Tunnel data processing method and system

Abstract

An embodiment of the present application provides a tunnel data processing method and system, which collects and obtains equipment construction data of construction equipment in a tunnel, as well as tunnel monitoring image data of the construction environment in the tunnel, wherein the equipment construction data includes trajectory data and working condition data of the construction equipment; converts the equipment construction data and the tunnel monitoring image data into optical signals and transmits them outside the tunnel; analyzes the optical signals outside the tunnel to obtain the equipment construction data and the tunnel monitoring image data; identifies, classifies, and analyzes the equipment construction data and the tunnel monitoring image data to obtain and output the construction monitoring data in the tunnel, thereby improving the effective transmission distance and transmission stability of the construction data in the tunnel.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a tunnel data processing method and system. Background Art

[0002] With the booming development of transportation and water conservancy infrastructure, tunnel engineering is increasingly being used in highways, railways, water conservancy, and energy storage. Tunnel construction generates massive amounts of data, including drilling rig data, shotcrete machine operating information, construction site video surveillance data, and environmental monitoring data.

[0003] However, traditional wireless data transmission technology has exposed numerous drawbacks at tunnel construction sites. Due to the complex geological structure and confined working space within tunnels, signal obstruction and interference are common. Conventional wireless transmission methods suffer from severe signal attenuation, leading to frequent packet loss and excessive transmission delays. This makes it difficult to meet the data timeliness requirements for real-time monitoring and precise control of equipment during construction. For example, the numerous rock and metal structures within tunnels can severely reflect and scatter wireless signals, disrupting the signal transmission path and significantly attenuating signal strength. This results in an effective transmission radius that cannot meet the required spacing between construction equipment on site. Furthermore, the 2.4 GHz frequency band commonly used by wireless technology is highly susceptible to electromagnetic interference from numerous electromechanical equipment on construction sites, such as electromagnetic noise generated by the operation of drilling rigs and concrete sprayers. This can lead to an increase in error frames in wireless transmission and packet loss, making it difficult to ensure the continuity and integrity of data from critical construction equipment.

[0004] Therefore, how to improve the effective transmission distance and transmission stability of construction data in tunnels is an urgent problem that needs to be solved. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a tunnel data processing method and system to improve the effective transmission distance and transmission stability of construction data in the tunnel.

[0006] In conjunction with the first aspect of the present application, a tunnel data processing method is provided, the method comprising:

[0007] Collecting and acquiring equipment construction data of construction equipment in the tunnel, as well as tunnel monitoring image data of the construction environment in the tunnel, wherein the equipment construction data includes trajectory data and working condition data of the construction equipment;

[0008] Converting the equipment construction data and the tunnel monitoring image data into optical signals and transmitting them outside the tunnel;

[0009] analyzing the optical signal outside the tunnel to obtain the equipment construction data and the tunnel monitoring image data;

[0010] Identification, classification and analysis operations are performed on the equipment construction data and the tunnel monitoring image data to obtain and output construction monitoring data in the tunnel.

[0011] Optionally, converting the equipment construction data and the tunnel monitoring image data into optical signals and transmitting them outside the tunnel includes:

[0012] performing a first conversion process on the equipment construction data and the tunnel monitoring image data to obtain a first electrical signal corresponding to the equipment construction data and the tunnel monitoring image data;

[0013] performing a second conversion process on the first electrical signal to obtain an optical signal corresponding to the equipment construction data and the tunnel monitoring image data;

[0014] transmitting the optical signal to outside the tunnel;

[0015] The step of analyzing the optical signal outside the tunnel to obtain the equipment construction data and the tunnel monitoring image data includes:

[0016] performing a third conversion process on the optical signal to obtain a first electrical signal corresponding to the equipment construction data and the tunnel monitoring image data;

[0017] The first electrical signal is subjected to a fourth conversion process to restore and obtain the equipment construction data and the tunnel monitoring image data.

[0018] Optionally, the collecting and acquiring equipment construction data of construction equipment in the tunnel and tunnel monitoring image data of the construction environment in the tunnel includes:

[0019] Collecting and acquiring initial equipment construction data of construction equipment in the tunnel, and initial tunnel monitoring image data of the construction environment in the tunnel;

[0020] Performing a first preprocessing operation on the initial equipment construction data to obtain the equipment construction data, wherein the first preprocessing operation includes at least one of the following: fault data diagnosis, abnormal data filtering, and data packaging;

[0021] A second preprocessing operation is performed on the initial tunnel monitoring image data to obtain the initial tunnel monitoring image data, wherein the second preprocessing operation includes at least one of the following: image denoising, image enhancement, image filtering, and image compression.

[0022] Optionally, after acquiring the equipment construction data of the construction equipment in the tunnel and the tunnel monitoring image data of the construction environment in the tunnel, the method further includes:

[0023] determining the spatiotemporal characteristics of the equipment construction data according to the trajectory data in the equipment construction data and the timestamp of the equipment construction data;

[0024] determining the spatiotemporal characteristics of the tunnel monitoring image data according to a timestamp of the tunnel monitoring image data and a scene position in the tunnel monitoring image data;

[0025] Determining, based on the spatiotemporal characteristics of the equipment construction data and the spatiotemporal characteristics of the tunnel monitoring image data, the equipment construction data and the tunnel monitoring image data that are correlated based on the spatiotemporal characteristics;

[0026] According to the equipment construction data and tunnel monitoring image data that are correlated with each other based on spatiotemporal features, the equipment construction data and the tunnel monitoring image data are correlated, summarized and stored.

[0027] Optionally, also include:

[0028] acquiring, based on the construction monitoring data in the tunnel, a remote control instruction related to the construction monitoring data, wherein the remote control instruction is used to remotely control target construction equipment in the tunnel to perform target construction operations;

[0029] converting the remote control command into an optical signal corresponding to the remote control command and transmitting the optical signal into the tunnel;

[0030] In the tunnel, the optical signal corresponding to the remote control instruction is restored to the remote control instruction, and based on the remote control instruction, the target construction equipment is controlled to perform the target construction operation.

[0031] Optionally, also include:

[0032] Acquiring historical equipment construction data of the construction equipment in the tunnel and historical tunnel monitoring image data of the construction environment in the tunnel;

[0033] converting the historical equipment construction data and the historical tunnel monitoring image data into optical signals and transmitting them outside the tunnel;

[0034] Analyzing the optical signal outside the tunnel to obtain the historical equipment construction data and the historical tunnel monitoring image data;

[0035] According to the historical equipment construction data, the timestamp of the historical equipment construction data, the historical tunnel monitoring image data, and the timestamp of the historical tunnel monitoring image data, the historical equipment construction data and the historical tunnel monitoring image data are restored to generate a digital twin environment based on the historical equipment construction data and the historical tunnel monitoring image data.

[0036] Optionally, also include:

[0037] Responding to the user's operation, obtaining a simulation construction operation instruction;

[0038] Based on the simulation construction operation instruction, the simulation construction operation instruction is simulated and executed in the digital twin environment based on the historical equipment construction data and the historical tunnel monitoring image data to generate simulated construction process data and simulated construction image data;

[0039] The simulated construction process data and the simulated construction image data are displayed or output.

[0040] Optionally, after transmitting the optical signal outside the tunnel, the method further includes:

[0041] When receiving the optical signal outside the tunnel, performing a transmission integrity check on the optical signal to generate an optical signal check result;

[0042] Extracting a data packet sequence number set and a check code set carried in the optical signal;

[0043] Comparing the data packet sequence number set with a pre-generated standard transmission sequence template to identify characteristic information of missing data packets;

[0044] Performing data integrity verification on the completely received data packet according to the verification code set to generate a data packet verification result;

[0045] When the optical signal verification result indicates that a data packet is lost or damaged, generating an optical signal retransmission request including characteristic information of the missing data packet;

[0046] converting the optical signal retransmission request into a retransmission instruction optical signal and sending it to a device in the tunnel;

[0047] An optical signal retransmitted according to the optical signal retransmission request is received, and a secondary integrity check is performed on the retransmitted optical signal until a preset transmission integrity threshold is met.

[0048] Optionally, after performing the first preprocessing operation on the initial equipment construction data, the method further includes:

[0049] Performing a data validity verification operation on the equipment construction data that has completed the first preprocessing operation to generate an equipment construction data verification result;

[0050] Comparing the equipment construction data verification result with the original check code of the initial equipment construction data to generate a data integrity comparison result;

[0051] When the data integrity comparison result indicates that the equipment construction data meets the preset data integrity condition, the equipment construction data is stored in the tunnel local cache queue;

[0052] When the data integrity comparison result indicates that the equipment construction data does not meet the preset data integrity condition, a data missing feature set is generated, where the data missing feature set includes a timestamp field of the missing data and a device identification field corresponding to the missing data;

[0053] Sending a data re-collection instruction to the corresponding construction equipment according to the data missing feature set, triggering re-collection of the initial equipment construction data;

[0054] The data validity verification operation includes a three-level verification process: data field integrity check, data timestamp continuity verification, and data value range compliance detection.

[0055] In combination with the second aspect of the present application, a tunnel data processing system is provided, which includes a machine-readable storage medium and a processor, wherein the machine-readable storage medium stores machine-executable instructions. When the processor executes the machine-executable instructions, the tunnel data processing system implements the aforementioned tunnel data processing method.

[0056] In conjunction with the third aspect of the present application, a computer-readable storage medium is provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed, the aforementioned tunnel data processing method is implemented.

[0057] In conjunction with the fourth aspect of the present application, a computer program product is provided, which implements the aforementioned tunnel data processing method when executed by a processor.

[0058] In combination with any of the above aspects, by collecting and acquiring equipment construction data of construction equipment within a tunnel and tunnel monitoring image data of the construction environment within the tunnel, the equipment construction data and the tunnel monitoring image data are converted into optical signals and transmitted outside the tunnel. Outside the tunnel, the optical signals are analyzed to obtain the equipment construction data and the tunnel monitoring image data, and the equipment construction data and the tunnel monitoring image data are identified, classified, and analyzed to obtain and output the construction monitoring data within the tunnel. Thus, by converting the construction-related data within the tunnel into optical signals with a longer effective transmission distance and greater transmission stability for transmission within and outside the tunnel, the effective transmission distance and transmission stability of the construction data within the tunnel are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained by combining these drawings without paying any creative work.

[0060] Figure 1 A flow chart of a tunnel data processing method provided in an embodiment of the present application

[0061] Figure 2 A flowchart of another tunnel data processing method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] To help those skilled in the art better understand the present invention, the following will provide a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. It is apparent that the described embodiments are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by those skilled in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0063] The terms "first," "second," and so on, in the specification and claims of the present invention and the accompanying drawings are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or end comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed therein, or may optionally include other steps or elements inherent to such process, method, product, or end.

[0064] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] Figure 1 The present invention provides a flow chart of a tunnel data processing method according to an embodiment of the present invention. It should be understood that in other embodiments, the order of some steps in the tunnel data processing method of this embodiment can be shared according to actual needs, or some steps can be omitted or maintained. The tunnel data processing method includes the following details:

[0066] S101 : Acquire equipment construction data of construction equipment in a tunnel, and tunnel monitoring image data of the construction environment in the tunnel.

[0067] The equipment construction data includes trajectory data and operating condition data of the construction equipment. Equipment construction data refers to data generated by construction equipment during operations within the tunnel, reflecting the operational status and working conditions of the construction within the tunnel. Trajectory data describes the temporal changes in the spatial position of the construction equipment within the tunnel, such as the latitude, longitude, and elevation coordinates of the shield machine at different times during tunnel excavation. Operating condition data reflects various parameters of the construction equipment's operating status, such as the shield machine's propulsion speed, cutterhead torque, and jack pressure.

[0068] Tunnel monitoring image data can be collected by cameras and other equipment installed in the tunnel, and can reflect image information of the construction environment in the tunnel, such as the excavation surface conditions in the tunnel, the working status of construction personnel, the operating position of construction equipment, etc.

[0069] In this step, various sensors can be installed on the construction equipment within the tunnel to collect equipment construction data. For example, GPS positioning sensors can be installed to obtain trajectory data, and pressure sensors and speed sensors can be installed to obtain operating condition data. Alternatively, motion collection sensors can be installed at various joints of the construction equipment to detect the equipment's motion trajectory and operating condition data.

[0070] For example, taking the construction equipment as a shield machine, torque sensors, speed sensors, inclination sensors, etc. can be installed on the rotating joints of the shield machine (such as the edge of the cutter head, the main drive bearing seat), displacement sensors (used to control the excavation distance of the shield machine by measuring the extension and contraction of the cylinder), pressure sensors (used to monitor the cylinder pressure to determine the propulsion resistance), etc. can be installed on the telescopic joints (such as the connection between the propulsion cylinder and the piston rod), angle sensors can be installed on the swing joints (such as the hinge between the front shield and the middle shield of the shield machine) to determine the turning radius of the shield machine, and speed sensors can be installed on the walking joints (such as the track drive wheels) to monitor the track speed to determine the travel speed, etc.

[0071] At the same time, cameras can be installed at appropriate locations in the tunnel, such as the tunnel top, side walls, etc., or a mobile communication monitoring vehicle can be added to the tunnel, and several sets of pan-tilt cameras can be installed to monitor the tunnel construction environment through the cameras to collect tunnel monitoring image data of the construction environment in the tunnel.

[0072] The equipment construction data of the construction equipment in the tunnel and the tunnel monitoring image data of the construction environment in the tunnel can be automatically collected at preset time intervals, or can be collected in response to a received data collection instruction, etc.

[0073] S102: Convert the equipment construction data and the tunnel monitoring image data into optical signals and transmit them to the outside of the tunnel.

[0074] Optical signals are signals that use light as a carrier, carrying information through changes in parameters such as light intensity, frequency, and phase. In data transmission, optical signals offer advantages such as high transmission speed and strong anti-interference capabilities.

[0075] In this step, the collected equipment construction data and tunnel monitoring image data can be digitized and converted into digital signals. Then, optical transmission equipment is used to convert the digital signals into optical signals. The optical signals are then transmitted outside the tunnel via optical transmission media such as optical fibers installed within the tunnel.

[0076] For example, the data to be converted can be input into a laser driver circuit in the form of an electrical signal. Based on this input electrical signal, the laser driver circuit precisely controls the laser's operating state, causing it to emit a corresponding optical signal in accordance with the electrical signal's changing patterns. For example, changes in the electrical signal's high and low levels can correspond to changes in the optical signal's on and off state, or varying light intensities, thereby encoding the data in an optical signal. Under control of the driver circuit, the laser emits an optical signal carrying the data, which can then be injected into an optical fiber for transmission.

[0077] For example, trajectory and operating condition data collected by the shield machine, as well as tunnel monitoring image data captured by cameras, can be converted into digital signals through a data processing module. These digital signals are then loaded onto a laser beam using an optical transmission module and converted into optical signals. These optical signals are then quickly and stably transmitted to a data reception center outside the tunnel via optical fibers pre-installed in the tunnel sidewalls.

[0078] S103: Analyze the optical signal outside the tunnel to obtain the equipment construction data and the tunnel monitoring image data.

[0079] Optical signal analysis refers to the process of restoring the received optical signal to the original equipment construction data and tunnel monitoring image data, which usually requires conversion from optical signals to electrical signals and subsequent data processing.

[0080] In this step, a light receiving device, such as a photodetector, can be set up outside the tunnel to convert the received light signal into an electrical signal. This electrical signal is then decoded and filtered to remove noise and interference, restoring the original equipment construction data and tunnel monitoring image data.

[0081] S104: Perform identification, classification, and analysis operations on the equipment construction data and the tunnel monitoring image data to obtain and output construction monitoring data in the tunnel.

[0082] Among them, identification and classification refers to dividing equipment construction data and tunnel monitoring image data into different categories based on the characteristics and attributes of the data. For example, trajectory data and working condition data are classified separately, and tunnel monitoring image data is classified according to different scenarios.

[0083] Parsing operations refer to in-depth analysis and processing of classified data to extract valuable information, such as analyzing whether the operating status of equipment is normal from working condition data, and identifying whether the construction workers' operations are standardized from tunnel monitoring image data.

[0084] Construction monitoring data refers to comprehensive data that can reflect the overall construction situation in the tunnel after identification, classification and analysis operations, and can be used for construction management and decision-making.

[0085] In this step, data analysis algorithms and image recognition technologies are used to process equipment construction data and tunnel monitoring image data. Statistical analysis, machine learning, and other methods can be used to identify, classify, and analyze equipment construction data. For example, thresholds can be applied to operating condition data to identify equipment failures. For tunnel monitoring image data, computer vision algorithms can be used for image recognition and analysis, such as identifying whether construction workers in images are wearing safety equipment. Finally, the processed data is integrated into construction monitoring data and output, which can be displayed on display screens, in reports, and other formats.

[0086] The method provided in an embodiment of the present application collects and acquires equipment construction data of construction equipment within a tunnel, as well as tunnel monitoring image data of the construction environment within the tunnel, and converts the equipment construction data and the tunnel monitoring image data into optical signals and transmits them outside the tunnel. The optical signals are then analyzed outside the tunnel to obtain the equipment construction data and the tunnel monitoring image data, and the equipment construction data and the tunnel monitoring image data are identified, classified, and analyzed to obtain and output the construction monitoring data within the tunnel. This method, by converting the construction-related data within the tunnel into optical signals with a longer effective transmission distance and greater transmission stability for transmission within and outside the tunnel, improves the effective transmission distance and transmission stability of the construction data within the tunnel.

[0087] The following is a detailed description of how the equipment construction data and the tunnel monitoring image data are converted into optical signals and transmitted outside the tunnel in the aforementioned step S102. The aforementioned step S102 can be implemented by the following sub-steps:

[0088] S1021. Perform a first conversion process on the equipment construction data and the tunnel monitoring image data to obtain a first electrical signal corresponding to the equipment construction data and the tunnel monitoring image data.

[0089] The first conversion process involves converting the collected raw data, such as equipment construction data and tunnel monitoring images, into electrical signals. These signals typically exhibit certain voltage and current variations, facilitating subsequent processing and transmission.

[0090] The first electrical signal is an electrical signal obtained after the first conversion process, and carries information of equipment construction data and tunnel monitoring image data.

[0091] Specifically, an analog-to-digital converter can be used to perform the first conversion process. The trajectory data and operating condition data in the equipment construction data are generally analog signals. For example, the analog voltage signal output by a pressure sensor is proportional to the equipment's pressure condition; the analog current signal output by a temperature sensor reflects the equipment's temperature condition. This analog equipment construction data is converted into digital electrical signals using an analog-to-digital converter.

[0092] For tunnel monitoring image data, the image captured by the camera enters the camera in the form of a light signal. The image sensor inside the camera can convert the light signal into an analog electrical signal, and then convert it into a digital electrical signal through an analog-to-digital converter.

[0093] For example, consider a highway tunnel construction project where the pressure sensor on a shield machine outputs an analog voltage signal ranging from 0 to 5V, corresponding to different pressure values ​​on the shield machine's jack. A 12-bit analog-to-digital converter converts this analog voltage signal into a digital electrical signal, represented in binary form and ranging from 0 to 4095. This converts the equipment construction data collected by the pressure sensor into a digital electrical signal. Simultaneously, after a high-definition camera installed in the tunnel captures an image, the optical signal is first converted into an analog electrical signal within the camera, and then converted back into a digital electrical signal via the analog-to-digital converter. These digital electrical signals constitute the first electrical signal corresponding to the tunnel monitoring image data.

[0094] S1022: Perform a second conversion process on the first electrical signal to obtain an optical signal corresponding to the equipment construction data and the tunnel monitoring image data.

[0095] The second conversion process is the process of converting the first electrical signal into an optical signal, which is usually achieved through optical modulation technology. Optical signals use light as a carrier and carry information through changes in parameters such as light intensity, frequency, and phase.

[0096] Specifically, an optical modulator can be used to complete the second conversion process. Common optical modulation methods include intensity modulation, which represents the information of an electrical signal by changing the intensity of light. For example, using a semiconductor laser as a light source, the first electrical signal is applied to the laser's drive current. When the electrical signal changes, the intensity of the light emitted by the laser also changes, thereby achieving conversion from an electrical signal to an optical signal.

[0097] For example, the digital electrical signal obtained after the first conversion process can be input into a Mach-Zehnder modulator (MZM). The MZM uses a continuous laser beam as its light source. The digital electrical signal controls the phase of the light wave in the MZM. Through interference, the information in the electrical signal is modulated into the intensity of the optical signal, ultimately outputting an optical signal corresponding to the equipment construction data and tunnel monitoring image data.

[0098] S1023. Transmit the optical signal to the outside of the tunnel.

[0099] Optical signal transmission refers to the process of transmitting optical signals from inside a tunnel to outside the tunnel using optical transmission media. Optical transmission media can effectively reduce the loss of optical signals during transmission, increase the effective transmission distance, and reduce packet loss caused by electromagnetic interference.

[0100] In this step, optical fiber can be used as the optical transmission medium. Optical fiber has low loss and high bandwidth, making it ideal for long-distance optical signal transmission. Optical fiber is laid in the tunnel, and the optical signal is coupled into the fiber. The optical signal is then transmitted to the data receiving end outside the tunnel.

[0101] Alternatively, a transmission path can be constructed using optical cables of varying lengths. Initially, a relatively short, flexible optical cable, such as a 50m cable, is used to carry the converted optical signal from the data collection point. This cable offers excellent flexibility and portability, facilitating connections and adjustments within the complex construction environment of the tunnel. The 50m cable is then connected to a longer-distance optical cable, such as a 1200m cable, using a quick-connect connector. This quick-connect connector ensures a fast and stable connection, minimizing optical signal loss at the connection point. Long-distance optical cables should feature low loss and high bandwidth to ensure high-quality optical signal transmission over extended distances. As tunnel construction progresses, the long-distance optical cable can be deployed and retracted according to the construction schedule. As construction progresses, the optical cable is deployed to extend the transmission distance; if the construction area changes or requires expansion, the cable can be partially retracted.

[0102] For example, continuing with the construction of an urban subway tunnel, the converted optical signal can be extracted from a data collection point near the shield machine via a 50m optical cable. The 50m optical cable is connected to a 1200m portable optical cable via a quick aviation plug. As the shield machine continues to dig forward, the 1200m optical cable can be released synchronously with the construction progress, ensuring that the optical signal can be continuously and stably transmitted from inside the tunnel to outside. If the position of the shield machine needs to be adjusted or excavation needs to be temporarily stopped during construction, a portion of the 1200m optical cable can be retracted as needed to prevent cable accumulation or obstruction in the tunnel. Ultimately, the optical signal is transmitted outside the tunnel for subsequent processing via this transmission path composed of optical cables of different lengths.

[0103] In a possible implementation, after transmitting the optical signal outside the tunnel, the method may further include:

[0104] When the optical signal is received outside the tunnel, a transmission integrity check is performed on the optical signal to generate an optical signal verification result. The data packet sequence number set and the check code set carried in the optical signal are extracted. The data packet sequence number set is compared with a pre-generated standard transmission sequence template to identify the characteristic information of the missing data packet. The data integrity of the completely received data packet is verified according to the check code set to generate a data packet verification result. When the optical signal verification result indicates that there is data packet loss or data corruption, an optical signal retransmission request containing the characteristic information of the missing data packet is generated. The optical signal retransmission request is converted into a retransmission instruction optical signal and sent to the device in the tunnel. The optical signal retransmitted according to the optical signal retransmission request is received, and a secondary integrity check is performed on the retransmitted optical signal until a preset transmission integrity threshold is met.

[0105] In this implementation, when receiving an optical signal outside the tunnel, a transmission integrity check can be performed to generate an optical signal verification result. This is a critical operation that verifies the integrity of the optical signal during transmission and checks for packet loss or data corruption. For example, a cyclic redundancy check (CRC) algorithm can be used to implement this. Specifically, the received optical signal is first converted into an electrical signal using a photodetector. A CRC algorithm is then used to calculate a checksum value for the received data, which is then compared with the original checksum value carried in the optical signal. If the two values ​​match, the optical signal transmission is intact; if they differ, packet loss or data corruption may have occurred. For example, in a tunnel construction project, the original optical signal carries a CRC checksum value of 0x1234, and the calculated checksum value matches it, indicating that the optical signal transmission is intact. If the calculated value is 0x5678, a possible problem may exist.

[0106] Next, the data packet sequence number set and check code set carried in the optical signal are extracted. After the optical signal is received and converted into an electrical signal, extraction is performed according to the pre-defined protocol format. For example, the protocol specifies that the packet sequence number is located in the first four bytes of the data frame, and the check code is located in the last two bytes. By parsing the electrical signal, the sequence number and check code of each packet are extracted and formed into corresponding sets. In the aforementioned tunnel project, if three packets are received, the extracted packet sequence number set might be [1, 2, 3], and the check code set might be [0xABCD, 0xEF01, 0x2345].

[0107] The data packet sequence number set is then compared with a pre-generated standard transmission sequence template to identify the signature information of missing data packets. The standard transmission sequence template is a pre-set standard order of data packet sequence numbers. By comparing them one by one, the sequence numbers that exist in the standard template but are missing from the data packet sequence number set are identified. These missing sequence numbers are the signature information of missing data packets. For example, if the standard transmission sequence template is [1,2,3,4,5] and the received data packet sequence number set is [1,3,5], then the signature information of the missing data packets is sequence numbers 2 and 4.

[0108] The data integrity of the completely received data packet is then verified using the check code set to generate a packet verification result. The check code for the completely received data packet is recalculated and compared with the corresponding check code in the check code set. If they match, the packet data is intact; if they do not, the data is corrupted. In this tunnel project, if the recalculated check code for the packet with sequence number 1 is 0xABCD, which matches the corresponding value in the set, the verification result is that the data is intact. If the recalculated value for sequence number 3 is 0x6789, which does not match 0xEF01 in the set, the packet data is corrupted.

[0109] When the optical signal verification results indicate packet loss or data corruption, an optical signal retransmission request containing the missing packet's characteristic information is generated. This request is encapsulated using a specific format, including the missing packet's sequence number, and then supplemented with necessary information such as a request identifier. For example, in this project, if packets with sequence numbers 2 and 4 are missing, they are encapsulated with the request identifier "RETRANSMIT" to form an optical signal retransmission request.

[0110] The optical signal retransmission request is then converted into a retransmission command optical signal and sent to the equipment inside the tunnel. Using an optical modulator, such as a Mach-Zehnder modulator, the optical signal retransmission request is input as an electrical signal. The modulator applies the electrical signal to a laser beam, generating a retransmission command optical signal that is then sent via the optical fiber laid within the tunnel.

[0111] Finally, the optical signal retransmitted in response to the optical signal retransmission request is received and subjected to a second integrity check until it meets the preset transmission integrity threshold. Using the same verification method as the first check, if the second check indicates that the optical signal transmission integrity meets the preset threshold (e.g., the packet integrity rate reaches 100%), the optical signal transmission is considered successful. Otherwise, further optical signal retransmission requests are sent until the requirement is met. In this tunnel project, if the retransmitted optical signal still does not meet the preset threshold, the above request retransmission and verification process is repeated to ensure that the optical signal is ultimately transmitted accurately and completely.

[0112] In one possible implementation, after performing a secondary integrity check on the retransmitted optical signal until it meets a preset transmission integrity threshold, the number of optical signal retransmissions can be counted to generate a transmission quality assessment indicator. Examples of such indicators include the number of optical signal retransmissions and a transmission path stability indicator. The transmission path stability indicator can be demonstrated by monitoring changes in the signal attenuation coefficient and data packet loss rate of an alternative transmission path (after switching paths when the number of retransmissions exceeds a threshold). The signal attenuation coefficient reflects the loss of signal strength during transmission, while the data packet loss rate reflects the proportion of data lost during transmission.

[0113] The signal attenuation coefficient can be obtained by installing specialized monitoring equipment (such as optical power meters and optical time domain reflectometers) at key nodes along the transmission path. The monitoring equipment can measure the signal strength in real time and calculate the signal attenuation coefficient based on the difference between the input and output signal strengths.

[0114] The packet loss rate can be determined by marking each transmitted packet during data transmission and counting the number of packets received at the receiving end. By comparing the number of packets sent by the sender with the number of packets received by the receiver, the packet loss rate can be calculated. For example, if the sender sent 1000 packets and the receiver only received 950, the packet loss rate is (1000 - 950) / 1000 = 5%.

[0115] When the number of optical signal retransmissions exceeds a preset retransmission threshold (which can be set according to actual needs and is not limited in this application), a transmission path optimization strategy is initiated. The transmission path optimization strategy may include the following steps:

[0116] a. Detect the path characteristic parameters of the currently used optical signal transmission path.

[0117] Among them, path characteristic parameters may include, for example, a signal attenuation coefficient that reflects the intensity loss of the optical signal during transmission (the larger the attenuation coefficient, the faster the signal strength decreases, resulting in a decrease in signal quality at the receiving end and affecting communication reliability), a data packet loss rate that represents the proportion of data packets lost during transmission to the total number of data packets sent, etc.

[0118] Specifically, you can use an optical power meter or optical time-domain reflectometer (OTDR) to measure the optical power at the transmitting and receiving ends of the optical signal along the transmission path. The difference between the two can be used to determine the signal attenuation coefficient. Monitoring points can be set up to capture and analyze data packets along the transmission path, counting the number of packets sent and received, and calculating the packet loss rate.

[0119] b. Select an alternative transmission path with the best channel quality from the set of backup transmission paths.

[0120] For each path in the backup transmission path set, measure its path characteristic parameters using the same method as in step a. Based on pre-defined evaluation criteria (such as minimum signal attenuation coefficient, lowest data packet loss rate, and minimum latency), select the path with the best channel quality from the backup paths as the replacement transmission path.

[0121] In this step, multiple fiber optic sensor nodes can be deployed within the tunnel to collect real-time optical signal strength data for each transmission path. A transmission path quality assessment matrix is ​​constructed, containing each path's real-time signal strength, historical outage counts, and current load ratio. A priority score for each path is calculated based on the transmission path quality assessment matrix. The priority ranking of the optical signal transmission paths is dynamically adjusted based on the priority scores, and an alternative transmission path with the best channel quality is determined.

[0122] Fiber optic sensor nodes are used to collect real-time optical signal strength data on each transmission path. This data can directly reflect the attenuation of optical signals during transmission. (For example, in a 10-kilometer-long tunnel, a fiber optic sensor node can be deployed every 500 meters to monitor the intensity of optical signals in real time.)

[0123] The transmission path quality assessment matrix comprehensively evaluates the quality of transmission paths. For example, it may include information such as each transmission path's real-time signal strength, historical outage counts, and current load factor. Based on this matrix, a priority score for each path can be calculated. A higher score indicates better channel quality and a more suitable alternative transmission path. For example, the three paths described above can be scored based on pre-set scoring rules, such as adding 1 point for every 1dBm increase in real-time signal strength, 2 points for every decrease in historical outage counts, and 1 point for every 10% decrease in current load factor.

[0124] Specifically, the optical signal strength data collected by the fiber optic sensor nodes for each backup transmission path can be used to calculate the historical outage counts and current load rate data for each backup transmission path. This data, along with the historical outage counts and current load rate data for each backup transmission path, can then be integrated to form a transmission path quality assessment matrix. Each backup transmission path in the transmission path quality assessment matrix is ​​then scored based on the aforementioned pre-set scoring rules, and the scoring results are ranked. If the current transmission path experiences a fault or quality degradation, an alternative transmission path with the best channel quality can be selected based on the ranking results.

[0125] c. Switch the subsequent optical signal transmission to an alternative transmission path.

[0126] d. Real-time monitoring of the signal attenuation coefficient and data packet loss rate of alternative transmission paths.

[0127] Deploy optical power meters, network performance analyzers, and other equipment at key nodes along the alternative transmission path to monitor signal attenuation and packet loss in real time. Based on the monitoring results, dynamically adjust the transmission path or optimize the strategy.

[0128] e. When the signal attenuation coefficient exceeds the safety threshold, a transmission path switching alarm is triggered.

[0129] The safety threshold can be set based on data transmission requirements and transmission path characteristics, and this application does not impose any restrictions on this. When the signal attenuation coefficient is detected to exceed the threshold, it indicates that the performance of the transmission path is poor, and the transmission path can be switched. A corresponding alarm message is output to inform the user that the transmission path needs to be switched or has been switched.

[0130] The following is a detailed description of how to analyze the optical signal outside the tunnel to obtain the equipment construction data and the tunnel monitoring image data in the aforementioned step S103. The aforementioned step S103 can be implemented by the following sub-steps:

[0131] S1031. Perform a third conversion process on the optical signal to obtain a first electrical signal corresponding to the equipment construction data and the tunnel monitoring image data.

[0132] The third conversion process refers to the process of converting the received optical signal back into an electrical signal, which is usually achieved through a photodetector.

[0133] The first electrical signal refers to the first electrical signal here, which is essentially the same as the first electrical signal obtained in S1021, but is restored from the optical signal after being transmitted through the optical signal.

[0134] Specifically, a photodetector such as a photodiode can be used for the third conversion process. The photodetector can convert the light signal into a current signal, and then convert the current signal into a voltage signal through a subsequent circuit, thereby obtaining the first electrical signal.

[0135] For example, a PIN photodiode is installed as a photodetector at the data receiving center outside the tunnel. When a light signal transmitted from inside the tunnel strikes the PIN photodiode, it generates a current signal proportional to the light signal intensity. This current signal is converted to a voltage signal by a transimpedance amplifier, resulting in a first electrical signal corresponding to the equipment construction data and tunnel monitoring image data.

[0136] S1032: Perform a fourth conversion process on the first electrical signal to restore and obtain the equipment construction data and the tunnel monitoring image data.

[0137] The fourth conversion process refers to the process of performing operations such as decoding and decompression on the first electrical signal to restore it to the original equipment construction data and tunnel monitoring image data.

[0138] Specifically, since the equipment construction data is digitized during the first conversion process, it can be decoded using digital signal processing algorithms, restoring the binary digital electrical signals to specific trajectory data and operating condition data. For example, data parsing software can be used to convert digital electrical signals into corresponding pressure values, speed values, etc. according to pre-set encoding rules.

[0139] For tunnel monitoring image data, if image compression or other operations were performed during the first conversion process, decompression and image reconstruction can be performed to achieve the fourth conversion process. For example, an image decoding algorithm, such as a JPEG decoding algorithm, can be used to restore the compressed digital electrical signal to the original image data.

[0140] The following is a detailed description of how to collect and obtain the equipment construction data of the construction equipment in the tunnel and the tunnel monitoring image data of the construction environment in the tunnel in step S101. Step S101 can be implemented by the following sub-steps:

[0141] S1011. Acquire initial equipment construction data of construction equipment in the tunnel, and initial tunnel monitoring image data of the construction environment in the tunnel.

[0142] In this step, various sensors can be installed on the construction equipment in the tunnel to collect initial equipment construction data. For example, GPS positioning sensors can be installed to obtain trajectory data, pressure sensors, speed sensors, etc. can be installed to obtain working condition data, or motion collection sensors can be installed at each motion joint position of the construction equipment to detect the motion trajectory, working condition data, etc. of the construction equipment through various sensors.

[0143] At the same time, cameras can be installed at appropriate locations in the tunnel, such as the tunnel top, side walls, etc., or a mobile communication monitoring vehicle can be added to the tunnel, and several sets of pan-tilt cameras can be installed to monitor the tunnel construction environment through the cameras to collect initial tunnel monitoring image data of the construction environment in the tunnel.

[0144] The initial equipment construction data of the construction equipment in the tunnel and the initial tunnel monitoring image data of the construction environment in the tunnel may be automatically collected at preset time intervals or collected in response to a received data collection instruction.

[0145] S1012: Perform a first preprocessing operation on the initial equipment construction data to obtain the equipment construction data.

[0146] The first preprocessing operation includes at least one of the following: fault data diagnosis, abnormal data filtering, and data packaging.

[0147] Specifically, for fault data diagnosis, anomaly detection models within machine learning algorithms can be employed. For example, using methods based on statistical distribution, a reasonable threshold range can be set by calculating the mean and standard deviation of the data. When the data exceeds this threshold range, it is considered that the equipment may be faulty. Taking the torque data of a shield machine as an example, if the torque value normally fluctuates within a relatively stable range, if at a certain moment the torque value suddenly and significantly exceeds this range, it may indicate that the shield machine's cutterhead has encountered an obstacle or a problem with a mechanical component.

[0148] A sliding average filter can be used to filter out abnormal data. This algorithm calculates the average value of data within a specific time window and uses this average value to replace the original data within the window. For example, the propulsion speed data of a shield machine may contain individual abnormal values ​​due to momentary interference from the sensor. Using a sliding average filter, these abnormal values ​​are smoothed out, making the data more representative of the actual operating status of the equipment.

[0149] Data packaging can be done by data type and time sequence. For example, shield machine trajectory data and working condition data (such as pressure, temperature, etc.) within the same time period can be packaged into a data packet and the corresponding timestamp and data identifier can be added to facilitate subsequent transmission and processing.

[0150] For example, for the initial equipment construction data collected for a shield machine, an anomaly detection model can first be used to diagnose the cutterhead torque data for faults. If the torque value suddenly increases and exceeds the normal range, the system will issue an alarm to indicate a possible fault. A sliding average filter algorithm is then used on the shield machine's propulsion speed data to remove outliers caused by sensor jitter. Finally, the processed trajectory data, pressure data, speed data, etc. are packaged into data packets every 10 minutes, and each data packet has a unique identifier and corresponding timestamp.

[0151] S1013: Perform a second preprocessing operation on the initial tunnel monitoring image data to obtain the initial tunnel monitoring image data.

[0152] The second preprocessing operation includes at least one of the following: image denoising, image enhancement, image filtering, and image compression.

[0153] Specifically, image denoising can be performed using a median filter. This algorithm replaces the value of each pixel in an image with the median of the pixels in its neighborhood, thereby removing random noise such as salt and pepper noise. For example, in tunnel surveillance images, random white or black noise may appear due to unstable lighting or interference from the camera itself. Median filtering can effectively remove this noise.

[0154] Image enhancement can be achieved through histogram equalization. This method adjusts the image's grayscale histogram to make the grayscale distribution more uniform, thereby enhancing the image's contrast. For example, an image captured in a dimly lit tunnel has low overall contrast. Histogram equalization can make the details in the image clearer.

[0155] Gaussian filtering can be used for image filtering. Gaussian filtering is a linear smoothing filter that smoothes images by performing a convolution operation on them while preserving their key features. For example, for edges in tunnel monitoring images, Gaussian filtering can smooth them to a certain extent and reduce jagged edges.

[0156] Image compression can use the JPEG compression algorithm. This algorithm significantly reduces the image data size by removing redundant information while maintaining a certain level of image quality. For example, using the JPEG compression algorithm can reduce the file size of large-scale tunnel monitoring images captured by high-definition cameras to a fraction of their original size, or even smaller.

[0157] For example, for initial tunnel monitoring image data, a median filter can be used to remove salt-and-pepper noise from the image, making it clearer. Histogram equalization can then be used to enhance image contrast, making the construction scene within the tunnel more distinct. Next, a Gaussian filter can be used to smooth the image, reducing noise and jagged edges. Finally, the JPEG compression algorithm can be used to compress the processed image, reducing the file size from several megabytes to a few hundred kilobytes, facilitating subsequent transmission and storage.

[0158] In one possible implementation, after performing the first preprocessing operation on the initial equipment construction data, the method of the present application may further include:

[0159] Perform a data validity verification operation on the equipment construction data that has completed the first preprocessing operation to generate an equipment construction data verification result. Compare the equipment construction data verification result with the original checksum of the initial equipment construction data to generate a data integrity comparison result. When the data integrity comparison result indicates that the equipment construction data meets the preset data integrity condition, the equipment construction data is stored in the tunnel local cache queue. When the data integrity comparison result indicates that the equipment construction data does not meet the preset data integrity condition, generate a data missing feature set, the data missing feature set including the timestamp field of the missing data and the device identification field corresponding to the missing data. Send a data re-collection instruction to the corresponding construction equipment according to the data missing feature set to trigger the re-collection of the initial equipment construction data. The data validity verification operation includes a three-level verification process of data field integrity check, data timestamp continuity verification, and data value range compliance detection.

[0160] In this implementation, a data validity verification operation can be performed on the equipment construction data that has completed the first pre-processing operation to generate an equipment construction data verification result. The data validity verification operation includes a three-level verification process: data field integrity check, data timestamp continuity check, and data value range compliance check.

[0161] Data field integrity verification checks whether all fields in equipment construction data are complete and free of omissions. For example, in subway tunnel construction, shield machine equipment construction data should include fields such as trajectory data (such as latitude, longitude, and elevation) and operating condition data (such as propulsion speed and cutterhead torque). If the propulsion speed field is missing from a data entry, the data will fail field integrity.

[0162] Data timestamp continuity verification checks whether the data timestamps are arranged in a reasonable and continuous sequence. For example, under normal circumstances, shield machine data should be recorded in chronological order. If timestamps jump or become disordered, such as the previous data timestamp is 9:00 AM, and the next data timestamp is 11:00 AM, with no transition data in between, then the timestamp continuity requirement is not met.

[0163] Data value range compliance testing determines whether the data value is within a reasonable range. For example, the cutterhead torque of a shield machine has a specific range during normal operation. If the collected data shows that the cutterhead torque is far beyond this range, the data is not compliant with the numerical range.

[0164] After completing data validity verification, the equipment construction data verification results can be compared with the original checksum of the initial equipment construction data to generate a data integrity comparison result. The original checksum is generated during the initial equipment construction data collection and is used to verify data integrity. By comparing the verification results with the original checksum, it is possible to determine whether the data was damaged or lost during the preprocessing process.

[0165] If the data integrity comparison indicates that the equipment construction data meets the pre-set data integrity criteria, it can be stored in the tunnel's local cache queue. This cache queue serves as a temporary storage area to facilitate subsequent data processing and transmission. For example, in a subway tunnel construction monitoring system, shield machine equipment construction data that meets the integrity criteria is stored in the local cache queue, awaiting transmission to a server outside the tunnel for analysis.

[0166] When the data integrity comparison results indicate that the equipment construction data does not meet the preset data integrity conditions, a missing data feature set is generated. This feature set contains the timestamp field of the missing data and the corresponding equipment identification field of the missing data. For example, if data for a shield machine is missing from 10:00 AM to 10:15 AM, the timestamp of this time period and the equipment identification field of the shield machine will be recorded in the missing data feature set.

[0167] Then, based on the missing data feature set, a data re-collection instruction can be sent to the corresponding construction equipment, triggering the re-collection of the initial equipment construction data. During subway tunnel construction, if a shield machine is detected to have some missing data, the monitoring system can send a data re-collection instruction to the shield machine based on the equipment identifier in the missing data feature set, instructing it to re-collect the initial equipment construction data for a specified time period. This ensures the integrity and accuracy of the data and provides a reliable basis for subsequent construction monitoring and decision-making.

[0168] Figure 2A flow chart of another tunnel data processing method provided in an embodiment of the present application is provided. After acquiring equipment construction data of construction equipment in the tunnel and tunnel monitoring image data of the construction environment in the tunnel, the method of the present application may further include the following steps:

[0169] S201 : Determine spatiotemporal characteristics of the equipment construction data according to trajectory data in the equipment construction data and a timestamp of the equipment construction data.

[0170] In this step, the coordinate information and corresponding timestamps in the trajectory data can be analyzed to calculate parameters such as the rate of change of the equipment's position and direction at different time points, thereby determining the spatiotemporal characteristics of the equipment's construction data. For example, the equipment's movement speed can be determined by calculating the distance and time difference between trajectory coordinates corresponding to adjacent timestamps; the equipment's direction of rotation can be determined by comparing the directional changes in trajectory coordinates at different time points.

[0171] For example, consider a railway tunnel construction project where the shield machine's trajectory data records the coordinates of different locations within the tunnel, with each coordinate data point accompanied by a corresponding timestamp. Analysis of this data reveals that the shield machine moved from coordinates (X1, Y1, Z1) to coordinates (X2, Y2, Z2) between 9:00 AM and 9:30 AM. Based on the distance and time difference between these two coordinate points, the average speed of the shield machine during this period can be calculated. Furthermore, by comparing the directions of these two coordinate points, the shield machine's direction of excavation during this period can be determined. This speed and direction information constitutes the spatiotemporal characteristics of the equipment construction data for that time period.

[0172] S202: Determine spatiotemporal features of the tunnel monitoring image data according to a timestamp of the tunnel monitoring image data and a scene position in the tunnel monitoring image data.

[0173] Specifically, the scene's location information can be first identified from tunnel monitoring image data. For example, the specific coordinates of the scene within the tunnel can be determined by using tunnel wall markings and the location of construction equipment in the image. Then, combined with the image data's timestamps, the scene's state changes at different times can be determined, thereby obtaining the spatiotemporal characteristics of the tunnel monitoring image data.

[0174] For example, suppose that during the construction of the aforementioned railway tunnel, cameras installed inside the tunnel captured a series of surveillance images at different times. For one image, taken at 2 p.m., the specific coordinates of the scene within the tunnel are determined based on the position of the tunnel boring machine (TBM) shown in the image and the mileage markings on the tunnel wall. Comparing images taken at adjacent times reveals that the TBM advanced a certain distance between 2 p.m. and 2:15 p.m., and the placement of surrounding construction materials changed. This scene location information, along with its state changes over time, constitutes the spatiotemporal characteristics of the tunnel surveillance image data.

[0175] S203 , determining the equipment construction data and the tunnel monitoring image data that are correlated based on the temporal and spatial characteristics according to the temporal and spatial characteristics of the equipment construction data and the temporal and spatial characteristics of the tunnel monitoring image data.

[0176] In this step, by comparing the timestamps and spatial location information of the equipment construction data and the tunnel monitoring image data, data at or near the same time point and the same spatial location can be found. For example, if the equipment construction data shows that the shield machine was at a certain position in the tunnel at a certain moment, and tunnel monitoring images taken at or near the same moment match the location of the shield machine, then these equipment construction data and tunnel monitoring image data can be considered to be interconnected based on temporal and spatial characteristics.

[0177] Specifically, this step can be implemented through the following sub-steps:

[0178] S2031. Extract a timestamp set and a three-dimensional space coordinate set from the spatiotemporal features of the equipment construction data.

[0179] The timestamp set refers to the set of time records corresponding to the generation of equipment construction data at different times. Each timestamp accurately identifies the specific time when the data is generated.

[0180] The three-dimensional space coordinate set refers to the set of position coordinates of the construction equipment in the three-dimensional space recorded in the equipment construction data, usually expressed as (x, y, z), which reflects the spatial position of the equipment in the tunnel.

[0181] Specifically, the equipment construction data can be traversed, and the timestamp information can be extracted to form a timestamp set. The corresponding 3D spatial coordinate information can also be extracted to form a 3D spatial coordinate set. For example, in subway tunnel construction, the equipment construction data of a shield machine contains its operating status and position information at different times. Program code parses this data, extracting the timestamp (e.g., "2025-04-10 09:00:00" or "2025-04-10 09:15:00") from each data record to form a timestamp set. The corresponding 3D spatial coordinates (e.g., (100, 200, 30) or (105, 202, 32)) can also be extracted to form a 3D spatial coordinate set.

[0182] S2032: Extracting image acquisition time sequence and image spatial positioning information from the spatiotemporal features of the tunnel monitoring image data.

[0183] Image spatial positioning information refers to the specific spatial location information of the scene captured by the tunnel monitoring image within the tunnel, which can be determined by landmarks, equipment locations, etc. in the image.

[0184] Specifically, the image acquisition time can be extracted from the metadata of tunnel monitoring image data and arranged chronologically to form an image acquisition time series. For image spatial positioning information, image recognition technology can be used to identify characteristic elements in the image (such as tunnel wall numbers, the location of specific construction equipment, etc.), and combined with tunnel map information to determine the spatial location of the scene captured by the image. For example, in the aforementioned subway tunnel construction, by reading the image metadata recorded by the surveillance camera, the image acquisition time (such as "2025-04-10 09:02:00" and "2025-04-10 09:18:00") is extracted to form an image acquisition time series. Simultaneously, image recognition algorithms are used to identify the position of the shield machine in the image and the mileage markings on the tunnel wall, determining the spatial coordinates of the image capture scene and forming the image spatial positioning information.

[0185] S2033: Perform dynamic time window alignment processing on the timestamp set and the image acquisition time series to generate a time synchronization feature set.

[0186] Dynamic time window alignment processing refers to matching and aligning the timestamp set and image acquisition time series according to a certain time window range to find data pairs within a similar time range.

[0187] The time synchronization feature set refers to a data feature set with a time synchronization relationship obtained after dynamic time window alignment processing.

[0188] In this step, a dynamic time window can be set, such as ±5 minutes. For each timestamp in the timestamp set, the image acquisition time sequence is searched for image acquisition times that fall within the range of ±5 minutes of the timestamp. These matching data pairs are recorded to form a time synchronization feature set. For example, if the timestamp set of equipment construction data contains a timestamp of "2025-04-10 09:10:00", and a search in the image acquisition time sequence reveals that "2025-04-10 09:12:00" falls within the range of ±5 minutes of the timestamp, then these two times form a time synchronization feature pair. All such feature pairs are aggregated to form a time synchronization feature set.

[0189] S2034: Perform spatial grid matching processing on the three-dimensional spatial coordinate set and the image spatial positioning information to generate a spatial correlation feature set.

[0190] Spatial grid matching processing refers to dividing the space inside the tunnel into several grids, mapping the three-dimensional spatial coordinate set and image space positioning information to these grids respectively, and finding data pairs in the same or adjacent grids.

[0191] The spatial correlation feature set refers to a data feature set with spatial correlation relationship obtained after spatial grid matching processing.

[0192] In this step, the space inside the tunnel can be divided into grids of uniform size, for example, each grid is 10m×10m×5m. Each coordinate point in the three-dimensional spatial coordinate set is mapped to the corresponding grid, and the image spatial positioning information is also mapped to the corresponding grid. For the equipment construction data coordinates and image spatial positioning information in the same grid or adjacent grids, they are recorded to form a spatial correlation feature set. For example, there is a coordinate point (102,203,31) in the three-dimensional spatial coordinate set mapped to grid A, and the coordinate corresponding to the image spatial positioning information is mapped to the adjacent grid B. Then these two data form a spatial correlation feature pair, and all such feature pairs are aggregated to form a spatial correlation feature set.

[0193] S2035: Input the time synchronization feature set and the space correlation feature set into a spatiotemporal correlation calculation model to generate a spatiotemporal correlation weight coefficient matrix.

[0194] The spatiotemporal correlation calculation model is a mathematical model used to calculate the temporal and spatial correlation between equipment construction data and tunnel monitoring image data. The spatiotemporal correlation weight coefficient matrix is ​​the matrix output by the spatiotemporal correlation calculation model. The elements in the matrix represent the spatiotemporal correlation weights between different data pairs.

[0195] In this step, a machine learning algorithm (such as a neural network) can be used to construct a spatiotemporal correlation calculation model. The model takes a set of time-synchronized features and a set of spatially correlated features as input. After training and calculation, the model outputs a matrix. The rows and columns of the matrix correspond to the equipment construction data and tunnel monitoring image data, respectively. Each element in the matrix represents the spatiotemporal correlation weight between the corresponding data pair. For example, the element (i, j) in the matrix represents the spatiotemporal correlation weight between the i-th piece of equipment construction data and the j-th piece of tunnel monitoring image data.

[0196] S2036 , screening the spatiotemporal correlation weight coefficient matrix based on a preset spatiotemporal correlation threshold to obtain a subset of equipment construction data and a subset of tunnel monitoring image data that meet the correlation conditions.

[0197] The preset spatiotemporal correlation threshold is used to determine whether the spatiotemporal correlation between data pairs meets the required level. The equipment construction data subset is a data set selected from the original equipment construction data to show a strong spatiotemporal correlation with the tunnel monitoring image data. The tunnel monitoring image data subset is a data set selected from the original tunnel monitoring image data to show a strong spatiotemporal correlation with the equipment construction data.

[0198] In this step, a spatiotemporal correlation threshold, such as 0.8, can be set. The spatiotemporal correlation weight coefficient matrix is ​​traversed. For data pairs with matrix element values ​​greater than or equal to 0.8, the corresponding equipment construction data and tunnel monitoring image data are extracted, forming the equipment construction data subset and the tunnel monitoring image data subset, respectively. For example, if the value of the matrix element (3,5) is 0.9, then the third piece of equipment construction data and the fifth piece of tunnel monitoring image data meet the correlation condition and are added to the equipment construction data subset and the tunnel monitoring image data subset, respectively.

[0199] S2037: Perform data fusion processing on the equipment construction data subset and the tunnel monitoring image data subset to form a spatiotemporal correlation data group.

[0200] The spatiotemporal correlation data set includes the association between the equipment construction data and tunnel monitoring image data, which are mutually correlated based on spatiotemporal features. Data fusion processing involves integrating the equipment construction data subset and the tunnel monitoring image data subset to associate the related data. The spatiotemporal correlation data set refers to the data set containing the association between the equipment construction data and tunnel monitoring image data, formed after the data fusion processing.

[0201] Specifically, the equipment construction data subset and the tunnel monitoring image data subset can be combined according to certain rules. For example, identification information of the corresponding tunnel monitoring image can be added to each equipment construction data record, and identification information of the corresponding equipment construction data can be added to each tunnel monitoring image record. These associated data are stored in a new data structure, forming a spatiotemporally correlated data set. For example, for each record in the equipment construction data subset, the number of the associated tunnel monitoring image is added; for each image record in the tunnel monitoring image data subset, the number of the associated equipment construction data is added. This establishes an association between the equipment construction data and the tunnel monitoring image data, forming a spatiotemporally correlated data set.

[0202] S204 : According to the equipment construction data and the tunnel monitoring image data that are correlated with each other based on the spatiotemporal characteristics, the equipment construction data and the tunnel monitoring image data are correlated, summarized, and stored.

[0203] Specifically, a database can be established to store related data in the same table or record. Within the database, different fields can be set up to store relevant information about equipment construction data and tunnel monitoring image data, such as timestamps, spatial locations, equipment operating conditions, and image file paths. Furthermore, a unique identifier can be assigned to each related data group to facilitate subsequent management and query.

[0204] Finally, after obtaining tunnel construction monitoring data using the methods of the aforementioned embodiments, this data can be used to implement real-time data monitoring, historical data backtracking, and simulation data simulation. The following details the specific implementation methods for these three uses: real-time data monitoring, historical data backtracking, and simulation data simulation.

[0205] Real-time data monitoring:

[0206] The construction monitoring data within the tunnel is stored in a local host and / or a cloud server to monitor the construction environment and the operating conditions of the construction equipment within the tunnel in real time. Based on the construction monitoring data within the tunnel, remote control instructions related to the construction monitoring data are obtained, and the remote control instructions are used to remotely control target construction equipment within the tunnel to perform target construction operations. The remote control instructions are converted into optical signals corresponding to the remote control instructions and transmitted into the tunnel. Within the tunnel, the optical signals corresponding to the remote control instructions are restored to the remote control instructions, and based on the remote control instructions, the target construction equipment is controlled to perform the target construction operations.

[0207] Under this implementation, the construction monitoring data in the tunnel can first be stored in the local host and / or cloud server. Among them, the local host can provide fast data access and processing capabilities, which is suitable for real-time data analysis and monitoring. The cloud server has powerful storage and computing capabilities and can be used for long-term data storage and complex data analysis. By storing the construction monitoring data on the local host and the cloud server at the same time, real-time monitoring of the construction environment and the working conditions of the construction equipment in the tunnel can be achieved. For example, in the construction of a subway tunnel, the construction monitoring data includes information such as the propulsion speed of the shield machine, the torque of the cutter head, the temperature and humidity in the tunnel. These data are stored in real time on the local monitoring host and the cloud server. The monitoring personnel can view the operating status of the shield machine in real time through the monitoring interface of the local host, or they can monitor it from anywhere with a network through the remote access function of the cloud server.

[0208] Next, based on the construction monitoring data within the tunnel, remote control commands related to that data are obtained. These remote control commands are generated based on the actual conditions reflected by the construction monitoring data and are used to remotely control the execution of targeted construction operations by target construction equipment within the tunnel. For example, if the construction monitoring data indicates that the shield machine is advancing too fast and may cause damage to the tunnel wall, the monitoring system can generate a remote control command to reduce the advance speed based on preset rules. This command is intended to control the shield machine to execute the reduced advance speed operation to ensure construction safety.

[0209] The remote control command is then converted into an optical signal corresponding to the command and transmitted into the tunnel. Optical signals, due to their high transmission speed and strong anti-interference capabilities, are well-suited for data transmission in complex environments like tunnels. An optical modulator can be used to load the remote control command in the form of an electrical signal onto an optical carrier, generating an optical signal. For example, in the aforementioned subway tunnel construction, the remote control command to reduce the shield machine's propulsion speed is converted into an optical signal using a Mach-Zehnder modulator. This signal is then transmitted via optical fiber laid within the tunnel to a receiving device within the tunnel.

[0210] Finally, within the tunnel, the optical signal corresponding to the remote control command is converted back into a remote control command, and the target construction equipment is controlled based on the remote control command to perform the target construction operation. Light receiving devices, such as photodetectors, are installed within the tunnel to convert the received optical signals into electrical signals. After decoding and other processing, the original remote control command is restored. The remote control command is then sent to the control system of the target construction equipment, which controls the equipment to perform the corresponding operation. For example, in a subway tunnel, a photodetector receives an optical signal and converts it into an electrical signal. After decoding, the signal is converted into a remote control command to reduce the propulsion speed of a shield machine. This command is then sent to the shield machine's control system, which adjusts the speed accordingly, thereby achieving remote control of the construction equipment and ensuring safe and smooth tunnel construction.

[0211] Historical data backtracking:

[0212] Obtain historical equipment construction data of construction equipment within the tunnel, as well as historical tunnel monitoring image data of the construction environment within the tunnel. Convert the historical equipment construction data and the historical tunnel monitoring image data into optical signals and transmit them outside the tunnel. Analyze the optical signals outside the tunnel to obtain the historical equipment construction data and the historical tunnel monitoring image data. Restore the historical equipment construction data and the historical tunnel monitoring image data based on the historical equipment construction data, the timestamps of the historical equipment construction data, the historical tunnel monitoring image data, and the timestamps of the historical tunnel monitoring image data, to generate a digital twin environment based on the historical equipment construction data and the historical tunnel monitoring image data.

[0213] In this implementation, historical equipment construction data includes operational information about construction equipment over different time periods. For example, historical trajectory data for a shield machine (TBM) records its latitude, longitude, and elevation at each moment, reflecting the machine's route within the tunnel. Furthermore, operating condition data, such as propulsion speed and cutterhead torque, reflects the machine's operating status at different construction stages. Historical tunnel monitoring image data consists of images captured at different times by cameras installed within the tunnel. These images record changes in the tunnel construction environment over time, such as the progress of the tunnel excavation surface and the work of construction personnel. This historical data can be accessed through the data storage devices built into the construction equipment and the storage devices of surveillance cameras. For example, during subway tunnel construction, the shield machine's control system can periodically store equipment construction data on its internal hard drive, and surveillance cameras can also store captured images on a local storage server.

[0214] Next, the acquired historical equipment construction data and historical tunnel monitoring image data are converted into optical signals and transmitted outside the tunnel. Optical signals, due to their advantages such as fast transmission speed and strong anti-interference capabilities, are well-suited for data transmission over long distances and in complex environments. Historical data can first be digitized and converted into digital signals suitable for transmission. Then, using optical transmission equipment such as a laser, the digital signals are loaded onto an optical carrier, achieving the conversion of electrical signals into optical signals. The optical signals are then transmitted to a data reception center outside the tunnel via optical fibers laid within the tunnel. For example, in the aforementioned subway tunnel construction, historical data stored on the shield machine's hard drive and the surveillance camera storage server is read, digitized, and converted into optical signals using an optical transmission module. This data is then transmitted via optical fiber to the monitoring room outside the tunnel.

[0215] Then, outside the tunnel, the optical signals are analyzed to obtain historical equipment construction data and historical tunnel monitoring image data. At the data receiving center outside the tunnel, optical receiving devices, such as photodetectors, convert the received optical signals into electrical signals. These electrical signals are then decoded and filtered to remove any noise and interference that may have occurred during transmission, restoring the original historical equipment construction data and historical tunnel monitoring image data. For example, after a photodetector converts the optical signal into an electrical signal, data processing software decodes and filters the electrical signal to ultimately obtain complete historical data.

[0216] Finally, based on historical equipment construction data, its timestamps, historical tunnel monitoring image data, and its timestamps, this historical data is restored to generate a digital twin environment based on the historical equipment construction data and tunnel monitoring image data. The digital twin environment is a virtual representation of the tunnel construction process. The historical equipment construction data and tunnel monitoring image data can be chronologically arranged and linked using timestamps. Using this linked data, a virtual environment highly similar to the actual tunnel construction process can be constructed on a computer, recreating the entire tunnel construction process. For example, in a digital twin environment for subway tunnel construction, the trajectory and operating status of a shield machine can be simulated based on historical equipment construction data. Simultaneously, historical tunnel monitoring image data can be combined to display construction scenes within the tunnel at different times. This provides construction managers with an intuitive historical overview of the construction process, enabling them to better analyze ongoing construction issues and optimize subsequent construction plans.

[0217] Simulation data simulation:

[0218] In response to a user operation, a simulated construction operation instruction is obtained. Based on the simulated construction operation instruction, the simulated construction operation instruction is simulated and executed in the digital twin environment based on the historical equipment construction data and the historical tunnel monitoring image data to generate simulated construction process data and simulated construction image data. The simulated construction process data and the simulated construction image data are displayed or output.

[0219] In tunnel construction data processing and application, the digital twin environment built based on historical equipment construction data and historical tunnel monitoring image data can be used to simulate construction and provide strong support for construction planning and decision-making.

[0220] In this step, the user operation can be initiated through an interactive interface, which can be a software interface specially designed for tunnel construction simulation, or a module integrated into a large-scale construction management system. The interactive interface can provide various operation options and input boxes to facilitate users to input or select the required simulation construction operation instructions. For example, on a subway tunnel construction simulation software interface, users can select different construction scenarios through a drop-down menu, such as normal tunneling of the shield machine, handling when encountering obstacles, etc.; they can also enter specific parameters in the input box, such as the propulsion speed of the shield machine, the cutterhead torque adjustment value, etc. When the user completes the operation and clicks the "Start Simulation" button, the system responds to this operation, extracts the information entered by the user from the interface, and organizes it into simulation construction operation instructions.

[0221] The digital twin environment is a virtual recreation of the actual tunnel construction scene. It is constructed based on a wealth of historical equipment construction data and historical tunnel monitoring image data. It includes tunnel geographic information, construction equipment models, and operating rules. Upon receiving simulated construction operation instructions, the system inputs these instructions into the digital twin environment's simulation engine. The simulation engine performs calculations and deductions based on the instructions and the models and rules within the digital twin environment. For example, if the instruction is to increase the shield machine's propulsion speed by 10%, the simulation engine calculates the changes in these parameters (such as cutterhead torque and jack pressure) based on the relationship between the shield machine's propulsion speed and other parameters (such as cutterhead torque and jack pressure) in historical data. It also considers the impact of factors such as the tunnel's geological conditions and the surrounding environment on construction.

[0222] During the simulated execution of operational instructions, the system records changes in various parameters in real time, generating simulated construction process data. This data includes operating parameters of the construction equipment (such as the shield machine's propulsion speed, cutterhead speed, and torque), tunnel deformation (such as tunnel wall displacement and stress), and construction progress. For example, during the simulation of the shield machine increasing its propulsion speed, the system can record the actual propulsion speed, changes in cutterhead torque, and displacement of the tunnel wall at different locations at regular intervals. This data is stored in chronological order, forming a complete dataset for the simulated construction process.

[0223] Furthermore, to more intuitively demonstrate the simulated construction process, the system can generate simulated construction image data based on the simulated construction process data. Computer graphics technology can be used to visualize the virtual model and simulation process within the digital twin environment. For example, 3D modeling software and a rendering engine can be used to visualize the position and posture of the shield machine at different times, as well as the deformation of the tunnel. This image can be static, showing the construction scene at a specific moment, or dynamic, showing the entire simulated construction process continuously.

[0224] Optionally, the simulated construction process data and simulated construction image data can be displayed on the interactive interface. The simulated construction process data can be displayed in the form of tables, charts, etc., which are convenient for users to view and analyze. For example, the parameters such as the propulsion speed and cutter head torque of the shield machine can be displayed in the form of a line graph, allowing users to intuitively see the changing trends of these parameters during the simulation process. For simulated construction image data, a dedicated area can be opened on the interface to display images or videos. Users can control the video through the controls on the operation interface, such as play, pause, fast forward, etc., so as to carefully observe the details of the simulated construction process.

[0225] In addition to displaying the data on the interface, simulated construction process data and simulated construction image data can also be exported to other storage devices or files. For example, saving simulated construction process data as a CSV file facilitates subsequent in-depth analysis using data analysis software; saving simulated construction image data as a video file can be used for reporting, training, and other purposes. Users can click the "Export" button on the interface to select the output file format and storage path to complete the data export operation.

[0226] Through the above steps, users can perform simulated construction in a digital twin environment, obtain simulated construction process data and simulated construction image data, and display and output them, providing a strong basis for tunnel construction planning and decision-making.

[0227] To facilitate understanding, the following describes a possible tunnel data processing system for implementing the above method. The tunnel data processing system includes: a first data acquisition unit, a second data acquisition unit, a first data processing unit, a second data processing unit, a signal transmission unit, a signal receiving unit, a first signal conversion unit, a second signal conversion unit, and a processing unit. In one possible implementation, the system may also include an operation unit and a display unit.

[0228] Among them, the first data acquisition unit is used to collect and obtain equipment construction data of construction equipment in the tunnel; the second data acquisition unit is used to collect tunnel monitoring image data of the construction environment in the tunnel; the first data processing unit is used to preprocess and store the collected equipment construction data; the second data processing unit is used to preprocess and store the collected tunnel monitoring image data; the first signal conversion unit is used to convert the equipment construction data and the tunnel monitoring image data into optical signals; the signal transmission unit is used to transmit the optical signals to the outside of the tunnel; the signal receiving unit is used to receive optical signals; the second signal conversion unit is used to analyze the optical signals to obtain the equipment construction data and the tunnel monitoring image data; the processing unit is used to identify, classify and analyze the equipment construction data and the tunnel monitoring image data, obtain and output the construction monitoring data in the tunnel, and based on the construction monitoring data in the tunnel, realize real-time data monitoring, historical data backtracking, simulation data simulation and other purposes.

[0229] The operating unit may include, for example, an operating handle, touch screen, keypad, toggle switch, and master switch in a control center outside the tunnel, providing remote control instructions in response to user operations. The display unit may be used to display real-time construction monitoring data and digital twin models within the tunnel.

[0230] Optionally, the first data acquisition unit and the second data acquisition unit may be integrated into the same data acquisition unit, or may be two different data acquisition units.

[0231] Optionally, the aforementioned first data processing unit and second data processing unit may be integrated into the same data processing unit, or may be two different data processing units.

[0232] The tunnel data processing system provided in the embodiment of the present application can execute the tunnel data processing method in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here.

[0233] In the above embodiment, the tunnel data processing system for executing the above method embodiment has at least one processor, a control module (chip set) coupled to at least one of the (at least one) processor, a memory coupled to the control module, a non-volatile memory (NVM) / storage device coupled to the control module, at least one input / output device coupled to the control module, and a network interface coupled to the control module.

[0234] The processor may include at least one single-core or multi-core processor, and the processor may include any combination of general-purpose processors or dedicated processors (such as graphics processors, application processors, baseband processors, etc.). For some alternative implementations, the tunnel data processing system can serve as a tunnel data processing system device such as the gateway described in the embodiments of this application.

[0235] For some alternative embodiments, the tunnel data processing system may include at least one computer-readable medium (e.g., a memory or NVM / storage device) having instructions and at least one processor integrated with the at least one computer-readable medium and configured to execute the instructions to implement a module to perform the actions described in the present disclosure.

[0236] For one embodiment, the control module may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) and / or any suitable device or component in communication with the control module.

[0237] The control module may include a memory controller module to provide an interface to the memory. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0238] The memory may be used, for example, to load and store data and / or instructions for the tunnel data processing system. For one embodiment, the memory may include any suitable volatile memory, such as a suitable DRAM.

[0239] For one embodiment, the control module may include at least one input / output controller to provide an interface to the NVM / storage device and the (at least one) input / output device.

[0240] For example, NVM / storage devices may be used to store data and / or instructions. The NVM / storage devices may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (at least one) non-volatile storage device (e.g., at least one hard disk drive (HDD), at least one compact disc (CD) drive, and / or at least one digital versatile disc (DVD) drive).

[0241] The NVM / storage device may include storage resources that are physically part of the device on which the tunnel data processing system is installed, or that are accessible to the device without being part of the device. For example, the NVM / storage device may be accessible via (at least one) input / output device over a network.

[0242] The (at least one) input / output device may provide an interface for the tunnel data processing system to communicate with any other appropriate device. The input / output device may include a communication component, a phonetic component, a sensor component, etc. The network interface may provide an interface for the tunnel data processing system to communicate via at least one network. The tunnel data processing system may wirelessly communicate with at least one component of a wireless network in accordance with any of at least one wireless network standard and / or protocol, for example, accessing a wireless network in accordance with a communication standard.

[0243] For one embodiment, at least one of the (at least one) processors may be loaded together with the logic of at least one controller of a control module (e.g., a memory controller module). For one embodiment, at least one of the (at least one) processors may be loaded together with the logic of at least one controller of a control module to form a system-level load. For one embodiment, at least one of the (at least one) processors may be integrated on the same die with the logic of at least one controller of a control module. For one embodiment, at least one of the (at least one) processors may be integrated on the same die with the logic of at least one controller of a control module to form a system-on-chip (SoC).

[0244] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0245] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the tunnel data processing method described in the aforementioned embodiment.

[0246] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the tunnel data processing method described in the above embodiment.

[0247] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0248] Through the detailed description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus the necessary general hardware platform, or of course, by hardware. Based on this understanding, the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. The software product can be stored in a computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM), or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of storing or storing data.

[0249] Finally, it should be noted that what is disclosed above is only a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A tunnel data processing method, characterized in that: include: Collecting and acquiring equipment construction data of construction equipment in the tunnel, as well as tunnel monitoring image data of the construction environment in the tunnel, wherein the equipment construction data includes trajectory data and working condition data of the construction equipment; Converting the equipment construction data and the tunnel monitoring image data into optical signals and transmitting them outside the tunnel; analyzing the optical signal outside the tunnel to obtain the equipment construction data and the tunnel monitoring image data; performing identification, classification, and analysis operations on the equipment construction data and the tunnel monitoring image data to obtain and output construction monitoring data within the tunnel; Acquiring historical equipment construction data of the construction equipment in the tunnel and historical tunnel monitoring image data of the construction environment in the tunnel; converting the historical equipment construction data and the historical tunnel monitoring image data into optical signals and transmitting them outside the tunnel; Analyzing the optical signal outside the tunnel to obtain the historical equipment construction data and the historical tunnel monitoring image data; According to the historical equipment construction data, the timestamp of the historical equipment construction data, the historical tunnel monitoring image data, and the timestamp of the historical tunnel monitoring image data, the historical equipment construction data and the historical tunnel monitoring image data are restored to generate a digital twin environment based on the historical equipment construction data and the historical tunnel monitoring image data.

2. The tunnel data processing method according to claim 1, characterized in that: The converting the equipment construction data and the tunnel monitoring image data into optical signals and transmitting them outside the tunnel includes: performing a first conversion process on the equipment construction data and the tunnel monitoring image data to obtain a first electrical signal corresponding to the equipment construction data and the tunnel monitoring image data; performing a second conversion process on the first electrical signal to obtain an optical signal corresponding to the equipment construction data and the tunnel monitoring image data; transmitting the optical signal to outside the tunnel; The step of analyzing the optical signal outside the tunnel to obtain the equipment construction data and the tunnel monitoring image data includes: performing a third conversion process on the optical signal to obtain a first electrical signal corresponding to the equipment construction data and the tunnel monitoring image data; The first electrical signal is subjected to a fourth conversion process to restore and obtain the equipment construction data and the tunnel monitoring image data.

3. The tunnel data processing method according to claim 2, characterized in that: The collecting and acquiring equipment construction data of construction equipment in the tunnel, and tunnel monitoring image data of the construction environment in the tunnel, includes: Collecting and acquiring initial equipment construction data of construction equipment in the tunnel, and initial tunnel monitoring image data of the construction environment in the tunnel; Performing a first preprocessing operation on the initial equipment construction data to obtain the equipment construction data, wherein the first preprocessing operation includes at least one of the following: fault data diagnosis, abnormal data filtering, and data packaging; A second preprocessing operation is performed on the initial tunnel monitoring image data to obtain the initial tunnel monitoring image data, wherein the second preprocessing operation includes at least one of the following: image denoising, image enhancement, image filtering, and image compression.

4. The tunnel data processing method according to any one of claims 1 to 3, characterized in that: After acquiring the equipment construction data of the construction equipment in the tunnel and the tunnel monitoring image data of the construction environment in the tunnel, the method further includes: determining the spatiotemporal characteristics of the equipment construction data according to the trajectory data in the equipment construction data and the timestamp of the equipment construction data; determining the spatiotemporal characteristics of the tunnel monitoring image data according to a timestamp of the tunnel monitoring image data and a scene position in the tunnel monitoring image data; Determining, based on the spatiotemporal characteristics of the equipment construction data and the spatiotemporal characteristics of the tunnel monitoring image data, the equipment construction data and the tunnel monitoring image data that are correlated based on the spatiotemporal characteristics; According to the equipment construction data and tunnel monitoring image data that are correlated with each other based on spatiotemporal features, the equipment construction data and the tunnel monitoring image data are correlated, summarized and stored.

5. The tunnel data processing method according to any one of claims 1 to 3, characterized in that: Also includes: acquiring, based on the construction monitoring data in the tunnel, a remote control instruction related to the construction monitoring data, wherein the remote control instruction is used to remotely control target construction equipment in the tunnel to perform target construction operations; converting the remote control command into an optical signal corresponding to the remote control command and transmitting the optical signal into the tunnel; In the tunnel, the optical signal corresponding to the remote control instruction is restored to the remote control instruction, and based on the remote control instruction, the target construction equipment is controlled to perform the target construction operation.

6. The tunnel data processing method according to claim 1, characterized in that: Also includes: Responding to the user's operation, obtaining a simulation construction operation instruction; Based on the simulation construction operation instruction, the simulation construction operation instruction is simulated and executed in the digital twin environment based on the historical equipment construction data and the historical tunnel monitoring image data to generate simulated construction process data and simulated construction image data; The simulated construction process data and the simulated construction image data are displayed or output.

7. The tunnel data processing method according to claim 2, characterized in that: After transmitting the optical signal to the outside of the tunnel, the method further includes: When receiving the optical signal outside the tunnel, performing a transmission integrity check on the optical signal to generate an optical signal check result; Extracting a data packet sequence number set and a check code set carried in the optical signal; Comparing the data packet sequence number set with a pre-generated standard transmission sequence template to identify characteristic information of missing data packets; Performing data integrity verification on the completely received data packet according to the verification code set to generate a data packet verification result; When the optical signal verification result indicates that a data packet is lost or damaged, generating an optical signal retransmission request including characteristic information of the missing data packet; converting the optical signal retransmission request into a retransmission instruction optical signal and sending it to a device in the tunnel; An optical signal retransmitted according to the optical signal retransmission request is received, and a secondary integrity check is performed on the retransmitted optical signal until a preset transmission integrity threshold is met.

8. The tunnel data processing method according to claim 3, characterized in that: After performing the first pre-processing operation on the initial equipment construction data, the method further includes: Performing a data validity verification operation on the equipment construction data that has completed the first preprocessing operation to generate an equipment construction data verification result; Comparing the equipment construction data verification result with the original check code of the initial equipment construction data to generate a data integrity comparison result; When the data integrity comparison result indicates that the equipment construction data meets the preset data integrity condition, the equipment construction data is stored in the tunnel local cache queue; When the data integrity comparison result indicates that the equipment construction data does not meet the preset data integrity condition, a data missing feature set is generated, where the data missing feature set includes a timestamp field of the missing data and a device identification field corresponding to the missing data; Sending a data re-collection instruction to the corresponding construction equipment according to the data missing feature set, triggering re-collection of the initial equipment construction data; The data validity verification operation includes a three-level verification process: data field integrity check, data timestamp continuity verification, and data value range compliance detection.

9. A tunnel data processing system, characterized in that: The method comprises a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a computer, the tunnel data processing method according to any one of claims 1 to 8 is implemented.

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