Well engineering processing method and device based on intelligent glasses and storage medium

Abstract

The invention discloses a well engineering processing method and device based on intelligent glasses and a storage medium. The well engineering processing method and device are used for improving the efficiency of underground operation. Receiving an acquisition instruction of a user, and acquiring a feature two-dimensional code on the abnormal well engineering equipment through an image acquisition module; acquiring operation data of the well engineering equipment according to the feature two-dimensional code, and displaying the operation data on the intelligent glasses; receiving a data real-time processing instruction of a user, performing real-time analysis on the operation data of the well engineering equipment, and displaying a real-time analysis result on the intelligent glasses; a directional collection instruction of a user is received, real-time working condition data collected by underground working condition collection equipment are obtained according to the real-time analysis result, and the working condition data are real-time operation occasion data of the working area of the well engineering equipment; and receiving a cloud analysis instruction of a user, uploading the operation data, the real-time analysis result and the real-time working condition data to a well engineering cloud for project anomaly analysis, and displaying an anomaly analysis result on the intelligent glasses.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of intelligent operation of well engineering, and particularly to a well engineering processing method, device and storage medium based on smart glasses. Background Art

[0002] Well engineering is an engineering for underground or underwater mineral extraction or underground construction, and is widely used in fields such as resource extraction, underground space utilization, environmental protection and scientific research.

[0003] In the prior art, a large number of heavy equipment are required for underground engineering construction. In order to realize intelligent well engineering, the operation parameters of well engineering equipment are usually collected in real time, and the well engineering equipment is controlled. However, such real-time data collection and real-time control are usually carried out in a ground control room far from the underground. With the continuous development of well engineering technology, the precision and complexity of well engineering projects are gradually increasing. A variety of precision well engineering equipment is required, and usually technicians need to perform on-site operations on the operation of well engineering equipment according to the actual situation. Especially in the case of abnormal well engineering equipment, there are usually two situations for underground equipment anomalies. One is the faults caused by the aging and wear of the equipment itself during long-term operation of well engineering, and the other is that the abnormal rock formation causes the underground equipment to be unable to continue working. And if there is an abnormal rock formation, the faults of well engineering equipment may also cause changes in the underground environment, bringing danger to researchers. These two anomalies and faults are difficult to determine only through the collection and analysis of remote operation data. Usually, a variety of sensors underground are required to collect data on the overall underground equipment, and then further analyze the causes of anomalies and faults. However, there are many types of underground sensors, and there are many contents to be detected. It is necessary for the staff to analyze according to the on-site status on site and then report to the ground control room. The ground control room conducts secondary data collection and analysis, which reduces the efficiency of underground operations. Summary of the Invention

[0004] The present application discloses a well engineering processing method, device and storage medium based on smart glasses, which are used to improve the efficiency of underground operations.

[0005] A well engineering processing method based on smart glasses proposed in the first aspect of the present application includes:

[0006] Receiving a collection instruction from a user through the smart glasses, and collecting a characteristic two-dimensional code on the well engineering equipment with anomalies through an image collection module;

[0007] Obtaining the operation data of the well engineering equipment according to the characteristic two-dimensional code, and displaying the operation data on the smart glasses;

[0008] Receive the real-time processing instructions of the user, perform real-time analysis on the operation data of the well engineering equipment, and display the real-time analysis results on the smart glasses;

[0009] Receive the directional acquisition instructions of the user, obtain the real-time working condition data collected by the downhole working condition acquisition equipment according to the real-time analysis results, and the working condition data is the real-time operation occasion data of the working area of the well engineering equipment;

[0010] Receive the cloud analysis instructions of the user, upload the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project anomaly analysis, and display the anomaly analysis results on the smart glasses.

[0011] Optionally, there is an interaction between the well engineering equipment and the downhole Internet of Things;

[0012] Obtain the operation data of the well engineering equipment according to the feature QR code and display the operation data on the smart glasses, including:

[0013] Read the feature QR code of the well engineering equipment and connect to the downhole Internet of Things through the feature QR code;

[0014] Obtain the operation data of the well engineering equipment from the service center through the downhole Internet of Things and display the operation data on the smart glasses.

[0015] Optionally, the well engineering equipment is a roadheader, and the roadheader has an activatable data transmission label;

[0016] Obtain the operation data of the well engineering equipment according to the feature QR code and display the operation data on the smart glasses, including:

[0017] Read the feature QR code of the well engineering equipment;

[0018] Generate low-frequency acquisition radio waves according to the feature QR code, and the low-frequency acquisition radio waves are used to activate the data transmission label on the roadheader;

[0019] Activate the data transmission label of the well engineering equipment through the low-frequency acquisition radio waves so that the data transmission label is activated;

[0020] Receive the operation data transmitted back by the well engineering equipment through the reader and antenna in the smart glasses;

[0021] Display the operation data on the smart glasses.

[0022] Optionally, the real-time working condition data includes the operation component working condition data and the tunneling area working condition data of the well engineering equipment;

[0023] Receive the cloud analysis instruction from the user, upload the operation data, real-time analysis results, and real-time working condition data to the well engineering cloud for project anomaly analysis, and display the anomaly analysis results on the smart glasses, including:

[0024] Receive the cloud analysis instruction from the user, and extract the underground tunneling rock layer image from the working condition data of the tunneling area;

[0025] Input the underground tunneling rock layer image into the AI model in the well engineering cloud for rock layer type feature analysis to generate rock layer type data;

[0026] When the rock layer type data shows that the rock hardness of the current underground tunneling rock layer exceeds the working range of the tunneling module, it is determined that there is an abnormal mutation in the rock quality of the rock layer;

[0027] Obtain the operating audio data of the roadheader from the operating component working condition data, and conduct a preliminary detection of drill bit anomalies on the audio data through the audio analysis model;

[0028] When the preliminary detection of drill bit anomalies shows that there is a frequency band with drill bit anomalies, obtain the tunneling component image from the operating component working condition data;

[0029] Determine the area to be detected in the tunneling component image according to the abnormal frequency band of the operating audio data;

[0030] Conduct drill bit defect detection on the area to be detected in the tunneling component image through the drill bit defect analysis model to generate drill bit detection results;

[0031] When the drill bit detection results show that there are anomalies in the drill bit of the roadheader, conduct an AI simulation of reverse drill bit wear on the underground tunneling rock layer image to generate wear analysis data;

[0032] Conduct drill bit damage matching on the drill bit detection results and wear analysis data to generate drill bit damage results;

[0033] Integrate the rock layer anomaly results and drill bit detection results to generate the anomaly analysis results of the well engineering project, and display the anomaly analysis results of the well engineering project on the smart glasses.

[0034] Optionally, after receiving the cloud analysis instruction from the user, uploading the operation data, real-time analysis results, and real-time working condition data to the well engineering cloud for project anomaly analysis, and displaying the anomaly analysis results on the smart glasses, the method further includes:

[0035] When the anomaly analysis results of the well engineering project indicate that there are component damages in the well engineering equipment, receive the well engineering equipment inventory query instruction from the user;

[0036] Access the well engineering inventory database to query the inventory situation in real time and locate the item position.

[0037] Optionally, after receiving the user's cloud analysis instruction, uploading the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project abnormality analysis, and displaying the abnormality analysis results on the smart glasses, the method further includes:

[0038] When the abnormal analysis results of the well engineering project indicate that there is an abnormal mutation of the rock formation, a collection signal is sent to the sensors of the downhole gas environment and rock formation environment;

[0039] The underground working environment data sent back by the sensors of the underground gas environment and rock environment are analyzed, and the underground environmental change data are displayed on the smart glasses.

[0040] The second aspect of the present application provides a well engineering treatment device based on smart glasses, comprising:

[0041] A collection unit, used to receive a collection instruction from a user through smart glasses, and collect a characteristic QR code on the well engineering equipment with abnormalities through an image collection module;

[0042] A first acquisition unit is used to acquire the operation data of the well engineering equipment according to the characteristic QR code, and display the operation data on the smart glasses;

[0043] The first analysis unit is used to receive the user's real-time data processing instruction, analyze the operation data of the well engineering equipment in real time, and display the real-time analysis results on the smart glasses;

[0044] The second acquisition unit is used to receive the user's directional acquisition instruction and acquire the real-time working condition data collected by the downhole working condition acquisition equipment according to the real-time analysis result. The working condition data is the real-time operation occasion data of the working area of ​​the well engineering equipment;

[0045] The second analysis unit is used to receive the user's cloud analysis instructions, upload the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project abnormality analysis, and display the abnormality analysis results on the smart glasses.

[0046] Optionally, there is interaction between the well engineering equipment and the underground Internet of Things;

[0047] The first acquisition unit includes:

[0048] Read the characteristic QR code of well engineering equipment and connect it with the underground Internet of Things through the characteristic QR code;

[0049] The operation data of well engineering equipment is obtained from the service center through the underground Internet of Things, and the operation data is displayed on smart glasses.

[0050] Optionally, the well engineering equipment is a tunnel boring machine, and the tunnel boring machine is provided with an activatable data transmission tag;

[0051] The first acquisition unit includes:

[0052] Read the characteristic QR code of the well engineering equipment;

[0053] Generate low-frequency acquisition radio waves according to the characteristic QR code, and the low-frequency acquisition radio waves are used to activate the data transmission tag on the roadheader;

[0054] Activate the data transmission tag of the well engineering equipment through the low-frequency acquisition radio waves so that the data transmission tag is activated;

[0055] Receive the operation data transmitted back by the well engineering equipment through the reader and antenna in the smart glasses;

[0056] Display the operation data on the smart glasses.

[0057] Optionally, the real-time working condition data includes the operation component working condition data of the well engineering equipment and the working condition data of the tunneling area;

[0058] The second analysis unit includes:

[0059] Receive the cloud analysis instruction of the user, and extract the underground tunneling rock layer image from the working condition data of the tunneling area;

[0060] Input the underground tunneling rock layer image into the AI model in the well engineering cloud for rock layer type feature analysis to generate rock layer type data;

[0061] When the rock layer type data shows that the rock hardness of the current underground tunneling rock layer exceeds the working range of the tunneling module, it is determined that there is an abnormal rock mass mutation;

[0062] Obtain the operation audio data of the roadheader from the operation component working condition data, and perform preliminary detection of drill bit abnormalities on the audio data through the audio analysis model;

[0063] When the preliminary detection of drill bit abnormalities shows the frequency band with drill bit abnormalities, obtain the tunneling component image from the operation component working condition data;

[0064] Determine the area to be detected of the tunneling component image according to the abnormal frequency band of the operation audio data;

[0065] Perform drill bit defect detection on the area to be detected of the tunneling component image through the drill bit defect analysis model to generate drill bit detection results;

[0066] When the drill bit detection result shows that there is an abnormality in the drill bit of the roadheader, perform an AI simulation of reverse drill bit wear on the underground tunneling rock layer image to generate wear analysis data;

[0067] Perform drill bit damage matching on the drill bit detection result and the wear analysis data to generate drill bit damage results;

[0068] Integrate the abnormal results of the rock formation and the drill bit detection results to generate the abnormal analysis results of the well engineering project, and display the abnormal analysis results of the well engineering project on the smart glasses.

[0069] Optionally, after the second analysis unit, the well engineering processing device further includes:

[0070] A receiving unit, configured to receive the user's well engineering equipment inventory query instruction when the abnormal analysis result of the well engineering project indicates that there is a component damage in the well engineering equipment;

[0071] A query unit, configured to access the well engineering inventory database to query the inventory situation in real time and locate the position of the item.

[0072] Optionally, after the second analysis unit, the well engineering processing device further includes:

[0073] A sending unit, configured to send a collection signal to the sensors of the downhole gas environment and the rock formation environment when the abnormal analysis result of the well engineering project indicates that there is an abnormal change in the rock quality of the rock formation;

[0074] A third analysis unit, configured to analyze the downhole working environment data transmitted back by the sensors of the downhole gas environment and the rock formation environment, and display the downhole environment change data on the smart glasses.

[0075] The third aspect of the present application provides a well engineering processing device based on smart glasses, including:

[0076] A processor, a memory, an input / output unit, and a bus;

[0077] The processor is connected to the memory, the input / output unit, and the bus;

[0078] The memory stores a program, and the processor calls the program to execute the well engineering processing method as described in the first aspect and any optional well engineering processing method of the first aspect.

[0079] The fourth aspect of the present application provides a computer-readable storage medium, on which a program is stored, and when the program is executed on a computer, it executes the well engineering processing method as described in the first aspect and any optional well engineering processing method of the first aspect.

[0080] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0081] In this application, first, the smart glasses receive the user's acquisition instruction, and the image acquisition module acquires the characteristic QR code on the abnormal well engineering equipment. The operation data of the well engineering equipment is obtained according to the characteristic QR code, and the operation data is displayed on the smart glasses. By having the downhole workers carry the smart glasses to scan the characteristic QR code of the abnormal well engineering equipment, the operation data of the well engineering equipment is obtained, and the operation data is displayed on the smart glasses for the workers to analyze first. Next, the smart glasses receive the user's real-time data processing instruction, perform real-time analysis on the operation data of the well engineering equipment, and display the real-time analysis result on the smart glasses. The workers can control the smart glasses by means of voice, etc., make the smart glasses perform a certain analysis of the well engineering equipment, and then display the numerical analysis result on the smart glasses. At this time, the smart glasses receive the user's directional acquisition instruction, obtain the real-time working condition data collected by the downhole working condition acquisition equipment according to the real-time analysis result, and the working condition data is the real-time operation occasion data of the working area of the well engineering equipment. That is, further data is obtained, and the downhole sensors are used to perform targeted data acquisition on the mechanical equipment. Finally, the smart glasses receive the user's cloud analysis instruction, upload the operation data, real-time analysis result and real-time working condition data to the well engineering cloud for project anomaly analysis, and display the anomaly analysis result on the smart glasses. That is, the cloud-edge-end collaboration method is used to enable the data acquisition and analysis of the downhole process to obtain the analysis result faster, improving the efficiency of downhole operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0083] Figure 1 It is a schematic diagram of an embodiment of the well engineering processing method based on smart glasses of the present application;

[0084] Figure 2 It is a schematic diagram of an embodiment of the method for obtaining the operation data of the well engineering equipment of the present application;

[0085] Figure 3 It is a schematic diagram of an embodiment of the method for obtaining the operation data of the well engineering equipment of the present application;

[0086] Figure 4 It is a schematic diagram of an embodiment of the method for project anomaly analysis of the present application;

[0087] Figure 5 It is a schematic diagram of an embodiment of the method for inventory query of the present application;

[0088] Figure 6 Schematic diagram of an embodiment of the method for well engineering environment monitoring of this application;

[0089] Figure 7 Schematic diagram of an embodiment of the device based on regional connectivity merging of this application;

[0090] Figure 8 Schematic diagram of another embodiment of the device based on regional connectivity merging of this application. Detailed implementation manners

[0091] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system architectures, technologies, etc. are presented to thoroughly understand the embodiments of this application. However, those skilled in the art should clearly understand that this application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of this application.

[0092] It should be understood that when used in the specification and appended claims of this application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0093] It should also be understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0094] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if detecting [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detecting [the described condition or event]", or "in response to detecting [the described condition or event]" depending on the context.

[0095] In addition, in the description of the specification and appended claims of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0096] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0097] In the prior art, a large number of heavy equipment are required for downhole engineering construction. To achieve an intelligent well project, it is usually chosen to collect the operating parameters of well project equipment in real time and control the well project equipment. However, this kind of real-time data collection and real-time control are usually carried out in a ground control room far from the downhole. With the continuous development of well engineering technology, the precision and complexity of well engineering projects are gradually increasing. A variety of precision well engineering equipment needs to be used, and usually technicians need to perform on-site operations on the operation of well engineering equipment according to the actual situation. Especially in the case of abnormal well engineering equipment, there are usually two situations for downhole equipment anomalies. One is the faults caused by the aging and wear of the equipment itself during long-term operation of the well project, and the other is that the abnormal formation causes the downhole equipment to be unable to continue working. Moreover, if there is a formation anomaly, the faults of the well engineering equipment may also cause changes in the downhole environment, bringing danger to researchers. It is difficult to determine these two anomalies and faults only through the collection and analysis of remote data. Usually, a variety of sensors downhole are needed to collect data on the overall downhole equipment, and then further analyze the causes of the anomalies and faults. However, there are many types of downhole sensors and a large amount of content to be detected. It requires the staff to analyze according to the on-site status on the spot and then report to the ground control room. The ground control room then conducts secondary data collection and analysis, which reduces the efficiency of downhole operations.

[0098] Based on this, the present application discloses a well engineering processing method, device, and storage medium based on smart glasses, which are used to improve the efficiency of downhole operations.

[0099] Next, the technical solutions in the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0100] The method of the present application can be applied to servers, devices, terminals, or other devices with logical processing capabilities, and the present application does not limit this. For the convenience of description, the following takes the execution entity as a terminal as an example for description.

[0101] Please refer to Figure 1 , an embodiment of a well engineering processing method based on smart glasses provided by the present application includes:

[0102] 101. Receive a collection instruction from the user through the smart glasses, and collect the characteristic two-dimensional code on the well engineering equipment with anomalies through the image collection module;

[0103] 102. Obtain the operation data of the well engineering equipment according to the characteristic two-dimensional code, and display the operation data on the smart glasses;

[0104] 103. Receive the user's real-time data processing instruction, perform real-time analysis on the operation data of the well engineering equipment, and display the real-time analysis result on the smart glasses;

[0105] The smart glasses terminal starts to perform a preliminary analysis on the first-hand collected operation parameters of the downhole equipment only after receiving the user's real-time data processing instruction, which belongs to the cloud-edge-terminal

[0106] 104. Receive the user's directional collection instruction, and obtain the real-time working condition data collected by the downhole working condition collection equipment according to the real-time analysis result. The working condition data is the real-time operation occasion data of the working area of the well engineering equipment;

[0107] 105. Receive the user's cloud analysis instruction, upload the operation data, real-time analysis result, and real-time working condition data to the well engineering cloud for project anomaly analysis, and display the anomaly analysis result on the smart glasses.

[0108] In this embodiment, the staff uses the AI smart glasses to perform analysis and detection work in the downhole environment. The smart glasses terminal receives the collection instruction from the user through the smart glasses. Today, the smart glasses can be controlled through data such as voice and gestures, and the characteristic two-dimensional code on the well engineering equipment with anomalies is collected through the image collection module. In this embodiment, the equipment for downhole engineering is all set with independent characteristic two-dimensional codes, and the staff can view the operation status, parameter indicators, and other information of the machine equipment in real time through the smart glasses, so as to quickly respond to faults or anomalies and improve work efficiency and accuracy.

[0109] Specifically, as an interaction terminal between the user and the system, the smart glasses can receive the user's instructions, such as collection instructions, data processing instructions, directional collection instructions, and cloud analysis instructions. The smart glasses can receive the user's instructions through voice, gesture, or touch, etc., providing a convenient human-computer interaction experience.

[0110] Instruction types include: 1. Acquisition instruction: Start the image acquisition module to scan the feature QR code on the device. 2. Data processing instruction: Perform real-time analysis on the acquired operation data. 3. Directed acquisition instruction: Further obtain downhole working condition data based on the analysis results. 4. Cloud analysis instruction: Upload the data to the cloud for in-depth analysis.

[0111] Next, use the image acquisition module on the smart glasses to scan the feature QR code on the well engineering equipment to obtain the unique identification information of the equipment. Among them, the feature QR code can contain the basic information of the equipment (such as equipment model, installation location, maintenance records, etc.) and the access link to the real-time operation data (the specific steps will be described in the subsequent embodiments). The image acquisition module needs to have high-precision recognition ability and be able to quickly and accurately identify the QR code in complex environments (such as insufficient light, damaged equipment surface). Among them, QR code technology can be combined with other identification technologies (such as RFID, NFC) to improve the efficiency and reliability of data acquisition.

[0112] Operation data includes the operation status of the equipment (such as temperature, pressure, vibration, etc.), fault alarm information, maintenance records, etc. The display interface of the smart glasses supports various forms such as charts, text, and color coding to help users quickly understand the data. The data can be obtained from the local device or the cloud database through a wireless network (such as 5G, Wi-Fi).

[0113] Next, the smart glasses terminal receive the user's real-time data processing instruction, perform real-time analysis on the operation data of the equipment, and display the analysis results on the smart glasses. Real-time analysis can include: Data trend analysis: Predict possible faults of the equipment. Anomaly detection: Identify abnormal patterns in the data through machine learning algorithms. Health assessment: Evaluate the operation health status of the equipment. The analysis results can be presented in a visual way (such as line charts, bar charts, heat maps) to help users make decisions quickly.

[0114] After the staff analyze the visual data, they make a decision on object acquisition for the smart glasses. According to the real-time analysis results, receive the user's directed acquisition instruction to obtain the real-time working condition data collected by the downhole working condition acquisition equipment. Specifically, the working condition data includes environmental parameters such as temperature, humidity, gas concentration, and pressure in the equipment working area. The directed acquisition instruction can be automatically triggered according to the real-time analysis results. For example, when it is detected that the equipment is operating abnormally, the working condition data acquisition is automatically started. The working condition data can be correlated with the equipment operation data to help users comprehensively understand the operation environment of the equipment.

[0115] After receiving the cloud analysis instruction from the user, the smart glasses upload the operation data, real-time analysis results, and working condition data to the well engineering cloud for project anomaly analysis and display the analysis results on the smart glasses. The well engineering cloud is also set up within the well engineering, and only internal data can be uploaded for analysis, ensuring security. Cloud analysis can deeply mine massive data based on big data and artificial intelligence technologies to discover potential anomaly patterns and fault causes. The anomaly analysis results can include: Fault types: such as mechanical faults, electrical faults, environmental anomalies, etc. Fault causes: such as equipment aging, improper operation, environmental changes, etc. Maintenance suggestions: such as replacing parts, adjusting parameters, shutting down for maintenance, etc. The cloud analysis results can be pushed to the user in real time through the smart glasses, supporting remote expert collaboration and improving the efficiency of fault diagnosis and handling.

[0116] Moreover, on-site workers can make video calls with remote experts through the smart glasses to obtain immediate support and problem-solving solutions, improving the speed of problem-solving.

[0117] The system advantages and application scenarios of this application are as follows: Real-time: Through smart glasses and cloud technologies, real-time data collection, analysis, and display are achieved. Intelligent: Based on artificial intelligence and big data technologies, accurate anomaly diagnosis and maintenance suggestions are provided. Convenience: As a portable terminal, smart glasses facilitate users to operate and make decisions on-site.

[0118] Application scenarios: Equipment monitoring of well engineering in energy industries such as oil and gas. Equipment maintenance of underground projects such as mines and tunnels. Remote inspection and fault diagnosis of industrial equipment.

[0119] In this embodiment, first, the user's collection instruction is received through the smart glasses, and the characteristic two-dimensional code on the well engineering equipment with abnormalities is collected through the image acquisition module. The operation data of the well engineering equipment is obtained according to the characteristic two-dimensional code, and the operation data is displayed on the smart glasses. The underground staff is asked to carry smart glasses to scan the characteristic two-dimensional code of the abnormal well engineering equipment, so as to obtain the operation data of the well engineering equipment, display the operation data on the smart glasses, and let the staff analyze it first. Next, the smart glasses will receive the user's real-time data processing instruction, analyze the operation data of the well engineering equipment in real time, and display the real-time analysis results on the smart glasses. The staff can control the smart glasses through voice and other means, let the smart glasses perform certain well engineering equipment analysis, and then display the numerical analysis results on the smart glasses. At this time, the smart glasses will receive the user's directional collection instruction, and obtain the real-time working condition data collected by the underground working condition collection equipment according to the real-time analysis results. The working condition data is the real-time operating occasion data of the working area of ​​the well engineering equipment. That is, further obtain data, and use underground sensors to collect targeted data on mechanical equipment. Finally, it receives the user's cloud analysis instructions, uploads the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project abnormality analysis, and displays the abnormal analysis results on the smart glasses. That is, the cloud-edge-end collaboration method is used to enable data collection and analysis of the underground process to obtain analysis results faster, thereby improving the efficiency of underground operations.

[0120] Secondly, according to the needs of underground engineering, targeted applications and functional modules are developed for smart glasses to improve the functionality of smart glasses. In underground engineering, they can combine cloud-edge data for data analysis while allowing staff to make judgments based on on-site data, which can further meet specific business processes and operational needs.

[0121] See also Figure 2 The present application provides an embodiment of a method for obtaining operation data of well engineering equipment, comprising:

[0122] 201. Read the characteristic QR code of the well engineering equipment and connect it with the underground Internet of Things through the characteristic QR code;

[0123] 202. Obtain the operating data of well engineering equipment from the service center through the underground Internet of Things, and display the operating data on smart glasses.

[0124] In this embodiment, information such as the operating status and parameter indicators of downhole machinery and equipment is uploaded to the service middle platform through the Internet of Things, and each downhole machinery and equipment corresponds to a unique characteristic QR code. When the smart glasses view the relevant machinery and equipment, the staff can call the camera of the glasses through voice, identify the QR code on the machine, and at this time, the service middle platform can push relevant operating status, parameter indicators and other information to the glasses through the Internet of Things.

[0125] This method requires building a dedicated Internet of Things for the downhole. By accessing the enterprise's private database and privately deployed AI, it can ensure that all downhole data is circulated within the enterprise, thus greatly enhancing data security and protecting the user's private data.

[0126] Moreover, by performing more AI calculations and data processing within the device or local area network, it reduces the dependence on Internet connection, improves the response speed, and reduces the impact brought by unstable external networks.

[0127] Please refer to Figure 3 , another embodiment of a method for obtaining the operating data of well engineering equipment provided by this application includes:

[0128] 301. Read the characteristic QR code of the well engineering equipment;

[0129] 302. Generate low-frequency acquisition radio waves according to the characteristic QR code, and the low-frequency acquisition radio waves are used to activate the data transmission label on the roadheader;

[0130] 303. Activate the data transmission label of the well engineering equipment through the low-frequency acquisition radio waves so that the data transmission label is activated;

[0131] 304. Receive the operating data transmitted back by the well engineering equipment through the reader and antenna in the smart glasses;

[0132] 305. Display the operating data on the smart glasses.

[0133] In this embodiment, through the image acquisition module on the smart glasses, the characteristic QR code on the well engineering equipment is scanned to obtain the unique identification information of the equipment. Specifically, the characteristic QR code can contain the basic information of the equipment (such as equipment model, installation location, maintenance record, etc.) and the access link of the operating data. The reading of the QR code requires the smart glasses to have high-precision image recognition ability and be able to quickly and accurately identify the QR code in a complex environment (such as insufficient light, damaged equipment surface). The QR code technology can be combined with other identification technologies (in this embodiment, the RFID technology is used) to improve the efficiency and reliability of data acquisition.

[0134] Next, the terminal generates low-frequency acquisition radio waves based on the information in the feature QR code to activate the data transmission tag on the well engineering equipment.

[0135] Low-frequency radio waves (with a frequency range generally from 30 kHz to 300 kHz) have the characteristics of strong penetration and good anti-interference ability, and are suitable for use in the complex underground environment. The intelligent glasses terminal generates low-frequency radio waves unique to the underground equipment through the feature QR code. The device for generating low-frequency radio waves can be a radio frequency module built into the intelligent glasses or an external low-frequency signal generator. The generation of low-frequency radio waves needs to be encoded according to the device information in the feature QR code to ensure that the signal can accurately activate the data transmission tag of the target device. Specifically, in this embodiment, RFID technology is used. The RFID system in this embodiment usually consists of the following parts:

[0136] 1. Tag: The tag is the core component of the RFID system and is usually composed of a chip and an antenna. The chip is used to store data (such as unique identifiers, device information, etc.), and the antenna is used to receive and send radio signals.

[0137] 2. Reader: The reader is used to send radio signals to the tag and receive the data returned by the tag. The reader usually consists of an antenna, a radio frequency module, and a control module. The reader can be fixed (such as an access control system) or portable (such as a handheld device).

[0138] 3. Antenna: The antenna is used to transmit radio signals between the tag and the reader. The performance of the antenna directly affects the reading distance and stability of the RFID system.

[0139] 4. Backend System: The backend system is used to process and manage the data obtained from the reader. The backend system can be a simple database or a complex enterprise resource planning (ERP) system.

[0140] Please refer to Figure 4 , an embodiment of a method for project anomaly analysis provided by this application includes:

[0141] 401. Receive the cloud analysis instruction of the user and extract the underground tunneling rock layer image in the tunneling area working condition data;

[0142] 402. Input the underground tunneling rock layer image into the AI model in the well engineering cloud for rock layer type feature analysis to generate rock layer type data;

[0143] 403. When the rock layer type data shows that the rock hardness of the current underground tunneling rock layer exceeds the working range of the tunneling module, determine that there is an abnormal rock mass mutation in the rock layer;

[0144] 404. Obtain the operating audio data of the roadheader from the operating component condition data, and perform preliminary detection of drill bit anomalies on the audio data through an audio analysis model;

[0145] 405. When the preliminary detection of drill bit anomalies shows that there are frequency bands with drill bit anomalies, obtain the image of the tunneling component from the operating component condition data;

[0146] 406. Determine the area to be detected in the tunneling component image according to the abnormal frequency band of the operating audio data;

[0147] 407. Perform drill bit defect detection on the area to be detected in the tunneling component image through a drill bit defect analysis model, and generate a drill bit detection result;

[0148] 408. When the drill bit detection result shows that there are anomalies in the drill bit of the roadheader, perform AI simulation of reverse drill bit wear on the underground tunneling rock formation image, and generate wear analysis data;

[0149] 409. Perform drill bit damage matching on the drill bit detection result and the wear analysis data, and generate a drill bit damage result;

[0150] 410. Integrate the rock formation anomaly result and the drill bit detection result to generate an abnormal analysis result for the well engineering project, and display the abnormal analysis result of the well engineering project on the smart glasses.

[0151] In this embodiment, the user sends a cloud analysis instruction through the smart glasses, and the system extracts the underground tunneling rock formation image from the condition data of the tunneling area. The condition data includes various sensor data such as real-time images, audio, and vibration in the tunneling area. The underground tunneling rock formation image can be collected by a camera or an underground imaging device, and the image contains information such as the texture, color, and structure of the rock formation. The extracted image needs to be preprocessed (such as denoising and enhancement) to improve the accuracy of subsequent analysis.

[0152] Upload the underground tunneling rock formation image to the well engineering cloud, and use the AI model to analyze the rock formation type characteristics of the enterprise to generate rock formation type data. The AI model is an image classification model based on deep learning (such as convolutional neural network CNN) according to the well engineering data of the current enterprise, and can identify the type of rock formation (such as sandstone, shale, granite, etc.). The rock formation type data includes physical properties such as the hardness, density, and compressive strength of the rock formation. Cloud analysis can combine historical data and geological databases to improve the accuracy of analysis.

[0153] The cloud determines whether the hardness of the rock stratum exceeds the working range of the tunneling module based on the rock stratum type data. If it exceeds, it is determined as an abnormal rock mass mutation. The working range of the tunneling module is usually determined by the performance parameters of the equipment (such as the bit material, motor power, etc.). When the hardness of the rock stratum exceeds the working range, it may cause equipment damage or efficiency decline. An abnormal rock mass mutation needs to be warned in time to adjust the tunneling strategy or replace the equipment.

[0154] In this embodiment, the cloud extracts audio data from the operating component condition data of the tunneling machine and uses an audio analysis model to detect whether there is an abnormality in the bit. The audio data can be collected by a microphone installed on the tunneling machine. The audio analysis model can be an anomaly detection model based on machine learning, which can identify abnormal sounds (such as friction sounds, fracture sounds). The preliminary detection result can mark the frequency band where an abnormality may exist.

[0155] If the audio analysis result shows an abnormal frequency band, the image of the tunneling component is extracted from the condition data. The image of the tunneling component can be collected by a camera or an industrial endoscope. The image needs to include key components such as the bit and the cutter head for subsequent analysis.

[0156] In this embodiment, the cloud locates the area that needs to be focused on detecting in the image of the tunneling component according to the abnormal frequency band of the audio data. There may be a corresponding relationship between the abnormal frequency band and specific parts of the bit (such as the cutter teeth, bearings). Determining the area to be detected can improve the efficiency of image analysis.

[0157] In this embodiment, the cloud uses a bit defect analysis model to perform defect detection on the area to be detected and generates a bit detection result. The bit defect analysis model can be an image segmentation model based on deep learning, which can identify defects such as cracks, wear, and fractures. The detection result can include the type, location, and severity of the defect.

[0158] Next, it is judged whether there is an abnormality in the bit. If the bit detection result shows an abnormality, an AI model is used to simulate the wear process of the bit in the rock stratum to generate wear analysis data. The reverse wear simulation can combine the rock stratum type data and the bit material parameters to predict the wear condition of the bit. The wear analysis data can be used to evaluate the remaining life and maintenance requirements of the bit. The bit detection result is matched with the wear analysis data to generate a bit damage result. The matching process can combine historical data and expert knowledge to improve the accuracy of the result. The bit damage result can include the cause, degree, and repair suggestions of the damage.

[0159] Integrate the abnormal results of the rock formation and the bit damage results to generate the abnormal analysis results of the well engineering project and display them on the smart glasses. The abnormal analysis results may include information such as rock formation mutation, bit damage, and equipment risks. The display interface of the smart glasses needs to be simple and intuitive, supporting various forms such as charts, text, and color coding. The display content can combine augmented reality (AR) technology to overlay the analysis results on real equipment.

[0160] Secondly, by utilizing the internal downhole knowledge base and data resources, the smart glasses can provide real-time information query and task guidance to help employees complete their work more efficiently.

[0161] Please refer to Figure 5 , an embodiment of a method for inventory query provided by this application includes:

[0162] 501. When the abnormal analysis result of the well engineering project indicates that there is a component damage in the well engineering equipment, receive the user's inventory query instruction for the well engineering equipment;

[0163] 502. Access the well engineering inventory database to query the inventory situation in real time and locate the position of the item.

[0164] In this embodiment, when the well engineering equipment is damaged, the terminal or the staff can access the inventory database through the smart glasses, and the worker can query the inventory situation in real time and locate the position of the item to realize the intelligent query of the enterprise database information.

[0165] Please refer to Figure 6 , an embodiment of a method for well engineering environment monitoring provided by this application includes:

[0166] 601. When the abnormal analysis result of the well engineering project indicates an abnormal mutation in the rock quality of the rock formation, send a collection signal to the sensors of the downhole gas environment and the rock formation environment;

[0167] 602. Analyze the downhole working environment data transmitted back by the sensors of the downhole gas environment and the rock formation environment, and display the downhole environment change data on the smart glasses.

[0168] In this embodiment, when there is an abnormal mutation in the rock quality of the rock formation, the smart glasses will focus on sending a collection signal to the sensors of the downhole gas environment and the rock formation environment to detect the changes in the well engineering environment in real time and analyze whether the changes will affect the work of the staff.

[0169] The smart glasses can collect and upload on-site data (such as images, sounds, temperatures), perform real-time analysis and feedback to the workers for optimizing processes or increasing production.

[0170] And it can monitor environmental conditions (such as noise level, concentration of harmful gases) or the wearing situation of personal protective equipment, and issue an alarm in a timely manner in case of danger.

[0171] Moreover, in terms of skills training and drills, smart glasses can use virtual and augmented reality technologies to provide immersive training experiences, enabling new employees to simulate real scenarios for learning and shortening the time to get on the job.

[0172] Project progress tracking: Provide real-time updates and visual displays of project progress to help employees better plan and execute tasks.

[0173] Personalized information push: According to the roles and permissions of users, push relevant reports, announcements or notifications to the glasses interface to achieve efficient transmission of information flow.

[0174] Through these functions, enterprises can use smart glasses to improve operational efficiency and reduce costs.

[0175] Please refer to Figure 7 , this application provides an embodiment of a device for regional connection and merging, including:

[0176] A collection unit 701, configured to receive a collection instruction of a user through smart glasses, and collect a feature two-dimensional code on the well engineering equipment with anomalies through an image collection module;

[0177] A first acquisition unit 702, configured to obtain operation data of the well engineering equipment according to the feature two-dimensional code, and display the operation data on the smart glasses;

[0178] Optionally, the well engineering equipment interacts with the underground Internet of Things;

[0179] The first acquisition unit 702 includes:

[0180] Read the feature two-dimensional code of the well engineering equipment, and connect to the underground Internet of Things through the feature two-dimensional code;

[0181] Obtain the operation data of the well engineering equipment from the service center through the underground Internet of Things, and display the operation data on the smart glasses.

[0182] Optionally, the well engineering equipment is a roadheader, and the roadheader is equipped with an activatable data transmission label;

[0183] The first acquisition unit 702 includes:

[0184] Read the feature two-dimensional code of the well engineering equipment;

[0185] Generate low-frequency acquisition radio waves according to the feature two-dimensional code, and the low-frequency acquisition radio waves are used to activate the data transmission label on the roadheader;

[0186] Activate the data transmission tag of the well engineering equipment by collecting radio waves at low frequency, so that the data transmission tag is activated;

[0187] Receive the operation data transmitted back by the well engineering equipment through the reader and antenna in the smart glasses;

[0188] Display the operation data on the smart glasses.

[0189] The first analysis unit 703 is used to receive the user's real-time data processing instruction, perform real-time analysis on the operation data of the well engineering equipment, and display the real-time analysis result on the smart glasses;

[0190] The second acquisition unit 704 is used to receive the user's directional acquisition instruction, and obtain the real-time working condition data collected by the downhole working condition acquisition equipment according to the real-time analysis result. The working condition data is the real-time operation occasion data of the working area of the well engineering equipment;

[0191] The second analysis unit 705 is used to receive the user's cloud analysis instruction, upload the operation data, real-time analysis result and real-time working condition data to the well engineering cloud for project anomaly analysis, and display the anomaly analysis result on the smart glasses.

[0192] Optionally, the real-time working condition data includes the operation component working condition data and the tunneling area working condition data of the well engineering equipment;

[0193] The second analysis unit 705 includes:

[0194] Receive the user's cloud analysis instruction, and extract the downhole tunneling rock layer image from the tunneling area working condition data;

[0195] Input the downhole tunneling rock layer image into the AI model in the well engineering cloud for rock layer type feature analysis, and generate rock layer type data;

[0196] When the rock layer type data shows that the rock layer hardness of the current downhole tunneling rock layer exceeds the working range of the tunneling module, it is determined that there is an abnormal rock mass mutation;

[0197] Obtain the operation audio data of the roadheader from the operation component working condition data, and perform preliminary detection of drill bit abnormality on the audio data through the audio analysis model;

[0198] When the preliminary detection of drill bit abnormality shows that there is a frequency band with drill bit abnormality, obtain the tunneling component image from the operation component working condition data;

[0199] Determine the area to be detected of the tunneling component image according to the abnormal frequency band of the operation audio data;

[0200] Perform drill bit defect detection on the area to be detected of the tunneling component image through the drill bit defect analysis model, and generate the drill bit detection result;

[0201] When the drill bit detection result shows that there is an abnormality in the drill bit of the roadheader, perform an AI simulation of reverse drill bit wear on the underground rock formation image during tunneling to generate wear analysis data;

[0202] Perform drill bit damage matching on the drill bit detection result and the wear analysis data to generate a drill bit damage result;

[0203] Integrate the rock formation abnormality result and the drill bit detection result to generate an abnormal analysis result for the well engineering project, and display the abnormal analysis result of the well engineering project on the smart glasses.

[0204] A receiving unit 706, configured to receive a well engineering equipment inventory query instruction from the user when the abnormal analysis result of the well engineering project indicates that there is a component damage in the well engineering equipment;

[0205] A query unit 707, configured to access the well engineering inventory database to query the inventory situation in real time and locate the position of the item;

[0206] A sending unit 708, configured to send a collection signal to the sensors of the underground gas environment and the rock formation environment when the abnormal analysis result of the well engineering project indicates that there is an abnormal change in the rock quality of the rock formation;

[0207] A third analysis unit 709, configured to analyze the underground working environment data transmitted back by the sensors of the underground gas environment and the rock formation environment, and display the underground environment change data on the smart glasses.

[0208] Please refer to Figure 8 , this application provides a well engineering processing device based on smart glasses, including:

[0209] A processor 801, a memory 802, an input / output unit 803, and a bus 804.

[0210] The processor 801 is connected to the memory 802, the input / output unit 803, and the bus 804.

[0211] The memory 802 stores a program, and the processor 801 calls the program to execute the well engineering processing method as described in Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 .

[0212] This application provides a computer-readable storage medium, on which a program is stored, and when the program is executed on a computer, it executes as described in Figure 1 , Figure 2 , Figure 3 , Figure 4 ,Figure 5 and Figure 6 the well engineering treatment method in

[0213] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0214] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0215] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0216] In addition, the functional units in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0217] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present 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 storage medium and includes several instructions for causing 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 described in each embodiment of the present application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, read-only memory), random access memories (RAM, random access memory), magnetic disks, or optical disks, and other various media that can store program codes.

Claims

1. A well engineering treatment method based on smart glasses, characterized in that: include: The smart glasses receive the user's collection instructions, and the image collection module collects the characteristic QR code on the well engineering equipment with abnormalities; Acquire the operation data of the well engineering equipment according to the characteristic QR code, and display the operation data on the smart glasses; receiving a user's real-time data processing instruction, analyzing the operating data of the well engineering equipment in real time, and displaying the real-time analysis results on the smart glasses; Receive a directional acquisition instruction from a user, and acquire real-time operating condition data collected by a downhole operating condition acquisition device according to the real-time analysis result, wherein the operating condition data is real-time operating occasion data of a working area of ​​the downhole engineering equipment; Receive the user's cloud analysis instructions, upload the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project abnormality analysis, and display the abnormality analysis results on the smart glasses.

2. The well engineering treatment method according to claim 1, characterized in that: The well engineering equipment interacts with the underground Internet of Things; Acquiring the operating data of the well engineering equipment according to the characteristic QR code, and displaying the operating data on the smart glasses, comprises: Read the characteristic QR code of the well engineering equipment and connect with the underground Internet of Things through the characteristic QR code; The operation data of the well engineering equipment is obtained from the service center through the underground Internet of Things, and the operation data is displayed on the smart glasses.

3. The well engineering treatment method according to claim 1, characterized in that: The well engineering equipment is a tunneling machine, and the tunneling machine is provided with an activatable data transmission tag; Acquiring the operating data of the well engineering equipment according to the characteristic QR code, and displaying the operating data on the smart glasses, comprises: Read the characteristic QR code of well engineering equipment; Generate a low-frequency acquisition radio wave according to the characteristic two-dimensional code, wherein the low-frequency acquisition radio wave is used to activate a data transmission tag on the tunnel boring machine; activating the data transmission tag of the well engineering equipment by using the low-frequency acquisition radio wave, so that the data transmission tag is activated; Receiving the operation data transmitted back by the well engineering equipment through the reader / writer and antenna in the smart glasses; The operating data is displayed on the smart glasses.

4. The well engineering treatment method according to claim 3, characterized in that: The real-time working condition data includes working condition data of operating components of well engineering equipment and working condition data of the excavation area; Receive the user's cloud analysis command, upload the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project abnormality analysis, and display the abnormality analysis results on the smart glasses, including: Receive the user's cloud analysis instructions and extract the underground excavation rock formation images from the excavation area working condition data; In the well engineering cloud, the underground excavation rock formation image is input into the AI ​​model to perform rock formation type feature analysis to generate rock formation type data; When the rock formation type data shows that the rock formation hardness of the current underground excavation rock formation exceeds the working range of the excavation module, it is determined that there is an abnormal rock formation mutation; Acquire the operation audio data of the tunnel boring machine from the operation component working condition data, and perform preliminary detection of drill bit abnormality on the audio data through an audio analysis model; When the drill bit abnormality preliminary detection shows that there is a frequency band of drill bit abnormality, acquiring a tunneling component image from the operating component working condition data; Determine the to-be-detected area of ​​the tunneling component image according to the abnormal frequency band of the operating audio data; Perform drill bit defect detection on the to-be-detected area of ​​the tunneling component image using a drill bit defect analysis model to generate a drill bit detection result; When the drill bit detection result shows that the drill bit of the tunnel boring machine is abnormal, an AI simulation of reverse drill bit wear is performed on the underground tunneling rock formation image to generate wear analysis data; Perform drill bit damage matching on the drill bit detection result and the wear analysis data to generate a drill bit damage result; The rock formation abnormality results and the drill bit detection results are integrated to generate the well engineering project abnormality analysis results, and the well engineering project abnormality analysis results are displayed on the smart glasses.

5. The well engineering treatment method according to any one of claims 1 to 4, characterized in that: After receiving the user's cloud analysis instruction, uploading the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project abnormality analysis, and displaying the abnormality analysis results on the smart glasses, the method further includes: When the abnormal analysis result of the well engineering project indicates that there is a component damage in the well engineering equipment, receiving a well engineering equipment inventory query instruction from the user; Access the well engineering inventory database to query inventory status and locate items in real time.

6. The well engineering treatment method according to any one of claims 1 to 4, characterized in that: After receiving the user's cloud analysis instruction, uploading the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project abnormality analysis, and displaying the abnormality analysis results on the smart glasses, the method further includes: When the abnormal analysis result of the well engineering project indicates that there is abnormal sudden change in rock quality of the rock formation, a collection signal is sent to sensors of the downhole gas environment and rock formation environment; The underground working environment data transmitted back by the sensors of the underground gas environment and the rock formation environment are analyzed, and the underground environment change data are displayed on the smart glasses.

7. A well engineering treatment device based on smart glasses, characterized in that: include: A collection unit, used to receive a collection instruction from a user through smart glasses, and collect a characteristic QR code on the well engineering equipment with abnormalities through an image collection module; A first acquisition unit, configured to acquire the operation data of the well engineering equipment according to the characteristic two-dimensional code, and display the operation data on the smart glasses; A first analysis unit, configured to receive a user's real-time data processing instruction, analyze the operation data of the well engineering equipment in real time, and display the real-time analysis results on the smart glasses; A second acquisition unit is used to receive a directional acquisition instruction from a user, and acquire the real-time working condition data collected by the downhole working condition acquisition equipment according to the real-time analysis result, wherein the working condition data is the real-time operation occasion data of the working area of ​​the well engineering equipment; The second analysis unit is used to receive the user's cloud analysis instructions, upload the operation data, real-time analysis results and real-time working condition data to the well engineering cloud for project abnormality analysis, and display the abnormality analysis results on the smart glasses.

8. The well engineering treatment device according to claim 7, characterized in that: The well engineering equipment interacts with the underground Internet of Things; The first acquisition unit includes: Read the characteristic QR code of the well engineering equipment and connect with the underground Internet of Things through the characteristic QR code; The operation data of the well engineering equipment is obtained from the service center through the underground Internet of Things, and the operation data is displayed on the smart glasses.

9. A well engineering treatment device for regional connection and merging, characterized in that: Includes processor, memory, input and output unit and bus; The processor is connected to the memory, the input and output unit, and the bus; The memory stores a program, and the processor calls the program to execute the well engineering treatment method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is executed on a computer, the well engineering treatment method according to any one of claims 1 to 6 is executed.

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