Cable protection pipe whole life two-dimensional code anti-counterfeiting traceability early warning system

Abstract

This invention relates to the field of full life-cycle management and operation and maintenance support technology for underground or utility tunnel cable protection pipes, specifically to a full life-cycle QR code anti-counterfeiting traceability and early warning system for cable protection pipes. This system assigns QR code identifiers to cable protection pipe units and sealing components; collects event data and sealing operation data through a life-cycle scanning anti-counterfeiting verification process; constructs a network topology parameterized hydraulic digital twin model; performs hydraulic numerical calculations and topology analysis to obtain propagation assessment results and head constraint indicators; constructs a risk factor graph to assess the credibility weight of evidence, estimate the intensity of inflow events, output the posterior risk spectrum of water hazard situation, and generate inspection frequency, dispatch priority, and disposal work order content. This invention uses QR code assignment for anti-counterfeiting and links it to full life-cycle information, collecting event / sealing operation data; performs hydraulic numerical calculations and topology analysis to obtain propagation assessment and head constraint indicators; and outputs the posterior risk spectrum of water hazard situation based on the credibility weight of evidence, linking inspections, dispatch, and disposal.

Description

Technical Field

[0001] This invention relates to the field of whole-life management and operation and maintenance support technology for underground or utility tunnel cable protection pipes, and more specifically, it relates to a QR code anti-counterfeiting traceability and early warning system for the whole life of cable protection pipes. Background Technology

[0002] Cable protection pipe units and cable protection pipe sealing components typically span multiple stages, including manufacturing, warehousing, transportation, arrival, installation, operation and maintenance, excavation and relocation, and decommissioning. Current management methods primarily utilize paper ledgers, enterprise resource planning records, mobile forms, and methods such as QR codes, barcodes, or RFID tags for numbering, identification, and registration. These methods effectively improve material identification efficiency and reduce errors from manual transcription. Simultaneously, for critical processes, some units retain on-site images, inspection records, and signature information for quality traceability and responsibility determination. On the other hand, in the face of water ingress incidents such as end-point leakage, current operation and maintenance work often incorporates strategies such as inspection experience, alarm thresholds, and segmented isolation for emergency response. When conditions permit, the impact range is assessed by referring to the pipeline topology or simplified hydraulic estimation to support emergency repair, investigation, and re-inspection arrangements.

[0003] However, from the perspective of continuous lifecycle traceability and closed-loop risk management, existing technologies may still have some shortcomings in engineering implementation: First, although the identification carrier can carry numbering information, when it is necessary to simultaneously meet the requirements of anti-counterfeiting verification, lifecycle-related storage, and cross-entity consistency, simple readable coding or single-point verification often cannot maintain the unity of "object uniqueness - evidence consistency - historical continuity" in the long term; Second, event records and sealing operation records are usually collected separately by different positions and at different time windows, and the standardization of data fields, time bases, spatial positioning, and evidence attachments is inconsistent. This results in a weak chain link between end-seepage evidence, anti-counterfeiting verification results, sealing installation evidence, and sealing re-verification evidence, making post-event verification difficult. Third, the intensity of water ingress events is often affected by the combined effects of water ingress height, water ingress duration, and the propagation process of pipeline topology, water level field, and flow field. In many scenarios, the propagation impact is still mainly based on empirical judgment or static thresholds, making it difficult to stably output downstream impact lists and corresponding quantifiable indicators such as arrival time windows, remaining head margin, and over-threshold duration for unified command. Fourth, when the sources of evidence are diverse and of varying quality, there is a lack of a mechanism to quantitatively assess the credibility of the evidence chain and constrain risk inference in the form of weights. This can easily lead to the amplification of high-noise evidence or the accumulation of uncertainty due to the lack of key evidence, thereby affecting the objectivity and interpretability of inspection frequency, dispatch priority, and handling work order content.

[0004] Therefore, there is a technical need in the industry to integrate anti-counterfeiting and traceability, evidence collection of incidents and blocking operations, hydraulic numerical calculation and topology analysis based on network topology, and post-hoc risk inference and dispatching under the constraint of evidence credibility, so as to better support the reliable management of the entire life cycle of cable protection pipes and the quantifiable and executable closed-loop handling of water damage risks. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a QR code anti-counterfeiting and traceability early warning system for the entire life cycle of cable protection pipes.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The cable protection pipe full-lifecycle QR code anti-counterfeiting traceability and early warning system includes: The coding and binding module is used to assign QR codes to cable protection pipe units and cable protection pipe sealing parts, generate QR code identifiers containing anti-counterfeiting verification information, and establish an association between QR code identifiers and full life-cycle information. The event acquisition module is used to scan the QR code of the cable protection pipe unit in the life cycle stage set to complete the anti-counterfeiting verification and collect event data, and to scan the QR code of the cable protection pipe sealing component when the sealing operation is performed in the life cycle stage set to complete the anti-counterfeiting verification and collect sealing operation data. The topology calculation module is used to construct a parametric hydraulic digital twin model, which includes network topology. Based on event data, hydraulic numerical calculations and topology analysis are performed on the parametric hydraulic digital twin model to generate propagation assessment results and head constraint indicators. The dispatching module is used to construct a risk factor map based on event data, closure operation data, propagation assessment results, and head constraint indicators. It also assesses the credibility of the evidence chain in the event data and closure operation data to obtain the credibility weight of the evidence. Under the constraint of the credibility weight of the evidence, it estimates the intensity of the water inflow event and outputs the posterior risk spectrum of the water hazard situation. Based on the posterior risk spectrum of the water hazard situation, it generates the inspection frequency, dispatch priority, and disposal work order content.

[0007] Furthermore, the life cycle stage set is a collection of multiple stages used to characterize the entire life cycle management process of the cable protection pipe unit. The set includes the manufacturing stage, warehousing stage, transportation stage, arrival stage, installation stage, operation and maintenance stage, excavation and relocation stage, and decommissioning stage. The installation stage, operation and maintenance stage, and excavation and relocation stage include sealing operations, which are used to complete the specification matching, installation, re-inspection, or replacement of the cable protection pipe sealing components.

[0008] Furthermore, the parameterized hydraulic digital twin model includes a directed network topology consisting of well chamber nodes, pipe segment nodes, and end nodes, as well as a set of geometric and hydraulic parameters corresponding to each pipe segment node. The set of hydraulic parameters includes at least pipe diameter, length, slope, roughness coefficient, local loss coefficient, low-point volume parameter, and seepage recharge parameter.

[0009] Furthermore, the topology calculation module performs hydraulic numerical calculations on the parameterized hydraulic digital twin model based on event data to obtain hydraulic numerical results, and performs topology analysis on the network topology to obtain topology analysis results. Based on the hydraulic numerical results and topology analysis results, the topology calculation module generates propagation evaluation results and head constraint indicators.

[0010] Furthermore, the propagation assessment results should include at least a list of downstream impacts and their corresponding arrival time windows, and the head constraint indicators should include at least the remaining head margin and the duration of exceeding the threshold.

[0011] Furthermore, the event data includes inflow height parameters and inflow duration parameters. The topology calculation module transforms the inflow height parameters and inflow duration parameters into time-varying inflow boundary conditions. Based on the hydraulic calculation model, the water level field and flow field of the parameterized hydraulic digital twin model are numerically solved to obtain hydraulic numerical results. The hydraulic numerical results include the dynamic response curves of water level at each node, path energy lines, and propagation uncertainties.

[0012] Furthermore, the topology calculation module maps the network topology of the parameterized hydraulic digital twin model to a weighted impedance graph and performs spectral analysis to obtain topology analysis results, which include propagation impedance tensor, node criticality parameters, and topology vulnerability parameters.

[0013] Furthermore, the control and dispatch module evaluates the credibility of the evidence chain by examining the end-seepage evidence and anti-counterfeiting verification results in the event data, as well as the sealing installation evidence and sealing re-verification evidence in the sealing operation data, and obtains the credibility weight of the evidence.

[0014] Furthermore, the control and dispatch module performs a fusion estimation of the intensity of the inflow event under the constraint of evidence credibility weight, obtains the confidence distribution of the inflow event intensity, and performs posterior inference based on the risk factor map to output the posterior risk spectrum of the flood situation. The posterior risk spectrum of the flood situation is used to characterize the risk situation of each end node in the preset risk level set.

[0015] Furthermore, the control and dispatch module performs time-series prediction based on the posterior risk spectrum of the flood situation to obtain the risk evolution trajectory within the prediction time window. Based on the risk evolution trajectory, the control and dispatch module optimizes and generates inspection frequency, dispatch priority, and disposal work order content through multi-objective constraints.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention assigns QR codes to cable protection pipe units and cable protection pipe sealing components to generate QR code identifiers containing anti-counterfeiting verification information, and establishes an association between the QR code identifiers and life-cycle information. This achieves the unification of object-level unique identification and life-cycle traceability, ensuring continuity and consistency in authenticity verification, circulation records, and responsibility traceability across manufacturing, warehousing, transportation, arrival, installation, operation and maintenance, excavation and relocation, and decommissioning stages. It also reduces quality distortion and traceability costs caused by identifier duplication, replacement, or information chain breaks.

[0017] In the lifecycle stage set, the QR code identification of the cable protection pipe unit is scanned to complete anti-counterfeiting verification and collect event data. At the same time, during the sealing operation, the QR code identification of the cable protection pipe sealing component is scanned to complete anti-counterfeiting verification and collect sealing operation data. This makes the event data and sealing operation data have the same source correlation in terms of time benchmark, object binding and operation evidence dimensions, providing a verifiable data foundation for subsequent dissemination assessment and risk inference, and reducing the uncertainty caused by manual supplementation, field inconsistency and missing evidence.

[0018] Based on event data, a parameterized hydraulic digital twin model incorporating network topology is constructed, and hydraulic numerical calculations and topology analysis are performed to generate propagation assessment results and head constraint indicators. Furthermore, based on event data, plugging operation data, propagation assessment results, and head constraint indicators, a risk factor map is constructed. Evidence chain credibility assessments are conducted on end-seepage evidence, anti-counterfeiting verification results, plugging installation evidence, and plugging re-verification evidence to obtain evidence credibility weights. Under these weight constraints, the intensity of the ingress event is estimated, and the posterior risk spectrum of the flood situation is output. This transforms the decision-making process for inspection frequency, dispatch priority, and work order content from experience-based decision-making to a quantifiable, interpretable, and closed-loop executable coordinated response, reducing misjudgments and ineffective dispatches, and improving the timeliness of risk response and resource allocation efficiency. Attached Figure Description

[0019] Figure 1 A structural block diagram of a QR code anti-counterfeiting and traceability early warning system for the entire lifespan of cable protection pipes; Figure 2 A flowchart of scanning anti-counterfeiting verification and data collection in the lifecycle stages; Figure 3 A schematic diagram of the network topology of a parameterized hydraulic digital twin model; Figure 4 This diagram illustrates how the dispatch module assesses the credibility of the evidence chain based on the risk factor graph and outputs the posterior risk spectrum of the flood situation, as well as generating inspection frequency, dispatch priority, and disposal work order content. Detailed Implementation

[0020] Reference Figures 1 to 4The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system includes: The coding and binding module is used to assign QR codes to cable protection pipe units and cable protection pipe sealing parts, generate QR code identifiers containing anti-counterfeiting verification information, and establish an association between QR code identifiers and full life-cycle information. By establishing a one-to-one correspondence between physical objects and their digital identities, and assigning QR codes to cable protection pipe units and sealing components, a scannable, identifiable, and verifiable QR code identifier can be created for each controlled object. This QR code identifier contains anti-counterfeiting verification information, ensuring that subsequent identification at any stage not only involves reading information but also provides the foundation for authenticity verification. Furthermore, by establishing a linked storage system between QR code identifiers and full-lifecycle information, records of the same object throughout its entire lifecycle—including manufacturing, circulation, installation, operation and maintenance, and disposal—can be continuously added to and traced, ensuring an unbroken data chain. This provides a stable data index entry point and traceability basis for subsequent event collection, analysis, calculation, and dispatching.

[0021] In one specific implementation, for cable protection pipe units and cable protection pipe sealing components produced in the same batch, a unique coded index value is generated for each physical item. Based on this unique coded index value, anti-counterfeiting verification information is generated. This information includes a verification digest and its digital signature calculated using a random challenge factor, a timestamp factor, and a key derivation factor, ensuring unpredictability and non-forgeability. The unique coded index value and the anti-counterfeiting verification information are jointly encoded into a QR code identifier, which is then solidified on the outer surface or end area of ​​the cable protection pipe unit in a weather-resistant and wear-resistant manner. Simultaneously, the QR code identifier is solidified on the visible surface or embedded carrier of the cable protection pipe sealing component to reduce the risk of replacement. After solidification, an association storage is established between the QR code identifier and the full lifecycle information. This association storage records at least the unique coded index value, the fingerprint of the anti-counterfeiting verification information generation parameters, the coding time, the coding object type identifier, and the corresponding full lifecycle information writing entry point. This allows any subsequent stage in the lifecycle stage set to locate the full lifecycle information of the same object by scanning the QR code identifier and continuously adding information. For example, an index value A-0001 is generated for cable protection pipe unit numbered A, and an index value B-0108 is generated for cable protection pipe sealing component of model B. Different anti-counterfeiting verification information is generated for each and written into the associated storage table, thereby ensuring that the same cable protection pipe unit and its corresponding cable protection pipe sealing component have a stable and unique data anchor point in subsequent traceability.

[0022] In one specific implementation, after completing the scan, the event acquisition module reads the verification digest and its digital signature from the QR code identifier, and uses the verification digest, random challenge factor, timestamp factor, and key derivation factor as verification inputs. Verification is then performed based on the verification key corresponding to the key derivation factor. If the verification passes and the timestamp factor is within a preset valid window, the anti-counterfeiting verification is deemed successful, and an anti-counterfeiting verification result is generated. Otherwise, the anti-counterfeiting verification is deemed unsuccessful, and the anti-counterfeiting verification result, failure reason, original QR code identifier, and acquisition terminal identifier are recorded in the lifecycle information for subsequent verification. The verification key and the signature key used to generate the digital signature are updated according to a preset rotation cycle, and the associated storage records the anti-counterfeiting verification information generation parameter fingerprint corresponding to each rotation to support historical verification.

[0023] In one specific implementation, the timestamp factor, within a preset valid window, constrains the expected occurrence time window of the QR code identifier within its lifecycle stage set. A stage time window table is formed by associating the coding time with the planned time range of each stage. When the scanning time falls within the corresponding stage time window table, the timestamp factor is determined to be within the valid window. When the scanning time falls within another stage time window or an abnormal cross-stage jump occurs, the anti-counterfeiting verification result is marked as suspicious, and the time consistency penalty item in the subsequent evidence chain credibility assessment is increased. Furthermore, to reduce the risk of QR codes being copied and reused across locations, historical scanning location sequences and scanning terminal sequences with the same unique coding index value are associated and stored. When the same unique coding index value is repeatedly scanned under impossible spatiotemporal constraints, a suspected clone alarm is output, and a clone risk evidence node is introduced into the risk factor graph to weaken the evidence credibility weight of the evidence chain corresponding to that QR code.

[0024] In one specific implementation, the QR code payload includes at least an object unique identifier, an object type identifier, a signature version number, an issuance time, a random challenge factor, a digest value, and a digital signature. After scanning by the acquisition terminal, signature verification is performed first, followed by verification of terminal time deviation, location credibility level, and lifecycle rules. When the key corresponding to the signature version number is in a revoked state or has expired, the anti-counterfeiting verification is directly deemed unsuccessful, and the reason for revocation is recorded. When offline, only local signature verification is performed, and the scanning event is marked as an event to be reviewed. After connecting to the network, location consistency and cross-link consistency checks are performed. When the same object unique identifier is scanned repeatedly within a preset impossible time window, a suspected clone event is generated, and this event is used as an evidence credibility penalty item and input into the evidence chain credibility assessment model.

[0025] The event acquisition module is used to scan the QR code of the cable protection pipe unit at each stage of the lifecycle, complete anti-counterfeiting verification, and collect event data. It also scans the QR code of the cable protection pipe sealing component during sealing operations, completing anti-counterfeiting verification and collecting sealing operation data. This ensures the system's full lifecycle management is implemented at the level of executable, recordable, and verifiable on-site actions. Firstly, scanning the QR code of the cable protection pipe unit at each stage of the lifecycle ensures the correct identity and reliable source of the recorded object, preventing data from incorrect or non-genuine objects from being mixed into the lifecycle information chain. Collecting event data after completing anti-counterfeiting verification provides reliable object binding relationships and on-site attributes such as time and location. Secondly, scanning the QR code of the cable protection pipe sealing component during sealing operations binds the sealing operation to the specific cable protection pipe sealing component and collects sealing operation data, creating a traceable record of the sealing operation's occurrence, object, and result at the data level. On the one hand, it provides the topology calculation module with event data input related to water inflow, and on the other hand, it provides the control and dispatch module with blocking operation data input related to blocking, so that subsequent calculations and decisions do not rely on subjective descriptions, but on verifiable structured acquisition results.

[0026] In one specific implementation, for the manufacturing, warehousing, transportation, arrival, installation, operation and maintenance, excavation and relocation, and decommissioning stages in the life cycle stage set, a data acquisition terminal with security authentication capabilities scans the QR code identification of the cable protection pipe unit. After scanning, the anti-counterfeiting verification information in the QR code identification is parsed and the anti-counterfeiting verification is completed. If the anti-counterfeiting verification is successful, event data is collected according to a unified field template and written into the life cycle information associated with the QR code identification. The event data includes at least the stage identification, scanning time, scanning location, data acquisition terminal identification, operator identification, and structured records related to the status of the cable protection pipe unit in the life cycle stage set. At the same time, on-site image evidence and environmental elements are allowed as supplementary fields to enhance traceability integrity. If the anti-counterfeiting verification fails, event data is still collected but marked as abnormal and the original evidence is retained for subsequent verification. When performing sealing operations during the installation, operation and maintenance, and excavation and relocation phases in the lifecycle stage set, the QR code identification of the cable protection pipe sealing component is scanned and anti-counterfeiting verification is completed. The operation process is organized in the order of specification matching, installation, re-inspection or replacement. At key operation nodes, sealing operation data is written into the full lifecycle information associated with the QR code identification of the cable protection pipe sealing component. At the same time, in order to avoid the separation of sealing operation data and event data, the QR code identification of the cable protection pipe unit is scanned simultaneously at the start and end of the sealing operation to establish a dual-object association within the same time window. The sealing operation data includes at least the specification matching result of the cable protection pipe sealing component, sealing installation evidence, sealing re-inspection evidence and replacement record, so as to ensure that the sealing operation trajectory can be traced back by the dimension of cable protection pipe unit and the authenticity and circulation trajectory can be traced back by the dimension of cable protection pipe sealing component. In one specific implementation, the event data uses a unified field template that includes at least the stage identifier, scanning time, scanning location, acquisition terminal identifier, operator identifier, end node number, and end-point seepage evidence from the lifecycle stage set. The end-point seepage evidence includes at least one of the following: seepage level, seepage duration, and on-site video evidence. When the event corresponds to a water ingress event, the event data further includes water ingress height and water ingress duration parameters. The water ingress height parameter is obtained from the liquid level gauge reading, liquid level sensor reading, or on-site video calibration at the end node. The water ingress duration parameter is obtained from the difference between the start and end times recorded by the acquisition terminal. The sealing operation data uses a unified field template that includes at least the cable protection pipe sealing component specification matching result, sealing installation evidence, sealing re-verification evidence, and replacement record. The sealing installation evidence and sealing re-verification evidence include at least one of the following: operation video, inspection record, and signature information. The control and dispatch module generates a sealing effectiveness score based on the consistency of the specification matching result, the completeness of the evidence, and the re-verification conclusion. The sealing effectiveness score ranges from zero to one and is used for estimation and inference under subsequent evidence credibility weight constraints. For example, after scanning the cable protection pipe unit upon arrival, event data is collected and the terminal status is recorded as normal. Subsequently, during the sealing operation in the installation stage, the QR code of the cable protection pipe sealing component is scanned to complete the anti-counterfeiting verification and generate specification matching results and sealing installation evidence. After the re-verification is completed, the QR code of the cable protection pipe unit is scanned again to associate the sealing re-verification data with the full life information of the cable protection pipe unit, so that the event data and sealing operation data of the same cable protection pipe unit in the arrival and installation stages form a continuous traceable chain.

[0027] The topology calculation module is used to construct a parametric hydraulic digital twin model, which includes network topology. Based on event data, the module performs hydraulic numerical calculations and topology analysis on the parametric hydraulic digital twin model, generating propagation assessment results and head constraint indicators. It transforms events into computable, assessable, and quantifiable propagation and constraint conclusions, thereby supporting subsequent risk assessment and dispatch control. Constructing the parametric hydraulic digital twin model provides the system with the foundation for numerically representing the pipeline network status. The inclusion of network topology in the parametric hydraulic digital twin model means that the system expresses nodes and connections in a network topology manner, facilitating the derivation of propagation paths and impact ranges. Performing hydraulic numerical calculations and topology analysis on the parametric hydraulic digital twin model based on event data allows event data to be used as a computational driving factor, simultaneously characterizing the propagation process at both the hydraulic numerical level and the topological relationship level. The system generates propagation assessment results and head constraint indicators, enabling the output to not only describe "where it may propagate to" but also provide two key conclusions: "propagation assessment" and "head constraint." This provides the necessary calculation basis and constraint information for the control and dispatch module to construct risk factor maps, estimate the intensity of inflow events, and output the posterior risk spectrum of the flood situation, thus avoiding omissions or misjudgments caused by relying solely on experience.

[0028] In one specific implementation, based on as-built drawings, on-site measurement records, and existing operation and maintenance ledgers, each manhole location is abstracted as a manhole node, each connected section corresponding to each cable protection pipe unit is abstracted as a pipe segment node, and each externally connected or closed port is abstracted as an end node. Directed connections between nodes are established according to actual connectivity, with the direction determined by the composite result of gravity slope and existing drainage organization direction, forming a network topology that can be used for propagation path determination. To ensure the traceability and maintainability of the network topology, each node is assigned a unique number and its source, spatial location, adjacency relationship, and topology version number are recorded, enabling subsequent network topology updates to form incremental records without altering historical versions. Establish a set of geometric and hydraulic parameters for each pipe segment node and complete parameter binding: For each pipe segment node, collect or calculate the pipe diameter, length and slope, where the slope is determined by the elevation difference between the upstream and downstream manhole nodes and the length. At the same time, determine the roughness coefficient based on the material and inner wall condition, determine the local loss coefficient based on the local structural configuration such as elbows, diameter changes, and tees, calculate the low point volume parameter based on the relative elevation and cross-sectional shape of the lowest point of the pipe segment, and establish seepage recharge parameters by combining the permeability of the surrounding soil, groundwater level changes and historical seepage records, so that the hydraulic parameter set can reflect the recharge and accumulation characteristics of the pipe segment node under different working conditions; For example, in a branch consisting of two well nodes and one end node, there is a pipe segment node G1 between well node J1 and well node J2. The pipe diameter of pipe segment node G1 is 110 mm, the length is 30 m, and the slope is calculated from the elevation difference between well node J1 and well node J2. The roughness coefficient is determined according to the inner wall material, the local loss coefficient is determined according to the end elbow structure configuration, the low point volume parameter is calculated according to the lowest point position and cross section, and the seepage recharge parameter is established according to the surrounding soil layer and water level data. Thus, a parameterized hydraulic digital twin model that can be adjusted with data updates is formed and includes the network topology.

[0029] In one specific implementation, event data is received and the inflow height parameter and inflow duration parameter are parsed. Based on the inflow height parameter, the inflow level driving amount and the inflow section filling degree are determined. Based on the inflow duration parameter, the inflow action time window is determined. The inflow height parameter and the inflow duration parameter are jointly transformed into time-varying inflow boundary conditions. The time-varying inflow boundary conditions are discretized into an inflow sequence according to a preset time step and bound to the inflow position of the end node or well chamber node. At the same time, to ensure the traceability of the results, the event data source, time reference and discrete parameter fingerprint are recorded. In one specific implementation, for each pipe segment node Establish the volume balance equation: in, For the pipe segment node at time The water storage volume, and These are inflow and outflow flows, respectively. This involves establishing a permeation supply term and defining energy loss relationships between adjacent nodes. in, , For the water head of adjacent nodes, The friction factor is related to the roughness coefficient, pipe diameter, and length. This is the local loss coefficient. The cross-sectional area of ​​the water passage. The elevation difference is used. The topology calculation module iteratively solves for the node liquid level, flow rate, and arrival time at each time step according to the above equations, and writes the solution results into the corresponding hydraulic numerical results.

[0030] Hydraulic numerical solution and generation of hydraulic numerical results: Using a parameterized hydraulic digital twin model as the computational object, the hydraulic calculation model is called to numerically solve the water level field and flow field. The numerical solution simultaneously considers the influence of pipe diameter, length, slope, roughness coefficient, local loss coefficient, low-point volume parameters, and seepage recharge parameters on energy loss, accumulation, and recharge at each time step. The nonlinear term error is controlled by the iterative convergence criterion to obtain hydraulic numerical results covering the entire computation time domain. The hydraulic numerical results output at least the dynamic response curve of water level at each node and the path energy line. Under the setting of parameter disturbance and observation bias, the propagation uncertainty is calculated to reflect the influence of uncertain factors such as inflow, roughness coefficient, and local loss coefficient on propagation time and peak water level. In one specific implementation, the hydraulic calculation model is a one-dimensional unsteady flow model or a volumetric equilibrium model. The preset time step is determined based on the pipe segment node length and the upper limit of propagation velocity, and meets the numerical stability requirements. The iterative convergence criteria include a residual threshold and a maximum number of iterations. When the residual is less than the residual threshold or the maximum number of iterations is reached, the iteration of the current time step is terminated. If the residual threshold is not met even after reaching the maximum number of iterations, the hydraulic numerical result of that time step is marked as non-convergent, and the preset time step is reduced before resolving. The propagation uncertainty is obtained by applying parameter perturbations to the influent height parameter, influent duration parameter, roughness coefficient, and local loss coefficient, and repeating the hydraulic numerical calculation. The interval between the arrival time statistics and the peak water level statistics of the dynamic response curves of each node is used as the propagation uncertainty output. In one specific implementation, the inlet height parameter With influent duration parameter Convert to time-varying inflow boundary ,when season or ,in, , and These are calibration parameters obtained through field tests or historical event inversion; when t>T, let The initial liquid level at each node is taken from the measured liquid level at the sampling moment prior to the event; if no measured liquid level is available, the historical steady-state baseline value is used. The roughness coefficient, local loss coefficient, and permeation recharge parameters are calibrated through historical event playback, field tests, or fitting of measured liquid level curves, and the calibrated parameter version number is written into the parameter configuration table to ensure traceability of the subsequent numerical solution process.

[0031] The preset time step, residual threshold, maximum number of iterations, parameter perturbation amplitude, observation bias setting, and arrival criteria of the propagation front of the water level field and flow field are all written as configurable parameters into the parameter configuration table of the parameterized hydraulic digital twin model and stored in association with the network topology version number. The preset time step can be set to 1 second to 60 seconds, the residual threshold can be set to 0.001 to 0.01, the maximum number of iterations can be set to 20 to 200, and the upper limit of the propagation speed can be obtained from historically measured water level front propagation speeds or field experiments and used to constrain the selection of the preset time step. Parameter perturbation can be performed using a relative perturbation method on the inflow height parameter and the inflow... The water duration parameter is subjected to a relative perturbation of ±0.05 to ±0.20, and the roughness coefficient and local loss coefficient are subjected to a relative perturbation of ±0.10 to ±0.30. Hydraulic numerical calculations are repeated under each set of perturbations to obtain the arrival time statistics and peak water level statistics. The arrival criteria for the propagation front of the water level field and flow field can be defined as the first time that the dynamic response curve of the nodal water level exceeds the preset water level increment threshold relative to the reference water level or the first time that its first difference exceeds the preset slope threshold. The preset water level increment threshold can be set to 0.01 m to 0.05 m, and the preset slope threshold can be set to 0.001 m / s to 0.01 m / s. Network topology mapping and spectral analysis generate topology analysis results: The network topology of the parameterized hydraulic digital twin model is mapped into a weighted impedance diagram. The edge weights of the weighted impedance diagram are determined by the equivalent impedance composed of pipe diameter, length, slope, roughness coefficient, local loss coefficient, and low-point volume parameters. The node weights are modified by the seepage supply parameters and node connectivity. Spectral analysis is performed on the weighted impedance diagram to obtain the eigenvalue spectrum and eigenvector distribution. Based on this, the propagation impedance tensor is calculated to characterize the comprehensive impedance differences of different propagation directions and paths. The node critical parameters are calculated to characterize the degree of control of well chamber nodes, pipe segment nodes, and end nodes on propagation connectivity. The topology vulnerability parameters are calculated to characterize the degradation sensitivity of propagation capacity under edge or node constraints, thus forming topology analysis results that can be mutually verified with hydraulic numerical results. In one specific implementation, the edge weights of the weighted impedance diagram are converted into equivalent impedances by the pipe diameter, length, slope, roughness coefficient, local loss coefficient, and low-point volume parameter of the pipe segment nodes according to a preset normalization rule. The equivalent impedance increases with increasing length, roughness coefficient, local loss coefficient, and low-point volume parameter, and decreases with increasing pipe diameter and slope. In one specific implementation, the preset normalization rule includes performing minimum-maximum normalization on length, pipe diameter, slope, roughness coefficient, local loss coefficient, and low-point volume parameter respectively, and applying reverse normalization on pipe diameter and slope. The equivalent impedance is calculated by weighted summation: Let the length be... Pipe diameter is The slope is Roughness coefficient is The local loss coefficient is Low point volume parameters are ,make , , , , , The minimum and maximum values ​​are obtained by statistically analyzing the parameter set of the pipe segment nodes corresponding to the current network topology version number. or or or or or When the corresponding normalization result is taken Equivalent impedance ,in , , , , and All weights are non-negative and the sum of the weights is Example: , , , , , Reasons for the values: Friction loss is mainly controlled by length and roughness, so it is given higher weight; local loss and pipe diameter have a secondary impact on impedance; low-point volume reflects the storage effect; slope affects gravity drive and drainage capacity, so it is used as an auxiliary factor. An adjacency matrix and a Laplace matrix are constructed from the weighted impedance graph. Spectral analysis of the Laplace matrix yields the eigenvalue spectrum and eigenvector distribution. The propagation impedance tensor is calculated jointly by the minimum path equivalent impedance between end node pairs and the eigenvector projection. The node criticality parameter is calculated jointly by the concentration of eigenvector distribution on well chamber nodes, pipe segment nodes, and end nodes and the node connectivity. The topological fragility parameter is calculated jointly by the change in eigenvalue spectrum and the change in propagation impedance tensor after removing a single edge or node. Results Fusion and Index Output: Based on hydraulic numerical results and topology analysis results, propagation assessment results and head constraint indicators are generated. The propagation assessment results include at least a downstream impact list and corresponding arrival time windows. The downstream impact list is obtained by jointly screening the arrival criteria of the propagation front of the water level field and the flow field and the key parameters of the nodes. The arrival time windows are jointly given by the arrival time statistics of the dynamic response curves of the water level of each node and the propagation uncertainty. The head constraint indicators include at least the remaining head margin and the duration of exceeding the threshold. The remaining head margin is obtained by comparing the node head converted from the path energy line with the preset head threshold. The duration of exceeding the threshold is obtained by the cumulative duration of the dynamic response curve of the water level or the node head time history exceeding the threshold. In one specific implementation, the preset head threshold is determined by the minimum allowable head in the design or the statistical quantile of the historical operating head, and is configured and written into the parameterized hydraulic digital twin model for different well chamber nodes, pipe section nodes, and end nodes respectively; the remaining head margin is the difference between the node head and the preset head threshold, and the over-threshold duration is the cumulative duration for which the node head is lower than the preset head threshold. The preset risk level set includes at least multiple ordered risk levels, and the posterior probability interval of the posterior risk spectrum of the water hazard situation at each risk level is mapped to the inspection frequency and dispatch priority. The posterior probability interval can be set by historical event statistics or expert experience, and a version number is retained in the life cycle information for traceability. For example, when the event data corresponding to a certain end node gives an inflow height parameter of 0.30 meters and an inflow duration parameter of 1200 seconds, the time-varying inflow boundary condition can be constructed as a platform inflow sequence that rises linearly for the first 600 seconds and remains constant for the next 600 seconds. After numerical solution, three end nodes and one well chamber node can be listed in the downstream impact list, and the arrival time windows are given as 300 to 420 seconds, 520 to 680 seconds, 900 to 1100 seconds, and 240 to 360 seconds, respectively. At the same time, the remaining head margin of the key well chamber node is given as 0.15 meters and the over-threshold duration is 180 seconds in the head constraint index, thereby realizing the synchronous quantitative output of propagation range, arrival time sequence and head constraint.

[0032] The control and dispatch module is used to construct a risk factor map based on event data, blockade operation data, propagation assessment results and head constraint indicators, and to evaluate the credibility of the evidence chain in the event data and blockade operation data to obtain the credibility weight of the evidence. Under the constraint of the credibility weight of the evidence, the intensity of the water inflow event is estimated and the posterior risk spectrum of the water hazard situation is output. Based on the posterior risk spectrum of the water hazard situation, the inspection frequency, dispatch priority and disposal work order content are generated. This approach integrates multi-source information, credibility, risk status, and dispatch execution into a closed-loop decision-making chain. A risk factor map is constructed based on event data, containment operation data, propagation assessment results, and head constraint indicators. Field data collection and calculation outputs are unified within the same risk expression framework, enabling information from different sources and dimensions to be correlated, constrained, and reasoned about within the same structure. Evidence chain credibility is assessed in the event data and containment operation data, and evidence credibility weights are obtained to differentiate evidence quality, avoiding disproportionate impacts of low-credibility evidence on risk judgment and improving the robustness of subsequent estimations and inferences. Under the constraint of evidence credibility weights, the intensity of the inflow event is estimated, and a posterior risk spectrum of the flood hazard situation is output. The key driver of "inflow event intensity" is estimated under credibility constraints, and the risk status is output in the form of a posterior risk spectrum of the flood hazard situation, thus forming a risk expression that can be used for dispatch and execution. Based on the post-hoc risk spectrum of the water hazard situation, the system generates inspection frequency, dispatch priority, and disposal work order content, directly transforming risk expression into action instructions: the inspection frequency reflects the pace of inspection resource investment, the dispatch priority reflects the disposal sequence, and the disposal work order content reflects the on-site disposal requirements, so that the system output not only "makes the risks visible" but also "can be implemented," forming a complete closed loop with the aforementioned data collection and calculation.

[0033] In one specific implementation, a risk factor diagram is constructed: a unified risk expression structure is established based on event data, sealing operation data, propagation assessment results, and head constraint indicators. Each end node is treated as a risk object node, end seepage evidence, anti-counterfeiting verification results, sealing installation evidence, and sealing re-verification evidence are treated as evidence nodes, the intensity of the inflow event is treated as an implicit state node, and the downstream impact list and corresponding arrival time window, remaining head margin, and over-threshold duration are treated as constraint nodes. Directed connections are established according to causal consistency and temporal correlation, so that end seepage evidence affects the head constraint indicators through the intensity of the inflow event and further affects the risk status of the end node. At the same time, sealing installation evidence and sealing re-verification evidence are connected in a way that weakens the intensity of the inflow event or changes the propagation constraints, thereby ensuring that the propagation assessment results and the head constraint indicators can constrain the risk status of the end node within the same structure. The credibility assessment of the evidence chain calculates sub-scores for the consistency verification of anti-counterfeiting verification results, the matching verification of collection time and links in the life cycle link set, the spatial consistency verification of collection location and end node, the verification of mutual corroboration relationship between evidence, and the compatibility verification of arrival time window with the propagation assessment results. The sub-scores are then normalized and fused according to preset coefficients to obtain the evidence credibility weight. The evidence credibility weight is between zero and one and is associated with the end seepage evidence, anti-counterfeiting verification results, sealing installation evidence, and sealing verification evidence. The prior range of the intensity of the flooding event is obtained based on historical evidence of seepage at the end point, the parameters of the flooding height and the duration of the flooding. The confidence distribution of the intensity of the flooding event is obtained by fusion estimation using the observations of the seepage at the end point and the effectiveness score of the sealing as weighted likelihood sources. In the posterior inference stage, the confidence distribution is used as the input of the implicit state nodes, and iterative inference is performed by combining the propagation assessment results and the head constraint index to output the posterior risk spectrum of the flood situation. The posterior risk spectrum of the flood situation includes at least the posterior probability of each end point node in the preset risk level set and the corresponding maximum posterior risk level. The scores for each item in the evidence chain credibility assessment were normalized to a range of 0 to 1: Assuming the anti-counterfeiting consistency score is... The time matching score for each stage is: Spatial consistency score The scores for mutual corroboration are: Arrival time window compatibility score: The credibility weight of evidence is defined as follows: ,in and , When there is time inversion, spatial deviation exceeding a preset threshold, or significant incompatibility with the arrival time window, a penalty will be applied. Join and make ,in The deviation magnitude is determined by a linear or piecewise function, making the weight calculation reproducible and providing deterministic penalties for critical violations; The credibility of the evidence chain is assessed and the credibility weight is obtained: The credibility of the evidence chain is assessed for the end-point water seepage evidence and anti-counterfeiting verification results in the event data, and the sealing installation evidence and sealing re-verification evidence in the sealing operation data. The credibility assessment of the evidence chain includes at least the consistency verification of the anti-counterfeiting verification results, the matching verification of the collection time and the links in the life cycle link set, the spatial consistency verification of the collection location and the end-point node, the verification of the mutual corroboration relationship between the evidence, and the compatibility verification with the arrival time window of the propagation assessment results. The above verification results are integrated into the credibility weight of the evidence chain. The evidence chain with the anti-counterfeiting verification results passed and the time sequence of the sealing re-verification evidence is consistent with that of the sealing re-verification evidence is given a higher credibility weight. The evidence chain with time inversion, spatial deviation or obvious incompatibility with the arrival time window is given a lower credibility weight to ensure that subsequent estimation and inference are not dominated by low credibility evidence. Fusion estimation and generation of disposal output under the constraint of evidence credibility weight: The intensity of the water ingress event is fused and estimated under the constraint of evidence credibility weight. A prior range for the intensity of the water ingress event is set. The observation of the end seepage evidence and the sealing installation evidence and sealing verification evidence reflecting the sealing effectiveness are used as the weighted likelihood source to obtain the confidence distribution of the intensity of the water ingress event. Based on the risk factor map, posterior inference is performed to output the posterior risk spectrum of the water hazard situation. The posterior risk spectrum of the water hazard situation is used to characterize the risk situation of each end node in the preset risk level set. The posterior risk spectrum of the water hazard situation is mapped to the inspection frequency, dispatch priority and disposal work order content. The inspection frequency is increased with the increase of risk level, the dispatch priority is increased with the extension of the over-threshold duration and the decrease of the remaining head margin, and the disposal work order content includes at least the requirements for re-verification or replacement of sealing operation and the requirements for verification of end seepage evidence. In one specific implementation, the intensity of the inflow event is defined as the equivalent inflow rate at the end node. or equivalent inflow volume And set the prior as or The upper bound is determined by the pipe diameter, end cross-section, and inlet height parameters; the observable evidence of end seepage is defined as the observation vector. Its likelihood adopts ,in The statistical data of the dynamic response curve of the nodal water level obtained from hydraulic numerical calculations are generated for the corresponding time period; the effectiveness score of the closure is obtained. As a weakening factor acting on the equivalent inflow rate, and use Alternative By incorporating it into post-hoc calculations, the mechanism by which occlusion evidence affects influent intensity can be quantified.

[0034] In one specific implementation, the preset risk level set is as follows: Define a risk state random variable for each end node. And construct conditional probability tables or factor functions based on risk factor graphs: evidence nodes are weighted according to the credibility of the evidence. Weight its likelihood by a power factor, let The constraint nodes map the remaining head margin and over-threshold duration as a penalty or gain term for the high-risk level; the posterior distribution is calculated using belief propagation or variational inference. and will As the output of the a posteriori risk spectrum of the flood situation, it also includes the version identifier of the parameters used for inference. To support audit review.

[0035] In one specific implementation, multi-objective constraint optimization uses a parameterized combination of inspection frequency, dispatch priority, and disposal work order content as decision variables. The first objective is to reduce the posterior risk spectrum of the flood situation within the prediction time window, and the second objective is to reduce the consumption of disposal resources. The constraints are the risk evolution trajectory within the prediction time window, the downstream impact list and its corresponding arrival time window, and the available personnel and available time periods. The optimal or near-optimal solution that satisfies the constraints is obtained. Among them, the inspection frequency is constrained by the risk level and the arrival time window at least, the dispatch priority is constrained by the over-threshold duration and the remaining head margin at least, and the disposal work order content includes at least the task items of re-inspection, replacement, or inspection and the corresponding execution time limit. For example, if there is evidence of water seepage at the end node P1 and the anti-counterfeiting verification result passes, but the sealing re-verification evidence shows that the sealing match is unstable, and the propagation assessment result shows that P1 is on the downstream impact list and the arrival time window is consistent with the water seepage occurrence time, and the head constraint index shows that the remaining head margin is low and the duration of exceeding the threshold is long, then the evidence chain credibility assessment obtains a high evidence credibility weight, the fusion estimation obtains a high confidence distribution of the water ingress event intensity, and the posterior inference adjusts the risk level corresponding to P1 in the preset risk level set, and generates a high-frequency inspection arrangement, the highest dispatch priority, and the disposal work order content including sealing replacement and re-verification steps accordingly.

[0036] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A QR code anti-counterfeiting and traceability early warning system for the entire life cycle of cable protection pipes, characterized in that, include: The coding and binding module is used to assign QR codes to cable protection pipe units and cable protection pipe sealing parts, generate QR code identifiers containing anti-counterfeiting verification information, and establish an association between QR code identifiers and full life-cycle information. The event acquisition module is used to scan the QR code of the cable protection pipe unit in the life cycle stage set to complete the anti-counterfeiting verification and collect event data, and to scan the QR code of the cable protection pipe sealing component when the sealing operation is performed in the life cycle stage set to complete the anti-counterfeiting verification and collect sealing operation data. The topology calculation module is used to construct a parametric hydraulic digital twin model, which includes network topology. Based on event data, hydraulic numerical calculations and topology analysis are performed on the parametric hydraulic digital twin model to generate propagation assessment results and head constraint indicators. The dispatching module is used to construct a risk factor map based on event data, closure operation data, propagation assessment results, and head constraint indicators. It also assesses the credibility of the evidence chain in the event data and closure operation data to obtain the credibility weight of the evidence. Under the constraint of the credibility weight of the evidence, it estimates the intensity of the water inflow event and outputs the posterior risk spectrum of the water hazard situation. Based on the posterior risk spectrum of the water hazard situation, it generates the inspection frequency, dispatch priority, and disposal work order content.

2. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 1, characterized in that, The life cycle stage set is a collection of multiple stages used to characterize the entire life cycle management process of cable protection pipe units. The set includes the manufacturing stage, warehousing stage, transportation stage, arrival stage, installation stage, operation and maintenance stage, excavation and relocation stage, and decommissioning stage. The installation stage, operation and maintenance stage, and excavation and relocation stage include sealing operations, which are used to complete the specification matching, installation, re-inspection, or replacement of cable protection pipe sealing components.

3. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 1, characterized in that, The parameterized hydraulic digital twin model includes a directed network topology consisting of well chamber nodes, pipe segment nodes, and end nodes, as well as a set of geometric and hydraulic parameters corresponding to each pipe segment node. The set of hydraulic parameters includes at least pipe diameter, length, slope, roughness coefficient, local loss coefficient, low-point volume parameter, and seepage recharge parameter.

4. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 1, characterized in that, The topology calculation module performs hydraulic numerical calculations on the parameterized hydraulic digital twin model based on event data to obtain hydraulic numerical results, and performs topology analysis on the network topology to obtain topology analysis results. Based on the hydraulic numerical results and topology analysis results, the topology calculation module generates propagation assessment results and head constraint indicators.

5. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 4, characterized in that, The propagation assessment results should include at least a list of downstream impacts and their corresponding arrival time windows, and the head constraint indicators should include at least the remaining head margin and the duration of exceeding the threshold.

6. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 4, characterized in that, The event data includes inflow height parameters and inflow duration parameters. The topology calculation module transforms the inflow height parameters and inflow duration parameters into time-varying inflow boundary conditions. Based on the hydraulic calculation model, the water level field and flow field of the parameterized hydraulic digital twin model are numerically solved to obtain hydraulic numerical results. The hydraulic numerical results include the dynamic response curves of water level at each node, path energy lines, and propagation uncertainties.

7. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 4, characterized in that, The topology calculation module maps the network topology of the parameterized hydraulic digital twin model to a weighted impedance graph and performs spectral analysis to obtain the topology analysis results, which include propagation impedance tensor, node critical parameters, and topology vulnerability parameters.

8. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 1, characterized in that, The control and dispatch module evaluates the credibility of the evidence chain by examining the end-seepage evidence and anti-counterfeiting verification results in the event data, as well as the sealing installation evidence and sealing re-verification evidence in the sealing operation data, and obtains the evidence credibility weight.

9. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 1, characterized in that, The control and dispatch module performs a fusion estimation of the intensity of the inflow event under the constraint of evidence credibility weight, obtains the confidence distribution of the inflow event intensity, and performs posterior inference based on the risk factor map to output the posterior risk spectrum of the flood situation. The posterior risk spectrum of the flood situation is used to characterize the risk situation of each end node in the preset risk level set.

10. The cable protection pipe full-life-cycle QR code anti-counterfeiting traceability and early warning system according to claim 1, characterized in that, The control and dispatch module performs time-series prediction based on the posterior risk spectrum of the flood situation, and obtains the risk evolution trajectory within the prediction time window. Based on the risk evolution trajectory, the control and dispatch module optimizes and generates inspection frequency, dispatch priority and disposal work order content through multi-objective constraints.

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