Data processing method and device for mine safety operation plan, medium and equipment

Abstract

The invention discloses a data processing method and device for a mine safety operation plan, a medium and equipment, and relates to the technical field of data processing.Collected multi-source data is divided into monitoring data and environment data, a geologic model and an engineering model in the environment data are combined, and the monitoring data and the environment data are combined; the method comprises the following steps: obtaining a model of an actual structure of a mine, introducing the model into a physical environment object, mapping real-time monitoring data to the model of the actual structure of the mine through a preset sensor position to realize the combination of a virtual digital object and the physical environment object, and visualizing the monitoring data by taking the model of the actual structure of the mine as a carrier. When the abnormal condition is monitored, the risk and the prediction result thereof are visually marked and reflected on the actual structure of the mine, the visualization effect is improved, data processing, risk identification and visualization are isolated to a certain extent, the processing difficulty of each process is reduced, and the processing efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a data processing method, apparatus, medium, and equipment for mine safety operation planning. Background Technology

[0002] Timely and accurate mine safety operation plans are a major guarantee for mine safety. Real-time collection and analysis of relevant data during mine operations allows for the timely identification of risks. The effectiveness of processing multi-source data acquired through monitoring is a crucial factor affecting the quality of mine safety operation plans. The coexistence of large amounts of heterogeneous data from multiple sources in mine operations exacerbates the processing difficulty. Manually sifting through individual data points is inefficient and not only increases the complexity of risk identification but also makes visualization more challenging, thus increasing the difficulty of mine safety management. Summary of the Invention

[0003] The main purpose of this application is to provide a data processing method, apparatus, medium and equipment for mine safety operation planning, which aims to solve the problem of poor processing effect of monitoring data in mine operations in the prior art.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a data processing method for mine safety operation planning, comprising the following steps: Acquire multi-source data on mining operations; this multi-source data includes real-time monitoring data and environmental data. By integrating geological models and engineering models from environmental data, an actual structural model of the mine can be obtained. Real-time monitoring data is mapped to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine. In response to anomalies in real-time monitoring data, risk outcomes are marked on the mine visualization digital model; these risk outcomes include information on actual anomalies and information on predicted anomalies.

[0005] In one possible implementation of the first aspect, before mapping real-time monitoring data to an actual mine structure model using sensor locations to obtain a visualized digital model of the mine, the method further includes: Establish a mapping relationship between the sensor and spatial coordinates; Real-time monitoring data is mapped to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine, including: Real-time monitoring data is mapped to the actual structural model of the mine using a sensor-spatial coordinate mapping relationship, resulting in a visualized digital model of the mine.

[0006] In one possible implementation of the first aspect, establishing a sensor-spatial coordinate mapping relationship includes: Based on the location acquisition equipment, the three-dimensional spatial coordinates of the sensors in the mining operation are obtained; Establish a sensor spatial information table based on three-dimensional spatial coordinates; Based on the three-dimensional spatial coordinates, the sensor is instantiated at the corresponding position in the actual mine structure model to obtain the sensor instance label; The sensor spatial information table is associated with the corresponding sensor instance tag to establish a sensor-spatial coordinate mapping relationship.

[0007] In one possible implementation of the first aspect, before mapping real-time monitoring data to an actual mine structure model using sensor locations to obtain a visualized digital model of the mine, the method further includes: Real-time monitoring data is visualized at the corresponding sensor locations to obtain visualized monitoring data. Real-time monitoring data is mapped to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine, including: The monitoring and visualization data is mapped to the actual structural model of the mine based on the sensor locations to obtain a visualized digital model of the mine.

[0008] In one possible implementation of the first aspect, after visualizing the real-time monitoring data at the corresponding sensor locations to obtain the visualized monitoring data, the method further includes: Based on the interpolation algorithm, discrete monitoring visualization data is transformed into a continuous three-dimensional surface of monitoring data. The monitoring and visualization data is mapped to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine, including: The monitoring data is mapped onto the three-dimensional surface of the sensor location onto the actual structural model of the mine to obtain a visualized digital model of the mine.

[0009] In one possible implementation of the first aspect, acquiring multi-source data on mining operations includes: With defined time and spatial references, raw multi-source data of mining operations are collected; the raw multi-source data is then tagged with spatiotemporal labels. Identify the source coordinate system of the original multi-source data and perform coordinate transformation so that the original multi-source data are located in the same coordinate system; Data preprocessing is performed on the raw multi-source data to obtain multi-source data on mining operations.

[0010] In one possible implementation of the first aspect, before marking the risk outcome on the mine visualization digital model in response to anomalies in real-time monitoring data, the method further includes: Based on the anomalies in the real-time monitoring data, the categories of the abnormal data are determined; Based on the data category, select the corresponding risk model for prediction and obtain prediction anomaly information.

[0011] Secondly, embodiments of this application provide a data processing apparatus for mine safety operation planning, comprising: The acquisition module is used to acquire multi-source data from mining operations; the multi-source data includes real-time monitoring data and environmental data. The integration module is used to integrate the geological model and engineering model in the environmental data to obtain the actual structural model of the mine; The visualization module is used to map real-time monitoring data to the actual structure model of the mine based on sensor locations, thereby obtaining a visualized digital model of the mine. The risk module is used to mark risk results on the mine visualization digital model in response to anomalies in real-time monitoring data; the risk results include information on anomalies that have occurred and information on predicted anomalies.

[0012] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the data processing method for mine safety operation planning provided in any of the first aspects above.

[0013] Fourthly, embodiments of this application provide an electronic device, including a processor and a memory, wherein, Memory is used to store computer programs; The processor is used to load and execute computer programs to cause electronic devices to perform data processing methods for mine safety operation plans as provided in any of the first aspects above.

[0014] Compared with the prior art, the beneficial effects of this application are: This application proposes a data processing method, apparatus, medium, and equipment for mine safety operation planning. The method includes: acquiring multi-source data of mine operations; wherein the multi-source data includes real-time monitoring data and environmental data; integrating the geological model and engineering model in the environmental data to obtain an actual mine structure model; mapping the real-time monitoring data to the actual mine structure model based on sensor locations to obtain a visualized digital model of the mine; and marking risk results on the visualized digital model of the mine in response to anomalies in the real-time monitoring data; wherein the risk results include information on actual anomalies and information on predicted anomalies. This application categorizes the collected multi-source data into monitoring data and environmental data. First, it combines the geological and engineering models from the environmental data to obtain a model of the actual mine structure, which is then introduced into the physical environment. Next, real-time monitoring data is mapped onto the actual mine structure model through pre-set sensor locations, achieving a combination of virtual digital objects and physical environment objects. The actual mine structure model serves as a carrier to visualize the monitoring data. When an anomaly is detected, the risk and its prediction results are intuitively marked on the actual mine structure, improving the visualization effect. This approach isolates data processing, risk identification, and visualization to a certain extent, reducing the processing difficulty of each process and improving processing efficiency. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application; Figure 2 A flowchart illustrating a data processing method for mine safety operation planning provided in an embodiment of this application; Figure 3 A schematic diagram of a data processing device for mine safety operation planning provided in an embodiment of this application; The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0016] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0017] See attached document Figure 1 , attached Figure 1This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0018] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0019] As attached Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a data processing device for mine safety operation plans.

[0020] In the appendix Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device. The electronic device calls the data processing device for mine safety operation plan stored in the memory 105 through the processor 101 and executes the data processing method for mine safety operation plan provided in the embodiment of this application.

[0021] See attached document Figure 2 Based on the hardware device of the foregoing embodiments, embodiments of this application provide a data processing method for mine safety operation planning, including the following steps: S10: Acquire multi-source data of mining operations; among which, multi-source data includes real-time monitoring data and environmental data.

[0022] In the specific implementation process, for the target mine, that is, the mine that needs to carry out safety operation planning, multi-source data is collected. In order to achieve intelligent monitoring, reduce the degree of human intervention, and effectively combine physical entities with virtual digital data, the acquired multi-source data is divided into real-time monitoring data and environmental data, that is, data monitored and fed back by sensors and data characterizing the mine structure.

[0023] In one embodiment, acquiring multi-source data on mining operations includes: With defined time and spatial references, raw multi-source data of mining operations are collected; the raw multi-source data is then tagged with spatiotemporal labels. Identify the source coordinate system of the original multi-source data and perform coordinate transformation so that the original multi-source data are located in the same coordinate system; Data preprocessing is performed on the raw multi-source data to obtain multi-source data on mining operations.

[0024] In the specific implementation process, due to the multi-source nature of the data, in order to improve the data processing effect and enable effective data combination and analysis, it is considered to unify the spatiotemporal labels of the data. All raw data timestamps are converted to a unified time zone, and spatial coordinates are transformed to a unified coordinate system, such as a coordinate system established based on the mine or a global coordinate system. The system server and data acquisition equipment use NTP for time synchronization, controlling clock errors within milliseconds. After the above processing, all raw multi-source data has a unified spatiotemporal label representation. Before application, data preprocessing is performed to improve data quality, such as invalid value removal, correction of obvious error values, and missing value completion.

[0025] S20: Integrate the geological model and engineering model from the environmental data to obtain the actual structural model of the mine.

[0026] In practical implementation, mine structure is a crucial data point for risk assessment, and a more realistic mine structure model can enhance the visualization of subsequent data. Integrating geological and engineering models—where the geological model is obtained through geological exploration, and the engineering model relates to mine design, tunnel layout, and goaf design—generates model data such as CAD drawings and 3D mesh drawings. A 3D engine can be used for lightweight modeling and overlaying, achieving model integration and ensuring precise spatial correspondence. In this way, the actual mine structure model not only represents the mine operation planning and design but also the geological conditions at various locations.

[0027] S30: Map real-time monitoring data to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine.

[0028] In the implementation process, real-time monitoring data such as gas concentration, displacement deformation, and ground pressure are mapped onto the actual structural model of the mine, realizing the combination of virtual data and physical structure, and constructing a virtual digital twin of the mine. Multiple sensors corresponding to various monitoring data will be deployed in the parts of the mine that need to be monitored. In this way, the visualization mapping based on sensor locations will more comprehensively and accurately reflect the real-time situation of the mine.

[0029] In one embodiment, before mapping real-time monitoring data to an actual mine structure model based on sensor locations to obtain a visualized digital model of the mine, the method further includes: Establish a mapping relationship between the sensor and spatial coordinates; Real-time monitoring data is mapped to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine, including: Real-time monitoring data is mapped to the actual structural model of the mine using a sensor-spatial coordinate mapping relationship, resulting in a visualized digital model of the mine.

[0030] In the specific implementation process, to improve visualization efficiency, a sensor-spatial coordinate mapping relationship is constructed. Visualization is then based directly on this mapping relationship to quickly acquire information carried by the data and locate spatial positions. Specifically, establishing the sensor-spatial coordinate mapping relationship includes: Based on the location acquisition equipment, the three-dimensional spatial coordinates of the sensors in the mining operation are obtained; Establish a sensor spatial information table based on three-dimensional spatial coordinates; Based on the three-dimensional spatial coordinates, the sensor is instantiated at the corresponding position in the actual mine structure model to obtain the sensor instance label; The sensor spatial information table is associated with the corresponding sensor instance tag to establish a sensor-spatial coordinate mapping relationship.

[0031] In the implementation process, high-precision GPS, total stations, or laser scanners are used to acquire location data, and the three-dimensional spatial coordinates of each sensor are measured on-site as its unique spatial identifier. A structured sensor spatial information table is then constructed, containing the following key fields: sensor unique identifier, type, three-dimensional spatial coordinates, installation date, orientation, and associated production unit. In the actual mine structure model, based on the obtained three-dimensional spatial coordinates, a marker representing the corresponding sensor, such as a 3D icon or model, is instantiated at the appropriate location. The sensor spatial information table is then associated with the corresponding sensor instance markers, adding the information carried by the instance markers, and this serves as a spatial and logical association, resulting in a sensor-spatial coordinate mapping.

[0032] In one embodiment, before mapping real-time monitoring data to an actual mine structure model based on sensor locations to obtain a visualized digital model of the mine, the method further includes: The real-time monitoring data is transformed into visual data at the corresponding sensor locations to obtain visualized monitoring data.

[0033] In the specific implementation process, the purpose of mapping is to visualize the monitoring data. First, a visualization transformation is performed, such as expressing it numerically at the corresponding location, or a more intuitive graphical expression method can be adopted. The data is directly and tangibly covered on the model, such as using a visual channel method, where the data value dynamically changes the color of the sensor icon. For example, green, yellow and red respectively indicate safety, warning and exceeding limit for gas concentration. Another example is that the ground pressure value can be represented by the height of a column extending upward from the sensor location.

[0034] Based on the aforementioned steps, real-time monitoring data is mapped to the actual structure model of the mine using sensor locations to obtain a visualized digital model of the mine, including: The monitoring and visualization data is mapped to the actual structural model of the mine based on the sensor locations to obtain a visualized digital model of the mine.

[0035] In one embodiment, after visualizing the real-time monitoring data at the corresponding sensor locations to obtain visualized monitoring data, the method further includes: Based on the interpolation algorithm, discrete monitoring visualization data is transformed into a continuous three-dimensional surface of monitoring data.

[0036] In practical implementation, from the perspective of monitoring accuracy alone, deploying more sensors can obviously improve the quality of collected data. However, in reality, sensors cannot be densely distributed in the mining environment. In order to improve data processing efficiency and enhance visualization, discrete sensor data is collected and supplemented. Data of the same type is supplemented by interpolation algorithms to generate continuous three-dimensional surfaces to represent the distribution of monitoring data, such as gas concentration distribution or stress field distribution. The three-dimensional surface can be generated by using isosurface extraction algorithms to generate isosurface grids. In this way, the visualization of data is no longer limited to the sensor locations, but can also be extended to all working environments where no sensors are deployed.

[0037] Based on the aforementioned steps, the monitoring and visualization data is mapped to the actual structure model of the mine according to the sensor locations, resulting in a visualized digital model of the mine, including: The monitoring data is mapped onto the three-dimensional surface of the sensor location onto the actual structural model of the mine to obtain a visualized digital model of the mine.

[0038] S40: In response to anomalies in real-time monitoring data, mark risk results on the mine visualization digital model; whereby risk results include information on actual anomalies and information on predicted anomalies.

[0039] In the specific implementation process, visualization and anomaly identification are separated to a certain extent to avoid excessive complexity of a single system. Anomaly identification is carried out in parallel using other methods. The visualization digital model of the mine only needs to obtain the risk results from another aspect. During risk identification, not only can the types of existing risks be identified, but their future development can also be predicted. Combined with the aforementioned method of mapping data visualization to all operating environments in the mine, the current anomaly situation and its future impact can be intuitively displayed on the visualization digital model of the mine, thereby improving the visualization effect.

[0040] In one embodiment, before marking a risk outcome on the mine visualization digital model in response to an anomaly in real-time monitoring data, the method further includes: Based on the anomalies in the real-time monitoring data, the categories of the abnormal data are determined; Based on the data category, select the corresponding risk model for prediction and obtain prediction anomaly information.

[0041] In the specific implementation process, to improve the efficiency of anomaly identification, different risk models are provided for prediction based on multi-source data. For example, the first risk model is used for risk prediction based on gas concentration data, and the second risk model is used for risk prediction based on ground pressure data. Since the spatiotemporal labels of the multi-source data have been unified in the previous steps, their data formats can be further unified and converted so that the risk models can be merged into a large model. The data enters through a single input port, and the large model is classified according to data categories. Different modules deployed in the large model then perform identification and prediction.

[0042] Each risk model can be obtained through training using machine learning algorithms, such as: The model for predicting ground pressure disaster risk combines microseismic events, stress monitoring data, and a three-dimensional geological model. It uses machine learning algorithms to train the risk prediction model, predicts the probability of rock bursts, roof falls, and rib collapses in the mining area and roadways in the future, and identifies high-risk areas in the three-dimensional model.

[0043] The model for predicting gas explosion risks analyzes the spatiotemporal correlation of data such as gas concentration, wind speed, and mining intensity, establishes a gas migration and accumulation model, provides real-time early warning of gas exceeding limits, and marks the risks on the visualization model, suggesting adjustments to ventilation plans or suspension of operations.

[0044] The model for predicting hydrogeological disaster risks integrates data on precipitation, geological structure, and distribution of mining subsidence areas. Through numerical simulation analysis, it analyzes the risk paths and probabilities of water inrush and seepage, and provides early warnings for potentially affected areas before extreme weather events such as rainstorms.

[0045] In this embodiment, the collected multi-source data is divided into monitoring data and environmental data. First, the geological model and engineering model in the environmental data are combined to obtain a model of the actual mine structure, which is then introduced into the physical environment object. Then, the real-time monitoring data is mapped onto the actual mine structure model through pre-set sensor locations, realizing the combination of virtual digital objects and physical environment objects. The monitoring data is visualized using the actual mine structure model as a carrier. When an anomaly is detected, the risk and its prediction result are intuitively marked on the actual mine structure, improving the visualization effect. Data processing, risk identification, and visualization are isolated to a certain extent, reducing the processing difficulty of each process and improving processing efficiency.

[0046] See attached document Figure 3 Based on the same inventive concept as in the foregoing embodiments, this application also provides a data processing device for mine safety operation planning, comprising: The acquisition module is used to acquire multi-source data from mining operations; the multi-source data includes real-time monitoring data and environmental data. The integration module is used to integrate the geological model and engineering model in the environmental data to obtain the actual structural model of the mine; The visualization module is used to map real-time monitoring data to the actual structure model of the mine based on sensor locations, thereby obtaining a visualized digital model of the mine. The risk module is used to mark risk results on the mine visualization digital model in response to anomalies in real-time monitoring data; the risk results include information on anomalies that have occurred and information on predicted anomalies.

[0047] Those skilled in the art should understand that the division of the various modules in the embodiments is merely a logical functional division. In actual applications, they can be fully or partially integrated into one or more actual carriers. These modules can be implemented entirely in software through processing unit calls, entirely in hardware, or a combination of software and hardware. It should be noted that each module in the data processing device for mine safety operation plans in this embodiment corresponds one-to-one with each step in the data processing method for mine safety operation plans in the aforementioned embodiments. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned data processing method for mine safety operation plans, which will not be repeated here.

[0048] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the data processing method for mine safety operation planning provided in the embodiments of this application.

[0049] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an electronic device, including a processor and a memory, wherein, Memory is used to store computer programs; The processor is used to load and execute computer programs to cause electronic devices to perform data processing methods for mine safety operation plans as provided in the embodiments of this application.

[0050] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0051] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0052] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0053] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0054] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0055] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0056] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0057] In summary, the embodiments of this application provide a data processing method, apparatus, medium, and equipment for mine safety operation planning. The method includes: acquiring multi-source data of mine operations; wherein the multi-source data includes real-time monitoring data and environmental data; integrating the geological model and engineering model in the environmental data to obtain an actual mine structure model; mapping the real-time monitoring data to the actual mine structure model based on sensor locations to obtain a visualized digital model of the mine; and marking risk results on the visualized digital model of the mine in response to anomalies in the real-time monitoring data; wherein the risk results include information on anomalies that have occurred and information on predicted anomalies. This application categorizes the collected multi-source data into monitoring data and environmental data. First, it combines the geological and engineering models from the environmental data to obtain a model of the actual mine structure, which is then introduced into the physical environment. Next, real-time monitoring data is mapped onto the actual mine structure model through pre-set sensor locations, achieving a combination of virtual digital objects and physical environment objects. The actual mine structure model serves as a carrier to visualize the monitoring data. When an anomaly is detected, the risk and its prediction results are intuitively marked on the actual mine structure, improving the visualization effect. This approach isolates data processing, risk identification, and visualization to a certain extent, reducing the processing difficulty of each process and improving processing efficiency.

[0058] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A data processing method for mine safety operation planning, characterized in that, Includes the following steps: Acquire multi-source data on mining operations; wherein the multi-source data includes real-time monitoring data and environmental data; By integrating the geological model and engineering model from the environmental data, an actual structural model of the mine is obtained; The real-time monitoring data is mapped to the actual structure model of the mine based on the sensor locations to obtain a visualized digital model of the mine. In response to anomalies in real-time monitoring data, risk results are marked on the mine visualization digital model; wherein, the risk results include information on anomalies that have occurred and information on predicted anomalies.

2. The data processing method for mine safety operation planning according to claim 1, characterized in that, Before mapping the real-time monitoring data to the actual mine structure model based on sensor locations to obtain a visualized digital model of the mine, the method further includes: Establish a mapping relationship between the sensor and spatial coordinates; The step of mapping the real-time monitoring data to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine includes: The real-time monitoring data is mapped to the actual structure model of the mine using the sensor-spatial coordinate mapping relationship to obtain a visualized digital model of the mine.

3. The data processing method for mine safety operation planning according to claim 2, characterized in that, The establishment of the sensor-spatial coordinate mapping relationship includes: Based on the location acquisition equipment, the three-dimensional spatial coordinates of the sensors in the mining operation are obtained; Based on the aforementioned three-dimensional spatial coordinates, a sensor spatial information table is established; Based on the three-dimensional spatial coordinates, the sensor is instantiated at the corresponding position in the actual mine structure model to obtain the sensor instance marker; The sensor spatial information table is associated with the corresponding sensor instance tag to establish a sensor-spatial coordinate mapping relationship.

4. The data processing method for mine safety operation planning according to claim 1, characterized in that, Before mapping the real-time monitoring data to the actual mine structure model based on sensor locations to obtain a visualized digital model of the mine, the method further includes: The real-time monitoring data is visualized at the corresponding sensor locations to obtain visualized monitoring data. The step of mapping the real-time monitoring data to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine includes: The monitoring and visualization data is mapped to the actual structure model of the mine based on the sensor locations to obtain a visualized digital model of the mine.

5. The data processing method for mine safety operation planning according to claim 4, characterized in that, After visualizing the real-time monitoring data at the corresponding sensor locations to obtain visualized monitoring data, the method further includes: Based on the interpolation algorithm, the discrete monitoring visualization data is used to generate a continuous three-dimensional surface of monitoring data. The step of mapping the monitoring visualization data to the actual structure model of the mine based on sensor locations to obtain a visualized digital model of the mine includes: The monitoring data is mapped onto the actual structure model of the mine using the sensor location to obtain a visualized digital model of the mine.

6. The data processing method for mine safety operation planning according to claim 1, characterized in that, The acquisition of multi-source data from mining operations includes: With defined time and spatial references, raw multi-source data of mining operations are collected; wherein, the raw multi-source data is tagged with spatiotemporal labels. Identify the source coordinate system of the original multi-source data and perform coordinate transformation so that the original multi-source data are located in the same coordinate system; The original multi-source data is preprocessed to obtain multi-source data on mining operations.

7. The data processing method for mine safety operation planning according to claim 1, characterized in that, Before marking risk results on the mine visualization digital model in response to anomalies in real-time monitoring data, the method further includes: Based on the anomalies in the real-time monitoring data, the categories of the abnormal data are obtained; Based on the data category, a corresponding risk model is selected for prediction to obtain the predicted anomaly information.

8. A data processing device for mine safety operation planning, characterized in that, include: The acquisition module is used to acquire multi-source data from mining operations; wherein, the multi-source data includes real-time monitoring data and environmental data; The integration module is used to integrate the geological model and engineering model in the environmental data to obtain the actual structure model of the mine; The visualization module is used to map the real-time monitoring data to the actual structure model of the mine based on the sensor location, so as to obtain a visualized digital model of the mine. The risk module is used to mark risk results on the mine visualization digital model in response to anomalies in real-time monitoring data; wherein the risk results include information on anomalies that have occurred and information on predicted anomalies.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the data processing method for mine safety operation planning as described in any one of claims 1-7.

10. An electronic device, characterized in that, Including processor and memory, among which, The memory is used to store computer programs; The processor is used to load and execute the computer program to cause the electronic device to perform the data processing method for mine safety operation planning as described in any one of claims 1-7.

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