Underground monitoring management method and system based on data twinning

Abstract

The invention provides an underground monitoring management method and system based on data twinning, and relates to the intelligent monitoring technology, and the method comprises the steps: extracting working condition basic data, and determining an underground reality system; carrying out twinborn modeling based on the working condition basic data and an underground reality system by combining a twinborn modeling rule to generate an underground twinborn model which comprises an edge twinborn body and a holder twinborn body; working condition monitoring is synchronously carried out, underground working condition data is transmitted back based on a classified data transmission channel, and collaborative management and control of edge decision and holder central control decision are carried out; response monitoring based on collaborative management is carried out, feedback management and control data are returned, and management and control deviation data are metered; and if the management and control deviation data does not meet the deviation tolerance interval, underground feedback compensation management and control are executed. The technical problems that the working condition supervision difficulty is large and the supervision precision is low due to the fact that the underground working condition is complex can be solved, the underground working condition supervision accuracy can be improved, and safe and stable operation of underground operation is guaranteed.

Description

Technical Field

[0001] The present disclosure relates to intelligent monitoring technology, and more specifically, to a downhole monitoring management method and system based on data twins. Background Art

[0002] Mine mining safety is the core element of production. It is not only related to the stability of mine production, but also to the safety of mining workers and mine equipment. Mine safety monitoring and management refers to the real-time monitoring and supervision of the underground construction conditions during the mining process to ensure the safety of mine production.

[0003] At present, due to the harsh underground construction environment and complex geological conditions, conventional underground supervision methods are difficult to achieve comprehensive monitoring and control of underground operations, resulting in low accuracy of underground operation supervision and an adverse impact on underground safety operations.

[0004] The shortcomings of the existing underground monitoring and management methods are that the underground working conditions are relatively complex, which makes it difficult to supervise the working conditions, resulting in low accuracy in underground working condition supervision. Summary of the invention

[0005] Therefore, in order to solve the above technical problems, the technical solutions adopted by the embodiments of the present disclosure are as follows: The method for downhole monitoring and management based on data twins comprises the following steps: reading downhole working condition planning, identifying and extracting basic working condition data after executing credible priors, and determining the downhole reality system; setting twin modeling rules, wherein the twin modeling rules include edge twin rules and pan-tilt twin rules; connecting to a visual simulation platform, combining the twin modeling rules, performing twin modeling based on the basic working condition data and the downhole reality system, and generating a downhole twin model, wherein the downhole twin model comprises an edge twin and a pan-tilt twin interactively connected based on a network protocol; configuring a classified data transmission channel between the edge twin and the pan-tilt twin; combining with a distributedly configured sensor monitoring device, synchronously performing working condition monitoring and returning downhole working condition data based on the classified data transmission channel, and performing coordinated management and control of edge decisions and pan-tilt central control decisions; performing response monitoring based on collaborative management, returning feedback control and control data to the downhole twin model and measuring control and control deviation data; and performing downhole feedback compensation control and control if the control and control deviation data does not meet the deviation tolerance interval.

[0006] The underground monitoring and management system based on data twins includes: a basic working condition data extraction module, which is used to read the underground working condition planning, identify and extract the basic working condition data after executing the trusted prior, and determine the underground reality system; a twin modeling rule setting module, which is used to set the twin modeling rules, and the twin modeling rules include edge twin rules and pan-tilt twin rules; an underground twin model generation module, which is used to connect to the visual simulation platform, combine the twin modeling rules, and perform twin modeling based on the basic working condition data and the underground reality system to generate an underground twin model, wherein the underground twin model includes an edge twin and a pan-tilt twin that are interactively connected based on a network protocol. ; A classified data transmission channel configuration module, which is used to configure the classified data transmission channels of the edge twin and the pan-tilt twin; a decision-making collaborative management and control module, which is used to combine the distributed configuration of the sensor monitoring device, synchronously perform working condition monitoring and return the downhole working condition data based on the classified data transmission channel, and perform collaborative management and control of edge decisions and pan-tilt central control decisions; a control deviation data metering module, which is used to perform response monitoring based on collaborative management, return feedback control data to the downhole twin model and measure the control deviation data; a feedback compensation control module, which is used to perform downhole feedback compensation control if the control deviation data does not meet the deviation tolerance interval.

[0007] Due to the adoption of the above technical method, the present disclosure has achieved the following technical advances compared with the prior art: (1) It can solve the technical problem in the prior art that the working condition supervision is difficult due to the complexity of the underground working condition, resulting in low accuracy of the underground working condition supervision. First, the underground working condition planning is obtained, the basic working condition data after the execution of the credible prior is identified and extracted, and the underground real system is determined; the twin modeling rules are set, and the twin modeling rules include the edge twin rule and the pan-tilt twin rule; then, in the visual simulation platform, the twin modeling rules are combined to perform twin modeling based on the basic working condition data and the underground real system to generate an underground twin model, wherein the underground twin model includes It includes an edge twin and a pan-tilt twin that are interactively connected based on a network protocol; and a classified data transmission channel is configured between the edge twin and the pan-tilt twin; combined with a distributed sensor monitoring device, the working condition monitoring is performed synchronously and the downhole working condition data is transmitted back based on the classified data transmission channel, and the edge decision and the pan-tilt central control decision are coordinated and managed; response monitoring is performed based on the coordinated management and control, and feedback control data is transmitted back to the downhole twin model, and the control deviation data is calculated based on the downhole twin model; when the control deviation data does not meet the deviation tolerance interval, downhole feedback compensation control is performed. The above method can improve the accuracy of downhole working condition data judgment, thereby improving the accuracy and quality of downhole working condition supervision, and ensuring the safe and stable operation of downhole operations.

[0008] (2) By building edge twins and gimbal twins to coordinate the management of edge decisions and gimbal central control decisions, computing resources can be rationally utilized, the speed of analysis and processing of returned data can be improved, and the efficiency and accuracy of underground working condition supervision can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for describing the embodiments are briefly introduced below.

[0010] Figure 1 A flow chart of a downhole monitoring management method based on data twin is provided for this application; Figure 2 A schematic diagram of a process for setting twin modeling rules in a downhole monitoring management method based on data twins is provided for this application; Figure 3 A structural schematic diagram of a downhole monitoring and management system based on data twin is provided for this application.

[0011] Explanation of the accompanying drawings: basic data extraction module for working conditions 01, twin modeling rule setting module 02, downhole twin model generation module 03, classification data transmission channel configuration module 04, decision-making collaborative management and control module 05, management and control deviation data measurement module 06, feedback compensation management and control module 07. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0013] Based on the above description, if Figure 1 As shown, the present disclosure provides a downhole monitoring management method based on data twins, comprising: Mine safety monitoring and management refers to the real-time monitoring and supervision of the underground construction conditions during the mining process to ensure the safety of mine production. The method provided in this application uses digital twin technology to build an underground twin model, and monitors and manages the underground operations of the mine based on the underground twin model, which can improve the accuracy and quality of underground working condition supervision and ensure the safe and stable operation of underground operations. The method is specifically implemented in an underground monitoring and management system based on data twins.

[0014] Read the underground working condition plan, identify and extract the basic working condition data after executing the credible priori, and determine the underground actual system; In the embodiment of the present application, first, the downhole working condition plan is read, and the downhole working condition plan includes information such as expected downhole operation specifications, operation processes, and equipment working status. Those skilled in the art can set it according to the actual downhole operation plan. Then, the target working condition data is obtained according to the downhole working condition plan, and the credibility of the target working condition data is verified and compensated to improve the accuracy of the working condition data and obtain the basic working condition data. The downhole reality system is determined based on the downhole equipment control system and the risk control management system, wherein the downhole reality system includes multiple entities performing downhole operations.

[0015] In one embodiment, the method further comprises: Reading target operating condition data based on the downhole operating condition planning; Conduct industrial big data retrieval, determine auxiliary reference data under the same source working conditions, and fit and determine universal working condition data; In combination with a trusted verification channel, the credibility verification and compensation of the target working condition data and the universal working condition data are performed to determine the working condition basic data; The equipment control system and risk control management system for interactive underground operations serve as the underground reality system.

[0016] In an embodiment of the present application, first, target operating condition data is extracted based on the downhole operating condition planning, and the target operating condition data is expected operating condition data, including expected downhole operation tasks, expected downhole operation processes, expected downhole equipment operating status and other data. Then, based on the industrial big data technology, underground operation information is retrieved to obtain operating condition data under multiple homologous working conditions as auxiliary reference data, wherein the homologous working conditions are underground operation conditions that are similar to the current underground working conditions in terms of mining environment, mining type, and mining scale. Industrial big data technology is a method and technology for mining valuable data contained in massive industrial data. Using industrial big data technology for information retrieval can improve the accuracy and practicality of retrieved data; then, the auxiliary reference data under multiple homologous working conditions are fitted to generate universal operating condition data, wherein the universal operating condition data is more comprehensive than the target operating condition data, and can supplement the data of multiple operating links in the target operating condition data; wherein the fitting method can be set by technicians in this field based on actual conditions, for example: by obtaining the mean data of multiple auxiliary reference data, and using the mean data as the universal operating condition data; or using the data with the highest frequency of occurrence in multiple auxiliary reference data as the universal operating condition data.

[0017] Construct a trusted verification channel, in which a trusted judgment subchannel and a trusted compensation subchannel are embedded, wherein the trusted judgment subchannel is used to judge the credibility of the data and output the data credibility; the trusted compensation subchannel is used to repair and restore data with low credibility. The trusted judgment subchannel can be constructed based on the BP neural network, the input data of the trusted judgment subchannel is the working condition data, and the output data is the data credibility; first, retrieve and obtain multiple sample data based on industrial big data technology, wherein the sample data includes multiple sample working condition data and multiple sample data that are credible, and the sample working condition data and the sample data credibility have a corresponding relationship; then use the multiple sample data as training data to supervise the trusted judgment subchannel constructed based on the BP neural network, and embed the trusted judgment subchannel that meets the expected requirements into the trusted verification channel. The trusted compensation subchannel includes multiple data compensation means, and technicians in this field can select an adaptive data compensation means based on the actual data type, such as: data re-collection analysis, data prediction compensation, homologous data fusion and other means.

[0018] First, the target working condition data and the universal working condition data are segmented according to the operation link and the type of operating equipment, and then the segmented data is input into the trusted judgment sub-channel in the trusted verification channel for data credibility identification to obtain multiple data credibility. Obtain a preset data credibility threshold, which can be set based on actual needs, wherein the higher the data requirement accuracy, the larger the preset data credibility threshold. Judge the multiple data credibility according to the preset data credibility threshold, mark the credibility of the multiple data credibility that is less than or equal to the preset data credibility threshold as abnormal credibility, and obtain multiple abnormal credibility. Then input the working condition data corresponding to the multiple abnormal credibility into the trusted compensation sub-channel for data compensation, for example: for abnormal credibility data, data re-collection analysis can be performed for data compensation; abnormal credibility data can also be predicted and compensated according to the overall trend of the data; or data compensation is performed based on the fusion of multiple homologous data. Obtain compensation data for multiple abnormal credibility data. And according to the compensation data of multiple abnormal credibility data, the abnormal credibility data in the target operating condition data and the universal operating condition data are replaced to obtain the target operating condition data and the universal operating condition data that meet the preset data credibility threshold.

[0019] Then, the target working condition data and the universal working condition data are fused, wherein the target working condition data is used as the framework data, and the universal working condition data is used as the detail data of the framework data for fusion to generate the basic working condition data. By constructing a trusted verification channel to perform credibility verification and compensation of the working condition data and generate the basic working condition data, the authenticity and accuracy of the basic working condition data can be improved, thereby further improving the accuracy of the construction of the downhole twin model.

[0020] The equipment control system and risk control management system of underground operation are interactively integrated to jointly build an underground reality system, where the equipment control system includes control modules, multiple underground operation equipment, etc.; the risk control management system includes risk control links, risk control parameters, risk control measures, etc. By generating the underground reality system, support is provided for the next step of building an underground twin model.

[0021] Setting twin modeling rules, wherein the twin modeling rules include edge twin rules and gimbal twin rules; In an embodiment of the present application, twin modeling rules are obtained, wherein the twin modeling rules include edge twin rules and gimbal twin rules, the edge twin rules are used to construct edge twins, wherein the edge twins perform data processing based on edge computing; the gimbal twin rules are used to construct gimbal twins, wherein the gimbal twins perform centralized data processing based on a central gimbal, wherein the gimbal includes a cloud computer, a cloud computing platform, etc.

[0022] like Figure 2 As shown, in one embodiment, the method further includes: In view of the safety of underground working conditions, the division threshold is determined based on the risk control level, impact range, and risk response time limit, and the first twin modeling rules are determined; For underground equipment control, the division threshold is determined based on computing power requirements, data confidentiality level, and control response time limit, and the second twin modeling rules are determined; Based on the first twin modeling rule and the second twin modeling rule, a configuration based on gimbal twin and edge twin is performed to generate the twin modeling rule.

[0023] In an embodiment of the present application, first, with respect to the safety of underground working conditions, the risk control level, affected scope, and risk response time limit are used as rule division indicators to set the first twin modeling rule. Since edge data processing has a faster response speed and higher data processing efficiency than pan-tilt data processing, the basic working condition data and the underground reality system with a high risk control level, a wide affected scope, and a short risk response time limit can be set as the edge twin rule in the first twin modeling rule; the basic working condition data and the underground reality system with a low risk control level, a small affected scope, and a long risk response time limit can be set as the pan-tilt twin rule in the first twin modeling rule.

[0024] For downhole equipment control, computing power requirements, data confidentiality level, and control response time limit are used as rule division indicators to set the second twin modeling rules. The basic working data and downhole reality system with small computing power requirements, high data confidentiality level, and short control response time limit can be set as edge twin rules in the second twin modeling rules; the basic working data and downhole reality system with large computing power requirements, low data confidentiality level, and long control response time limit are set as pan-tilt twin rules in the second twin modeling rules.

[0025] Then, the edge twin rule and the pan-tilt twin rule in the first twin modeling rule and the second twin modeling rule are integrated to generate a twin modeling rule, wherein the twin modeling rule includes the edge twin rule and the pan-tilt twin rule. By generating the twin modeling rule, support is provided for building an underground twin model.

[0026] Connecting to the visual simulation platform, combining the twin modeling rules, performing twin modeling based on the basic working condition data and the downhole reality system, and generating a downhole twin model, wherein the downhole twin model includes an edge twin and a pan-tilt twin that are interactively connected based on a network protocol; In the embodiments of the present application, visual simulation is a technical means for simulating and modeling entities or systems based on data twin technology. Through visual simulation, real-time monitoring, simulation analysis, and optimized decision-making can be achieved.

[0027] First, the basic working condition data and the downhole reality system are divided in combination with the twin modeling rules to obtain the edge working condition basic data, the pan-tilt working condition basic data, the edge downhole reality system, and the pan-tilt downhole reality system; then, in the visual simulation platform, simulation modeling is performed based on the edge working condition basic data and the edge downhole reality system to generate an edge twin; simulation modeling is performed based on the pan-tilt working condition basic data and the pan-tilt downhole reality system to generate a pan-tilt twin. The edge twin and the pan-tilt twin are interactively connected based on a network protocol, and then a downhole twin model is generated based on the edge twin and the pan-tilt twin.

[0028] In one embodiment, the method further comprises: Determine effective loss data based on downhole equipment and system coordination; Based on the effective loss data, locate and compensate the loss of the working condition basic data and the downhole actual system, and determine compensation modeling data; In combination with the twin modeling rules, the downhole twin model is constructed in the visual simulation platform.

[0029] In the embodiment of the present application, first, the effective loss data based on the coordination of downhole equipment and the system is determined, and the effective loss data includes equipment technical loss data, service status loss data, loss data under the coordinated operation of multiple equipment, etc. Then, according to the effective loss data, the data of the corresponding links of the basic working condition data and the downhole reality system are compensated, that is, the effective loss data of the corresponding links are subtracted from the basic working condition data and the downhole reality system to obtain the compensation modeling data. By performing loss location and data compensation on the basic working condition data and the downhole reality system according to the effective loss data, the authenticity and accuracy of the compensation modeling data can be further improved, thereby further improving the accuracy of the construction of the downhole twin model.

[0030] Then, in the visual simulation platform, based on the twin modeling rules, the compensation modeling data is simulated and modeled to construct a downhole twin model.

[0031] In one embodiment, the method further comprises: Based on the edge twin rule and the gimbal twin rule, the compensation modeling data is divided into attribution groups to construct the gimbal twin and the edge twin; In the pan-tilt twin, mapping of the edge twin is performed to establish a virtual mapping node, wherein at least one of the virtual mapping nodes is included; Based on the network protocol, a mapping association between the virtual mapping node and the edge twin is established.

[0032] In the embodiment of the present application, firstly, based on the edge twin rule and the gimbal twin rule, the compensation modeling data is divided into edge twin modeling data and gimbal twin modeling data. And in the visual simulation platform, simulation modeling is performed based on the edge twin modeling data to generate an edge twin; simulation modeling is performed based on the gimbal twin modeling data to generate a gimbal twin.

[0033] Then, in the PTZ twin, the edge twin is mapped, and a virtual mapping node of the edge twin in the PTZ twin is generated. The virtual mapping node is used for information exchange between the PTZ twin and the edge twin, wherein the virtual mapping node includes at least one, that is, the number of the edge twin is at least one. Based on the network protocol, the virtual mapping node is mapped and associated with the edge twin to realize the data exchange function. The network protocol is used to ensure the security of data transmission, and technicians in this field can make a selection based on actual data requirements.

[0034] Configuring a classified data transmission channel between the edge twin and the gimbal twin; In an embodiment of the present application, a classified data transmission channel is configured for the edge twin and the pan-tilt twin. The classified data transmission channel is used to transmit different types of data and can be set according to the actual situation of the data type, such as: digital channel, analog channel, etc. By configuring the classified data transmission channel, the efficiency and security of data transmission can be improved.

[0035] Combined with the distributed sensor monitoring device, the working condition monitoring is carried out synchronously and the downhole working condition data is transmitted back based on the classified data transmission channel, so as to carry out the coordinated management and control of the edge decision and the pan-tilt central control decision; In an embodiment of the present application, when performing underground operations, operating condition monitoring is synchronously performed through distributed sensor monitoring devices. The type and distribution location of the sensor monitoring devices can be set by a technician in this field according to the type of monitoring data, to obtain operating condition monitoring data, i.e., underground operating condition data, and to transmit the underground operating condition data back to the underground twin model through the classified data transmission channel, and to coordinate the edge decision-making and the pan-tilt central control decision-making through the underground twin model.

[0036] In one embodiment, the method further comprises: Based on the edge twin and the pan-tilt twin, real-time abnormal analysis and tracing of the mapped downhole working condition data and prediction of future risk points are performed, and the edge decision plan and the pan-tilt decision plan are determined and implemented; Set up a precursor verification node based on the risk point prediction results; Based on the precursor verification node, risk occurrence trend verification is performed and feedback decision management is carried out.

[0037] In an embodiment of the present application, first, the edge twin and the pan-tilt twin are used to perform abnormal state analysis and future risk point prediction on the downhole operating condition data with mapping association, that is, the edge operating condition data in the downhole operating condition data is compared and analyzed by the edge twin to determine whether there is a deviation with the basic operating condition data in the edge twin. When there is deviation data, the deviation data is analyzed for abnormality, and future risk points are predicted based on the abnormal analysis results. The future risk point refers to the time point when the operating condition risk is about to occur, wherein the technicians in this field can predict the future risk point by constructing a neural network model through machine learning; the pan-tilt twin is used to compare and analyze the pan-tilt operating condition data in the downhole operating condition data, and the abnormal analysis results and future risk point prediction results are determined based on the comparison and analysis results.

[0038] Based on historical risk cases, an abnormal data-edge decision plan comparison table and an abnormal data-gimbal decision plan comparison table are constructed; the edge abnormality analysis results and the edge risk point prediction results are input into the abnormal data-edge decision plan comparison table for decision plan matching, and an edge decision plan is generated; the gimbal abnormality analysis results and the gimbal risk point prediction results are input into the abnormal data-gimbal decision plan comparison table for decision plan matching, and a gimbal decision plan is generated; and abnormality handling is performed according to the edge decision plan and the gimbal decision plan.

[0039] A precursor verification node based on the risk point prediction result is set, wherein the precursor verification node is a verification node for the time period before the risk prediction occurrence time node, and the precursor verification node can be set by a person skilled in the art according to the severity of the risk prediction result, wherein the higher the risk level, the faster the risk occurrence speed, and the shorter the response time, the longer the time interval between the precursor verification node and the risk prediction occurrence time node. The precursor verification node is provided with an expected risk judgment indicator.

[0040] At the precursor verification node, the abnormal analysis results of the real-time downhole operating condition data are judged according to the expected risk judgment index and the risk occurrence trend is verified. When the abnormal analysis result is greater than the expected risk judgment index, it indicates that the risk situation is more serious than the previous prediction, and the previous decision-making plan needs to be optimized, for example: increasing the risk control and adjustment efforts, etc., and generating a feedback decision plan, and further performing abnormal processing according to the feedback decision plan.

[0041] By setting up a precursor verification node to verify the risk occurrence trend and generating a feedback decision plan based on the trend verification results to optimize the previous decision plan, the timeliness and accuracy of abnormal working condition processing can be improved, thereby further reducing the risk of underground operations.

[0042] In one embodiment, the method further comprises: The collaborative control includes a first control mode and a second control mode, wherein the first control mode is a relatively independent control mode based on the edge twin and the gimbal twin, and the second control mode is an interactive control mode based on the edge twin and the gimbal twin; Setting the first control mode as a conventional control mode; If the edge twin or the gimbal twin has insufficient scene computing power support, the second control mode is temporarily activated.

[0043] In an embodiment of the present application, the collaborative control includes a first control mode and a second control mode, wherein the first control mode is a relatively independent control mode of the edge twin and the gimbal twin. Under the first control mode, the edge twin and the gimbal twin respectively perform independent analysis and on-site control management on the returned data, and at the same time, the edge twin will feed back the edge analysis results to the gimbal, and set the first control mode as the conventional control mode.

[0044] The second control is the interactive control method between the edge twin and the gimbal twin, that is, under special circumstances, the two can achieve interactive control through data transmission, wherein when the edge twin or the gimbal twin has insufficient scene computing power support, that is, the current data processing volume is too large and cannot be effectively processed in time, the second control method is temporarily activated for data processing. For example: when there is an abnormality in the on-site working conditions, and the edge twin is performing analysis and processing of a large amount of data, and cannot effectively allocate computing power for the remaining processing requirements, then secondly, the relevant data is temporarily transmitted to the gimbal for processing and decision management through the gimbal.

[0045] By setting an interactive control mode for the edge twin and the gimbal twin, computing resources can be reasonably utilized, the speed of analysis and processing of returned data can be improved, thereby improving the efficiency and accuracy of underground working condition supervision.

[0046] Conduct response monitoring based on collaborative management, feedback control data to the downhole twin model and measure control deviation data; In the embodiment of the present application, the response monitoring of the downhole operation condition is performed based on the coordinated management of the edge twin and the pan-tilt twin, and feedback control data is obtained. The feedback control data refers to the working condition data processed by the decision-making scheme, and the feedback control data is transmitted back to the downhole twin model. The feedback control data is compared with the downhole twin model to calculate the control deviation data. By obtaining the control deviation data, support is provided for the next step of judging the control accuracy.

[0047] If the control deviation data does not satisfy the deviation tolerance interval, downhole feedback compensation control is performed.

[0048] In an embodiment of the present application, a deviation tolerance interval is obtained, and the deviation tolerance interval can be set by a person skilled in the art according to the actual control accuracy requirement, wherein the higher the actual control accuracy requirement, the smaller the deviation tolerance interval. Then, the control deviation data is judged according to the deviation tolerance interval. When the control deviation data is greater than or equal to the deviation tolerance interval, it indicates that the control accuracy does not meet the expected requirements, and a feedback compensation scheme is generated, and downhole control compensation is performed according to the feedback compensation scheme. By performing downhole control compensation according to the feedback compensation scheme, the accuracy of downhole operation control can be further improved.

[0049] The above method can solve the technical problem in the prior art that the underground working conditions are relatively complex, which makes it difficult to supervise the working conditions, resulting in low accuracy of underground working condition supervision. It can improve the accuracy of underground working condition data judgment, thereby improving the accuracy and quality of underground working condition supervision, and ensuring the safe and stable operation of underground operations.

[0050] In one embodiment, Figure 3 The figure shows a downhole monitoring and management system based on data twins, including: a basic data extraction module 01 for working conditions, a twin modeling rule setting module 02, a downhole twin model generation module 03, a classified data transmission channel configuration module 04, a decision-making collaborative management and control module 05, a management and control deviation data metering module 06, and a feedback compensation management and control module 07, wherein: The basic working condition data extraction module 01 is used to read the underground working condition planning, identify and extract the basic working condition data after the reliable prior is executed, and determine the underground actual system; A twin modeling rule setting module 02, wherein the twin modeling rule setting module 02 is used to set twin modeling rules, wherein the twin modeling rules include edge twin rules and gimbal twin rules; A downhole twin model generation module 03, the downhole twin model generation module 03 is used to connect to the visual simulation platform, combine the twin modeling rules, perform twin modeling based on the basic working condition data and the downhole reality system, and generate a downhole twin model, wherein the downhole twin model includes an edge twin and a pan-tilt twin that are interactively connected based on a network protocol; A classified data transmission channel configuration module 04, wherein the classified data transmission channel configuration module 04 is used to configure a classified data transmission channel between the edge twin and the gimbal twin; The decision-making collaborative control module 05 is used to combine the distributed sensor monitoring device, synchronously monitor the working condition and return the downhole working condition data based on the classified data transmission channel, and perform collaborative control of edge decision-making and pan-tilt central control decision-making; A control deviation data metering module 06, which is used to perform response monitoring based on collaborative management, feed back control data to the downhole twin model and measure the control deviation data; The feedback compensation control module 07 is used to perform downhole feedback compensation control if the control deviation data does not meet the deviation tolerance interval.

[0051] In one embodiment, the system further comprises: A target working condition data reading module, the target working condition data reading module is used to read the target working condition data based on the downhole working condition planning; A universal working condition data determination module, which is used to perform industrial big data retrieval, determine auxiliary reference data under the same source working condition, and fit and determine universal working condition data; A working condition basic data determination module, the working condition basic data determination module is used to perform credibility verification and compensation of the target working condition data and the universal working condition data in combination with a trusted verification channel, and determine the working condition basic data; A system interaction module, wherein the system interaction module is used for interacting with the equipment control system and the risk control management system of the actual operation underground, as the underground reality system.

[0052] In one embodiment, the system further comprises: A first twin modeling rule determination module, wherein the first twin modeling rule determination module is used to determine a division threshold based on risk control level, affected scope, and risk response time limit for underground working condition safety, and determine a first twin modeling rule; A second twin modeling rule determination module, wherein the second twin modeling rule determination module is used to determine a division threshold based on computing power requirements, data confidentiality level, and control response time limit for downhole equipment control, and determine a second twin modeling rule; A twin modeling rule generation module, wherein the twin modeling rule generation module is used to generate the twin modeling rules based on the configuration of the gimbal twin and the edge twin based on the first twin modeling rule and the second twin modeling rule.

[0053] In one embodiment, the system further comprises: An effective loss data determination module, the effective loss data determination module is used to determine effective loss data based on downhole equipment and system collaboration; A compensation modeling data determination module, the compensation modeling data determination module is used to locate and compensate for the loss of the working condition basic data and the downhole real system based on the effective loss data, and determine the compensation modeling data; A downhole twin model construction module, wherein the downhole twin model construction module is used to construct the downhole twin model in the visual simulation platform in combination with the twin modeling rules.

[0054] In one embodiment, the system further comprises: A twin construction module, wherein the twin construction module is used to perform attribution division of the compensation modeling data based on the edge twin rule and the gimbal twin rule, and to construct the gimbal twin and the edge twin; A virtual mapping node establishment module, the virtual mapping node establishment module is used to perform mapping of the edge twin in the pan-tilt twin and establish virtual mapping nodes, including at least one virtual mapping node; A mapping association establishing module, wherein the mapping association establishing module is used to establish a mapping association between the virtual mapping node and the edge twin based on a network protocol.

[0055] In one embodiment, the system further comprises: A decision-making scheme determination module, which is used to perform real-time abnormal analysis and tracing of the mapped downhole working condition data and prediction of future risk points based on the edge twin and the pan-tilt twin, determine the edge decision scheme and the pan-tilt decision scheme and implement them; A precursor verification node setting module, the precursor verification node setting module is used to set a precursor verification node based on the risk point prediction result; A feedback decision management module is used to verify the risk occurrence trend and perform feedback decision management based on the predecessor verification node.

[0056] In one embodiment, the system further comprises: A control mode module, wherein the collaborative control includes a first control mode and a second control mode, wherein the first control mode is a relatively independent control mode based on the edge twin and the gimbal twin, and the second control mode is an interactive control mode based on the edge twin and the gimbal twin; A first control mode setting module, the first control mode setting module is used to set the first control mode as a conventional control mode; The second control mode activation module is used to temporarily activate the second control mode if the edge twin or the gimbal twin has insufficient scene computing power support.

[0057] In summary, compared with the prior art, the embodiments of the present disclosure have the following technical effects: (1) By building a downhole twin model to monitor and control downhole operations, the accuracy of downhole operating data judgment can be improved, thereby improving the accuracy and quality of downhole operating supervision and ensuring the safe and stable operation of downhole operations.

[0058] (2) By building a trusted verification channel to verify and compensate the credibility of the working condition data and generate basic working condition data, the authenticity and accuracy of the basic working condition data can be improved; by locating the loss and compensating the data of the basic working condition data and the downhole real system based on the effective loss data, the authenticity and accuracy of the compensation modeling data can be further improved, thereby further improving the accuracy of the downhole twin model construction.

[0059] (3) By setting up a precursor verification node to verify the risk occurrence trend and generating a feedback decision plan based on the trend verification results to optimize the previous decision plan, the timeliness and accuracy of abnormal working condition processing can be improved, thereby further reducing the risk of underground operations.

[0060] (4) By setting an interactive control mode for the edge twin and the gimbal twin, computing resources can be reasonably utilized, the speed of analysis and processing of returned data can be improved, and the efficiency and accuracy of underground working condition supervision can be improved.

[0061] The above-described embodiments only express several implementation methods of the present disclosure, but they cannot be understood as limiting the scope of the invention patent. Therefore, without departing from the scope of the present disclosure concept as defined by the attached claims, a person of ordinary skill in the art may make various types of substitutions, modifications and changes, and these substitutions, modifications and changes all belong to the protection scope of the present disclosure.

Claims

1. The underground monitoring and management method based on data twin is characterized by: The method comprises: Read the underground working condition plan, identify and extract the basic working condition data after executing the credible priori, and determine the underground actual system; Setting twin modeling rules, wherein the twin modeling rules include edge twin rules and gimbal twin rules; Connecting to the visual simulation platform, combining the twin modeling rules, performing twin modeling based on the basic working condition data and the downhole reality system, and generating a downhole twin model, wherein the downhole twin model includes an edge twin and a pan-tilt twin that are interactively connected based on a network protocol; Configuring a classified data transmission channel between the edge twin and the gimbal twin; Combined with the distributed sensor monitoring device, the working condition monitoring is carried out synchronously and the downhole working condition data is transmitted back based on the classified data transmission channel, so as to carry out the coordinated management and control of the edge decision and the pan-tilt central control decision; Conduct response monitoring based on collaborative management, feedback control data to the downhole twin model and measure control deviation data; If the control deviation data does not satisfy the deviation tolerance interval, downhole feedback compensation control is performed.

2. The method according to claim 1, characterized in that The method includes reading the downhole working condition plan, identifying and extracting the basic working condition data after executing the credible priori, and determining the downhole actual system. Reading target operating condition data based on the downhole operating condition planning; Conduct industrial big data retrieval, determine auxiliary reference data under the same source working conditions, and fit and determine universal working condition data; In combination with a trusted verification channel, the credibility verification and compensation of the target working condition data and the universal working condition data are performed to determine the working condition basic data; The equipment control system and risk control management system for interactive underground operations serve as the underground reality system.

3. The method according to claim 1, characterized in that The twin modeling rule is set, and the method includes: In view of the safety of underground working conditions, the division threshold is determined based on the risk control level, impact range, and risk response time limit, and the first twin modeling rules are determined; For underground equipment control, the division threshold is determined based on computing power requirements, data confidentiality level, and control response time limit, and the second twin modeling rules are determined; Based on the first twin modeling rule and the second twin modeling rule, a configuration based on gimbal twin and edge twin is performed to generate the twin modeling rule.

4. The method according to claim 1, characterized in that The method includes: Determine effective loss data based on downhole equipment and system coordination; Based on the effective loss data, locate and compensate the loss of the working condition basic data and the downhole actual system, and determine compensation modeling data; In combination with the twin modeling rules, the downhole twin model is constructed in the visual simulation platform.

5. The method according to claim 4, characterized in that In combination with the twin modeling rules, the downhole twin model is constructed in the visual simulation platform. The method includes: Based on the edge twin rule and the gimbal twin rule, the compensation modeling data is divided into attribution groups to construct the gimbal twin and the edge twin; In the pan-tilt twin, mapping of the edge twin is performed to establish a virtual mapping node, wherein at least one of the virtual mapping nodes is included; Based on the network protocol, a mapping association between the virtual mapping node and the edge twin is established.

6. The method according to claim 1, characterized in that The method for collaboratively managing edge decision-making and pan-tilt central control decision-making includes: Based on the edge twin and the pan-tilt twin, real-time abnormal analysis and tracing of the mapped downhole working condition data and prediction of future risk points are performed, and the edge decision plan and the pan-tilt decision plan are determined and implemented; Set up a precursor verification node based on the risk point prediction results; Based on the precursor verification node, risk occurrence trend verification is performed and feedback decision management is carried out.

7. The method according to claim 6, characterized in that The method includes: The collaborative control includes a first control mode and a second control mode, wherein the first control mode is a relatively independent control mode based on the edge twin and the gimbal twin, and the second control mode is an interactive control mode based on the edge twin and the gimbal twin; Setting the first control mode as a conventional control mode; If the edge twin or the gimbal twin has insufficient scene computing power support, the second control mode is temporarily activated.

8. The underground monitoring and management system based on data twin is characterized by: The system is used to perform the steps of any one of the methods for downhole monitoring and management based on data twins described in claims 1-7, the system comprising: A working condition basic data extraction module, which is used to read the underground working condition plan, identify and extract the working condition basic data after executing the credible prior, and determine the underground actual system; A twin modeling rule setting module, wherein the twin modeling rule setting module is used to set twin modeling rules, wherein the twin modeling rules include edge twin rules and gimbal twin rules; A downhole twin model generation module, the downhole twin model generation module is used to connect to the visual simulation platform, combine the twin modeling rules, perform twin modeling based on the basic working condition data and the downhole reality system, and generate a downhole twin model, wherein the downhole twin model includes an edge twin and a pan-tilt twin that are interactively connected based on a network protocol; A classified data transmission channel configuration module, wherein the classified data transmission channel configuration module is used to configure the classified data transmission channel between the edge twin and the gimbal twin; A decision-making collaborative control module, which is used to combine with a distributed sensor monitoring device to synchronously monitor the working condition and return the downhole working condition data based on the classified data transmission channel, so as to perform collaborative control of edge decision-making and pan-tilt central control decision-making; A control deviation data metering module, which is used to perform response monitoring based on collaborative management, feed back control data to the downhole twin model and measure the control deviation data; A feedback compensation control module is used to perform downhole feedback compensation control if the control deviation data does not meet the deviation tolerance interval.

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