accuracy and reliability of the information.

[0076] The emergency training unit is used to use virtual reality (VR) technology to build a simulated environment for when danger occurs in a certain area of ​​the mine, and enhance the mine staff's ability to respond to emergencies. The emergency training unit will train the mine staff on emergency response plans, use VR equipment to build a simulated environment for when danger occurs in a certain area of ​​the mine, and activate the emergency plan at the same time, so that the mine staff can acquire the ability to respond to emergencies in the simulated environment and enhance the mine staff's emergency handling capabilities. VR equipment can also provide immersive emergency training responses for mine staff, improving the effectiveness and realism of the training.

[0077] Through the above technical solution, this embodiment divides the mining personnel and environmental safety management and monitoring system into a mining personnel safety management and monitoring subsystem, an environmental safety management and monitoring subsystem and a control center. The control center coordinates the two subsystems and uniformly analyzes the personnel safety and environmental safety data of the mining area, thereby ensuring a rapid response when an emergency occurs in the mining area. Moreover, through the collaborative work of the two subsystems, comprehensive monitoring and management in the mining area is achieved.

[0078] Example 2

[0079] Reference Figure 2 The data integration and analysis unit uses the internally deployed mine safety judgment model to judge the safety hazards of the integrated subsystem data. The generation process of the mine safety judgment model includes:

[0080] Data preparation: Collect historical data, including historical environmental parameter data such as temperature, humidity, and harmful gas concentration collected by various environmental sensors, RFID tags, and readers installed in the mining area, as well as personnel location information, accident records in the mining area, personnel reputation, etc.; Data cleaning: Process missing values ​​and abnormal values ​​in the data. For example, if a sensor has no data for a period of time, use interpolation methods to fill in the gaps, or if a sensor has a reading error due to a fault, identify and correct it by comparing other sensor data; Feature engineering: Extract features that are helpful for model prediction, such as extracting daily average temperature, maximum temperature, minimum temperature, etc. from temperature data; Data annotation: Mark historical data with corresponding labels, such as marking data with safety levels: safe, warning, dangerous, etc. based on historical safety time records; Data segmentation: Divide the processed data set into a training set and a test set.

[0081] Model training: Use the training set data to train the gradient boosting decision tree model, where the calculation formula of the gradient boosting decision tree model is:

[0082] F(x)=h1(x)+h2(x)+…+h t (x),

[0083] Among them, F(x) is the final gradient boosting machine model, which accumulates the decision tree h learned at each step t (x) is the prediction of the security risk level; t is the total number of decision trees; h t (x) is the decision tree learned in step t. Each decision tree h t (x) is to reduce the previous step model F t-1 (x) is constructed based on the residual to correct the prediction error of the previous model on the safety risk level; the negative gradient, i.e. the residual, is calculated in each step of the calculation, where the residual calculation formula is:

[0084]

[0085] Among them, x i represents the characteristic vector of the i-th data point, i.e., the environmental monitoring parameters of the mining area, including temperature, humidity, concentration of harmful gases, personnel location, and equipment status; y i represents the target variable of the i-th data point, that is, the label added to the data point, including the safety risk level of the mining area or the occurrence of a specific safety time; r ti Represents the residual of the i-th data point at the t-th step, and the residual is the model F t-1 (x) The negative gradient at this data point is the error between the safety risk prediction and the actual safety status of the i-th data point in the current step in the system; L(y i ,F(x i )) is the loss function, which is a measure of the predicted value F(x) at the i-th data point i ) and the true value y i The function of the error between .

[0086] Model evaluation: Use the test set to test the trained model and evaluate the model's prediction performance. The specific evaluation indicators are accuracy, recall, and F1 score. The accuracy rate indicates the ability of the mine safety judgment model to correctly identify safety conditions, and the accuracy rate is calculated as follows:

[0087] Precision = the number of correctly predicted environmental parameters / the number of predicted environmental parameters * 100%,

[0088] The recall rate can reflect the proportion of actual safety issues captured by the mine safety judgment model, and the recall rate is calculated as follows:

[0089] Recall = number of correctly predicted environmental parameters / total number of environmental parameters * 100%,

[0090] The F1 score can balance the accuracy and recall rate, and can specifically measure the overall performance of the mine safety judgment model. The evaluation calculation of the F1 score is:

[0091] F1=2*Precision*Recall / (Precision+Recall)*100%.

[0092] Model deployment: Deploy the trained model to the data processing module, process the collected environmental parameter data in real time, and output the prediction results.

[0093] Real-time prediction: Preprocess and extract features from the environmental parameters collected in real time by environmental sensors to ensure that the data collected in real time is consistent with the data during model training. Then, input the processed data into the deployed gradient boosting decision tree model for prediction to obtain the dangerous situation in a certain area within the mining area.

[0094] Model update and maintenance: Monitor the prediction performance of the mining area safety judgment model. If a performance decline is found, retrain and optimize the mining area safety judgment model to ensure the accuracy and reliability of the mining area safety judgment model.

[0095] When the mining area safety judgment model judges potential safety hazards for the data integrated by the subsystem, the predicted value output is the probability score Score. The magnitude of the probability score Score represents different safety levels, and the safety levels are preset, including normal S, warning W, and danger D. Although the probability score represents the safety level, a threshold needs to be set to convert the probability score into a specific safety level level so as to accurately distinguish the mining area safety. Therefore, when the probability score Score > 0.7, the safety level is safe S; when 0.3 < Score <= 0.7, the safety level is warning W; when Score < 0.3, the safety level is danger D. The control center notifies the mining area management and staff by real-time monitoring of the safety level changes, and selects the appropriate emergency response plan according to the current safety level, and guides the actions of the mining area management and staff through intelligent wearable devices.

[0096] Through the above embodiments, the mining area safety judgment model continuously learns using historical data and self-adjusts according to real-time feedback, which can continuously improve the accuracy and efficiency of the mining area safety judgment model prediction; at the same time, by using real-time data analysis, the mine personnel and environmental safety management monitoring system can quickly predict potential safety risks such as gas explosions and rock collapses, so as to take preventive measures in advance; and the probability score output by the mining area safety judgment model is divided into different safety levels through the level threshold. Once the threshold is exceeded, the emergency response plan is immediately activated. Such an operation can significantly improve the safety management level of the mining area and provide rapid and effective measures in case of emergencies.

[0097] Embodiment 3

[0098] Mining in a mining area has always been a very dangerous job, and during the mining process, the responsibility for the lives and safety of the mining area staff needs to be taken. Therefore, the mine personnel safety management monitoring subsystem needs to monitor the positions and behaviors of the mining area staff in real time. Refer to Appendix Figure 3 、 4 . At the same time, the positioning and tracking unit and behavior monitoring unit of the mine personnel safety management monitoring subsystem monitor the mining area staff with abnormal behaviors simultaneously.

[0099] First, the positioning and tracking unit will obtain the latest map of the mining area, divide different working areas according to the specific conditions of the mining area, mark the boundaries of safe areas and dangerous areas, set corresponding safety rules for each area, and set attributes for each area. Only mining workers who meet the attributes can enter or enter within a limited time; safety rules include setting a stay time threshold in the area. The behavior monitoring unit will capture the actions of mining workers through high-definition cameras installed at key locations in the mining area, which can fully monitor the images in a certain area without blind spots; and by synchronizing the high-definition camera with the smart wearable device, that is, the high-definition camera can move with the movement of the smart wearable device, so as to obtain multi-dimensional data.

[0100] Every worker in the mining area needs to wear a smart wearable device, set personnel information in the device, and interact with the positioning device in the device in real time through the positioning base station to obtain the real-time location of the mining area workers.

[0101] The data of the smart wearable device is received by the positioning base station, and the data is transmitted to the mine personnel safety management monitoring system to determine the location of the mine workers. For example, when the mine workers stay in a high-risk area for more than the specified time threshold, the control center obtains the video data of the mine workers captured by the high-definition camera in the high-risk area; then the edge detection algorithm is used to pre-process the video image, and feature vectors, including contours, key points, and motion patterns, are extracted from the processed image and the data obtained by the smart wearable device; the processed video data of the mine workers and the smart device monitoring data are input into the deep learning model for behavior recognition, and the recognition results are analyzed according to the abnormal behavior judgment rules to determine whether there is abnormal behavior. When abnormal behavior is detected, the corresponding emergency plan is activated according to different abnormal behaviors, such as the control center remotely shutting down mechanical equipment, arranging the evacuation of mine workers in high-level areas, and notifying relevant personnel through sound, light or electronic means.

[0102] There is also a situation where mining workers enter a high-risk area without being authorized to do so. The alarm unit of the control center will immediately notify the mining manager of the worker's personal information and location, and send out sound and light signal alarms through the worker's smart wearable device to remind the worker to enter an unauthorized area and urge him to leave as soon as possible.

[0103] The abnormal behavior of the above-mentioned mine workers deviates from the baseline behavior to a certain extent, including: not wearing safety equipment, that is, not wearing safety equipment as required by the mine regulations; activities in unauthorized areas, that is, entering restricted areas without permission; abnormal work events, that is, appearing in the work area or during working hours during non-working hours; abnormal behavior patterns, that is, abnormal stay time in dangerous areas or abnormal movement speed, etc. If the mine workers have these abnormal behaviors twice or less, the mine will only criticize and educate the personnel. When the abnormal behavior occurs more than twice, the mine will take measures to transfer the mine workers from their posts or dismiss them.

[0104] In the above technical solution, the smart wearable device integrates a positioning device, a physiological monitoring device, a behavior monitoring device, a communication device, and an alarm device; the alarm device uses situational awareness for communication, and intelligently adjusts the information transmission method according to the current position and status of the mine workers, including noise level, light intensity, and personal health status, including vibration, light signal, sound or temperature change. For example, in a noisy area, a vibration or visual signal is used to remind, and in the case of poor visibility, a sound broadcast is used; at the same time, the transmission method can adjust the reminder intensity according to different situations to warn the mine workers. For example, when the mine workers are in a dangerous area, the alarm device warns by vibrating frequency or changing color.

[0105] In the above embodiment, monitoring the behavior and activity areas of mining area workers can effectively improve the safety management level of the mining area, reduce the probability of accidents, protect the lives of mining area workers, and also promote safe production and sustainable development of the mining area.

[0106] The above-mentioned embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person skilled in the art, several modifications, improvements and substitutions can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. Mine personnel and environmental safety management and monitoring system, characterized by: Including mining personnel safety management monitoring subsystem, environmental safety management monitoring subsystem and control center, The mine personnel safety management and monitoring subsystem is used to monitor the safety of mine workers in real time to ensure the safety of mine workers; The environmental safety management and monitoring subsystem is used to conduct real-time monitoring and management of the environmental safety of the mining area, including comprehensive monitoring of the environmental parameter data within the mining area; The control center is used to uniformly manage the mining personnel safety management and monitoring subsystem and the environmental safety management and monitoring subsystem, analyze the data information of the two subsystems, issue early warnings in a timely manner, direct the emergency response of the mining area, and provide decision support for management personnel.

2. The mining personnel and environment safety management and monitoring system according to claim 1 is characterized in that: The mine personnel safety management and monitoring subsystem includes: a positioning tracking unit, a behavior monitoring unit, a health monitoring unit, and an intelligent alarm unit; The positioning and tracking unit is used to track the location information of mining personnel in real time; The behavior monitoring unit is used to monitor the behavior of the mining staff in real time and determine whether the mining staff has any dangerous behavior; The health monitoring unit is used to monitor the vital signs of the mining workers and to warn them of health risks; The intelligent alarm unit assesses the location, dangerous behaviors, and health risks of mining workers, and issues early warnings to prevent accidents.

3. The mining personnel and environment safety management and monitoring system according to claim 2 is characterized in that: The specific steps of the positioning and tracking unit to track the location information of mining area staff in real time are: S1 first obtains the latest map of the mining area, divides different working areas according to the specific conditions of the mining area, marks the boundaries of safe areas and dangerous areas, sets corresponding safety rules for each area, and sets attributes for each area. Only mining area staff who meet the attributes can enter or enter within a limited time; Safety rules include setting a dwell time threshold in the zone; S2 distributes smart wearable devices to each mining worker. The devices have built-in personnel information and obtain the location of mining workers in real time through positioning base stations; S3 receives data from the smart wearable device through the positioning base station, and transmits the data to the mining personnel safety management and monitoring subsystem to determine the location of the mining personnel; S4 When mining workers enter unauthorized areas, the intelligent alarm unit notifies mining managers and sends out sound and light signal alarms through smart wearable devices to remind mining workers who enter unauthorized areas to evacuate the area as soon as possible.

4. The mining personnel and environment safety management and monitoring system according to claim 3 is characterized in that: The behavior monitoring unit is used to monitor whether the mining workers have dangerous behaviors or the tendency of dangerous behaviors, including: S1 installs a high-definition camera at a key position in the mining area to ensure that the camera can monitor the entire area without blind spots and capture the actions of the mining area workers; synchronizes the high-definition camera with the smart wearable device, and the high-definition camera can move with the movement of the smart wearable device to obtain multi-dimensional data; S2 uses edge detection algorithms to preprocess video images and extract feature vectors, including contours, key points, and motion patterns, from processed images and data acquired from smart wearable devices; S3 inputs the real-time collected and processed data into the deep learning model to perform behavior recognition, and analyzes the recognition results according to the abnormal behavior judgment rules to determine whether there is abnormal behavior; S4 When abnormal behavior is detected, the corresponding emergency plan is activated according to different abnormal behaviors, such as evacuation, shutdown of mechanical equipment, and relevant personnel are notified through sound, light or electronic means.

5. The mining personnel and environment safety management and monitoring system according to claim 4 is characterized in that: The abnormal behavior of the mine staff deviates from the baseline behavior to a certain extent, including: failure to wear safety equipment, that is, failure to wear safety equipment as required by the mine; activities in unauthorized areas, that is, entering restricted areas without permission; abnormal work events, that is, appearing in the work area or during working hours during non-working hours; abnormal behavior patterns, that is, abnormal stay time in dangerous areas or abnormal movement speed; when the abnormal behavior of the mine staff occurs twice or less, the mine will only criticize and educate the mine staff; when the abnormal behavior occurs more than twice, the mine will take measures to transfer or dismiss the mine staff.

6. The mining personnel and environment safety management and monitoring system according to claim 5 is characterized in that: The smart wearable device integrates a positioning device, a physiological monitoring device, a behavior monitoring device, a communication device, and an alarm device; the alarm device uses situational awareness for communication, and intelligently adjusts the information transmission method according to the current location and status of the mine workers, including noise level, light intensity, and personal health status, including vibration, light signals, sound or temperature changes. At the same time, the transmission method can adjust the reminder intensity according to different situations to alert the mine workers.

7. The mining personnel and environment safety management and monitoring system according to claim 1 is characterized in that: The environmental safety management and monitoring subsystem integrates multiple sensors to monitor environmental parameter data of potential safety hazards within the mining area, such as gas explosions and rock collapse, and environmental parameters such as temperature, humidity, concentration of harmful gases, and dust concentration; it can also monitor geological dynamics and vegetation coverage to provide more comprehensive data support for disaster warning; and the environmental safety management and monitoring subsystem can monitor the impact of the mining area on the surrounding environment, such as water pollution and air pollution, automatically generate environmental protection reports based on the monitoring results, and compare them with the environmental protection laws and regulations and standards in the area to evaluate the environmental compliance of the mining area; The environmental safety management monitoring subsystem can also automatically adjust the sensor layout and monitoring strategy according to the monitoring objectives and environmental conditions.

8. The mining personnel and environment safety management and monitoring system according to claim 1 is characterized in that: The control center includes a data integration and analysis unit, a decision support unit, a communication coordination unit, an information management unit, an emergency response unit, an emergency training unit, and a user visualization unit; The data integration and analysis unit is used to collect the data provided by the subsystem, process the received data, and determine whether there are safety hazards through the mine safety judgment model deployed in the data integration and analysis unit; The decision support unit is used to provide decision support, including emergency response plans and early warning information release, when the mine safety judgment model detects safety hazards; and uses augmented reality AR technology to build a real-time three-dimensional model of the mine area. The three-dimensional model integrates more types of data, including equipment operation status, ore quality and location distribution, and geological structure data. According to the data collected by the environmental safety management and monitoring subsystem, the environmental conditions in the three-dimensional model, such as ventilation, temperature, and water quality, are adjusted in real time to simulate the real environment. The three-dimensional model built by AR technology can be operated by custom gestures or voice commands, including zooming and rotating, to provide a visual display to help managers make decisions; The emergency response unit is used to respond to the formulated emergency response plan when the mine safety judgment model detects potential safety hazards, help workers evacuate, and reduce losses in the mine; The communication coordination unit is used to serve as a communication hub between the mining personnel safety management monitoring subsystem and the environmental safety monitoring subsystem to ensure information flow transmission and coordinate the execution of emergency response measures; The information management unit is used to manage the mine staff information, equipment information, and safety records to ensure the accuracy and reliability of the information; The emergency training unit is used to use virtual reality (VR) technology to construct a simulated environment when a danger occurs in a certain area of ​​the mining area, thereby enhancing the ability of mining staff to respond to emergencies.

9. The mining personnel and environment safety management and monitoring system according to claim 8, characterized in that: The data integration and analysis unit uses the internally deployed mine safety judgment model to judge the safety hazards of the integrated subsystem data. The generation process of the mine safety judgment model includes: S1 data preparation: collecting historical data, including environmental parameters, the location of mine workers and related safety event records; data cleaning, processing missing values ​​and abnormal values ​​in the data; feature engineering, extracting features that are helpful for model prediction; data annotation, marking the historical data with corresponding labels; data segmentation, dividing the processed data set into a training set and a test set; S2 model training: Use the training set data to train the gradient boosting decision tree model, where the calculation formula of the gradient boosting decision tree model is: F(x)=h1(x)+h2(x)+…+h t (x), Among them, F(x) is the final gradient boosting machine model, which accumulates the decision tree h learned at each step t (x) is the prediction of the security risk level; t is the total number of decision trees; h t (x) is the decision tree learned in step t. Each decision tree h t (x) is to reduce the previous step model F t-1 (x) is constructed based on the residual to correct the prediction error of the previous model on the safety risk level; the negative gradient, i.e. the residual, is calculated in each step of the calculation, where the residual calculation formula is: Among them, x i represents the characteristic vector of the i-th data point, i.e., the environmental monitoring parameters of the mining area, including temperature, humidity, concentration of harmful gases, location of mining area staff, and equipment status; y i represents the target variable of the i-th data point, that is, the label added to the data point, including the safety risk level of the mining area or the occurrence of a specific safety incident; r ti Represents the residual of the i-th data point at the t-th step, and the residual is the model F t-1 (x) The negative gradient at this data point is the error between the safety risk prediction and the actual safety status of the i-th data point in the current step in the mining personnel and environmental safety management monitoring system; L(y i ,F(x i )) is the loss function, which is a measure of the predicted value F(x) at the i-th data point i ) and the true value y i The function of the error between S3 model evaluation: Use the test set to test the trained model and evaluate the prediction performance of the model. The specific evaluation indicators used are accuracy, recall rate, and F1 score. The accuracy rate indicates the ability of the mine safety judgment model to correctly identify safety conditions, and the accuracy rate is calculated as follows: Precision = number of correctly predicted environmental parameters / number of predicted environmental parameters * 100%. The recall rate can reflect the proportion of actual safety issues captured by the mine safety judgment model, and the recall rate is calculated as follows: Recall = number of correctly predicted environmental parameters / total number of environmental parameters * 100%, The F1 score can balance the accuracy and recall rate, and can specifically measure the overall performance of the mine safety judgment model. The evaluation calculation of the F1 score is: F1=2*Precision*Recall / (Precision+Recall)*100%; S4 Model Deployment: Deploy the trained model to the control center to process the collected environmental parameters and the location data of mining area workers in real time; S5 Real-time Prediction: Preprocess and extract features from the environmental parameters and the location data of mining area workers collected in real time by environmental sensors, and then input the processed data into the mining area safety judgment model for prediction; S6 Model Update and Maintenance: Monitor the prediction performance of the model. If the performance is found to decline, retrain and optimize the model.

10. The mining personnel and environment safety management and monitoring system according to claim 9, characterized in that: When the mining area safety judgment model judges potential safety hazards for the data integrated by the subsystem, the predicted value output is the probability score Score. The magnitude of the probability score Score represents different safety levels. The safety levels are preset and include normal S, warning W, and danger D. When the probability score Score > 0.7, it is safe S; when 0.3 < Score <= 0.7, it is warning W; when Score < 0.3, it is danger D.

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