Fusion geological prediction method, system and device, storage medium and program product

Abstract

The invention provides a fusion geological prediction method, system and device, a storage medium and a program product, and relates to the technical field of tunnel advanced geological prediction. The method comprises the following steps: an upper computer receives multiple groups of seismic data and acquisition time of each group of seismic data sent by a data acquisition instrument; the upper computer determines the type of seismic data according to the acquisition time, the pre-recorded working time period of the heading machine and the vibration time period of the seismic source; the upper computer extracts micro-seismic analysis data, active source analysis data and passive source analysis data from the multiple sets of seismic data according to the data types; the upper computer generates rockburst risk data according to the micro-seismic analysis data, and generates geological risk data according to the active source analysis data and the passive source analysis data; and reconstructing and displaying a three-dimensional visual image according to the rockburst risk data and the geological risk data. The problems that in the prior art, due to the fact that sensors are arranged repeatedly, equipment layout is disordered, construction streamlines are blocked, and construction efficiency and equipment safety are seriously affected are solved.

Description

Technical Field

[0001] This application relates to the technical field of advanced geological prediction for tunnels, and particularly to a combined geological prediction method, system, device, storage medium, and program product. Background Art

[0002] During the tunnel construction process, the advanced geological prediction technology plays a decisive role in ensuring construction safety. Since faults, broken zones, boulders, and sudden changes between hard and soft rocks that may exist in front of the tunnel face are likely to cause jamming accidents of tunneling equipment, and the unique high in-situ stress environment of deep-buried tunnels will further induce destructive rock bursts, which may cause multiple risks such as face collapse, mechanical damage, and casualties. Therefore, implementing multi-dimensional advanced geological detection has become an essential part of the safety control of rock engineering.

[0003] In the prior art, rock burst risk warning and adverse geological risk warning are realized through two independent hardware systems. This not only separates from the tunneling machine structure and construction but also the repeated layout of sensors will occupy the limited working space, leading to chaotic equipment layout and blocked construction flow lines, seriously affecting construction efficiency and equipment safety. Summary of the Invention

[0004] This application provides a combined geological prediction method, system, device, storage medium, and program product to solve the problems of chaotic equipment layout and blocked construction flow lines caused by the repeated layout of sensors in the prior art, thus seriously affecting construction efficiency and equipment safety.

[0005] In a first aspect, this application provides a combined geological prediction method, which is applied to a combined geological prediction system. The combined geological prediction system includes: a host computer, signal sensors, a data acquisition instrument, a seismic source, and a seismic source trigger. The signal sensors are arranged on the tunnel sidewall. The host computer is communicatively connected to the data acquisition instrument and the seismic source trigger. The data acquisition instrument is communicatively connected to the signal sensors. The seismic source trigger is communicatively connected to the seismic source. The host computer is used to control the seismic source through the seismic source trigger. The method includes:

[0006] The host computer receives multiple groups of seismic data sent by the data acquisition instrument and the acquisition time of each group of the seismic data. The seismic data is obtained by the data acquisition instrument converting the analog seismic signals collected by the signal sensors. The analog seismic signals are seismic signals propagating in the tunnel surrounding rock;

[0007] The host computer determines the data type of the seismic data according to the acquisition time, the pre-recorded working time period of the tunneling machine, and the vibration time period of the seismic source. The data type is used to indicate whether the seismic data is collected during the operation of the tunneling machine and / or during the vibration of the seismic source;

[0008] The host computer extracts microseismic analysis data, active source analysis data, and passive source analysis data from the multiple sets of seismic data respectively according to the data type. The microseismic analysis data includes the active source analysis data and the passive source analysis data. The active source analysis data is the data collected when the seismic source vibrates and the roadheader stops working. The passive source analysis data is the data collected when the seismic source stops vibrating and the roadheader is working;

[0009] The host computer generates rockburst risk data according to the microseismic analysis data, and generates geological risk data according to the active source analysis data and the passive source analysis data;

[0010] Reconstruct a three-dimensional visual image according to the rockburst risk data and the geological risk data, and display the three-dimensional visual image.

[0011] In a possible design, the generating the rockburst risk data according to the microseismic analysis data, and generating the geological risk data according to the active source analysis data and the passive source analysis data includes:

[0012] Generate vibration energy point cloud data according to the microseismic analysis data, and generate rockburst risk data according to the vibration energy of each to-be-mined position point included in the vibration energy point cloud data. The rockburst risk data includes: rockburst risk points, the rockburst risk levels of the rockburst risk points. The rockburst risk level is positively correlated with the vibration energy. The rockburst risk points include the to-be-mined position points with vibration energy greater than or equal to the energy threshold;

[0013] Generate physical property parameter point cloud data according to the active source analysis data and the passive source analysis data, and generate geological risk data according to the physical property parameters of each to-be-mined position point included in the physical property parameter point cloud data. The geological risk data includes: geological risk points and the geological risk types of the geological risk points.

[0014] In a possible design, the generating the physical property parameter point cloud data according to the active source analysis data and the passive source analysis data includes:

[0015] Generate active source physical property parameter point cloud data according to the active source analysis data, and generate passive source physical property parameter point cloud data according to the passive source analysis data;

[0016] Fuse the active source physical property parameter point cloud data and the passive source physical property parameter point cloud data according to the to-be-mined position points to obtain the physical property parameter point cloud data. The physical property parameter of a to-be-mined position point in the physical property parameter point cloud data is obtained by fusing the physical property parameter of the to-be-mined position point in the active source physical property parameter point cloud data and the physical property parameter of the to-be-mined position point in the passive source physical property parameter point cloud data.

[0017] In a possible design, the reconstructing the three-dimensional visual image according to the rock burst risk data and the geological risk data includes:

[0018] Fuse the rock burst risk data and the geological risk data according to the to-be-mined position points to obtain risk fusion data. The risk fusion data includes: risk position points and the risk information of the risk position points. The risk position points are the rock burst risk points and / or the geological risk points, and the risk information includes the rock burst risk level of the rock burst risk points and / or the geological risk types;

[0019] For each of the risk position points, generate a risk description image and a text label of the risk position point according to the risk information. The text label is used to describe the risk information in words, and the risk description image is an image generated by fusing the rock burst risk level and / or the geological risk types included in the risk information;

[0020] Generate a three-dimensional visual image according to the risk position points, the risk description images and the text labels.

[0021] In a possible design, it further includes:

[0022] When the host computer sends control instructions to the seismic source trigger and the roadheader, record the seismic source vibration time period and the roadheader working time period according to the control instructions. The start time of the seismic source vibration time period is the control instruction sending time when the control instruction is a vibration start instruction, and the end time of the seismic source vibration time period is the control instruction sending time when the control instruction is a vibration stop instruction. The start time of the roadheader working time period is the control instruction sending time when the control instruction is a roadheader start instruction, and the end time of the roadheader working time period is the control instruction sending time when the control instruction is a roadheader stop instruction.

[0023] In a possible design, it further includes:

[0024] The host computer receives the acquisition parameters and the seismic source trigger parameters set by the user;

[0025] The host computer sends the acquisition parameters to the data acquisition instrument, so as to control the data acquisition instrument to receive the analog seismic signal sent by the signal sensor according to the acquisition parameters when the host computer receives the acquisition start instruction set by the user, and the acquisition parameters include the acquisition frequency and the acquisition duration;

[0026] The host computer sends the vibration parameters to the vibration source trigger, so that the vibration source trigger vibrates according to the vibration parameters.

[0027] In a second aspect, the present application provides a fusion geological prediction system, including: a host computer, a signal sensor, a data acquisition instrument, a vibration source, and a vibration source trigger. The signal sensor is arranged on the side wall of the tunnel. The host computer is communicatively connected to the data acquisition instrument and the vibration source trigger. The data acquisition instrument is communicatively connected to the signal sensor. The vibration source trigger is communicatively connected to the vibration source. The host computer is used to control the vibration source through the vibration source trigger. The host computer is used for:

[0028] Receiving multiple groups of seismic data sent by the data acquisition instrument and the acquisition time of each group of the seismic data. The seismic data is obtained by the data acquisition instrument converting the analog seismic signal collected by the signal sensor, and the analog seismic signal is the seismic signal propagating in the tunnel surrounding rock;

[0029] Determining the data type of the seismic data according to the acquisition time, the pre-recorded working time period of the roadheader and the vibration time period of the vibration source. The data type is used to indicate whether the seismic data is collected when the roadheader is working and / or when the vibration source is vibrating;

[0030] Respectively extracting microseismic analysis data, active source analysis data and passive source analysis data from the multiple groups of seismic data according to the data type. The microseismic analysis data includes the active source analysis data and the passive source analysis data. The active source analysis data is the data collected when the vibration source vibrates and the roadheader stops working. The passive source analysis data is the data collected when the vibration source stops vibrating and the roadheader is working;

[0031] Generating rock burst risk data according to the microseismic analysis data, and generating geological risk data according to the active source analysis data and the passive source analysis data;

[0032] Reconstructing a three-dimensional visual image according to the rock burst risk data and the geological risk data, and displaying the three-dimensional visual image.

[0033] In a third aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0034] The memory stores computer execution instructions;

[0035] The processor executes the computer-executable instructions stored in the memory to implement the integrated geological prediction method provided in the first aspect of the present application.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the integrated geological prediction method provided in the first aspect of the present application when executed by a processor.

[0037] In a fifth aspect, the present application provides a computer program product including a computer program, which is used to implement the integrated geological prediction method provided in the first aspect of the present application when executed by a processor.

[0038] The present application provides an integrated geological prediction method, system, device, storage medium and program product. The integrated geological prediction method includes: the host computer receives multiple sets of seismic data sent by a data collector and the acquisition time of each set of seismic data; the host computer determines the data type of the seismic data according to the acquisition time, the pre-recorded working time period of the roadheader and the vibration time period of the seismic source; the host computer respectively extracts microseismic analysis data, active source analysis data and passive source analysis data from the multiple sets of seismic data according to the data type; the host computer generates rock burst risk data according to the microseismic analysis data, and generates geological risk data according to the active source analysis data and the passive source analysis data; reconstructs a three-dimensional visual image according to the rock burst risk data and the geological risk data, and displays the three-dimensional visual image. Based on the above method, the following technical effects are achieved: based on the installation method carried by the roadheader, it can simultaneously perform three different seismic data processing and geological interpretation, namely microseismic monitoring, passive source seismic wave method detection and active source seismic wave method detection, which can reduce the hardware cost, improve the work efficiency, and effectively avoid the problems of equipment layout disorder and construction streamline obstruction caused by repeated sensor layout, which seriously affect the construction efficiency and equipment safety. At the same time, based on the host computer, construction personnel can view the collected data and processing results in real time, greatly improving the practicability and effectiveness of the geological forecasting system; using multi-source data and multi-source information for microseismic monitoring, active source seismic wave method detection and passive source seismic wave method detection result analysis, the advantages of different detection methods can be fully utilized to make up for the deficiencies of a single method and improve the accuracy of rock burst early warning and bad geological structure forecasting; the acceleration sensor adopts a wall-mounted installation design, which does not require drilling construction, protects the integrity of the building structure and simplifies the installation process, and the overall operation is convenient and efficient; a wireless network communication device such as a wireless router is used to realize wireless communication and data transmission between the host computer, the data collector and the seismic source trigger, and the device networking can be completed without manual wiring, and at the same time, remote data transmission and real-time communication functions are supported. Description of the Drawings

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the attached drawings required for use in the description of the embodiments or the prior art. Obviously, the attached drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other attached drawings can also be obtained based on these attached drawings.

[0040] Figure 1 Schematic diagram of the application scenario of the integrated geological prediction method provided by the embodiment of the present application;

[0041] Figure 2 Flow schematic of the integrated geological prediction method provided by the embodiment of the present application Figure 1 ;

[0042] Figure 3 Schematic diagram of the installation of the acceleration sensor provided by the embodiment of the present application;

[0043] Figure 4 Flow schematic of the integrated geological prediction method provided by the embodiment of the present application Figure 2 ;

[0044] Figure 5 Flow schematic of the integrated geological prediction method provided by the embodiment of the present application Figure 3 ;

[0045] Figure 6 Flow schematic of the integrated geological prediction method provided by the embodiment of the present application Figure 4 ;

[0046] Figure 7 Schematic diagram of the structure of the host computer provided by the embodiment of the present application;

[0047] Figure 8 Schematic diagram of the structure of the electronic device provided by the embodiment of the present application.

[0048] Explanation of reference numerals:

[0049] 100 - device; 110 - seismic source; 111 - seismic source trigger; 112 - wireless network communication device; 113 - host computer; 114 - data acquisition instrument; 115 - signal sensor; 116 - bad geology; 117 - microseismic event; 118 - first acceleration sensor; 119 - second acceleration sensor; 120 - third acceleration sensor; 121 - fourth acceleration sensor; 122 - tunnel; 801 - processor; 802 - memory; 803 - communication component; 804 - bus. Detailed implementation manners

[0050] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0051] In the embodiments of the present application, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and effects. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and "first", "second", etc. do not necessarily mean different. It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner. In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more.

[0052] First, relevant concepts or terms involved in the present application are explained:

[0053] Face: The forefront working surface in mine, tunnel or underground construction operations. The face in mine operations refers to the most front-end working surface in the mine, which is the main working area for mining workers. Workers carry out drilling and blasting operations at the face and continuously advance forward to obtain ore resources; the face in tunnel construction refers to the area at the forefront of tunnel excavation. Tunnel excavation usually starts from the face and advances the tunneling work through blasting and mechanical excavation, etc.

[0054] To clearly understand the technical solution of the present application, the solutions of the prior art are first introduced.

[0055] In the prior art, the rock burst risk warning and the adverse geological risk warning are achieved through two sets of independent hardware systems. Such a design not only lacks integration with the electromechanical system of the roadheader, but also squeezes the limited working space due to the repeated arrangement of sensors, resulting in chaotic equipment layout and blocked construction flow lines, forming a technical bottleneck that restricts both safety hazards and construction efficiency.

[0056] In summary, in a construction site with limited space, how to design a solution to the problems in the prior art, such as the disordered equipment layout and blocked construction flow caused by repeated sensor arrangement, which seriously affect the construction efficiency and equipment safety, is an urgent problem to be solved in this application.

[0057] Therefore, in view of the above technical problems existing in the prior art, the embodiments of this application provide a combined geological prediction method, system, device, storage medium and program product, which can be used in the field of tunnel advanced geological prediction technology. The aim is to collect various types of seismic data using a set of hardware devices, thus effectively avoiding the problems of disordered equipment layout and blocked construction flow caused by repeated sensor arrangement, which seriously affect the construction efficiency and equipment safety.

[0058] Next, the application scenario of a combined geological prediction method provided by the embodiments of this application will be introduced. The following application scenarios are only examples, aiming to help those skilled in the art understand the technical content of this application, but it does not mean that the embodiments of this application cannot be used in other devices, systems, environments or scenarios.

[0059] Applied to a device including a host computer, 4 signal sensors, 4 data acquisition instruments, a seismic source, a seismic source trigger and a wireless network communication device. Figure 1 This is a schematic diagram of the application scenario of the combined geological prediction method provided by the embodiments of this application. As Figure 1 shown, device 100 is the device to which the combined geological prediction method provided by the embodiments of this application is applied. The device includes a seismic source 110, a seismic source trigger 111, a wireless network communication device 112, a host computer 113, 4 data acquisition instruments 114, and signal sensors 115 installed on each data acquisition instrument 114. The working face of device 100 is the heading face. The combined geological prediction method provided by the embodiments of this application and this device 100 are used for geological interpretation of bad geology 116 and microseismic events 117.

[0060] Next, the embodiments of this application will be introduced in conjunction with the accompanying drawings of the specification.

[0061] Next, the technical solution of this application and how the technical solution of this application solves the above technical problems will be described in detail with specific embodiments. These specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Next, the embodiments of this application will be described in conjunction with the drawings.

[0062] Figure 2 This is the flow diagram of the combined geological prediction method provided by the embodiments of this application Figure 1, this method is applied to a fusion geological prediction system, which includes: a host computer, signal sensors, a data collector, a seismic source, and a seismic source trigger. The signal sensors are arranged on the sidewall of the tunnel. The host computer is communicatively connected to the data collector and the seismic source trigger. The data collector is communicatively connected to the signal sensors. The seismic source trigger is communicatively connected to the seismic source. The host computer is used to control the seismic source through the seismic source trigger. Then, the fusion geological prediction method provided in this embodiment includes the following steps:

[0063] S101. The host computer receives multiple groups of seismic data sent by the data collector and the acquisition time of each group of seismic data.

[0064] In this embodiment, the seismic data is obtained by the data collector converting the analog seismic signals collected by the signal sensors. The analog seismic signals are the seismic signals propagating in the tunnel surrounding rock.

[0065] The host computer is the general term for a workstation and software system that supports multi-task parallelism, and is used to receive the acquisition parameters and acquisition start instructions set by the user, receive the seismic data collected by the data collector, process the seismic data, present early warning information, and monitor the working status and power of the data collector.

[0066] The seismic source is an impact seismic source, a controllable seismic source, or manual hammering, and is used to generate an active seismic source signal in the function of the active source seismic wave method to obtain active source analysis data. The main difference between it and the passive source analysis data lies in the frequency of the seismic wave signal. The former has a wide frequency band, a high main frequency, and a controllable excitation frequency, while the latter has a relatively low main frequency.

[0067] The data collector receives the vibration signals generated by the seismic source. When the intensity of the seismic signal exceeds the acquisition threshold, it performs acquisition according to the acquisition parameters, and at the same time takes the current moment as the trigger zero point and stops acquisition after reaching the acquisition duration.

[0068] The signal sensor can be an acceleration sensor or a velocity sensor. In this embodiment, an acceleration sensor is adopted. The acceleration sensor is a single-component or three-component acceleration sensor, and is installed in a wall-mounted form including an anchor agent and expansion screws. It is arranged in two spatial arrangements along the axial direction of the tunnel, and 4 acceleration sensors are evenly arranged in each arrangement, which are used to receive the seismic signals propagating in the surrounding rock and transmit them to the data collector. Figure 3 It is a schematic diagram of the installation of the acceleration sensor provided in the embodiment of the present application, as Figure 3 shown, the first acceleration sensor 118, the second acceleration sensor 119, the third acceleration sensor 120, and the fourth acceleration sensor 121 are 4 acceleration sensors evenly arranged in the axial direction of the tunnel 122. In this embodiment, each data collector is connected to two acceleration sensors.

[0069] When collecting multiple sets of seismic data, the acceleration sensors are arranged in two spatial arrays along the tunnel axis. The first array is approximately 30 m away from the tunnel face, and the second array is behind the first array, 10 - 30 m away from the first array. Four acceleration sensors are evenly arranged in each array. After the tunneling machine advances 20 m, the second array automatically retracts and moves to a position approximately 30 m away from the tunnel face. The original first array becomes the second array, and they move forward in sequence. The acceleration sensors adopt a wall-mounted installation design, eliminating the need for drilling construction, which not only protects the integrity of the building structure but also simplifies the installation process, making the overall operation convenient and efficient.

[0070] The data acquisition instrument can collect and transmit data for 24 hours. It converts the analog seismic signals received by the acceleration sensors into digital signals for recording and uploads them to the host computer. Through the host computer, the working status and power of the data acquisition instrument can be viewed, and it can be replaced in a timely manner when the power of the data acquisition instrument is insufficient.

[0071] Wireless network communication devices such as wireless routers are used to achieve wireless communication and data transmission between the host computer, the data acquisition instrument, and the seismic source trigger. The device networking can be completed without manual wiring, and at the same time, it supports remote data transmission and real-time communication functions.

[0072] S102. The host computer determines the data type of the seismic data according to the acquisition time, the pre-recorded working time period of the tunneling machine, and the vibration time period of the seismic source.

[0073] In this embodiment, the data type is used to indicate whether the seismic data is collected during the operation of the tunneling machine and / or during the vibration of the seismic source.

[0074] When the tunneling machine is tunneling, the cutter head rotates to break the rock, generating seismic wave signals. At this time, it belongs to the passive source seismic wave method.

[0075] The working time period of the tunneling machine refers to the starting time and the ending time of the tunneling machine's operation, and the vibration time period of the seismic source refers to the starting time and the ending time of the seismic source's vibration.

[0076] If the acquisition time of a certain set of seismic data is entirely within the pre-recorded working time period of the tunneling machine, the data type of this set of seismic data indicates that this set of seismic data is collected during the operation of the tunneling machine; if the acquisition time of a certain set of seismic data is entirely within the pre-recorded vibration time period of the seismic source, the data type of this set of seismic data indicates that this set of seismic data is collected during the vibration of the seismic source; if a part of the acquisition time of a certain set of seismic data is within the pre-recorded working time period of the tunneling machine and the other part is within the pre-recorded vibration time period of the seismic source, the data type of this set of seismic data indicates that this set of seismic data is collected during the operation of the tunneling machine and the vibration of the seismic source.

[0077] S103. The host computer extracts microseismic analysis data, active source analysis data, and passive source analysis data from multiple groups of seismic data according to the data type.

[0078] In this embodiment, the microseismic analysis data includes active source analysis data and passive source analysis data. The active source analysis data is the data collected when the seismic source vibrates and the roadheader stops working, and the passive source analysis data is the data collected when the seismic source stops vibrating and the roadheader works.

[0079] If the data type of a certain group of seismic data indicates that the group of seismic data is collected when the roadheader is working and the seismic source stops vibrating at this time, then this group of seismic data belongs to the passive source analysis data; if the data type of a certain group of seismic data indicates that the group of seismic data is collected when the seismic source vibrates and the roadheader stops working at this time, then this group of seismic data belongs to the active source analysis data; the microseismic analysis data is the sum of the active source analysis data and the passive source analysis data.

[0080] S104. The host computer generates rockburst risk data based on the microseismic analysis data, and generates geological risk data based on the active source analysis data and the passive source analysis data.

[0081] Configure a mechanical seismic source, arrange 8 acceleration sensors in a spatial arrangement plus surface mounting method to receive seismic waves propagating in the tunnel surrounding rock, and realize the recording and transmission of data through a data acquisition instrument, which can meet the needs of collecting data for at least three different systems such as microseismic monitoring, passive source seismic wave method detection, and active source seismic wave method detection. The collected seismic data is transmitted to the host computer in real time based on a wireless network. The preprocessing algorithm in the host computer will automatically classify the seismic data into microseismic analysis data, active source analysis data, and passive source analysis data for data processing of different systems such as microseismic monitoring, passive source seismic wave method detection, and active source seismic wave method detection.

[0082] After obtaining the microseismic analysis data, perform preprocessing such as denoising and filtering on the collected microseismic analysis data, then extract relevant characteristics such as the source location, magnitude, epicenter depth, and source mechanism of microseismic events. By analyzing the source mechanism of microseismic events and through inversion analysis, the active areas of fractures and ruptures and the expansion mode of fractures can be obtained. The rockburst risk analysis model that has been pre-trained can be used to set the rockburst risk threshold, and a rockburst risk is prompted when the threshold is exceeded; regression analysis, probability models, etc. can also be used to establish a prediction model through the relationship between historical data and on-site microseismic analysis data; machine learning models such as support vector machines, decision trees, and neural networks can also be used to train the model using microseismic analysis data, so as to predict the rockburst risk data under different working conditions.

[0083] After obtaining the active source analysis data and the passive source analysis data, the active source analysis data and the passive source analysis data can be fused by means of weighted fusion, Kalman filtering, Bayesian fusion method, fuzzy logic method or machine learning method to obtain the fused data. Similar to the method of generating rockburst risk data based on microseismic analysis data as described above, geological risk data can also be obtained based on the fused data.

[0084] By using multi-source data and multi-source information for microseismic monitoring, active source seismic wave method detection and passive source seismic wave method detection result analysis, the advantages of different detection methods can be fully utilized, the deficiencies of a single method can be made up for, and the accuracy of rockburst early warning and bad geological structure prediction can be improved.

[0085] S105. Reconstruct a three-dimensional visual image based on the rockburst risk data and the geological risk data, and display the three-dimensional visual image.

[0086] After obtaining the rockburst risk data and the geological risk data, data cleaning and data conversion are performed on the two, and then geographical coordinate conversion and three-dimensional space modeling are performed on the preprocessed data. By selecting a suitable visualization tool such as Unity 3D or ParaView to render the data, a three-dimensional visual image can be generated, and the three-dimensional visual image can be displayed through an interactive interface.

[0087] Based on the installation method carried by the roadheader, three different seismic data processing and geological interpretation, namely microseismic monitoring, passive source seismic wave method detection and active source seismic wave method detection, can be carried out simultaneously, effectively avoiding problems such as equipment layout disorder and construction streamline obstruction caused by repeated sensor layout, which seriously affect construction efficiency and equipment safety. At the same time, based on the upper computer, construction personnel can view the collected data and processing results in real time, greatly improving the practicability and effectiveness of the geological prediction system.

[0088] The present application provides a fusion geological prediction method, which includes: the host computer receives multiple groups of seismic data sent by a data collector and the acquisition time of each group of seismic data; the host computer determines the data type of the seismic data according to the acquisition time, the pre-recorded working time period of the roadheader and the vibration time period of the seismic source; the host computer extracts microseismic analysis data, active source analysis data and passive source analysis data from the multiple groups of seismic data respectively according to the data type; the host computer generates rock burst risk data according to the microseismic analysis data, and generates geological risk data according to the active source analysis data and the passive source analysis data; reconstructs a three-dimensional visual image according to the rock burst risk data and the geological risk data, and displays the three-dimensional visual image. Based on the above method, the following technical effects are achieved: based on the installation method carried by the roadheader, three different seismic data processing and geological interpretation, namely microseismic monitoring, passive source seismic wave method detection and active source seismic wave method detection, can be carried out simultaneously, which can reduce the hardware cost, improve the work efficiency, and effectively avoid the problems of disordered equipment layout and blocked construction flow caused by repeated sensor layout, thus seriously affecting the construction efficiency and equipment safety. At the same time, based on the host computer, construction personnel can view the collected data and processing results in real time, greatly improving the practicability and effectiveness of the geological forecasting system; using multi-source data and multi-source information to analyze the results of microseismic monitoring, active source seismic wave method detection and passive source seismic wave method detection, the advantages of different detection methods can be fully utilized, the deficiencies of a single method can be made up for, and the accuracy of rock burst early warning and bad geological structure forecasting can be improved; the acceleration sensor adopts a wall-mounted installation design, without the need for drilling construction, which not only protects the integrity of the building structure but also simplifies the installation process, and the overall operation is convenient and efficient; a wireless network communication device such as a wireless router is used to realize wireless communication and data transmission between the host computer, the data collector and the seismic source trigger, and the equipment networking can be completed without manual wiring, and at the same time, remote data transmission and real-time communication functions are supported.

[0089] Figure 4 Flow schematic of the fusion geological prediction method provided by the embodiment of the present application Figure 2 。This embodiment further explains the fusion geological prediction method on the basis of the above embodiment. Then as Figure 4 shown, S104 includes:

[0090] S201. Generate vibration energy point cloud data according to the microseismic analysis data, and generate rock burst risk data according to the vibration energy of each to-be-mined position point included in the vibration energy point cloud data.

[0091] In this embodiment, the rock burst risk data includes: rock burst risk points, the rock burst risk levels of the rock burst risk points, the rock burst risk levels are positively correlated with the vibration energy, and the rock burst risk points include to-be-mined position points with vibration energy greater than or equal to the energy threshold.

[0092] The host computer receives the microseismic analysis data. First, it conducts data quality verification, and then inputs the microseismic analysis data into the processing system for automated analysis. Through processing procedures such as microseismic event picking, PS wave separation, microseismic event location, and energy calculation, 3D vibration energy point cloud data within a range of 20 meters before and after the tunnel face as the center is generated, completely recording the spatial coordinates and vibration energy of each position point to be excavated. An energy threshold is preset. If the vibration energy of a certain position point to be excavated is greater than or equal to this energy threshold, then this position point to be excavated is a rockburst risk point, the risk level corresponding to this position point to be excavated is the rockburst risk level of this rockburst risk point, and the rockburst risk data is the position points to be excavated whose vibration energy is greater than or equal to this energy threshold, and the risk level corresponding to this position point to be excavated. The rockburst risk level is positively correlated with the vibration energy. The higher the vibration energy of each position point to be excavated, the greater the corresponding risk level.

[0093] The image segmentation algorithm based on the U-Net deep learning network is used to perform block segmentation on the vibration energy point cloud data to obtain subsets of the vibration energy point cloud data. Then, the object detection algorithm based on the Single Shot MultiBox Detector (SSD) traverses the subsets of the vibration energy point cloud data to automatically identify and classify the rockburst risk points and the rockburst risk levels of the rockburst risk points, obtaining the rockburst risk data.

[0094] Vibration energy point cloud data is generated according to the microseismic analysis data. The vibration energy point cloud data includes the vibration energy of each position point to be excavated. By associating the vibration energy with the geological spatial position and combining the rockburst incubation mechanism, high energy density areas, microseismic event dense zones, and areas with abnormal energy release rates can be identified, thereby accurately locating the rockburst risk areas.

[0095] S202. Generate physical property parameter point cloud data according to the active source analysis data and the passive source analysis data, and generate geological risk data according to the physical property parameters of each position point to be excavated included in the physical property parameter point cloud data.

[0096] In this embodiment, the geological risk data includes: geological risk points and the geological risk types of the geological risk points.

[0097] The geological risk types include faults, fracture zones, and karst caves. Different geological risk types correspond to different physical property parameters.

[0098] The image segmentation algorithm based on the U-Net deep learning network is used to block and segment the physical property parameter point cloud data to obtain subsets of the physical property parameter point cloud data. Then, based on the object detection algorithm of the Single Shot MultiBox Detector (SSD), the subsets of the physical property parameter point cloud data are traversed to automatically identify and classify geological risk points and geological risk types such as faults, fracture zones, and karst caves, etc., to obtain geological risk data.

[0099] Figure 5 Flow schematic of the fusion geological prediction method provided by the embodiment of the present application Figure 3 . Based on the above embodiment, this embodiment further explains the fusion geological prediction method. As Figure 5 shown, generating the physical property parameter point cloud data according to the active source analysis data and the passive source analysis data in S202 includes:

[0100] S301. Generate the active source physical property parameter point cloud data according to the active source analysis data, and generate the passive source physical property parameter point cloud data according to the passive source analysis data.

[0101] After the host computer receives the active source analysis data and the passive source analysis data, the active source analysis data and the passive source analysis data are separated according to the data types of the seismic data. The separated active source analysis data is input into the active source analysis data processing system. After operations such as noise removal, direct wave excision, wave field separation, reflected wave extraction, velocity analysis, migration imaging, and geological interpretation, etc., the active source physical property parameter point cloud data within the range of 80 - 100 m in front of the heading face can be obtained, which completely records the spatial coordinates and physical property parameters of each point to be excavated. The physical property parameters of each point to be excavated are used to represent the physical characteristics of the point to be excavated. The separated passive source analysis data is input into the passive source analysis data processing system. After operations such as cross-correlation between channels, multi-channel stacking, noise removal, wave field separation, reflected wave extraction, velocity analysis, migration imaging, and geological interpretation, etc., the passive source physical property parameter point cloud data within the range of 80 - 100 m in front of the heading face can be obtained, which completely records the spatial coordinates and physical property parameters of each point to be excavated. The physical property parameters of each point to be excavated are used to represent the physical characteristics of the point to be excavated.

[0102] S302. Fuse the active source physical property parameter point cloud data and the passive source physical property parameter point cloud data according to the points to be excavated to obtain the physical property parameter point cloud data.

[0103] In this embodiment, the physical property parameters of a point to be excavated in the physical property parameter point cloud data are obtained by fusing the physical property parameters of the point to be excavated in the active source physical property parameter point cloud data and the physical property parameters of the point to be excavated in the passive source physical property parameter point cloud data.

[0104] For the fusion of active-source physical property parameter point cloud data and passive-source physical property parameter point cloud data in this embodiment, methods such as removing the maximum value, removing the minimum value, or taking the average value can be adopted, and no specific limitation is made here.

[0105] By fusing the active-source physical property parameter point cloud data and the passive-source physical property parameter point cloud data according to the points at the positions to be mined, the limitations of a single data source can be broken through, forming a multi-dimensional perception advantage in tunnel geological risk warning, thereby enhancing the complementarity of spatial information.

[0106] Figure 6 It is a schematic flow of the fusion geological prediction method provided by the embodiment of the present application. Figure 4 On the basis of the above embodiment, this embodiment further explains the fusion geological prediction method. As Figure 6 shown, S105 includes:

[0107] S401. Fuse the rock burst risk data and the geological risk data according to the points at the positions to be mined to obtain risk fusion data.

[0108] In this embodiment, the risk fusion data includes: risk position points and risk information of the risk position points. The risk position points are rock burst risk points and / or geological risk points, and the risk information includes the rock burst risk level of the rock burst risk points and / or the geological risk type.

[0109] If a certain point at the position to be mined has both a rock burst risk level and a geological risk type at the same time, then this point at the position to be mined is used as a risk position point including rock burst risk points and geological risk points, and the comprehensive risk value obtained after fusing the rock burst risk level and the geological risk type is used as the risk information of this risk position point. A juxtaposed fusion method can be adopted, that is, directly juxtaposing the rock burst risk level and the geological risk type. For example: "Rock burst risk level: high, geological risk type: karst cave"; or a weighted fusion method can be adopted, that is, weighting according to the severity of different geological risk types to obtain a comprehensive risk value. For example, the weight of the rock burst risk level is larger and the weight of the geological risk type is smaller, and a comprehensive risk value is obtained after fusion. The present application does not make specific limitations on the fusion method of the rock burst risk level and the geological risk type.

[0110] If a certain point at the position to be mined only has a rock burst risk level, then this point at the position to be mined is used as a risk position point including rock burst risk points, and the rock burst risk level is used as the risk information of this risk position point; if a certain point at the position to be mined only has a geological risk type, then this point at the position to be mined is used as a risk position point including geological risk points, and the geological risk type is used as the risk information of this risk position point.

[0111] S402. For each risk location point, generate a risk description image and a text label for the risk location point according to the risk information.

[0112] In this embodiment, the text label is used to describe the risk information in words, and the risk description image is an image generated by fusing the rockburst risk level and / or geological risk type included in the risk information.

[0113] For a risk location point including a rockburst risk point and / or a geological risk point, perform three-dimensional visualization processing on the risk information of the risk location point according to the digital twin technology to obtain a risk description image and a text label for the risk location point. The text label is used to describe the rockburst risk level and / or geological risk type of the risk location point in words, and the risk description image is used to vividly and vividly display the comprehensive risk value obtained after fusing the rockburst risk level and / or geological risk type. Describing the risk information through images, for example, a fault image or a karst cave image can be generated.

[0114] S403. Generate a three-dimensional visual image according to the risk location point, the risk description image and the text label.

[0115] There are preset trigger conditions for the rockburst risk level, the geological risk type, or the comprehensive risk value obtained after fusing the rockburst risk level and the geological risk type. During the process of collecting seismic data, it is judged in real time. When the system determines that the trigger condition is activated, that is, an early warning needs to be triggered, a pop-up window will automatically appear on the upper computer display screen to prompt the corresponding early warning information, such as the type of early warning event, the occurrence location, the expected impact degree, and the recommended measures to be taken, etc.

[0116] For each risk location point, obtain a risk description image and a text label for the risk location point based on multiple automated algorithms, so as to realize the intuitive display and automatic early warning functions of the rockburst risk level and the geological risk type, and enable non-geophysical exploration professionals to timely understand the geological conditions in the tunnel, greatly improving the practicability and effectiveness of the system.

[0117] The embodiment of the present application provides a fusion geological prediction method, which further explains the fusion geological prediction method provided in the above embodiment. The fusion geological prediction method provided in the embodiment of the present application includes:

[0118] S501. When the host computer sends control instructions to the seismic source trigger and the roadheader, it records the seismic source vibration time period and the roadheader working time period according to the control instructions. The start time of the seismic source vibration time period is the control instruction sending time when the control instruction is the vibration start instruction, and the end time of the seismic source vibration time period is the control instruction sending time when the control instruction is the vibration stop instruction. The start time of the roadheader working time period is the control instruction sending time when the control instruction is the roadheader start instruction, and the end time of the roadheader working time period is the control instruction sending time when the control instruction is the roadheader stop instruction.

[0119] In this embodiment, when the host computer sends a vibration start instruction to the seismic source trigger, the seismic source trigger starts to work. When the host computer sends a vibration stop instruction to the seismic source trigger, the seismic source trigger stops working. The time period from when the seismic source trigger starts to work to when it stops working is the seismic source vibration time period. When the host computer sends a roadheader start instruction to the roadheader, the roadheader starts to work. When the host computer sends a roadheader stop instruction to the roadheader, the roadheader stops working. The time period from when the roadheader starts to work to when it stops working is the roadheader working time period. By precisely defining the seismic source vibration time period and the roadheader working time period, the independent working time sequences of the seismic source trigger and the roadheader can be clarified: the seismic source trigger forms an active seismic source excitation period based on the vibration start instruction and the vibration stop instruction, and the roadheader forms a passive seismic source excitation period based on the roadheader start instruction and the roadheader stop instruction. This time sequence isolation mechanism first provides a spatio-temporal reference for the type determination of seismic data, and at the same time facilitates the management and monitoring of the seismic source trigger and the roadheader.

[0120] The embodiment of the present application provides a fusion geological prediction method to further explain the fusion geological prediction method provided in the above embodiment. The fusion geological prediction method provided in the embodiment of the present application includes:

[0121] S601. The host computer receives the acquisition parameters and the seismic source trigger parameters set by the user.

[0122] The user sets the acquisition parameters and the seismic source trigger parameters through the host computer display screen.

[0123] S602. The host computer sends the acquisition parameters to the data acquisition instrument to control the data acquisition instrument to receive the analog seismic signals sent by the signal sensor according to the acquisition parameters when the host computer receives the acquisition start instruction set by the user.

[0124] In this embodiment, the acquisition parameters include the acquisition frequency and the acquisition duration. Optionally, the acquisition parameters further include an acquisition threshold. When the intensity of the seismic signal exceeds the acquisition threshold, the data acquisition instrument and the seismic source trigger start to acquire seismic data.

[0125] After receiving the set acquisition parameters, the host computer wakes up the data acquisition instrument and checks the working status and power of the data acquisition instrument. When the data acquisition instrument is in the working state and has sufficient power, the host computer sends the acquisition parameters to the data acquisition instrument through the wireless network communication device. The host computer transmits the acquisition start instruction to the data acquisition instrument through the wireless network communication device. When the data acquisition instrument receives the acquisition start instruction and the intensity of the seismic signal exceeds the acquisition threshold, it receives the analog seismic signal sent by the signal sensor according to the acquisition parameters, converts it into a digital signal for recording, and finally uploads the recorded seismic data to the host computer through the wireless network communication device.

[0126] S603. The host computer sends the vibration parameters to the seismic source trigger so that the seismic source trigger vibrates according to the vibration parameters.

[0127] After receiving the set vibration parameters, the host computer sends the vibration parameters to the seismic source trigger through the wireless network communication device. When receiving the acquisition start instruction and the intensity of the seismic signal exceeds the acquisition threshold, the seismic source trigger starts to vibrate according to the vibration parameters.

[0128] Collect seismic data according to the set acquisition parameters and vibration parameters, and match the optimal acquisition mode for different physical quantities such as voltage and pulse frequency, so as to improve the accuracy of seismic data acquisition.

[0129] An embodiment of the present application provides a fusion geological prediction system, including: a host computer, a signal sensor, a data acquisition instrument, a seismic source, and a seismic source trigger. The signal sensor is arranged on the side wall of the tunnel. The host computer is communicatively connected to the data acquisition instrument and the seismic source trigger. The data acquisition instrument is communicatively connected to the signal sensor. The seismic source trigger is communicatively connected to the seismic source. The host computer is used to control the seismic source through the seismic source trigger. Figure 7 It is a schematic structural diagram of the host computer provided by an embodiment of the present application. The host computer includes:

[0130] A data receiving module 701, configured to receive multiple groups of seismic data sent by the data acquisition instrument and the acquisition time of each group of seismic data. The seismic data is obtained by the data acquisition instrument converting the analog seismic signal collected by the signal sensor. The analog seismic signal is the seismic signal propagating in the tunnel surrounding rock;

[0131] A data type determination module 702, configured to determine the data type of the seismic data according to the acquisition time, the pre-recorded working time period of the roadheader, and the vibration time period of the seismic source. The data type is used to indicate whether the seismic data is collected when the roadheader is working and / or when the seismic source is vibrating;

[0132] A data extraction module 703 is configured to separately extract microseismic analysis data, active source analysis data, and passive source analysis data from multiple groups of seismic data according to the data type. The microseismic analysis data includes the active source analysis data and the passive source analysis data. The active source analysis data is the data collected when the seismic source vibrates and the tunneling machine stops working, and the passive source analysis data is the data collected when the seismic source stops vibrating and the tunneling machine is working.

[0133] A risk data generation module 704 is configured to generate rockburst risk data according to the microseismic analysis data, and generate geological risk data according to the active source analysis data and the passive source analysis data.

[0134] A three-dimensional visual image reconstruction module 705 is configured to reconstruct a three-dimensional visual image according to the rockburst risk data and the geological risk data, and display the three-dimensional visual image.

[0135] The integrated geological prediction system provided in this embodiment can execute Figure 2 the technical solution of the integrated geological prediction method embodiment shown in Figure 2 The implementation principle and technical effect are similar to those of the integrated geological prediction method embodiment shown in

[0136] and will not be elaborated here one by one.

[0137] Optionally, in this embodiment, when the risk data generation module 704 generates rockburst risk data according to the microseismic analysis data, and generates geological risk data according to the active source analysis data and the passive source analysis data, it generates vibration energy point cloud data according to the microseismic analysis data, and generates rockburst risk data according to the vibration energy of each to-be-excavated position point included in the vibration energy point cloud data. The rockburst risk data includes: rockburst risk points and the rockburst risk levels of the rockburst risk points. The rockburst risk level is positively correlated with the vibration energy. The rockburst risk points include the to-be-excavated position points with vibration energy greater than or equal to the energy threshold.

[0138] It generates physical property parameter point cloud data according to the active source analysis data and the passive source analysis data, and generates geological risk data according to the physical property parameters of each to-be-excavated position point included in the physical property parameter point cloud data. The geological risk data includes: geological risk points and the geological risk types of the geological risk points.

[0139] Optionally, in this embodiment, when the risk data generation module 704 generates physical property parameter point cloud data according to the active source analysis data and the passive source analysis data, it generates active source physical property parameter point cloud data according to the active source analysis data, and generates passive source physical property parameter point cloud data according to the passive source analysis data.

[0140] Fuse the active source physical property parameter point cloud data and the passive source physical property parameter point cloud data according to the points at the positions to be mined to obtain physical property parameter point cloud data. The physical property parameter of a point at the position to be mined in the physical property parameter point cloud data is obtained by fusing the physical property parameter of the point at the position to be mined in the active source physical property parameter point cloud data and the physical property parameter of the point at the position to be mined in the passive source physical property parameter point cloud data.

[0141] Optionally, in this embodiment, when reconstructing the three-dimensional visual image according to the rock burst risk data and the geological risk data, the three-dimensional visual image reconstruction module 705 fuses the rock burst risk data and the geological risk data according to the points at the positions to be mined to obtain risk fusion data. The risk fusion data includes: risk position points and risk information of the risk position points. The risk position points are rock burst risk points and / or geological risk points, and the risk information includes the rock burst risk level of the rock burst risk points and / or the geological risk type;

[0142] For each risk position point, generate a risk description image and a text label of the risk position point according to the risk information. The text label is used to describe the risk information in words, and the risk description image is an image generated by fusing the rock burst risk level and / or the geological risk type included in the risk information;

[0143] Generate a three-dimensional visual image according to the risk position points, the risk description images and the text labels.

[0144] Optionally, in this embodiment, it further includes: when the host computer sends control instructions to the seismic source trigger and the roadheader, record the seismic source vibration time period and the roadheader working time period according to the control instructions. The start time of the seismic source vibration time period is the control instruction sending time when the control instruction is a vibration start instruction, and the end time of the seismic source vibration time period is the control instruction sending time when the control instruction is a vibration stop instruction. The start time of the roadheader working time period is the control instruction sending time when the control instruction is a roadheader start instruction, and the end time of the roadheader working time period is the control instruction sending time when the control instruction is a roadheader stop instruction.

[0145] Optionally, in this embodiment, it further includes: the host computer receives the acquisition parameters and the seismic source trigger parameters set by the user;

[0146] The host computer sends the acquisition parameters to the data acquisition instrument to control the data acquisition instrument to receive the analog seismic signals sent by the signal sensor according to the acquisition parameters when the host computer receives the acquisition start instruction set by the user. The acquisition parameters include the acquisition frequency and the acquisition duration;

[0147] The host computer sends the vibration parameters to the seismic source trigger to make the seismic source trigger vibrate according to the vibration parameters.

[0148] The integrated geological prediction system provided in this embodiment can execute the technical solutions of the above-mentioned integrated geological prediction method embodiment. Its implementation principle and technical effects are similar to those of the above-mentioned integrated geological prediction method embodiment, and will not be elaborated here one by one.

[0149] Figure 8 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. This electronic device is intended for various electronic devices that can execute the integrated geological prediction method, such as a microcomputer, a single-chip microcomputer, and other suitable computers. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0150] As Figure 8 shown, the electronic device includes: at least one processor 801 and a memory 802. The electronic device also includes a communication component 803. Among them, the processor 801, the memory 802, and the communication component 803 are connected through a bus 804.

[0151] In the specific implementation process, at least one processor 801 executes the computer execution instructions stored in the memory 802, so that at least one processor 801 executes the integrated geological prediction method executed on the electronic device side as above.

[0152] For the specific implementation process of the processor 801, reference can be made to the above-mentioned integrated geological prediction method embodiment. Its implementation principle and technical effects are similar, and will not be elaborated here in this embodiment.

[0153] In the above embodiment, it should be understood that the processor 801 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), etc. The general-purpose processor 801 may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0154] The memory 802 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory.

[0155] The bus 804 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 804 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus 804 in the drawings of the present application is not limited to only one bus or one type of bus.

[0156] The functions implemented for the electronic device and the master device are described above for the solution provided by the embodiments of the present application. It can be understood that in order for the electronic device or the master device to implement the above functions, it includes the corresponding hardware structure and / or software module for executing each function. Combining the units and algorithm steps of each example described in the embodiments disclosed in the embodiments of the present application, the embodiments of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiments of the present application.

[0157] The present application also provides a computer-readable storage medium storing computer-executable instructions, and when the processor executes the computer-executable instructions, the above-mentioned geological prediction method is implemented.

[0158] For the above-mentioned computer-readable storage medium, the above-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disc. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0159] An exemplary readable storage medium is coupled to the processor, enabling the processor to read information from the readable storage medium and write information to the readable storage medium. The readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an Application Specific Integrated Circuit (ASIC). The processor and the readable storage medium can also exist as discrete components in an electronic device or a master device.

[0160] The memory 802 is the non-transitory computer-readable storage medium provided by the present invention. The non-transitory computer-readable storage medium of the present invention stores computer instructions, and the computer instructions are used to cause a computer to execute the integrated geological prediction method provided by the present invention.

[0161] As a non-transitory computer-readable storage medium, the memory 802 can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the integrated geological prediction method in the embodiments of the present application (for example, Figure 7 the data receiving module 701, the data type determination module 702, the data extraction module 703, the risk data generation module 704, and the three-dimensional visual image reconstruction module 705 shown). By running the non-transitory software programs, instructions, and modules stored in the memory 802, the processor 801 executes various functional applications and data processing, that is, implements the integrated geological prediction method in the above method embodiments.

[0162] Meanwhile, the present embodiment also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it is used to implement the integrated geological prediction method in the above embodiments.

[0163] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards, and corresponding operation entrances are provided for users to select authorization or rejection.

[0164] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0165] It should be further noted that although the steps in the flowchart are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in rotation with at least a part of other steps or sub-steps or stages of other steps.

[0166] It should be understood that the above device embodiments are illustrative only, and the devices of the present application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0167] In addition, unless otherwise specified, in each embodiment of the present application, each functional unit / module can be integrated in one unit / module, or each unit / module can exist physically alone, or two or more units / modules can be integrated together. The above integrated unit / module can be implemented in the form of hardware or in the form of a software program module.

[0168] When the integrated unit / module is implemented in the form of hardware, the hardware can be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0169] When the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of this application. And the aforementioned memory includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM), random access memories (RAM), mobile hard disks, magnetic disks, or optical discs.

[0170] In the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0171] Those skilled in the art will readily think of other implementation schemes of this application after considering the specification and practicing the invention disclosed herein. This application aims to cover any variations, uses, or adaptive changes of this application, and these variations, uses, or adaptive changes follow the general principles of this application and include the common general knowledge or conventional technical means in this technical field that are not disclosed in this application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of this application are pointed out by the following claims.

[0172] It should be understood that this application is not limited to the exact structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is only limited by the appended claims.

Claims

1. A geological prediction method for fusion, characterized in that, Applied to a fusion geological prediction system, the fusion geological prediction system includes: a host computer, a signal sensor, a data collector, a seismic source, and a seismic source trigger. The signal sensor is arranged on the side wall of the tunnel. The host computer is communicatively connected to the data collector and the seismic source trigger. The data collector is communicatively connected to the signal sensor. The seismic source trigger is communicatively connected to the seismic source. The host computer is used to control the seismic source through the seismic source trigger. The method includes: The host computer receives multiple groups of seismic data sent by the data collector and the acquisition time of each group of the seismic data. The seismic data is obtained by the data collector converting the analog seismic signals collected by the signal sensor. The analog seismic signals are seismic signals propagating in the tunnel surrounding rock; The host computer determines the data type of the seismic data according to the acquisition time, the pre-recorded working time period of the roadheader and the seismic source vibration time period. The data type is used to indicate whether the seismic data is collected when the roadheader is working and / or when the seismic source is vibrating; The host computer respectively extracts microseismic analysis data, active source analysis data, and passive source analysis data from the multiple groups of seismic data according to the data type. The microseismic analysis data includes the active source analysis data and the passive source analysis data. The active source analysis data is the data collected when the seismic source is vibrating and the roadheader stops working. The passive source analysis data is the data collected when the seismic source stops vibrating and the roadheader is working; The host computer generates rockburst risk data according to the microseismic analysis data, and generates geological risk data according to the active source analysis data and the passive source analysis data; Reconstruct a three-dimensional visual image according to the rockburst risk data and the geological risk data, and display the three-dimensional visual image.

2. The integrated geological prediction method according to claim 1, wherein The generating the rockburst risk data according to the microseismic analysis data, and generating the geological risk data according to the active source analysis data and the passive source analysis data includes: Generating vibration energy point cloud data according to the microseismic analysis data, and generating rockburst risk data according to the vibration energy of each excavation position point included in the vibration energy point cloud data. The rockburst risk data includes: rockburst risk points, the rockburst risk levels of the rockburst risk points. The rockburst risk level is positively correlated with the vibration energy. The rockburst risk points include the excavation position points with vibration energy greater than or equal to the energy threshold; Generating physical property parameter point cloud data according to the active source analysis data and the passive source analysis data, and generating geological risk data according to the physical property parameters of each excavation position point included in the physical property parameter point cloud data. The geological risk data includes: geological risk points and the geological risk types of the geological risk points.

3. The integrated geological prediction method according to claim 2, wherein, The generating the physical property parameter point cloud data according to the active source analysis data and the passive source analysis data includes: Generating active source physical property parameter point cloud data according to the active source analysis data, and generating passive source physical property parameter point cloud data according to the passive source analysis data; Fuse the active source physical property parameter point cloud data and the passive source physical property parameter point cloud data according to the points at the positions to be mined to obtain the physical property parameter point cloud data. The physical property parameter of a point at the position to be mined in the physical property parameter point cloud data is obtained by fusing the physical property parameter of the point at the position to be mined in the active source physical property parameter point cloud data and the physical property parameter of the point at the position to be mined in the passive source physical property parameter point cloud data.

4. The integrated geological prediction method according to any one of claims 1 to 3, characterized in that Reconstructing a three-dimensional visual image based on the rock burst risk data and the geological risk data includes: Fuse the rock burst risk data and the geological risk data according to the points at the positions to be mined to obtain risk fusion data. The risk fusion data includes: risk position points and risk information of the risk position points. The risk position points are the rock burst risk points and / or the geological risk points, and the risk information includes the rock burst risk level of the rock burst risk points and / or the geological risk types; For each of the risk position points, generate a risk description image and a text label of the risk position point according to the risk information. The text label is used to describe the risk information in words, and the risk description image is an image generated by fusing the rock burst risk level and / or the geological risk types included in the risk information; Generate a three-dimensional visual image according to the risk position points, the risk description images and the text labels.

5. The fusion geological prediction method according to any one of claims 1 to 3, characterized in that, Further included: When the host computer sends control commands to the shock source trigger and the roadheader, record the shock source vibration time period and the roadheader working time period according to the control commands. The start time of the shock source vibration time period is the control command sending time when the control command is a vibration start command, and the end time of the shock source vibration time period is the control command sending time when the control command is a vibration stop command. The start time of the roadheader working time period is the control command sending time when the control command is a roadheader start command, and the end time of the roadheader working time period is the control command sending time when the control command is a roadheader stop command.

6. The integrated geological prediction method according to claim 1, wherein Further included: The host computer receives the acquisition parameters and the shock source trigger parameters set by the user; The host computer sends the acquisition parameters to the data acquisition instrument to control the data acquisition instrument to receive the analog seismic signals sent by the signal sensor according to the acquisition parameters when the host computer receives the acquisition start command set by the user. The acquisition parameters include the acquisition frequency and the acquisition duration; The host computer sends the vibration parameters to the shock source trigger to enable the shock source trigger to vibrate according to the vibration parameters.

7. A geological prediction integration system, characterized in that, Including: A host computer, a signal sensor, a data acquisition instrument, a shock source, and a shock source trigger. The signal sensor is arranged on the tunnel sidewall. The host computer is communicatively connected to the data acquisition instrument and the shock source trigger. The data acquisition instrument is communicatively connected to the signal sensor. The shock source trigger is communicatively connected to the shock source. The host computer is used to control the shock source through the shock source trigger. The host computer is used for: Receiving multiple groups of seismic data sent by the data acquisition instrument and the acquisition time of each group of the seismic data, where the seismic data is obtained by the data acquisition instrument through converting the analog seismic signals collected by the signal sensor, and the analog seismic signals are seismic signals propagating in the tunnel surrounding rock; Determining the data type of the seismic data according to the acquisition time, the pre-recorded working time period of the roadheader and the vibration time period of the seismic source, where the data type is used to indicate whether the seismic data is collected during the operation of the roadheader and / or during the vibration of the seismic source; Respectively extracting microseismic analysis data, active source analysis data and passive source analysis data from the multiple groups of seismic data according to the data type, where the microseismic analysis data includes the active source analysis data and the passive source analysis data, the active source analysis data is the data collected when the seismic source vibrates and the roadheader stops working, and the passive source analysis data is the data collected when the seismic source stops vibrating and the roadheader is working; Generating rockburst risk data according to the microseismic analysis data, and generating geological risk data according to the active source analysis data and the passive source analysis data; Reconstructing a three-dimensional visual image according to the rockburst risk data and the geological risk data, and displaying the three-dimensional visual image.

8. An electronic device, characterized in that, Comprising: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the integrated geological prediction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, they are used to implement the integrated geological prediction method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it is used to implement the integrated geological prediction method according to any one of claims 1 to 6.

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