A real-time monitoring method and system for underground cable tunnel construction process

Abstract

The application relates to a real-time monitoring method and system for an underground cable channel construction process, wherein the method comprises the following steps: configuring a unique passive coding mark for each cable and a pipe; reading the passive coding information in real time during the construction process, recording the passive coding mark and position information of the cable and the pipe, and collecting auxiliary data to obtain the positioning characteristics of the pipe and the multi-modal characteristics of the cable; based on the positioning characteristics of the pipe and the multi-modal characteristics of the cable, adopting an automatic positioning matching algorithm to perform dynamic trajectory matching, three-dimensional fusion scoring and self-learning dynamic threshold adaptation, so that the automatic matching of the cable and the pipe is realized; and generating a visual three-dimensional construction model according to the matching result, so that dynamic digital monitoring of the whole construction process is realized. Compared with the prior art, the application can realize accurate matching of the cable and the pipe, has the advantages of effectively improving the construction precision and efficiency, reducing manual operation errors and the like.

Description

Technical Field

[0001] This invention relates to the field of power engineering construction and information monitoring technology, and in particular to a method and system for real-time monitoring of the construction process of underground cable tunnels. Background Technology

[0002] In existing underground cable tunnel construction, the correspondence between cables and ducts typically relies on manual recording and labeling. This method suffers from incomplete data recording, manual input errors, and difficulties in identification due to complex construction site conditions, easily leading to information confusion during later maintenance and repair. Furthermore, traditional monitoring methods largely depend on video and manual inspections, lacking real-time capabilities and automation, and failing to achieve effective digital management of construction progress, installation accuracy, and cable laying paths. Chinese patent CN119849011A provides a precise handover method for underground cable construction based on high-precision positioning equipment. By deploying high-precision positioning equipment at the construction site, it collects real-time positioning data of underground cables, environmental data, and underground facility data, performs data preprocessing and integration, and compares it with design drawings to correct potential deviations, constructing a three-dimensional cable path model to provide the construction team with accurate cable path handover information. While this method can effectively identify the relationship between the cable's burial location, direction, depth, and surrounding environment, it can only compare and correct measured data with design drawings and cannot achieve proactive matching and determination of duct-cable configuration.

[0003] Therefore, there is an urgent need for a real-time monitoring method that can automatically identify and match cables and ducts and monitor the construction status in real time, so as to improve construction accuracy and information traceability, and ensure the construction quality and operational safety of cable tunnel projects. Summary of the Invention

[0004] The purpose of this invention is to overcome the defects of the existing technology and provide a method and system for real-time monitoring of the construction process of underground cable tunnels.

[0005] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a method for real-time monitoring of the construction process of underground cable tunnels is provided, the method comprising the following steps: Each cable and conduit should be assigned a unique passive coding identifier; During construction, passive coding information is read in real time, the passive coding identifiers and their location information of cables and ducts are recorded, and auxiliary data is collected to obtain the positioning characteristics of ducts and the multimodal characteristics of cables. Based on the positioning characteristics of the ductwork and the multimodal characteristics of the cable, an automated positioning matching algorithm is used to perform dynamic trajectory matching, three-dimensional fusion scoring, and self-learning dynamic threshold adaptation to achieve automatic matching between the cable and the ductwork. Based on the matching results, a visualized 3D construction model is generated, enabling dynamic digital monitoring of the entire construction process.

[0006] The method for obtaining the positioning features of the pipe is as follows: Based on the passive coding identifier of the read pipe, determine whether it is a pipe. If it is, extract the pipe attributes from the passive coding identifier and obtain the corresponding location information; otherwise, send an invalid coding signal. Based on the continuously acquired location information of the pipes, the linear least squares method is used to fit the baseline trajectory of the pipes, and the pipe direction vector is calculated to model the cylindrical space of the pipes. The aforementioned pipe attributes, pipe baseline trajectory line, and pipe direction vector are used as the positioning features of the pipe.

[0007] The pipe attributes include the maximum allowable cable cross section, construction section, and purpose of the pipe. The maximum allowable cable cross section is calculated based on the pipe diameter.

[0008] The method for obtaining the multimodal characteristics of the cable is as follows: Based on the passive coding identifier of the read cable, determine whether it is a cable. If it is, extract the cable attributes from the passive coding identifier of the cable and obtain the corresponding location information; otherwise, send an invalid adapter signal. Based on the continuously acquired cable location information, the actual cable trajectory is fitted using the linear least squares method, and the cable direction vector is calculated. The real-time tension data of the cable threading machine corresponding to the continuously collected cable position information is used as auxiliary data to calculate the tension fluctuation coefficient; The cable properties, actual cable trajectory, cable direction vector, and tensile fluctuation coefficient are used as the multimodal characteristics of the cable.

[0009] The cable properties include cross-sectional area and cable application.

[0010] The automated location matching algorithm performs the following steps: Calculate the dynamic trajectory matching degree based on the pipeline baseline trajectory line and the actual cable trajectory line; The matching score is calculated by weighting attribute matching degree, dynamic trajectory matching degree and working condition stability, wherein the attribute matching degree is determined based on pipe attributes and cable attributes, and the working condition stability is determined based on the tension fluctuation coefficient. Determine the adaptive threshold by using an effective learning pool; If the matching score is greater than or equal to the adaptive threshold, the match is considered successful. If the matching score is less than the adaptive threshold and greater than or equal to the difference between the adaptive threshold and the preset value, a secondary trajectory verification is triggered. Additional cable location information is collected to supplement the trajectory, the matching score is recalculated, and the judgment is made again. If the matching score is less than the difference between the adaptive threshold and the preset value, the matching is deemed to have failed.

[0011] The calculation of dynamic trajectory matching degree based on the pipeline baseline trajectory line and the actual cable trajectory line includes the following steps: Calculate the overlap length of the actual cable trajectory within the cylindrical space of the duct, and calculate the percentage of trajectory overlap length based on the overlap length; Calculate the angle between the duct direction vector and the cable direction vector, and calculate the consistency of the trajectory direction based on the angle; The dynamic trajectory matching degree is calculated based on the proportion of trajectory overlap length and the consistency of trajectory direction.

[0012] Determining the adaptive threshold through an effective learning pool includes the following steps: Initialize the general threshold; Each time an automatic match is completed, the match score and the corresponding match result are stored in the sample pool of the corresponding group, wherein the grouping is based on the construction section and the pipe laying type; Calculate the mean and standard deviation of all positive samples in the current group, and remove extreme positive samples; Calculate the mean and standard deviation of all negative samples in the current group, and remove extreme negative samples; If the number of samples in both the positive and negative sample sets after removing any group is greater than the preset threshold, then threshold learning is triggered according to the sliding window; otherwise, the initial general threshold is used as the threshold for matching judgment, while accumulating samples. The threshold learning specifically involves: calculating the difference between the mean and standard deviation of all positive samples within the current sliding window in the current group, as the lower bound of positive samples; calculating the sum of the mean and standard deviation of all negative samples in the current group, as the upper bound of negative samples; if the lower bound of positive samples is greater than or equal to the upper bound of negative samples, then the average of the lower bound of positive samples and the upper bound of negative samples is taken as the threshold for matching judgment; if the lower bound of positive samples is less than the upper bound of negative samples and the difference between the upper bound of negative samples and the lower bound of positive samples is less than or equal to a preset threshold, then the lower bound of positive samples is used as the threshold for matching judgment; if the lower bound of positive samples is less than the upper bound of negative samples and the difference between the upper bound of negative samples and the lower bound of positive samples is greater than a preset threshold, then the threshold is not updated temporarily, and the previously updated threshold is used.

[0013] The method also includes: after construction is completed, all matching data and construction records are stored in a database for rapid location and information traceability during later operation, maintenance and inspection phases.

[0014] According to a second aspect of the present invention, a real-time monitoring system for the construction process of underground cable tunnels is provided, the system comprising: Encoding and Identification Module: Configures each cable and duct with a unique passive encoding identifier; Data acquisition module: During construction, passive coding information is read in real time, the passive coding identifiers and location information of cables and ducts are recorded, and auxiliary data is collected to obtain the positioning characteristics of ducts and the multimodal characteristics of cables. Automatic matching module: Based on the positioning characteristics of the duct and the multimodal characteristics of the cable, an automated positioning matching algorithm is used to perform dynamic trajectory matching, three-dimensional fusion scoring and self-learning dynamic threshold adaptation to achieve automatic matching between the cable and the duct. Monitoring module: Generates a visualized 3D construction model based on the matching results, enabling dynamic digital monitoring of the entire construction process.

[0015] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0016] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0017] Compared with the prior art, the present invention has the following beneficial effects: (1) Unique Identification: This invention provides a unique identification for each cable and duct through passive coding technology, so as to achieve accurate information correspondence; (2) Automated identification and matching: This invention adopts an automated positioning and matching algorithm, integrates trajectory overlap, attribute matching, and three-dimensional features of working condition stability to accurately evaluate the matching, and uses the sliding window method to calculate the dynamic adaptive threshold to automatically adapt to different scenarios. Combined with secondary verification, it intelligently identifies the correspondence between cables and pipes, reduces manual intervention and misoperation, and improves matching accuracy. (3) Real-time digital monitoring: This invention can monitor construction progress, matching status and location information in real time, improving the transparency and controllability of the construction process; (4) Full-cycle information traceability: Data collected during the construction phase can directly serve operation and maintenance, realizing information closure in the "construction-operation" phase; (5) Improved construction efficiency and reliability: This invention can significantly improve the automation level and data accuracy of construction management and reduce the risk of rework caused by human operation. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0021] Example 1 This embodiment provides a method for real-time monitoring of the construction process of underground cable tunnels, such as... Figure 1 As shown, the method includes the following steps: S1 assigns a unique passive coding identifier to each cable and conduit.

[0022] In this embodiment, passive coded identifiers (such as RFID / NFC) need to embed key attributes: For example, the passive coding identifier for pipe laying can include: P-ID (unique identifier), pipe diameter D, construction section S, and pipe laying purpose U. p .

[0023] For passive cable coding, it can include: C-ID (unique identifier), cross-sectional area A, and cable application U. c .

[0024] S2 reads passive coding information in real time during construction, records the passive coding identifiers and location information of cables and ducts, and collects auxiliary data to obtain the positioning characteristics of ducts and the multimodal characteristics of cables.

[0025] In practical applications, construction units first deploy passive coded tags during the duct laying stage; during cable laying, the reading equipment collects the coded information of each cable and the duct it passes through in real time and transmits it to the monitoring host.

[0026] This embodiment implements process control using a finite state automaton. The specific implementation process is described below.

[0027] First, perform algorithm initialization and load hardware interface mapping: bind the serial port, positioning module, and OLED / buzzer data stream interfaces; import general parameters; initialize the pipe feature cache, cable feature cache, and matching result cache, and clear historical temporary data.

[0028] Then, an algorithm initialization completion signal is sent to the state machine, triggering the state machine OLED to display "Scan HoleFirst" and enter the WAIT_FEATURE stage.

[0029] During the WAIT_FEATURE phase, the positioning features of the pipe arrangement are collected and preprocessed, specifically including: S211, based on the passive coding identifier of the read pipe, determine whether it is a pipe (hole1 / hole2). If so, extract the pipe attributes from the passive coding identifier of the pipe, and use the positioning module to continuously collect 5 positioning points (sampling interval 0.5s) to obtain the corresponding location information P={(X1,Y1,Z1),(X2,Y2,Z2),...,(X5,Y5,Z5)}, where (X i ,Y i Z i ) represents the location information collected for the i-th time, i=1,2,3,4,5; otherwise, it indicates that G1 or invalid code has been scanned, and an invalid code signal is sent to the state machine, triggering a 2000Hz pulse alarm of 0.5s, and the OLED displays Invalid Code\nScan HoleFirst, and the WAIT_FEATURE stage is maintained to continue waiting for valid positioning features.

[0030] In this embodiment, the pipe laying attributes include the maximum allowable cable laying cross section, construction section, and pipe laying purpose, wherein the maximum allowable cable laying cross section A is... max =0.8×π×(D / 2)2 .

[0031] S212, based on continuously acquired pipe layout location information, uses the linear least squares method to fit the pipe layout baseline trajectory line L. p And calculate the pipe orientation vector V p =(ΔX,ΔY,ΔZ), where ΔX=X5-X1, ΔY=Y5-Y1, ΔZ=Z5-Z1, modeling the cylindrical space of the pipe arrangement (axis L). p (Radius r = D / 2, where D is the pipe diameter).

[0032] S213, the aforementioned pipe attributes (A) max S, U p ), Pipeline baseline trajectory line L p and the direction vector V of the pipe p As a positioning feature of the pipeline.

[0033] After the above steps are completed, a positioning feature ready signal is sent to the state machine, triggering the OLED display to show "Hole: XX\nScan G1", and entering the WAIT_COMPONENT stage to perform multi-modal feature acquisition and preprocessing of the cable, specifically including: S221, based on the passive coding identifier of the read cable, determine whether it is a cable (G1). If it is, extract the cable attributes from the passive coding identifier of the cable, and use the positioning module to continuously collect 5 positioning points (sampling interval 0.5s) to obtain the corresponding position information C={(X'1,Y'1,Z'1),...,(X'5,Y'5,Z'5)}; otherwise, send an invalid adapter element signal, and the OLED displays "Hole: XX\nScan G1 Only", and maintain the WAIT_COMPONENT stage.

[0034] In this embodiment, the cable properties include the cross-sectional area A and the cable application U. c .

[0035] S222, based on continuously acquired cable location information C, uses the linear least squares method to fit the actual cable trajectory line L. c And calculate the cable direction vector V. c =(ΔX',ΔY',ΔZ'), where, ΔX'=X'5-X'1, ΔY'=Y'5-Y'1, ΔZ'=Z'5-Z'1.

[0036] S223, acquire the real-time tension data F1~F5 of the cable threading machine corresponding to the continuously collected cable position information as auxiliary data, and calculate the tension fluctuation coefficient CV, which is the ratio of the standard deviation to the mean of the real-time tension data F1~F5.

[0037] S224, the cable properties (A and U) c ), actual cable trajectory line L c The cable direction vector and tensile fluctuation coefficient CV are used as multimodal characteristics of the cable.

[0038] S3, based on the positioning characteristics of the duct and the multimodal characteristics of the cable, adopts an automated positioning matching algorithm for dynamic trajectory matching, three-dimensional fusion scoring and self-learning dynamic threshold adaptation, to achieve automatic matching between the cable and the duct.

[0039] S3 specifically includes the following steps: S31 calculates the dynamic trajectory matching degree based on the pipeline baseline trajectory line and the actual cable trajectory line.

[0040] Specifically, the following steps are included: Calculate the actual cable trajectory line L c The overlap length L within the cylindrical space of the pipe arrangement overlap The proportion L of trajectory overlap length is calculated based on the overlap length. overlap / L, where L is the total length. ; Calculate the pipe orientation vector V p and cable direction vector V c The included angle θ = arccos[(V p ・V c ) / (|V p |×|V c |)], based on the included angle, the trajectory direction consistency is calculated; The dynamic trajectory matching degree is calculated based on the proportion of trajectory overlap length and the consistency of trajectory direction: S = S1 + S2.

[0041] The method for determining S1 is as follows: if the proportion of overlapping trajectory lengths L overlap If L is greater than or equal to 90%, then S1 = 50. If the proportion of trajectory overlap length is L... overlap If / L is less than 60%, then S1 = 0; otherwise, the S1 fraction is determined by linear interpolation. S2 is determined as follows: if θ is less than or equal to 10°, S2 = 50; if θ is greater than 30°, S2 = 0; otherwise, the S2 fraction is determined by linear interpolation.

[0042] S32, the matching score is calculated based on a weighted average of attribute matching degree, dynamic trajectory matching degree, and working condition stability: T = W1 × A + W2 × S + W3 × B, Where W1, W2, and W3 are weighting coefficients, A is attribute matching degree, S is dynamic trajectory matching degree, and B is working condition stability.

[0043] For attribute matching degree A, if the purpose of pipe laying is Up and cable applications U c The same cross-sectional area A is less than or equal to the maximum allowable cable cross-section A of the duct. max If A=100, and the purpose of the pipe is U p and cable applications U c The cross-sectional area A is the same and greater than the maximum allowable cable cross-section A of the duct. max If the result is positive, then A = 50; otherwise, A = 0.

[0044] For the working condition stability B, if the tensile force fluctuation coefficient CV ≤ 5%, then B = 100; if 5% < CV ≤ 10%, then B = 50; if CV > 10%, then B = 0.

[0045] S33, determine the adaptive threshold through an effective learning pool.

[0046] Specifically, the following steps are included: S331, Initialize the general threshold.

[0047] By initializing a general threshold, an initial threshold that can be used directly is provided when there are no historical samples at the beginning of construction.

[0048] In this embodiment, the pipes are grouped according to the construction section and the type of pipe laying. Different initial thresholds are assigned to different groups. The groups that can be divided include, but are not limited to, tunnel pipe laying, direct buried pipe laying, and mixed construction section.

[0049] S332, after each automatic matching is completed, the matching score T and the corresponding matching result (match successful / match failed) are stored in the sample pool of the corresponding group.

[0050] S333, Calculate the mean μ of all positive samples in the current group. p and standard deviation σ p And remove T p <μ p -2σ p or T p >μ p +2σ p Extreme positive samples; S334, Calculate the mean μ of all negative samples in the current group. n and standard deviation σ n And remove T n <μ n -2σ n or T n >μ n +2σ n Extreme negative samples; S335, if the number of samples in both the positive and negative sample sets after removing any group is greater than the preset threshold, then threshold learning is triggered according to the sliding window; otherwise, the initial general threshold is used as the threshold for matching judgment, while accumulating samples.

[0051] The threshold learning specifically involves: Calculate the difference between the mean and standard deviation of all positive samples within the current sliding window in the current group, and use this as the lower bound for the positive samples: L p =μ p -σ p ; Calculate the sum of the mean and standard deviation of all negative samples in the current group, and use it as the upper bound for negative samples: U n =μ n +σ n .

[0052] If the lower bound of positive samples is greater than or equal to the upper bound of negative samples, L p ≥U n If there is no overlapping boundary, then the average of the lower bound of the positive samples and the upper bound of the negative samples is taken as the threshold T for matching judgment. opt =(L p +U n ) / 2; If the lower bound of positive samples is less than the upper bound of negative samples, and the difference between the upper bound of negative samples and the lower bound of positive samples is less than or equal to a preset threshold, for example, L... p <U n And U n -L p If the value is ≤5, it indicates a slight overlap in the boundary, so the lower bound L for positive samples is used. p The threshold used for matching should prioritize ensuring a high success rate and avoid missed matches. If the lower bound of positive samples is less than the upper bound of negative samples and the difference between the upper bound of negative samples and the lower bound of positive samples is greater than a preset threshold, for example, L... p <U n And U n -L p If the threshold is greater than 5, it indicates that there is a serious overlap boundary. In this case, the threshold will not be updated for the time being, and the samples will continue to be accumulated, using the threshold updated previously.

[0053] In this embodiment, to avoid interference from old samples (early construction scenarios), a sliding mechanism with a fixed window size is adopted. When the window is full, the earliest sample is removed and the latest sample is added to perform threshold learning.

[0054] S34, if the matching score T is greater than or equal to the adaptive threshold T opt If so, the match is considered successful; If the matching score T is less than the adaptive threshold T opt And greater than or equal to the adaptive threshold T opt-10 triggers secondary trajectory verification, sends a prompt signal to the state machine, displays "Slow Scan Again" on the OLED, collects 3 additional cable positioning points to supplement the trajectory, recalculates the matching score T, and makes a judgment again; If the matching score T is less than the adaptive threshold T opt -10 indicates a failed match.

[0055] After matching is completed, the matching result is sent to the state machine. If the matching is successful, the OLED displays "MatchSuccess\nHole:XX-G1" and the buzzer emits a short 1kHz tone (0.2s). If the matching fails, the OLED displays "Match Fail:\nXX (reason)" and the buzzer emits a long 2kHz tone (0.3s per tone, 0.1s interval).

[0056] The results show that after 3 seconds, the pipe feature cache and cable feature cache are cleared, the sample pool and self-learning parameters are retained, and a resettable signal is sent to the state machine.

[0057] Subsequently, the state machine automatically resets, and after the reset is complete, the OLED displays "Scan Hole First" and returns to the WAIT_FEATURE stage, waiting for the next round of localization feature scanning.

[0058] S4 generates a visualized 3D construction model based on the matching results, enabling dynamic digital monitoring of the entire construction process.

[0059] In one preferred embodiment, a GIS visualization platform can be used to transform the matching results into an intuitive monitoring view, thereby achieving dynamic digital monitoring of the entire construction process.

[0060] Specifically, the visual content includes: 1. GIS map overlay display: The base map is a satellite image / topographic profile of the construction area, marking key locations such as construction sections, benchmark piles, and inspection wells; Layer 1 (Pipeline Layer): The location of the pipeline is indicated by cylindrical icons, and the status is distinguished by color: green represents matched cables, yellow represents cables to be matched, and red represents matching error. Layer 2 (Cable Layer): The cable path is displayed using line segment icons, with the color matching the corresponding duct. C-ID and installation progress are marked. Layer 3 (Abnormal Layer): Use "blinking warning icons" to mark abnormal locations (such as incorrect pipe routing, positioning drift), and hover the mouse over the abnormal location to display abnormal details.

[0061] 2. Detailed information panel: including but not limited to matching relationship, location coordinates, attribute parameters, construction records, and matching score.

[0062] During construction, the cable laying progress can be calculated by matching the real-time location of the cable with the duct path, and the cable laying trajectory can be updated in real time on the GIS map to verify whether it is always within the matching duct cylindrical space. If the trajectory deviates from the duct range, an abnormal trajectory warning will be triggered immediately. The actual cable laying time can also be compared with the construction plan period to indicate the current progress.

[0063] In one preferred embodiment, after construction is completed, all matching data and construction records are stored in a database for rapid location and information traceability during the later operation, maintenance and inspection phases.

[0064] Example 2 This embodiment, based on Embodiment 1, provides a process description for implementing flow control using a finite state automaton, divided into five stages: initialization (INIT), waiting for positioning features (WAIT_FEATURE, i.e., the positioning features of the pipe), waiting for adapter elements (WAIT_COMPONENT, i.e., the multimodal features of the cable), displaying results (SHOW_RESULT), and automatic reset. The core automated positioning and matching algorithm is executed by an external controller, and returns to the state machine after execution. The core logic is as follows: 1. State Machine Design 1) Initialization (INIT): Triggering conditions: Power on or reset; Actions performed: Initialize serial port, OLED, and buzzer; Next state: OLED displays "Scan Hole First", waiting for localization feature (WAIT_FEATURE).

[0065] 2) Waiting for localization feature (WAIT_FEATURE) 21) Triggering condition: "hole1" or "hole2" is detected during scanning; Execution action: Store location feature identifier; Next state: OLED displays “Hole: XX\nScan G1” waiting for adapter (WAIT_COMPONENT).

[0066] 22) Triggering condition: "G1" or invalid code is detected during scanning. Actions performed: OLED displays "Invalid Code\nScan Hole First"; 2000Hz pulse alarm sounds for 0.5 seconds. Next state: Maintain WAIT_FEATURE.

[0067] 3) Waiting for adapter (WAIT_COMPONENT) 31) Trigger condition: "G1" is detected during scanning; Actions performed: matching judgment; OLED display of results; buzzer alarm based on the results; Next state: Show result (SHOW_RESULT).

[0068] 32) Triggering condition: "hole1" / "hole2" is detected during scanning; Action performed: Update location feature identifier; OLED displays "Hole: XX\nScan G1"; Next state: Keep WAIT_COMPONENT.

[0069] 33) Triggering condition: Invalid code detected; Action performed: The OLED displays "Hole: XX\nScan G1 Only"; Next state: Keep WAIT_COMPONENT.

[0070] 4) Display the results (SHOW_RESULT) Action performed: Result displayed for 3 seconds; location feature markers cleared; OLED displays "Scan Hole First"; Next state: WAIT_FEATURE.

[0071] 2. Multimodal Feedback Strategy Matching results OLED display content buzzer action feedback duration Location feature + G1 matching (hole1 + G1) First line: "Hole: hole1" Second line: "Pipe: G1" Third line: "PASS" (green) Silence for 3 seconds Location feature + G1 mismatch (hole2 + G1) First line: "Hole: hole2" Second line: "Pipe: G1" Third line: "FAIL" (red) Continuous beeping at 1000Hz for 1 second, followed by 3 seconds of silence. Operation error (Scan G1 first) First line: "Invalid Code" Second line: "Scan Hole First" 2000Hz pulse beep (0.2 seconds on, 0.3 seconds off) 1 second Invalid code scan: First line: "Invalid Code"; Second line: "Scan Again"; 2000Hz pulse beep for 0.5 seconds to 1 second. 3. Complete, executable code #include<SPI.h> #include<Wire.h> #include<Adafruit_GFX.h> #include<Adafruit_SSD1306.h> #include<SoftwareSerial.h> / / 1. Pin definitions (no "D" prefix, use numbers directly, compatible with all compilation environments) #define QR_TX_PIN 11 / / GM65 TX → Arduino 11 (software serial port TX) #define QR_RX_PIN 10 / / GM65 RX → Arduino 10 (software serial port RX) #define BUZZER_PIN 2 / / Buzzer control pin (with a 1kΩ resistor in series) #define OLED_ADDR 0x3C / / OLED default I2C address (can be changed to 0x3D if not displayed) #define SCREEN_WIDTH 128 / / OLED width (pixels) #define SCREEN_HEIGHT 64 / / OLED height (pixels) / / 2. Module Initialization Adafruit_SSD1306 display(SCREEN_WIDTH, SCREEN_HEIGHT, &Wire, -1); / / OLED object SoftwareSerial qrSerial(QR_RX_PIN, QR_TX_PIN); / / Software serial port (RX, TX) String storedFeature = ""; / / Stores the current scan's localization features (hole1 / hole2) / / 3. System state enumeration (core of finite state automaton) enum SystemState { INIT, / / Initialization state WAIT_FEATURE, / / Waiting for the scan to locate features WAIT_COMPONENT, / / Waiting for adapter scanning SHOW_RESULT / / Display the matching results }; SystemState currentState = INIT; / / Initial state void setup() { / / Initialize the serial port (for debugging, can be kept) Serial.begin(9600); qrSerial.begin(9600); / / Initialize the buzzer (initially low level, off state) pinMode(BUZZER_PIN, OUTPUT); digitalWrite(BUZZER_PIN, LOW); / / Initialize OLED (detect whether the module is functioning correctly) if (!display.begin(SSD1306_SWITCHCAPVCC, OLED_ADDR)) { Serial.println(F("OLED init failed! Check wiring or address.")); while (1); / / If initialization fails, the process will pause for troubleshooting. } display.clearDisplay(); display.setTextSize(1); / / Font size (1 = 8 x 8 pixels) display.setTextColor(SSD1306_WHITE); / / White text (black background on OLED) display.setCursor(0, 0); / / Cursor position (x=0, y=0) / / Initial state switch to waiting for feature localization currentState = WAIT_FEATURE; updateOLED("Scan Hole First", "", ""); Serial.println("System initialized. Ready to scan hole."); } void loop() { / / State machine driven (executes logic based on the current state) switch (currentState) { case WAIT_FEATURE: handleWaitFeature(); break case WAIT_COMPONENT: handleWaitComponent(); break case SHOW_RESULT: handleShowResult(); break default: currentState = WAIT_FEATURE; updateOLED("Scan Hole First", "", ""); break } delay(100); / / Reduce loop frequency and decrease resource consumption. } / / Handling the "Waiting for Localization Features" state void handleWaitFeature() { if (qrSerial.available() > 0) { String scanData = readAndTrimData(); / / Read and process scan data Serial.print("Scanned in WAIT_FEATURE: "); Serial.println(scanData); / / Valid localization features (hole1 / hole2) detected if (scanData == "hole1" || scanData == "hole2") { storedFeature = scanData; currentState = WAIT_COMPONENT; updateOLED("Hole: " + storedFeature, "Scan G1 Now", ""); Serial.println("Switch to WAIT_COMPONENT. Scan G1."); } / / Scanned G1 (operation sequence error) else if (scanData == "G1") { updateOLED("Error: Scan Hole First", "", ""); beep(2000, 500); / / 2000Hz alarm with a 500ms delay. Serial.println("Error: Scan hole before G1."); } Invalid code detected else { updateOLED("Invalid Code", "Scan Hole (hole1 / hole2)", ""); beep(2000, 300); / / 2000Hz alarm for 300 milliseconds Serial.println("Invalid code. Scan hole1 / hole2."); } } } / / Handling the "Waiting for adapter" state void handleWaitComponent() { if (qrSerial.available() > 0) { String scanData = readAndTrimData(); Serial.print("Scanned in WAIT_COMPONENT: "); Serial.println(scanData); / / Valid adapter found (G1) if (scanData == "G1") { currentState = SHOW_RESULT; get_result(); / / Call the external controller to perform matching and judgment if (get_result()=isMatch) { updateOLED("Hole: " + storedFeature, "Pipe: G1", "Result:PASS (GREEN)"); beep(0, 0); / / Do not trigger an alarm Serial.println("Match PASS: hole1+G1."); } else { updateOLED("Hole: " + storedFeature, "Pipe: G1", "Result:FAIL (RED)"); beep(1000, 1000); / / 1000Hz alarm, 1000 milliseconds Serial.println("Match FAIL: hole2+G1."); } } / / Rescan and locate features (update hole) else if (scanData == "hole1" || scanData == "hole2") { storedFeature = scanData; updateOLED("Hole Updated: " + storedFeature, "Scan G1 Now",""); Serial.println("Hole updated. Scan G1."); } Invalid code detected else { updateOLED("Hole: " + storedFeature, "Invalid Code", "Scan G1Only"); beep(2000, 300); Serial.println("Invalid code. Scan G1 only."); } } } / / Handle the "Display Results" status (automatically resets after 3 seconds) void handleShowResult() { delay(3000); / / Keep the result displayed for 3 seconds / / Reset system state storedFeature = ""; currentState = WAIT_FEATURE; updateOLED("Scan Hole First", "", ""); Serial.println("Result shown. Reset to WAIT_FEATURE."); } / / Read the scanned data and remove invalid characters (spaces, newlines) at the beginning and end. String readAndTrimData() { String data = qrSerial.readStringUntil('\n'); data.trim(); / / Removes spaces, carriage returns, and newlines. return data; } / / Update OLED display (supports 3 lines of content) void updateOLED(String line1, String line2, String line3) { display.clearDisplay(); display.setCursor(0, 0); / / First line (y=0) display.println(line1); display.setCursor(0, 20); / / Second line (y=20, 20-pixel spacing to prevent overlap) display.println(line2); display.setCursor(0, 40); / / Third line (y=40) display.println(line3); display.display(); / / Refresh the display } / / Buzzer control (no alarm when frequency=0, duration=alarm duration (milliseconds)) void beep(int frequency, int duration) { if (frequency > 0 && duration > 0) { tone(BUZZER_PIN, frequency, duration); delay(duration); noTone(BUZZER_PIN); / / Ensure alarms are turned off } } Example 3 This embodiment provides a real-time monitoring system for the construction process of underground cable tunnels, such as... Figure 2 As shown, the system includes: Encoding and Identification Module: Configures each cable and duct with a unique passive encoding identifier; Data acquisition module: During construction, passive coding information is read in real time, the passive coding identifiers and location information of cables and ducts are recorded, and auxiliary data is collected to obtain the positioning characteristics of ducts and the multimodal characteristics of cables. Automatic matching module: Based on the positioning characteristics of the duct and the multimodal characteristics of the cable, an automated positioning matching algorithm is used to perform dynamic trajectory matching, three-dimensional fusion scoring and self-learning dynamic threshold adaptation to achieve automatic matching between the cable and the duct. Monitoring module: Generates a visualized 3D construction model based on the matching results, enabling dynamic digital monitoring of the entire construction process.

[0072] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0073] However, this embodiment provides a detailed description of the specific hardware composition of the system, including: Main control module: Arduino Nano (ATmega328P), operating voltage 5V; 14 digital pins; integrated LM1117-3.3 voltage regulator chip, pin definitions without "D" prefix, compatible with all compilation environments.

[0074] QR code scanning module: adopts GM65 embedded module, working voltage 5V±0.5V; recognition distance 5-30cm; supports ±30° rotation recognition; baud rate 9600bps, software serial communication to avoid occupying hardware serial port resources.

[0075] OLED display module: 128×64 pixels (SSD1306 driver), operating voltage 3.3V; I2C interface; 180° viewing angle; operating current ≤10mA, forced 3.3V power supply, driver chip protected against burnout.

[0076] Alarm module: Employs a passive buzzer + 1kΩ carbon film resistor. Buzzer: 5V, sound pressure level ≥85dB; Resistor: 1 / 4W, accuracy ±5%, resistor current limiting to prevent pin damage from overcurrent. Power module: Uses a DC power adapter with an output voltage of 5V±0.25V and a maximum output current of 1A; USB interface, overload protection to prevent voltage fluctuations from affecting the module.

[0077] Passive coding identification for pipes: QR code label (20mm×20mm), error correction level H; print resolution ≥300dpi; content: "hole1" "hole2", matte coated paper material, wear-resistant and anti-reflective. Passive coding identification for cables: QR Code label (20mm×20mm), error correction level H; print resolution ≥300dpi; content: "G1", consistent with the size of the positioning feature label, for easy scanning operation.

[0078] Example 4 The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0079] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0080] The processing unit executes the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S4 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S4 by any other suitable means (e.g., by means of firmware).

[0081] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0082] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0083] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0084] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for real-time monitoring of the construction process of underground cable tunnels, characterized in that, The method includes the following steps: Each cable and conduit should be assigned a unique passive coding identifier; During construction, passive coding information is read in real time, the passive coding identifiers and their location information of cables and ducts are recorded, and auxiliary data is collected to obtain the positioning characteristics of ducts and the multimodal characteristics of cables. Based on the positioning characteristics of the ductwork and the multimodal characteristics of the cable, an automated positioning matching algorithm is used to perform dynamic trajectory matching, three-dimensional fusion scoring, and self-learning dynamic threshold adaptation to achieve automatic matching between the cable and the ductwork. Based on the matching results, a visualized 3D construction model is generated, enabling dynamic digital monitoring of the entire construction process.

2. The method for real-time monitoring of the construction process of an underground cable tunnel according to claim 1, characterized in that, The method for obtaining the positioning features of the pipe is as follows: Based on the passive coding identifier of the read pipe, determine whether it is a pipe. If it is, extract the pipe attributes from the passive coding identifier of the pipe and obtain the corresponding location information. Otherwise, send an invalid encoded signal; Based on the continuously acquired location information of the pipes, the linear least squares method is used to fit the baseline trajectory of the pipes, and the pipe direction vector is calculated to model the cylindrical space of the pipes. The aforementioned pipe attributes, pipe baseline trajectory line, and pipe direction vector are used as the positioning features of the pipe.

3. The method for real-time monitoring of the construction process of an underground cable tunnel according to claim 2, characterized in that, The pipe attributes include the maximum allowable cable cross section, construction section, and purpose of the pipe. The maximum allowable cable cross section is calculated based on the pipe diameter.

4. The method for real-time monitoring of the construction process of an underground cable tunnel according to claim 1, characterized in that, The method for obtaining the multimodal characteristics of the cable is as follows: Based on the passive coding identifier of the read cable, determine whether it is a cable. If it is, extract the cable attributes from the passive coding identifier of the cable and obtain the corresponding location information. Otherwise, send an invalid adapter signal; Based on the continuously acquired cable location information, the actual cable trajectory is fitted using the linear least squares method, and the cable direction vector is calculated. The real-time tension data of the cable threading machine corresponding to the continuously collected cable position information is used as auxiliary data to calculate the tension fluctuation coefficient; The cable properties, actual cable trajectory, cable direction vector, and tensile fluctuation coefficient are used as the multimodal characteristics of the cable.

5. The method for real-time monitoring of the construction process of an underground cable tunnel according to claim 4, characterized in that, The cable properties include cross-sectional area and cable application.

6. The method for real-time monitoring of the construction process of an underground cable tunnel according to claim 1, characterized in that, The automated location matching algorithm performs the following steps: Calculate the dynamic trajectory matching degree based on the pipeline baseline trajectory line and the actual cable trajectory line; The matching score is calculated by weighting attribute matching degree, dynamic trajectory matching degree and working condition stability, wherein the attribute matching degree is determined based on pipe attributes and cable attributes, and the working condition stability is determined based on the tension fluctuation coefficient. Determine the adaptive threshold by using an effective learning pool; If the matching score is greater than or equal to the adaptive threshold, the match is considered successful. If the matching score is less than the adaptive threshold and greater than or equal to the difference between the adaptive threshold and the preset value, a secondary trajectory verification is triggered. Additional cable location information is collected to supplement the trajectory, the matching score is recalculated, and the judgment is made again. If the matching score is less than the difference between the adaptive threshold and the preset value, the matching is deemed to have failed.

7. A method for real-time monitoring of the construction process of an underground cable tunnel according to claim 6, characterized in that, The calculation of dynamic trajectory matching degree based on the pipeline baseline trajectory line and the actual cable trajectory line includes the following steps: Calculate the overlap length of the actual cable trajectory within the cylindrical space of the duct, and calculate the percentage of trajectory overlap length based on the overlap length; Calculate the angle between the duct direction vector and the cable direction vector, and calculate the consistency of the trajectory direction based on the angle; The dynamic trajectory matching degree is calculated based on the proportion of trajectory overlap length and the consistency of trajectory direction.

8. A method for real-time monitoring of the construction process of an underground cable tunnel according to claim 6, characterized in that, Determining the adaptive threshold through an effective learning pool includes the following steps: Initialize the general threshold; Each time an automatic match is completed, the matching score and the corresponding matching result are stored in the sample pool of the corresponding group, wherein the grouping is based on the construction section and the pipe laying type; Calculate the mean and standard deviation of all positive samples in the current group, and remove extreme positive samples; Calculate the mean and standard deviation of all negative samples in the current group, and remove extreme negative samples; If the number of samples in both the positive and negative sample sets after removing any group is greater than the preset threshold, then threshold learning is triggered according to the sliding window; otherwise, the initial general threshold is used as the threshold for matching judgment, while accumulating samples. The threshold learning specifically involves: calculating the difference between the mean and standard deviation of all positive samples within the current sliding window in the current group, as the lower bound of positive samples; calculating the sum of the mean and standard deviation of all negative samples in the current group, as the upper bound of negative samples; if the lower bound of positive samples is greater than or equal to the upper bound of negative samples, then the average of the lower bound of positive samples and the upper bound of negative samples is taken as the threshold for matching judgment; if the lower bound of positive samples is less than the upper bound of negative samples and the difference between the upper bound of negative samples and the lower bound of positive samples is less than or equal to a preset threshold, then the lower bound of positive samples is used as the threshold for matching judgment; if the lower bound of positive samples is less than the upper bound of negative samples and the difference between the upper bound of negative samples and the lower bound of positive samples is greater than a preset threshold, then the threshold is not updated temporarily, and the previously updated threshold is used.

9. A method for real-time monitoring of the construction process of an underground cable tunnel according to claim 1, characterized in that, The method also includes: after construction is completed, all matching data and construction records are stored in a database for rapid location and information traceability during later operation, maintenance and inspection phases.

10. A real-time monitoring system for the construction process of underground cable tunnels, characterized in that, The system includes: Encoding and Identification Module: Configures each cable and duct with a unique passive encoding identifier; Data acquisition module: During construction, passive coding information is read in real time, the passive coding identifiers and location information of cables and ducts are recorded, and auxiliary data is collected to obtain the positioning characteristics of ducts and the multimodal characteristics of cables. Automatic matching module: Based on the positioning characteristics of the duct and the multimodal characteristics of the cable, an automated positioning matching algorithm is used to perform dynamic trajectory matching, three-dimensional fusion scoring and self-learning dynamic threshold adaptation to achieve automatic matching between the cable and the duct. Monitoring module: Generates a visualized 3D construction model based on the matching results, enabling dynamic digital monitoring of the entire construction process.

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