Bridge tunnel defect measurement and detection method and system

Abstract

The invention provides a bridge and tunnel defect measurement and detection method and system, and belongs to the technical field of image recognition, and the method specifically comprises the steps: carrying out the setting processing of wireless network relay nodes in sub-regions based on a setting strategy, and carrying out the setting processing of the wireless network relay nodes in the sub-regions through prestressed pipelines with abnormal detection results in different sub-regions, according to the method, when it is determined that a data abnormal risk exists in the detection result according to the detection result of the prestressed pipeline associated with the wireless network relay nodes in different sub-regions, whether optimization processing of the wireless network can be carried out by using the detection equipment is determined according to the detection result of the prestressed pipeline associated with the wireless network relay nodes in the different sub-regions, and the comprehensiveness of defect measurement processing is improved.

Description

Technical Field

[0001] This invention belongs to the field of image recognition technology, and in particular relates to a method and system for measuring and detecting defects in bridges and tunnels. Background Technology

[0002] Prestressed ducts are frequently used in bridge and tunnel construction. These ducts are pipes or channels pre-embedded in concrete components during concrete pouring. Their sole purpose is to provide space for the subsequent insertion and tensioning of prestressed steel strands or reinforcing bars, thereby establishing prestress within the concrete.

[0003] The casting quality of prestressed ducts is crucial to the structural strength. In invention patent application CN201410815167.3, "Method for Detecting the Grouting Quality of Prestressed Ducts in Bridges Based on Combined Seismic Source Method," a hammer-driven seismic source and a Chirp seismic source are effectively combined, fully leveraging the advantages of both while effectively avoiding their disadvantages. This method can accurately determine the location of the first arrival wave and obtain high-resolution elastic wave field maps, thus enabling precise detection of the grouting quality of prestressed ducts in bridges. However, the following drawbacks exist: In order to detect and process prestressed ducts in bridges or tunnels, using a single measuring point is often insufficient to effectively detect and process anomalies. Therefore, how to combine the prestressed duct data that has not been detected in the past to achieve network processing between different detection nodes, thereby improving the reliability of the transmission of detection results, has become an urgent technical problem to be solved.

[0004] To address the aforementioned technical problems, this application provides a method and system for measuring and detecting defects in bridge tunnels. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for measuring and detecting defects in bridge tunnels, which includes: S1 uses historical inspection data of prestressed ducts in the target area as a basis to determine the distribution data of undetected ducts in different sub-areas. When determining the need to set up wireless network relay nodes without detection function based on the distribution data of undetected ducts, the setting strategy of wireless network relay nodes in the sub-area is determined based on the relay relationship of the undetected duct data in the sub-area and the network data between the sub-area and other sub-areas. S2 performs setting processing of the wireless network relay node in the sub-area based on the setting policy, and determines whether the detection result of the prestressed pipe associated with the wireless network relay node in different sub-areas is abnormal, and whether the wireless network can be optimized by the detection device based on the detection result of the prestressed pipe associated with the wireless network relay node in different sub-areas when the detection result is abnormal.

[0006] The beneficial effects of the present application are: Based on the distribution data of the undetected pipe, it is determined whether the setting of the wireless network relay node without detection function is needed, so as to avoid the case that there are too many undetected pipes, and it is difficult to determine the abnormal positioning processing when there are too many undetected pipes with abnormal detection results. By setting the wireless network relay node without detection function, the efficiency and reliability of the abnormal positioning processing are improved.

[0007] Based on the detection result of the prestressed pipe associated with the wireless network relay node in different sub-areas, it is determined whether the wireless network can be optimized by the detection device, which not only considers the coincidence of the wireless network relay node with abnormal detection result, but also further considers the difference in the number of detection devices with wireless network relay function in different sub-areas, which leads to the difference in the available degree of the wireless network relay processing by the detection device, so as to realize the determination of the optimization processing method of the wireless network from the perspective of the available degree and the coincidence of the wireless network relay node, and lay a foundation for further improving the reliability of the detection result of the prestressed pipe.

[0008] Further, the target area includes a bridge area and a tunnel area.

[0009] Further, the historical detection data includes the detection result of the grouting quality of the prestressed pipe, and the detection result includes whether the grouting quality of the prestressed pipe is suspected to be abnormal.

[0010] Further, the undetected pipe is a prestressed pipe that has not been detected in history or the interval between the detection date and the current date is greater than a preset interval threshold, and needs to be detected this time.

[0011] Further, the determination of the setting of the wireless network relay node without detection function includes: Based on the distribution data of the undetected pipe, a sub-area with undetected pipe is determined as a risk sub-area; According to the composition data of the undetected pipe in different sub-areas, the pipe risk factor of different sub-areas is determined. According to the distribution data of the risk sub-regions in the sub-regions and the pipeline risk factors in different risk sub-regions, it is determined whether the setting of the wireless network relay node without detection function is needed.

[0012] Further, it is determined whether the optimization processing of the wireless network can be performed by using the detection equipment, and specifically includes: According to the detection result of the prestressed pipeline associated with the wireless network relay node in the sub-region, an abnormal pipeline in the prestressed pipeline associated with the wireless network relay node in the sub-region is determined and is taken as an associated abnormal pipeline; According to the associated abnormal pipeline data of the wireless network relay node in different sub-regions, the number of the associated abnormal pipelines of the wireless network relay node is determined. Based on the number of the associated abnormal pipelines of the wireless network relay node in different sub-regions, it is determined whether the optimization processing of the wireless network can be performed by using the detection equipment.

[0013] In a second aspect, the present application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to perform the bridge tunnel defect measurement and detection method.

[0014] Other features and advantages will be set forth in the descriptions that follow, and in part will be apparent from the description, or can be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

[0015] So that the foregoing and other features and advantages of the present application can be understood, a more particular description will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0016] The foregoing and other features and advantages of the present application will become more apparent from the detailed description that follows, when taken in conjunction with the accompanying drawings.

[0017] Figure 1 is a flowchart of an online monitoring and analysis method of a transformer; Figure 2 is a flowchart of a method for determining a monitoring deviation load interval corresponding to a fault type; Figure 3 is a framework diagram of an intelligent sensor. DETAILED DESCRIPTION

[0018] Example implementations are now described with reference to the drawings. Example implementations can, however, be implemented in many different forms and should not be construed as limited to the implementations set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example implementations to those skilled in the art. Like reference numerals refer to like elements throughout the specification. Detailed descriptions of structures, functions, and methods that are well known in the art are omitted herein to avoid obscuring the concepts of example implementations.

[0019] The terms "a," "an," "the," and "said" are used to refer to one or more than one (i.e., to "at least one") of the referenced elements / constituents / etc.; the terms "comprising," "having," and "including" are used to mean "including but not limited to;" and the term "consisting essentially of" means including an element or ingredient in addition to those recited.

[0020] Example 1 To solve the above problems, according to one aspect of the present application, as shown in the drawings, a bridge tunnel defect measurement detection method is provided, specifically comprising: Figure 1 S1, based on historical detection data of prestressed pipes in a target area, determine the distribution data of undetected pipes in different sub-areas, and based on the distribution data of the undetected pipes, determine the setting strategy of the wireless network relay nodes in the sub-areas based on the undetected pipe data in the sub-areas and the relay relationship between the network data and other sub-areas; S2, based on the setting strategy, perform setting processing of the wireless network relay nodes in the sub-areas, and based on the coincidence of the associated wireless network relay nodes of the prestressed pipes with abnormal detection results in different sub-areas, determine whether the detection results have data abnormality risks, and based on the detection results of the prestressed pipes associated with the wireless network relay nodes in different sub-areas, determine whether the detection equipment can be used to optimize the wireless network.

[0021] Further, the target area includes a bridge area and a tunnel area.

[0022] Further, the historical detection data includes detection results of grouting quality of the prestressed pipes, wherein the detection results include whether the grouting quality of the prestressed pipes is suspected to be abnormal.

[0023] Further, the undetected pipes are prestressed pipes that have not been detected in history or have not been detected for a time interval greater than a preset time interval threshold, and need to be detected this time.

[0024] Specifically, as shown in the drawings, Figure 2 ​As shown, determining the need for setting up a wireless network relay node with no-detection function, specifically comprising: With the distribution data of the undetected pipelines, determining sub-regions where there exist undetected pipelines and taking them as risk sub-regions; According to the composition data of the undetected pipelines in different sub-regions, determining pipeline risk factors of different sub-regions; According to the distribution data of the risk sub-regions in sub-regions and the pipeline risk factors in different risk sub-regions, determining whether to need to set up a wireless network relay node with no-detection function.

[0025] It can be understood that the pipeline risk factor of the sub-region is determined according to the product of the number of undetected pipelines in the sub-region and a preset proportion factor, wherein the more the number of undetected pipelines, the greater the pipeline risk factor of the sub-region.

[0026] Specifically, according to the distribution data of the risk sub-regions in sub-regions and the pipeline risk factors in different risk sub-regions, determining whether to need to set up a wireless network relay node with no-detection function, specifically comprising: it can also be understood that when the risk proportion is not greater than a preset risk proportion threshold, determining a comprehensive risk factor based on the average of the risk proportion and the average of the pipeline risk factors in different risk sub-regions, wherein when the comprehensive risk factor is greater than a preset risk factor threshold, it is determined that a wireless network relay node with no-detection function needs to be set up.

[0027] This embodiment will convert the abstract text description into a specific, numerically supported calculation process, so that the entire decision-making process can be clearly understood.

[0028] Embodiment: 1. Scenario and data assumptions Assume that the total number of sub-regions in the managed area is 100. Through preliminary investigation, the following data is obtained: risk sub-regions: after detection, it is found that there are undetected pipelines in 15 sub-regions. These 15 are "risk sub-regions".

[0029] Pipeline risk factor calculation: According to the rules, the sub-region pipeline risk factor = the number of undetected pipelines in the sub-region × the preset proportion factor, and the preset proportion factor is set to 0.8. The number of undetected pipelines in the 15 risk sub-regions is [5, 3, 7, 2, 10, 4, 1, 6, 3, 8, 2, 1, 12, 4, 2].

[0030] Decision threshold: preset risk proportion threshold: 0.2 (i.e. 20%), preset risk factor threshold: 3.0 2. Calculation process First step: calculate the risk proportion, the number of risk sub-regions = 15, the total number of sub-regions = 100, the risk proportion = 15 / 100 = 0.15; Conclusion: the risk proportion (0.15) is less than the preset risk proportion threshold (0.2), so we enter the next step of calculating the "comprehensive risk factor". If the risk proportion is greater than 0.2, it may mean that the problem is already widespread and direct action needs to be taken without calculating the comprehensive factor.

[0031] Second step: calculate the pipeline risk factor of each risk sub-region, according to the formula Risk factor = Undetected pipeline number × 0.8, we calculate the risk factor of each risk sub-region: Table 1. Calculation results of risk factors

[0032] Third step: calculate the comprehensive risk factor; According to the description, the comprehensive risk factor is the average of "risk proportion" and "average of risk sub-region pipeline risk factors", calculate the average of pipeline risk factors: the sum of all risk factors = 4.0 + 2.4 + 5.6+... + 1.6 = 56.0, the number of risk sub-regions = 15, the average of pipeline risk factors = 56.0 / 15 ≈ 3.73, calculate the comprehensive risk factor: risk proportion = 0.15, average of pipeline risk factors = 3.73, comprehensive risk factor = (0.15 + 3.73) / 2 = 3.88 / 2 = 1.94; Fourth step: decision Comprehensive risk factor (1.94); preset risk factor threshold (3.0), decision result: because 1.94 < 3.0, so currently there is no need to set up a wireless network relay node without detection function.

[0033] This embodiment clearly shows the complete chain from raw data to final decision: data input: total sub-region number, risk sub-region list, number of undetected pipelines in each region, parameter setting: proportion factor, risk proportion threshold, risk factor threshold, which takes into account both the universality of risk areas (risk proportion) and the severity of risk (average of pipeline risk factors), avoiding making one-sided decisions due to extreme risk in individual areas or large-area low risk, so that the deployment of relay nodes is more scientific and economical.

[0034] Specifically, as shown in Figure 3 The method for determining the setting strategy of the wireless network relay node in the sub-region comprises the following steps: determining the number of undetected pipelines in the sub-region based on the undetected pipeline data in the sub-region; determining other sub-regions requiring network data transmission by the wireless network relay node in the sub-region according to the relay relationship of the network data between the sub-region and other sub-regions, and taking the other sub-regions as the associated sub-regions; determining the setting strategy of the wireless network relay node in the sub-region based on the number of undetected pipelines in the sub-region and the associated sub-region data.

[0035] It should be noted that the associated sub-region is the sub-region requiring network signal relay processing according to the data sent by the other sub-regions to the detection device.

[0036] It can be understood that when the number of undetected pipelines in the sub-region does not meet the requirement, i.e., the number of undetected pipelines is greater than the preset pipeline quantity threshold, it is determined that the sub-region requires the setting processing of the wireless network relay node.

[0037] In addition, it should be noted that if the number of undetected pipelines in the sub-region meets the requirement, it is determined that the sub-region has the associated sub-region, and if the number of undetected pipelines in the associated sub-region of the sub-region is greater than the preset quantity threshold, it is determined that the sub-region requires the setting processing of the wireless network relay node.

[0038] Specifically, when the number of undetected pipelines in the associated sub-region of the sub-region is not greater than the preset quantity threshold, the pipeline risk factor in different associated sub-regions is determined based on the number of undetected pipelines in different associated sub-regions, and when there is an associated sub-region with a pipeline risk factor greater than the preset factor threshold, it is determined that the sub-region requires the setting processing of the wireless network relay node.

[0039] In addition, it should be noted that when there is no associated sub-region with a pipeline risk factor greater than the preset risk factor threshold, the associated risk coefficient of the associated sub-region of the sub-region is determined based on the sum of the pipeline risk factors in different associated sub-regions, and when the associated risk coefficient is greater than the preset risk coefficient threshold, it is determined that the sub-region requires the setting processing of the wireless network relay node, and when the associated risk coefficient is not greater than the preset risk coefficient threshold, it is determined that the sub-region does not require the setting processing of the wireless network relay node.

[0040] This embodiment focuses on deciding whether a wireless network relay node needs to be set for a certain sub-region. The decision process not only looks at itself, but also depends on the risk situation of the "associated sub-region" it serves. Embodiment: Single sub-region relay node setting strategy decision.

[0041] 1. Scenario and data assumption The current need to make relay node settings policy for sub-area A, preset threshold: preset pipe number threshold (N_threshold): 5, preset number threshold (M_threshold): 5 (for judging the risk of associated areas), preset factor threshold (F_threshold): 4.0, preset risk coefficient threshold (R_threshold): 10.0, scale factor: used to calculate pipe risk factor, assuming 1.0. (That is, pipe risk factor = undetected pipe number x 1.0); Sub-area A's own data: sub-area A's undetected pipe number: 3; Network topology and associated sub-area data: according to the network relay relationship, it is determined that there are 3 associated sub-areas that need to be relayed through sub-area A. They are: associated area B, associated area C, and associated area D.

[0042] The undetected pipe numbers of these associated sub-areas are: associated area B: 7, associated area C: 2, and associated area D: 4.

[0043] 2. Decision-making process and calculation First layer judgment: whether the risk of sub-area itself is extremely high, judgment condition: sub-area A's undetected pipe number (3) > preset pipe number threshold (5), judgment result: 3 < 5, condition not met, conclusion: the risk of sub-area A itself does not reach the standard of directly setting nodes. Enter the next layer of judgment.

[0044] Second layer judgment: whether there is a generally high risk in the associated sub-area, judgment condition: whether there is any associated sub-area whose undetected pipe number > preset number threshold (5), judgment result: check associated area B (7), C (2), D (4). The pipe number of associated area B is 7 > 5, the condition is met, the conclusion: since there is a generally high risk in associated area B, it is determined that sub-area A needs to be set up wireless network relay node, decision-making ends: finally decide to

need to set up relay node in sub-area A

[0045] 3. Scenario changes and subsequent logical deduction To demonstrate the complete decision tree, we assume a different scenario: the number of undetected pipes in the associated region B is not 7, but 3, then the decision flow will continue down: the first layer judgment: the number of sub-regions A itself (3) < threshold (5), not satisfied. The second layer judgment: all associated sub-regions number (B=3, C=2, D=4) < threshold (5), not satisfied. Enter the next layer of judgment, the third layer judgment: whether there is an individual extremely high risk point in the associated sub-region, calculate the pipe risk factor of each associated sub-region (since the proportion factor = 1.0, the risk factor = the number of undetected pipes): the risk factor of the associated region B is 3, the risk factor of the associated region C is 2, and the risk factor of the associated region D is 4. The judgment condition is: whether there is any associated sub-region whose pipe risk factor > preset factor threshold (4.0), the judgment result: the maximum value is 4.0 of the associated region D, which is not greater than 4.0. The condition is not satisfied. Enter the final judgment.

[0046] The fourth layer judgment: whether the comprehensive risk of the associated region is too high, calculate the associated risk coefficient: the sum of the risk factors of all associated sub-regions = 3 + 2 + 4 = 9, the judgment condition: the associated risk coefficient (9) > the preset risk coefficient threshold (10.0), the judgment result: 9 < 10.0, the condition is not satisfied, the final conclusion: it is determined that sub-region A does not need to be set up wireless network relay node processing, the final decision is

no need to set up relay node in sub-region A

[0047] This embodiment shows a multi-level and rigorous decision-making process for setting relay node strategy for a single sub-region. The core idea is: self-preference: first check whether the risk of the target region itself is high enough to set up, associated region risk transmission: if the self-risk is not high, the necessity of setting up the node comes from the risk of its "customers" (i.e. associated sub-regions), risk evaluation layer by layer: first see if there is a general high risk in the associated region (the number exceeds the threshold), then see if there is an extreme risk point in the associated region (the risk factor exceeds the threshold), and finally see the overall risk level of the associated region (the risk coefficient exceeds the threshold).

[0048] This method ensures that the relay node is set in strategic positions that are crucial to the overall network robustness, taking into account both key risk points and the aggregation effect of risk, thus achieving optimal allocation of resources.

[0049] Further, based on the setting strategy, the setting processing of the wireless network relay node in the sub-region is performed, specifically including: Based on the setting strategy, determine the sub-region that needs to be set up wireless network relay node processing, and take it as a setting sub-region; According to the number of undetected pipes in the setting sub-region and the number of prestressed pipes to be detected this time, the number of wireless network relay nodes to be set in the sub-region is determined.

[0050] It can be understood that, based on the number of undetected pipes in the setting sub-region, the setting proportion of the total wireless network relay nodes in the sub-region is determined, and the product of the setting proportion and the number of prestressed pipes to be detected this time is used to determine the number of wireless network relay nodes to be set in the setting sub-region.

[0051] It should be noted that the more the number of undetected pipes in the setting sub-region, the higher the setting proportion of the total wireless network relay nodes in the sub-region.

[0052] Specifically, the prestressed pipes with abnormal detection results are prestressed pipes whose detection results may have grouting quality problems.

[0053] 1. Scene and data assumptions Based on the previous decision results, we assume that through the previous round of strategy analysis, the following two sub-regions need to set relay nodes: Setting sub-region A: Undetected pipe number = 8, Setting sub-region B: Undetected pipe number = 15 Add this time's detection task data: The number of prestressed pipes to be detected this time: In the entire project scope, the total number of prestressed pipes to be detected using this wireless network this time is 50. These are the pipes that need to be monitored and may have grouting quality problems.

[0054] Setting proportion rule assumptions: We define a basic setting proportion factor (k), such as k = 0.1, and the calculation formula of the setting proportion is: Setting proportion = k × (number of undetected pipes in the setting sub-region / total number of undetected pipes in all setting sub-regions). This formula reflects that the more the number of undetected pipes in a sub-region, the greater its "share" in all risk pipes, and therefore the higher the node setting proportion it is allocated.

[0055] 2. Calculation process First step: Determine the total wireless network relay node setting proportion; Calculate the total number of undetected pipes in all setting sub-regions: Total = Subregion A (8) + Subregion B (15) = 23, Calculate the setting proportion of each setting subregion: Setting proportion of Subregion A = 0.1 × (8 / 23) ≈ 0.1 × 0.3478 ≈ 0.03478, Setting proportion of Subregion B = 0.1 × (15 / 23) ≈ 0.1 × 0.6522 ≈ 0.06522; Verification: The sum of setting proportions of Subregion A and Subregion B ≈ 0.03478 + 0.06522 = 0.1, equals to our preset base proportion factor. This means we plan to allocate 10% of the total number of detection pipelines as the "budget" for node deployment.

[0056] Step 2: Calculate the number of relay nodes for each setting subregion; According to the formula: Setting number = Setting proportion × Number of prestressed pipelines for this detection, Setting number of Subregion A = 0.03478 × 50 ≈ 1.739, Setting number of Subregion B = 0.06522 × 50 ≈ 3.261 Step 3: Round the calculation results (rationalization processing); The calculated number of nodes is usually not an integer, and needs to be rounded to meet the actual deployment requirements. A reasonable strategy is to round up or ensure at least one node per region. Scheme (round up): Subregion A: 1.739 → 2 wireless network relay nodes, Subregion B: 3.261 → 3 wireless network relay nodes, Total number of deployed nodes: 2 + 3 = 5. This accounts for 10% of the 50 detection pipelines, consistent with our total proportion budget.

[0057] 3. Summary and Explanation This example clearly demonstrates the complete closed loop from "whether to set" to "how much to set": Input: List of setting subregions and their undetected pipeline numbers (from the decision output of the previous stage), total number of this detection task (number of prestressed pipelines for this detection), a configurable base setting proportion factor (k) that determines the overall investment level of node deployment.

[0058] Risk allocation: Allocate node numbers according to the relative proportion of undetected pipelines in each setting subregion. The more concentrated the risk of a subregion, the more network coverage resources it will get. The final number of nodes is proportional to the overall size of this detection task (50 pipelines), making resource allocation more reasonable and scalable, output: The specific number of wireless network relay nodes deployed in each setting subregion.

[0059] Further, the determination of the data abnormality risk of the detection result specifically comprises: The prestressed pipeline with the abnormal detection result is regarded as an abnormal pipeline, and a sub-region with the abnormal pipeline is determined based on distribution data of the abnormal pipeline, and the sub-region is regarded as an abnormal sub-region; A wireless network relay node used by the abnormal pipeline in the abnormal sub-region is determined based on the distribution data of the abnormal pipeline in the abnormal sub-region, and the relay node is regarded as an associated relay node; Based on coincidence of the associated relay nodes in the abnormal sub-region, it is determined whether the detection result has a data abnormality risk.

[0060] It can be understood that when the number of abnormal pipelines does not meet the requirement, there is a certain data abnormality risk even if the associated relay nodes of different abnormal pipelines do not coincide, and thus it is determined that the detection result has a data abnormality risk.

[0061] In addition, it should be noted that when the number of abnormal pipelines meets the requirement, it is further determined that the coincidence of the associated relay nodes of different abnormal pipelines, and the abnormal pipelines using the same associated relay node are divided into the same abnormal pipeline combination, and when there is an abnormal pipeline combination with a number of abnormal pipelines greater than a preset abnormal pipeline number threshold, it is determined that the detection result has a data abnormality risk.

[0062] Further, when there is no abnormal pipeline combination with a number of abnormal pipelines greater than the preset abnormal pipeline number threshold, the number of coincidences between different abnormal pipelines and other abnormal pipelines is determined according to the coincidence of the associated relay nodes between different abnormal pipelines and other abnormal pipelines, and a coincidence risk coefficient of the abnormal pipelines is determined based on the number of coincidences, and when the sum of the coincidence risk coefficients of different abnormal pipelines does not meet the requirement, i.e., greater than a preset risk coefficient threshold, it is determined that the detection result has a data abnormality risk.

[0063] In addition, it can be understood that when the sum of the coincidence risk coefficients of different abnormal pipelines meets the requirement, a region risk coefficient in different abnormal sub-regions is determined based on the sum of the coincidence risk coefficients of the abnormal pipelines in the different abnormal sub-regions, and when there is no abnormal sub-region with a region risk coefficient not meeting the requirement, it is determined that the detection result has no data abnormality risk.

[0064] Further, when there is an abnormal sub-region whose regional risk coefficient does not meet the requirement, and the number of abnormal sub-regions whose regional risk coefficients do not meet the requirement is greater than the preset abnormal sub-region number threshold, it is determined that the detection result has a data anomaly risk, and when the number of abnormal sub-regions whose regional risk coefficients do not meet the requirement is not greater than the preset abnormal sub-region number threshold, it is determined that the detection result does not have a data anomaly risk.

[0065] In one possible embodiment, 1. Scenario and data assumptions: Suppose that after a detection task is completed, a number of prestressed pipes with abnormal detection results (i.e., abnormal pipes) are found.

[0066] Preset threshold: abnormal pipe number total threshold (N_total): 10 (used to determine whether the total number of abnormal pipes meets the requirement), preset abnormal pipe number threshold (N_group): 3 (used to determine the size of a single abnormal pipe combination), preset risk coefficient threshold (R_total): 15 (used to determine the total coincidence risk of all abnormal pipes), preset regional risk coefficient threshold (R_zone): 8 (used to determine the risk of a single abnormal sub-region), and preset abnormal sub-region number threshold (Z_count): 2 (used to determine the number of high-risk sub-regions). Abnormal data: A total of 8 abnormal pipes are found.

[0067] They are distributed in three abnormal sub-regions: sub-region X, sub-region Y, and sub-region Z, and each abnormal pipe transmits data through one or more wireless network relay nodes (associated relay nodes).

[0068] 2. Risk assessment process The analysis will be strictly based on the logic tree provided by you. First layer judgment: whether the total number of abnormal pipes exceeds the limit, judgment condition: total number of abnormal pipes (8) > total threshold (N_total=10), judgment result: 8<10, condition not met (i.e., the number meets the requirement), conclusion: since the total number of abnormal pipes is within the acceptable range, we need to further analyze its distribution pattern. Enter the next layer of judgment.

[0069] Second layer judgment: whether there is a large-scale abnormal pipe combination? Divide abnormal pipe combinations: divide abnormal pipes using the same associated relay node into the same combination, through Node_A's pipes: P1, P2, P3 → Combination A (number = 3) through Node_D's pipes: P4, P5 → Combination D (number = 2), through Node_B's pipes: P1 → Combination B (number = 1), through Node_C's pipes: P3 → Combination C (number = 1), through Node_E's pipes: P5 → Combination E (number = 1), through Node_F's pipes: P6 → Combination F (number = 1), through Node_G's pipes: P7 → Combination G (number = 1), through Node_H's pipes: P8 → Combination H (number = 1).

[0070] Judgment condition: whether there is any combination whose pipe number > preset abnormal pipe number threshold (N_group = 3), judgment result: the number of Combination A is equal to 3, which is not greater than 3. The condition is not met. Enter the next layer of judgment.

[0071] Third layer judgment: whether the total coincidence risk of all abnormal pipes is too high, calculate the coincidence risk coefficient of each abnormal pipe: the coefficient is based on the number of relay nodes shared by the pipe and other abnormal pipes (i.e. coincidence number), P1: with P2 (share A), P3 (share A, B) -> share A is calculated once, and share A and B with P3 is calculated twice, we need to define it clearly. Here, the total number of shared nodes with all other abnormal pipes is used, P1's associated nodes: [A, B], shared nodes with P2: A (count 1), shared nodes with P3: A, B (count 2), no sharing with P4~P8 (count 0), P1 coincidence number = 1 + 2 = 3, similarly calculate other pipes: P2: [A] -> share A with P1 (1), share A with P3 (1) -> coincidence number = 2, P3: [A, C] -> share A, B with P1 (2), share A with P2 (1) -> coincidence number = 3, P4: [D] -> share D with P5 (1) -> coincidence number = 1, P5: [D, E] -> share D with P4 (1) -> coincidence number = 1, P6: [F] -> no sharing -> coincidence number = 0, P7: [G] -> no sharing -> coincidence number = 0, P8: [H] -> no sharing -> coincidence number = 0, suppose we define the coincidence risk coefficient = coincidence number.

[0072] Calculate the total coincidence risk coefficient: sum = 3 (P1) + 2 (P2) + 3 (P3) + 1 (P4) + 1 (P5) + 0 + 0 + 0 = 10, judgment condition: total coincidence risk coefficient (10) > preset risk coefficient threshold (R_total = 15), judgment result: 10 < 15, condition not met. Enter the next layer of judgment.

[0073] Fourth layer judgment: whether there is an abnormal sub-region with too high risk? Calculate the regional risk coefficient of each abnormal sub-region: we define the sum of the coincidence risk coefficients of all abnormal pipelines in a sub-region as its regional risk coefficient. Sub-region X (contains P1, P2, P3): 3 + 2 + 3 = 8, sub-region Y (contains P4, P5, P6): 1 + 1 + 0 = 2, sub-region Z (contains P7, P8): 0 + 0 = 0, judge "regional risk coefficient does not meet the requirements": "Does not meet the requirements" means: regional risk coefficient > preset regional risk coefficient threshold (R_zone = 8), sub-region X: 8 = 8, not greater than threshold. Sub-regions Y and Z do not meet the condition of "not meeting the requirements": the risk coefficients are much lower than 8.

[0074] Judgment condition: whether there is an abnormal sub-region with regional risk coefficient not meeting the requirements, judgment result: there is none. The risk coefficients of all sub-regions do not exceed 8. Final decision: according to the logical operation result, when this step is reached and the judgment is "none", it is determined that the detection result does not exist data abnormal risk.

[0075] This embodiment demonstrates a risk assessment model that progresses layer by layer from macro to micro: total screening: if there are too many abnormal pipelines, it is directly determined that the system is at risk, node aggregation screening: if the abnormality is highly concentrated in a certain relay node, it is very likely that the node is faulty, and it is determined that there is data abnormal risk, network correlation screening: if the abnormal pipelines are highly correlated through relay nodes, forming an "abnormal network", it also indicates that it may be a network problem rather than a simple pipeline problem, regional risk screening: finally, locate to a specific sub-region to see if the abnormal pipeline correlation of a certain region is particularly strong. This method effectively helps operation and maintenance personnel to distinguish between "real defects in the physical world" and "collection errors in the digital world", thereby guiding them to take the correct follow-up action.

[0076] Further, it is determined whether the detection device can be used for wireless network optimization processing, specifically including: determine an abnormal pipeline in the associated prestressed pipeline of the wireless network relay node in the sub-region based on the detection result of the associated prestressed pipeline of the wireless network relay node in the sub-region, and take it as an associated abnormal pipeline; determine the number of associated abnormal pipelines of the wireless network relay node based on the associated abnormal pipeline data of the wireless network relay node in different sub-regions; determine whether the optimization processing of the wireless network can be performed by the detection device based on the number of associated abnormal pipelines of the wireless network relay node in different sub-regions.

[0077] Further, when the average number of associated abnormal pipelines of different wireless relay nodes is greater than the preset associated abnormal pipeline number threshold, at this time, since the degree of coincidence between the wireless relay node and the associated abnormal pipeline is high, the signal relay processing is performed by combining the detection device which has the wireless network relay function except the wireless relay node, so as to reduce the degree of coincidence between the wireless relay node and the associated abnormal pipeline, and therefore it is determined that the optimization processing of the wireless network can be performed by the detection device.

[0078] Further, when the average number of associated abnormal pipelines of different wireless relay nodes is not greater than the preset associated abnormal pipeline number threshold, the number of detection devices which have the wireless network relay function in different regions is determined based on the detection devices which have the wireless network relay function in different regions, and the detection device matching factor is determined based on the proportion of the detection devices which have the wireless network relay function in the devices which have the wireless network relay function in the region, wherein when there is no sub-region in which the detection device matching factor is greater than the preset matching factor threshold, it is determined that the optimization processing of the wireless network cannot be performed by the detection device.

[0079] Further, if there is a sub-region in which the detection device matching factor is greater than the preset matching factor threshold, and the proportion of the number of sub-regions in which the detection device matching factor is greater than the preset matching factor threshold in all sub-regions is greater than the preset proportion threshold, it is determined that the optimization processing of the wireless network can be performed by the detection device, and if the proportion of the number of sub-regions in which the detection device matching factor is greater than the preset matching factor threshold in all sub-regions is not greater than the preset proportion threshold, it is determined that the optimization processing of the wireless network cannot be performed by the detection device.

[0080] Assume a wireless sensor network is managing a pipeline with multiple sub-regions, and the network is monitoring the pipeline. The pre-set thresholds are: a pre-set threshold for the average number of associated abnormal pipelines (N_avg_threshold): 2.5 (to determine if the average number of associated abnormal pipelines is too high), a pre-set threshold for the matching factor (F_match_threshold): 0.6 (i.e., 60%, to determine if the relay capability of the detection devices in a sub-region is strong enough), and a pre-set threshold for the ratio (P_ratio_threshold): 0.5 (i.e., 50%, to determine if the sub-regions with optimization potential are in the majority). Network and device data: Focus on four sub-regions: Region_1, Region_2, Region_3, Region_4, each with fixed wireless network relay nodes, and a large number of detection devices. Some of them are basic, and some have wireless network relay functions (can act as temporary or auxiliary relay nodes).

[0081] Abnormal data and associated relationships: All abnormal pipelines have been identified, and the data for each abnormal pipeline is transmitted through which wireless relay node. The number of associated abnormal pipelines for each wireless relay node is shown in the following table: Table 2 Number of Associated Abnormal Pipelines for Wireless Relay Nodes

[0082] Device relay capability data for each sub-region: | Sub-region | Number of detection devices with wireless network relay function in this region (A) | Total number of detection devices in this region (B) | Detection device matching factor (A / B) |

[0083] First step: Determine if the average number of associated abnormal pipelines for wireless relay nodes is too high. Calculate the average number: the sum of the number of associated abnormal pipelines for all relay nodes = 4+1+3+0+2+1 = 11, the total number of wireless relay nodes = 6, the average number of associated abnormal pipelines = 11 / 6 ≈ 1.83, the judgment condition: the average number (1.83) > the pre-set threshold (2.5), the judgment result: 1.83 < 2.5, the condition is not met.

[0084] Conclusion: Overall, the association between abnormal pipelines and relay nodes is not extremely high. Further analysis of the hardware foundation for network optimization is needed. Proceed to the next level of judgment.

[0085] Second step: Analyze the relay capability of detection devices in each sub-region (calculate the detection device matching factor) Calculate the matching factor for each sub-region (as shown in the table above): Region_1: 8 / 15 ≈0.533, Region_2: 12 / 18 ≈ 0.667, Region_3: 5 / 20 = 0.250, Region_4: 10 / 10= 1.000 Judgment condition: Is there a sub-region with a matching factor > preset matching factor threshold (0.6)? Judgment result: Region_2 (0.667>0.6) -> Yes, Region_4 (1.000>0.6) -> Yes, Region_1 and Region_3 do not meet.

[0086] Therefore, there is such a sub-region. Enter the next layer of judgment.

[0087] Step 3: Judge the universality of sub-regions with optimization potential Calculate the proportion of high matching factor sub-regions: the number of sub-regions with matching factor > 0.6 = 2 (Region_2, Region_4), the total number of sub-regions = 4, the proportion of high matching factor sub-regions = 2 / 4 = 0.5, judgment condition: the proportion (0.5) > preset proportion threshold (0.5), judgment result: 0.5 is not greater than 0.5 (i.e. equal to the threshold). According to the "not greater than" condition in the description, it is determined that the wireless network cannot be optimized using the detection equipment. Final decision: this analysis concludes that the wireless network cannot be optimized using the detection equipment.

[0088] Problem-driven assessment: First, see if the "abnormal hotspots" in the network are serious enough to immediately start optimization. If so, directly determine "feasible". Resource-driven assessment: If the problem is not urgent, assess whether there are hardware conditions for optimization. Microscopic resources: Check if a sufficient proportion of detection equipment in each sub-region can be upgraded to auxiliary relay nodes. Macroscopic resources: Check if sub-regions with such potential account for the majority to ensure that the optimization scheme can effectively cover the entire network.

[0089] The significance of the final conclusion: In the original data of this case, although two sub-regions (Region_2 and Region_4) have very good hardware conditions, the other two sub-regions (Region_1 and Region_3) have insufficient potential. If forced to optimize, it will lead to uneven network optimization effect, forming a "bucket effect". Therefore, the system will give a "not feasible" suggestion, prompting the manager to consider network optimization after upgrading or re-planning Region_1 and Region_3 first. This method ensures that the decision is not only technically feasible, but also effective and balanced in the overall range.

[0090] Embodiment 2 In a second aspect, the present application provides a computer system, comprising a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to perform the bridge tunnel defect measurement method.

[0091] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments. Especially, the device, equipment, and non-volatile computer storage medium embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0092] The above describes specific embodiments of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different than the order in which the actions or steps are recited in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous or necessary.

[0093] The above only describes one or more embodiments of the specification and does not limit the specification. One or more embodiments of the specification can have various changes and variations for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of one or more embodiments of the specification shall be included in the scope of the claims of the specification.

Claims

1. A method for measuring and detecting defects in bridge tunnels, characterized in that, Specifically, it includes: Based on the historical inspection data of prestressed ducts in the target area, the distribution data of undetected ducts in different sub-areas is determined. When it is determined that wireless network relay nodes without detection function need to be set up based on the distribution data of undetected ducts, the setting strategy of wireless network relay nodes in the sub-area is determined based on the relay relationship of the undetected duct data in the sub-area and the network data between the sub-area and other sub-areas. Based on the setting strategy, the wireless network relay nodes in the sub-regions are configured. When the detection results of abnormal prestressed pipes in different sub-regions overlap with the associated wireless network relay nodes, it is determined whether the detection results pose a risk of data anomalies. Then, based on the detection results of the associated prestressed pipes in the wireless network relay nodes in different sub-regions, it is determined whether the detection equipment can be used for wireless network optimization.

2. The bridge tunnel defect measurement and detection method as described in claim 1, characterized in that, The target area includes the bridge area and the tunnel area.

3. The bridge and tunnel defect measurement and detection method as described in claim 1, characterized in that, The historical testing data includes the test results of the grouting quality of the prestressed duct.

4. The bridge and tunnel defect measurement and detection method as described in claim 1, characterized in that, The undetected pipelines are prestressed pipelines that have not been inspected in the past or whose interval between the inspection date and the current date is longer than a preset interval threshold, but which need to be inspected this time.

5. The bridge and tunnel defect measurement and detection method as described in claim 1, characterized in that, Determine the configuration requirements for wireless network relay nodes without detection capabilities, specifically including: Based on the distribution data of the undetected pipelines, sub-regions containing undetected pipelines are identified and designated as risk sub-regions; Based on the composition data of undetected pipelines in different sub-regions, determine the pipeline risk factors for different sub-regions; Based on the distribution data of the risk sub-regions within the sub-regions and the pipeline risk factors in different risk sub-regions, determine whether it is necessary to set up wireless network relay nodes without detection functions.

6. The bridge and tunnel defect measurement and detection method as described in claim 5, characterized in that, The pipeline risk factor for the sub-region is determined by multiplying the number of undetected pipelines in the sub-region by a preset proportional factor.

7. The bridge and tunnel defect measurement and detection method as described in claim 1, characterized in that, The configuration process for wireless network relay nodes in the sub-area based on the aforementioned configuration strategy specifically includes: Based on the aforementioned setting strategy, sub-regions that require setting up wireless network relay nodes are identified and designated as setting sub-regions. The number of wireless network relay nodes in the sub-region is determined based on the number of undetected pipes in the sub-region and the number of prestressed pipes being detected in this test.

8. The bridge and tunnel defect measurement and detection method as described in claim 7, characterized in that, Based on the number of undetected pipes in the designated sub-region, the total setting ratio of wireless network relay nodes in the sub-region is determined. The number of wireless network relay nodes in the designated sub-region is determined by multiplying the setting ratio by the number of prestressed pipes detected in this test.

9. The bridge and tunnel defect measurement and detection method as described in claim 8, characterized in that, Determine whether wireless network optimization can be performed using testing equipment, specifically including: Based on the detection results of the prestressed pipes associated with the wireless network relay nodes in the sub-region, abnormal pipes in the prestressed pipes associated with the wireless network relay nodes in the sub-region are identified and identified as associated abnormal pipes. The number of associated abnormal pipes of the wireless network relay node is determined based on the associated abnormal pipe data of the wireless network relay node in different sub-regions. Based on the number of associated abnormal pipelines in wireless network relay nodes in different sub-regions, it is determined whether wireless network optimization can be performed using detection equipment.

10. A computer system, comprising: A memory and processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a bridge tunnel defect measurement and detection method according to any one of claims 1-9.

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