A municipal pipe network intelligent monitoring and data analysis management platform

Abstract

The present invention discloses an intelligent monitoring and data analysis management platform for municipal pipe networks. The system includes multiple monitoring areas, an environmental sensor group, an environmental data processing unit, a corrosion risk assessment module, an intelligent patrol unit, a data transmission unit and a cloud platform. The environmental sensor group collects environmental data in the monitoring area in real time, and the environmental data processing unit analyzes the data in real time through edge computing technology and generates historical data. The corrosion risk assessment module evaluates the corrosion risk based on real-time data and historical data, determines high-risk areas and generates detection tasks. The intelligent patrol unit automatically navigates to the target area for spot projection detection, and the deformation analysis module analyzes the image data through computer vision algorithms to identify the corrosion area and its information. The data transmission unit uploads the detection information to the cloud platform. The present invention improves the efficiency and accuracy of corrosion detection through automated patrol and spot projection, ensuring the safe operation of pipe corridor equipment.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring, and in particular to a municipal pipe network intelligent monitoring and data analysis management platform. Background Art

[0002] Underground utility corridors are a vital component of modern urban infrastructure, primarily used to carry and maintain various urban pipelines, such as water pipes, cables, and gas pipelines. Due to the unique environment of underground utility corridors, pipelines and equipment are exposed to complex conditions such as humidity, low temperatures, and low light levels for extended periods, making them highly susceptible to corrosion and aging. In severe cases, these issues can lead to equipment damage and even safety accidents. Therefore, timely and effective corrosion detection and maintenance are crucial to ensuring the normal operation of underground utility corridors.

[0003] Currently, traditional methods for detecting corrosion in underground pipeline corridors mostly rely on manual inspections or simple automated monitoring systems. Manual inspections are not only labor-intensive and inefficient, but also susceptible to environmental factors, posing the risk of missed and false detections. Some automated monitoring systems, on the other hand, typically use visual monitoring devices such as cameras to inspect the pipeline surface. However, this camera-based monitoring method faces numerous limitations in underground pipeline corridors, characterized by low lighting and complex environments.

[0004] First, underground pipeline corridors are often located in extremely dimly lit environments, making it difficult for cameras to obtain clear images. This is especially true when obstructions such as dirt and rust are present on the pipeline surface, significantly impacting image quality. Furthermore, cameras require high image resolution and contrast, and in low-light environments, problems such as image blur and excessive color aberration can easily occur, impacting the accuracy of detection results.

[0005] Secondly, the complex environment of underground pipe corridors, where pipes and equipment are often located in cramped spaces, can prevent cameras from covering all areas requiring inspection, leading to missed inspections or inability to fully inspect the pipes from all angles. This is especially true at pipe joints and hidden locations, where cameras struggle to effectively monitor and inspect them.

[0006] Furthermore, traditional camera-based monitoring systems require extensive data storage and transmission. This creates significant storage and computing pressures when dealing with large amounts of data and demanding processing requirements, placing high demands on the real-time and scalability of monitoring systems. Furthermore, camera-based monitoring typically only provides static images or video streams, failing to capture timely deformation information on pipeline equipment, making it difficult to accurately determine whether a pipeline is corroded or damaged.

[0007] Therefore, the existing detection method of monitoring through cameras has significant limitations in the low-light and complex environment of underground pipeline corridors, and cannot meet the needs of efficient, accurate and real-time detection of corrosion risks. More intelligent and precise technical means are urgently needed to improve the effectiveness of underground pipeline corridor corrosion detection. Summary of the Invention

[0008] The purpose of the present invention is to provide an intelligent monitoring and data analysis management platform for municipal pipe networks, which has the advantages of high accuracy and reliability in corrosion detection and can detect early corrosion and deal with it in a timely manner.

[0009] The above technical objectives of the present invention are achieved through the following technical solutions:

[0010] A municipal pipe network intelligent monitoring and data analysis management platform, including:

[0011] Several monitoring areas, each of which includes at least one environmental sensor group, and the environmental sensor group is used to collect environmental data in the area in real time;

[0012] An environmental data processing unit, electrically connected to the environmental sensor group, for receiving and processing in real time environmental data from each monitored area, wherein the environmental data processing unit processes and analyzes the environmental data in real time using edge computing technology and stores the data as historical data;

[0013] A corrosion risk assessment module, connected to the environmental data processing unit, is used to rate the corrosion risk of the monitored area based on the real-time collected environmental data and historical data, determine the corrosion risk area, and sort the inspection order of the monitored area according to the corrosion risk level to generate inspection tasks;

[0014] An intelligent inspection unit, which can automatically navigate within the underground pipeline corridor and proceed to the monitoring area to perform spot projection detection according to the detection task. The intelligent inspection unit includes a spot projection module and a deformation analysis module. The spot projection module is used to project a spot of a specific shape onto the surface of the pipeline or equipment. The deformation analysis module is used to obtain image data after the spot is projected onto the surface of the equipment or pipeline, and perform real-time image processing and deformation analysis based on the image data using a computer vision algorithm to determine the corrosion area and corresponding corrosion information.

[0015] A data transmission unit, used to transmit the corrosion information determined by the deformation analysis module to the environmental data processing unit and the cloud platform, wherein the data transmission unit performs bidirectional data transmission with the cloud platform through the Internet of Things technology;

[0016] The cloud platform is connected to the data transmission unit and is used to store the uploaded corrosion information, generate corrosion monitoring reports and provide real-time warnings.

[0017] Further configuration: the environmental data processing unit includes a processing module and a storage module, the processing module is used to preprocess the received environmental data, and output the preprocessed environmental data to the corrosion risk assessment module and the storage module; the storage module stores the corrosion information and the corresponding environmental data as historical data, and uses a time series database for storage.

[0018] It is further configured that the preprocessing of the received environmental data specifically includes noise filtering, outlier detection and data standardization, wherein the outlier detection adopts the Z-Score method.

[0019] Further setting: rating the corrosion risk of the monitoring area, determining the corrosion risk area, and sorting the inspection order of the monitoring area according to the corrosion risk level to generate the inspection task specifically includes the following steps:

[0020] The corrosion risk score of each monitoring area is calculated using a linear regression model based on historical data and real-time environmental data;

[0021] The corrosion risk scores are classified into corrosion risk levels according to preset classification rules, wherein the corrosion risk levels specifically include high risk, medium risk and no risk;

[0022] Identify high-risk and medium-risk monitoring areas as corrosion risk areas;

[0023] The detection tasks are generated after sorting the corrosion risk areas according to their specific corrosion risk scores. The priority of the corrosion risk areas in the detection tasks is in direct proportion to the corrosion risk scores.

[0024] Further configuration: the light spot projection module includes a partitioning submodule, a shape selection submodule and a projection submodule. The partitioning submodule obtains the structural information corresponding to the preset corrosion risk area, and divides each equipment and pipeline in the corrosion risk area into multiple projection areas according to the structural information and the preset partitioning rules; the shape selection submodule calculates the specific shape used for each projection area based on the structural information and environmental data analysis, and the specific shapes specifically include dot light spots, grid light spots and stripe light spots; the projection submodule is used to project the selected specific shape of the light spot to the corresponding projection area.

[0025] Further configuration: the shape selection submodule calculates the specific shape used for each projection area based on the structural information and environmental data analysis, specifically comprising the following steps:

[0026] Get structure parameters from structure information;

[0027] Calculate the judgment index of each projection area based on structural parameters and environmental data;

[0028] A specific shape for each projection area is selected based on a judgment index.

[0029] Further configuration: the deformation analysis module is used to obtain image data after the light spot is projected onto the surface of the equipment or pipeline, and perform real-time image processing and deformation analysis on the image data based on a computer vision algorithm to determine the corrosion occurrence area and the corresponding corrosion information. Specifically, the steps include:

[0030] Preprocessing the image data, wherein the preprocessing includes denoising and contrast enhancement;

[0031] Perform illumination normalization on the preprocessed image data;

[0032] According to the specific shape of the light spot in the image, different deformation analysis methods are selected to analyze and calculate the image data to obtain corrosion information;

[0033] For the dot light spot, analysis and calculation are performed based on the displacement of the dots and the change of the dot spacing;

[0034] For grid spots, analysis and calculation are performed based on the displacement of intersection points and geometric fitting;

[0035] For stripe light spots, analysis and calculation are performed based on the changes in stripe angle and spacing.

[0036] In summary, the present invention has the following beneficial effects:

[0037] 1. Traditional camera monitoring systems are prone to image blur and excessive color difference in the low-light and complex environments of underground pipe corridors, resulting in poor detection results. In contrast, the present invention uses spot projection technology to project a specific light spot onto the surface of the pipe or equipment, combined with a deformation analysis module for real-time image processing. This technology is independent of ambient light and can provide clear detection results in low-light or even dark environments, effectively overcoming the impact of low-light environments on image quality.

[0038] 2. Through intelligent inspection units, the system can automatically navigate to the target monitoring area for inspection, eliminating reliance on manual inspections. This significantly reduces labor costs and workload, and avoids potential oversights and misjudgments during manual inspections. The intelligent inspection system not only improves inspection efficiency but also ensures comprehensive inspections of all monitoring areas in the underground pipeline corridor, effectively enhancing overall system efficiency.

[0039] 3. Through the corrosion risk assessment module, the system uses a linear regression model to accurately calculate the corrosion risk score for each monitored area based on real-time environmental data and historical data, and generates corresponding inspection tasks based on the score. By sorting and prioritizing the monitored areas, the system can efficiently schedule inspection tasks, ensuring that high-risk areas receive priority inspection, effectively avoiding the risk of missed inspections and false detections.

[0040] 4. The deformation analysis module combines spot projection with computer vision algorithms to accurately detect surface deformation of pipes or equipment and, based on this deformation information, determine the location and extent of corrosion. This technical solution not only detects surface corrosion but also analyzes its specific morphology and distribution through image data, ensuring precise extraction of corrosion information and improving the accuracy and reliability of corrosion detection.

[0041] 5. By using a time series database to store historical data, the system can track the corrosion status of pipeline equipment over time, conduct trend analysis, and review historical data. This not only provides data support for long-term monitoring of pipeline corridor equipment, but also helps analyze the cyclical patterns of corrosion and provide a basis for future maintenance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is an overall flow chart of the embodiment. DETAILED DESCRIPTION

[0043] The present invention will be further described in detail below with reference to the accompanying drawings.

[0044] Example:

[0045] like Figure 1 As shown, a municipal pipe network intelligent monitoring and data analysis management platform includes:

[0046] Several monitoring areas, each of which includes at least one environmental sensor group, and the environmental sensor group is used to collect environmental data in the area in real time;

[0047] An environmental data processing unit, electrically connected to the environmental sensor group, for receiving and processing in real time environmental data from each monitored area, wherein the environmental data processing unit processes and analyzes the environmental data in real time using edge computing technology and stores the data as historical data;

[0048] A corrosion risk assessment module, connected to the environmental data processing unit, is used to rate the corrosion risk of the monitored area based on the real-time collected environmental data and historical data, determine the corrosion risk area, and sort the inspection order of the monitored area according to the corrosion risk level to generate inspection tasks;

[0049] An intelligent inspection unit, which can automatically navigate within the underground pipeline corridor and proceed to the monitoring area to perform spot projection detection according to the detection task. The intelligent inspection unit includes a spot projection module and a deformation analysis module. The spot projection module is used to project a spot of a specific shape onto the surface of the pipeline or equipment. The deformation analysis module is used to obtain image data after the spot is projected onto the surface of the equipment or pipeline, and perform real-time image processing and deformation analysis based on the image data using a computer vision algorithm to determine the corrosion area and corresponding corrosion information.

[0050] A data transmission unit, used to transmit the corrosion information determined by the deformation analysis module to the environmental data processing unit and the cloud platform, wherein the data transmission unit performs bidirectional data transmission with the cloud platform through the Internet of Things technology;

[0051] The cloud platform is connected to the data transmission unit and is used to store the uploaded corrosion information, generate corrosion monitoring reports and provide real-time warnings.

[0052] The environmental data processing unit includes a processing module and a storage module. The processing module is used to pre-process the received environmental data and output the pre-processed environmental data to the corrosion risk assessment module and the storage module; the storage module stores the corrosion information and the corresponding environmental data as historical data, and uses a time series database for storage. Environmental data includes humidity, temperature, oxygen concentration, harmful gas concentration and other environmental parameters related to corrosion. The environmental data (humidity H, temperature T, oxygen concentration 0, harmful gas concentration G, etc.) collected by the sensor group in each monitoring area is transmitted to the processing module through a communication network (such as LoRa, Wi-Fi, etc.). The data is first subjected to noise filtering, outlier detection and standardization to ensure the accuracy of the data. The Z-Score method is used to standardize the collected environmental data and detect abnormal points in the data that exceed the threshold. The Z-Score calculation formula is:

[0053]

[0054] Where X is the current data value, μ is the mean of the environmental data, and σ is the standard deviation of the environmental data. If |Z| > 3, the data is considered an outlier and is processed or removed.

[0055] The processed environmental data is mapped one-to-one with the corrosion information calculated by the subsequent deformation analysis module. The environmental data and specific corrosion conditions of the monitored areas where corrosion occurs are stored in the storage module as historical data. This serves as one of the foundations for future corrosion risk assessments, improving the accuracy of corrosion assessments based on historical data. Data storage utilizes a time series database (such as InfluxDB) for efficient storage, ensuring scalability for large-scale data processing. Data compression techniques, such as the Zlib compression algorithm, are used during storage to reduce data storage space and improve data access efficiency.

[0056] Whenever new environmental data is collected, the environmental data processing unit dynamically updates historical data using a weighted moving average (WMA). It regularly checks data consistency using a hash check method to prevent data corruption or tampering, improving data stability and forecasting accuracy. It also optimizes the storage and query of large-scale data by integrating with a time series database. Data integrity and accuracy are ensured by generating a data hash value and comparing it with previously stored hash values.

[0057] The corrosion risk assessment module is a key component of the entire system. It assesses corrosion risks based on real-time environmental data and historical data, using corrosion prediction models, and generates inspection tasks. This module processes the input data and outputs a corrosion risk rating (none, medium, or high), which is then prioritized for subsequent intelligent inspection units. Specifically, it includes the following steps:

[0058] The corrosion risk score of each monitoring area is calculated using a linear regression model based on historical data and real-time environmental data;

[0059] The corrosion risk scores are classified into corrosion risk levels according to preset classification rules, wherein the corrosion risk levels specifically include high risk, medium risk and no risk;

[0060] Identify high-risk and medium-risk monitoring areas as corrosion risk areas;

[0061] The detection tasks are generated after sorting the corrosion risk areas according to their specific corrosion risk scores. The priority of the corrosion risk areas in the detection tasks is in direct proportion to the corrosion risk scores.

[0062] To accurately assess corrosion risk, multiple regression analysis or support vector machines (SVM) are used as mathematical models for corrosion risk prediction. The corrosion risk assessment model is based on the relationship between environmental parameters (humidity, temperature, oxygen concentration, and harmful gas concentration) and corrosion rate. In this embodiment, a linear regression model in the regression analysis model is used to model the relationship between corrosion risk and environmental factors. The evaluation formula of the linear regression model is:

[0063] R(t)=β0+β1·H(t)+β2·T(t)+β3·0(t)+β4·G(t)

[0064] Where R(t) is the corrosion risk score, β0 is the intercept term of the regression model, which represents the baseline impact of environmental parameters on corrosion risk, H(t), T(t), 0(t), and G(t) are humidity, temperature, oxygen concentration, and harmful gas concentration, respectively. β1, β2, β3, and β4 are regression coefficients, which represent the degree of influence of each environmental parameter on corrosion risk and are obtained through training with historical data.

[0065] Based on the corrosion risk value R(t) obtained from the assessment, it is divided into different corrosion risk levels. The corrosion risk grading standards are as follows: High risk (R(t) ≥ 0.8): The area requires an immediate and comprehensive inspection; Medium risk (0.5 ≤ R(t) < 0.8): The area also requires inspection, but the risk is not as high and is prioritized after the high risk; No risk (R(t) < 0.5): The risk of corrosion in this area is low and does not require inspection for the time being. The risk can be eliminated during the subsequent overall inspection. For corrosion risk areas that are also high risk, they are ranked according to the size of their corrosion risk scores. The higher the corrosion risk score, the higher the priority.

[0066] The intelligent inspection unit is required to be able to automatically navigate in the underground pipeline corridor and go to the monitoring area to perform spot projection detection according to the detection task. The intelligent inspection unit can be implemented by selecting an inspection robot, an intelligent car or a drone. In this embodiment, a drone is selected as the carrier of the intelligent inspection unit, which can better perform spot projection from various angles. The spot projection module includes a partitioning submodule, a shape selection submodule and a projection submodule. The partitioning submodule obtains the structural information corresponding to the preset corrosion risk area, and divides each device and pipeline in the corrosion risk area into multiple projection areas according to the structural information and the preset partitioning rules; the shape selection submodule calculates the specific shape used for each projection area based on the structural information and environmental data analysis. The specific shapes specifically include dot spots, grid spots and stripe spots; the projection submodule is used to project the selected specific shape of the spot to the corresponding projection area. The preset structural information specifically includes position information and structural parameters. The position information represents the specific position distribution of each pipeline and each device. The structural parameters include the three-dimensional structural information of the pipeline and equipment, the type of material used for the outer layer, and the shape. The preset zoning rules are based on the three-dimensional structural information and shape of pipes and equipment. For interfaces and connection points, for example, at a pipe interface, both sides of the connection should be divided into separate zones, and the interface itself should be inspected as a separate zone. This prevents the complex shape of the interface from interfering with the analysis of other parts. For equipment with complex shapes (such as pipes connected to equipment, equipment connected to external cabinets, etc.), segmentation should be based on the equipment's geometry. For example, the area around the external cabinet is treated as a separate zone, and the different surfaces of the cabinet are treated as separate zones. The areas where pipes bend or transition are located should be treated as separate zones to reduce interference with the curved parts of the pipes during deformation analysis. Pipes and equipment are divided into multiple projection zones based on their shape and three-dimensional structure, depending on the complexity of each location. Through the segmentation of the projection zones, the structure of the projection surface of each independent projection zone is made as simple and smooth as possible, reducing complexity and the interference of complex structures on the spot projection analysis of corrosion, ensuring that each part is independently and accurately inspected.

[0067] The shape selection submodule calculates the specific shape used in each projection area based on the structural information and environmental data analysis, specifically comprising the following steps:

[0068] Get structure parameters from structure information;

[0069] Calculate the judgment index of each projection area based on structural parameters and environmental data;

[0070] A specific shape for each projection area is selected based on a judgment index.

[0071] Humidity, temperature and oxygen concentration are selected from the environmental parameters and added to the calculation of the judgment index. Humidity is usually one of the most important factors affecting the corrosion rate, especially in a humid environment. When the humidity is high, corrosion is accelerated, and the temperature will directly affect the oxidation reaction rate of the metal surface. In an environment with a higher temperature, the corrosion rate is usually faster, and the oxygen concentration determines the intensity of the oxidation reaction. In a low-oxygen environment, corrosion is slower, while in an environment with sufficient oxygen, corrosion is faster. The corrosion rate affects the specific degree and scope of corrosion if it occurs. Different shapes of light spots are required for projection for different degrees of corrosion. For more complex corrosion conditions, grid light spots can be used to obtain more detailed data in various places. For areas with large and light corrosion areas, striped light spots can be used to obtain results faster. Therefore, these parameters are added to the calculation of the judgment index. A smoothness index is converted from the structural parameters according to the type of material used for the outer layer, and the smoothness index and the area of ​​the projection area are added to the calculation of the judgment index. The calculation formula of the judgment index is:

[0072] CRS=w1·H+w2·T+w3·02+w4·S+w5·D

[0073] CRS: judgment index, w1, w2, w3, w4, w5 are the weights of each parameter, calculated based on their influence on the accuracy of the spot shape, H is humidity, T is temperature, O2 is oxygen concentration, S is the projection area, and D is the smoothness index.

[0074] If CRS < low threshold, select stripe or dot light spots. If low threshold ≤ CRS < high threshold, select grid light spots. If CRS ≥ high threshold, select dense grid light spots or adaptive light spots. Adjust the size of the light spot projection according to the size and shape of the projection area. Adjust the density of the light spot according to the judgment index. Areas with high judgment indexes have high light spot density, while areas with low judgment indexes have low light spot density.

[0075] The deformation analysis module is used to obtain image data after the light spot is projected onto the surface of the equipment or pipeline, and perform real-time image processing and deformation analysis based on the computer vision algorithm image data to determine the corrosion area and the corresponding corrosion information. Specifically, the steps include:

[0076] Preprocessing the image data, wherein the preprocessing includes denoising and contrast enhancement;

[0077] Perform illumination normalization on the preprocessed image data;

[0078] According to the specific shape of the light spot in the image, different deformation analysis methods are selected to analyze and calculate the image data to obtain corrosion information;

[0079] For a circular spot, the deformation analysis of the circular spot is usually based on the displacement and shape change of the spot. The specific steps are as follows:

[0080] Step 1: Circle detection and fitting Use Hough transform or circle fitting algorithm to detect circles in the image and fit the center position and radius of the circles.

[0081] Step 2: Calculate the displacement of the dots. Compare the ideal and actual positions of the dots and calculate the displacement Δx, Δy of each dot.

[0082]

[0083] The area where corrosion occurs can be determined by the change in displacement. Displacement is the displacement of the dot. (x i ,y i ) is the position of the dot in the actual image, (x ideal ,y ideal ) is the ideal dot position.

[0084] Step 3: Calculate the change in dot spacing. The distance between the dots can also reflect surface deformation. If the dots shift, the distance between the dots will change, further inferring the area of ​​corrosion.

[0085] Δd=d t -d0

[0086] Among them, d t and d0 are the distances between the dots after deformation and in the ideal case, respectively.

[0087] Step 4: Output the corrosion type and area. According to the pattern of dot offset, determine the corrosion type (such as pitting corrosion, localized corrosion, etc.) and calculate the area of ​​the corroded area.

[0088] A corrosion =∑Δd i

[0089] Among them, A corrosion is the corrosion area, Δd i The spacing between each dot varies.

[0090] Step 5: Determine the degree of corrosion based on the changes in offset and dot spacing to determine the degree of corrosion (mild, moderate, severe).

[0091] A grid spot is composed of multiple interlaced horizontal and vertical lines, forming a rectangular or square grid structure. When the surface of a pipe or equipment deforms, the grid intersections will be displaced or distorted. Deformation analysis of the grid spot is mainly based on the displacement of the grid intersections, usually through feature point detection and geometric fitting methods. The specific steps are as follows:

[0092] Step 1: Detect grid intersections. Use a feature point detection algorithm (such as SIFT, SURF, or ORB) to extract the intersections in the grid spot. Each intersection represents the intersection position of the grid, and deformation analysis is based on the displacement of these intersections.

[0093] Step 2: Intersection displacement calculation Compare the original (ideal) grid with the grid intersection position in the actual image, and calculate the displacement of each intersection Δx, Δy,

[0094]

[0095] Where Displacement is the displacement of the grid intersection, (x i ,y i ) is the intersection point in the actual image, (x ideal ,y ideal ) is the intersection position in the reference grid.

[0096] Step 3: Geometric fitting and deformation model Use the least squares method to fit the ideal grid and the actual grid, and calculate the offset and deformation of the intersection. Through the fitting method, the deformation pattern of the entire grid can be obtained, and the location and area of ​​corrosion can be further inferred. Least squares fitting:

[0097] Residual=∑((x i -x ideal ) 2 +(y i -y ideal ) 2 )

[0098] Residual: The residual of the least squares method, used to measure the error of the fit.

[0099] Calculate the corrosion area: Based on the displacement and offset of the intersection point and the area of ​​the grid area, the actual corrosion area is estimated:

[0100] A corrosion =∑Δx·Δy

[0101] Among them, A corrosion is the area of ​​the corrosion region, and Δx·Δy is the displacement of the intersection position.

[0102] Step 4: Output the type and degree of corrosion. Based on the deformation pattern of the grid intersection, determine the type of corrosion (such as uniform corrosion, crevice corrosion, etc.) and output the degree of corrosion (mild, moderate, severe).

[0103] Stripe light spots are usually composed of a series of parallel bright or dark lines with a fixed spacing. When the stripe light spots are projected onto the pipe surface, if the surface deforms (such as corrosion, cracks, etc.), these stripes will bend or stretch. The deformation analysis of stripe light spots is mainly based on the changes in the stripes, including the curvature of the stripes, the change in spacing, and the change in angle. The specific steps are as follows:

[0104] Step 1: Extract the edges of the stripes. Use an edge detection algorithm (such as Canny edge detection) to extract the edges of the stripes. Edge detection can extract the outline of the stripes, which is convenient for subsequent analysis.

[0105] Step 2: Calculate the geometric deformation of the stripes:

[0106] Angle change calculation: For the projected stripe light spot, calculate the tilt angle of each stripe and compare it with the angle of the reference image. Calculate the offset angle Δθ of each stripe,

[0107] Δθ=θ observed -θ ideal

[0108] Among them, θ observed is the angle of the actual fringe, θ ideal is the angle of the reference fringe. The change in the offset angle reflects the degree of surface deformation.

[0109] Stripe spacing change calculation: Calculate the stripe spacing change Δd to determine the compression or expansion of the stripes. The spacing change directly reflects the expansion or contraction of the surface.

[0110] Δd=d observed -d ideal

[0111] Among them, d observed is the actual fringe spacing, d ideal is the spacing of the reference stripes.

[0112] Step 3: Calculate the corrosion position and area Based on the change in stripe angle and spacing, the corrosion position and area can be estimated. Especially in the area where the corrosion is more uniform, the stripe deformation is more regular and the area calculation is more intuitive.

[0113] A corrosion =∫Δd(x)dx

[0114] Where Δd(x) is the change in fringe spacing, A corrosion is the area of ​​the corroded region.

[0115] The deformation analysis results of each light spot will output corrosion information, including: corrosion type (such as uniform corrosion, pitting corrosion, crevice corrosion, etc.); corrosion area (the area of ​​the area calculated based on deformation); corrosion degree (such as mild, moderate, severe); corrosion location.

[0116] An intelligent monitoring method for corrosion detection of underground pipe gallery equipment, applied to the aforementioned municipal pipe network intelligent monitoring and data analysis management platform, specifically includes the following steps:

[0117] By setting up environmental sensor groups in multiple monitoring areas, environmental data in the monitoring areas are collected in real time, and the environmental data include temperature, humidity, and gas concentration;

[0118] The collected environmental data are preprocessed by an environmental data processing unit, wherein the preprocessing includes noise filtering, outlier detection and data standardization, wherein the outlier detection adopts the Z-Score method;

[0119] Through edge computing technology, the processed environmental data is analyzed in real time and historical data records are generated;

[0120] Calculating a corrosion risk score for each monitored area using a linear regression model based on the real-time collected environmental data and historical data;

[0121] According to the corrosion risk score, the monitoring area is divided into three corrosion risk levels, specifically including high risk, medium risk and no risk, and the high risk and medium risk areas are identified as corrosion risk areas;

[0122] The inspection order of the monitoring areas is sorted according to the corrosion risk scores of the corrosion risk areas, and the inspection tasks are generated based on the sorting. The priority of the corrosion risk areas is directly proportional to their corrosion risk scores.

[0123] Through the intelligent inspection unit, it automatically navigates to the target monitoring area according to the inspection task and performs spot projection inspection. The intelligent inspection unit includes a spot projection module and a deformation analysis module. The spot projection module projects a spot of a specific shape onto the surface of the pipeline or equipment;

[0124] The deformation analysis module obtains image data after the light spot is projected onto the surface of the pipe or equipment, and performs image processing based on computer vision algorithms to analyze the deformation information in the image data and determine the corrosion area and its degree.

[0125] The corrosion information obtained by the deformation analysis module is uploaded to the environmental data processing unit and the cloud platform through the data transmission unit. The cloud platform generates a corrosion monitoring report and provides real-time warnings.

[0126] In summary, the present invention has the following beneficial effects:

[0127] Traditional camera monitoring systems are prone to image blur and excessive color chromatic aberration in the low-light and complex environments of underground pipe corridors, resulting in poor detection results. In contrast, the present invention utilizes spot projection technology. By projecting a specifically shaped spot onto the surface of a pipe or device, combined with a deformation analysis module for real-time image processing, it is independent of ambient light and can provide clear detection results in low-light or even dark environments, effectively overcoming the impact of low-light environments on image quality.

[0128] Through intelligent inspection units, the system automatically navigates to the target monitoring area for inspection, eliminating reliance on manual inspections. This significantly reduces labor costs and workload, and avoids potential oversights and misjudgments during manual inspections. The intelligent inspection system not only improves inspection efficiency but also ensures comprehensive inspections of every monitoring area in the underground utility corridor, effectively enhancing overall system efficiency.

[0129] The corrosion risk assessment module uses a linear regression model to accurately calculate the corrosion risk score for each monitored area based on real-time environmental and historical data. It then generates corresponding inspection tasks based on the score. By sorting and prioritizing monitored areas, the system efficiently schedules inspections, ensuring that high-risk areas receive priority inspections, effectively avoiding the risk of missed and incorrect inspections.

[0130] The deformation analysis module combines spot projection with computer vision algorithms to accurately detect surface deformation on pipes or equipment and, based on this deformation information, determine the location and extent of corrosion. This technical solution not only detects surface corrosion but also analyzes its specific form and distribution through image data, ensuring precise extraction of corrosion information and improving the accuracy and reliability of corrosion detection.

[0131] By using a time series database to store historical data, the system can track the corrosion status of pipeline equipment over time, conduct trend analysis, and review historical data. This not only provides data support for long-term monitoring of pipeline corridor equipment, but also helps analyze the cyclical patterns of corrosion and provide a basis for future maintenance decisions.

[0132] The above-described embodiments do not constitute a limitation on the scope of protection of this technical solution. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the above-described embodiments shall be included in the scope of protection of this technical solution.

Claims

1. A municipal pipe network intelligent monitoring and data analysis management platform, characterized by: include: Several monitoring areas, each of which includes at least one environmental sensor group, and the environmental sensor group is used to collect environmental data in the area in real time; An environmental data processing unit, electrically connected to the environmental sensor group, for receiving and processing in real time environmental data from each monitored area, wherein the environmental data processing unit processes and analyzes the environmental data in real time using edge computing technology and stores the data as historical data; A corrosion risk assessment module, connected to the environmental data processing unit, is used to rate the corrosion risk of the monitored area based on the real-time collected environmental data and historical data, determine the corrosion risk area, and sort the inspection order of the monitored area according to the corrosion risk level to generate inspection tasks; An intelligent inspection unit, which can automatically navigate in the underground pipeline corridor and go to the monitoring area to perform spot projection detection according to the detection task. The intelligent inspection unit includes a spot projection module and a deformation analysis module; The light spot projection module is used to project a light spot of a specific shape onto the surface of a pipe or equipment; the deformation analysis module is used to obtain image data after the light spot is projected onto the surface of the equipment or pipe, and perform real-time image processing and deformation analysis on the image data based on a computer vision algorithm to determine the corrosion area and the corresponding corrosion information; A data transmission unit, used to transmit the corrosion information determined by the deformation analysis module to the environmental data processing unit and the cloud platform, wherein the data transmission unit performs bidirectional data transmission with the cloud platform through the Internet of Things technology; A cloud platform connected to the data transmission unit is used to store uploaded corrosion information, generate corrosion monitoring reports and provide real-time warnings; The light spot projection module includes a partitioning submodule, a shape selection submodule and a projection submodule. The partitioning submodule obtains the structural information corresponding to the preset corrosion risk area, and divides each equipment and pipeline in the corrosion risk area into multiple projection areas according to the structural information and the preset partitioning rules; the shape selection submodule calculates the specific shape used for each projection area based on the structural information and environmental data analysis. The specific shapes specifically include dot light spots, grid light spots and stripe light spots; the projection submodule is used to project the selected specific shape of the light spot to the corresponding projection area.

2. A municipal pipe network intelligent monitoring and data analysis management platform according to claim 1, characterized in that: The environmental data processing unit includes a processing module and a storage module. The processing module is used to preprocess the received environmental data and output the preprocessed environmental data to the corrosion risk assessment module and the storage module; the storage module stores the corrosion information and the corresponding environmental data as historical data and uses a time series database for storage.

3. A municipal pipe network intelligent monitoring and data analysis management platform according to claim 2, characterized in that: The preprocessing of the received environmental data specifically includes noise filtering, outlier detection and data standardization, wherein the outlier detection adopts the Z-Score method.

4. A municipal pipe network intelligent monitoring and data analysis management platform according to claim 2, characterized in that: The steps of rating the corrosion risk of the monitoring area, determining the corrosion risk area, and sorting the inspection order of the monitoring area according to the corrosion risk level to generate the inspection task specifically include the following steps: The corrosion risk score of each monitoring area is calculated using a linear regression model based on historical data and real-time environmental data; The corrosion risk scores are classified into corrosion risk levels according to preset classification rules, wherein the corrosion risk levels specifically include high risk, medium risk and no risk; Identify high-risk and medium-risk monitoring areas as corrosion risk areas; The detection tasks are generated after sorting the corrosion risk areas according to their specific corrosion risk scores. The priority of the corrosion risk areas in the detection tasks is in direct proportion to the corrosion risk scores.

5. A municipal pipe network intelligent monitoring and data analysis management platform according to claim 4, characterized in that: The environmental data specifically include humidity, temperature, oxygen concentration and harmful gas concentration. The calculation formula of the corrosion risk score is specifically: R(t)=β0+β1·H(t)+β2·T(t)+β3·O(t)+β4·G(t) Among them, R(t) is the corrosion risk score, β0 is the intercept term of the linear regression model, which represents the baseline impact of environmental parameters on corrosion risk, H(t), T(t), O(t), and G(t) are humidity, temperature, oxygen concentration, and harmful gas concentration, respectively. β1, β2, β3, and β4 are regression coefficients, which represent the degree of influence of each environmental data on corrosion risk and are obtained through training with historical data.

6. A municipal pipe network intelligent monitoring and data analysis management platform according to claim 1, characterized in that: The shape selection submodule calculates the specific shape used in each projection area based on the structural information and environmental data analysis, specifically comprising the following steps: Get structure parameters from structure information; Calculate the judgment index of each projection area based on structural parameters and environmental data; A specific shape for each projection area is selected based on a judgment index.

7. A municipal pipe network intelligent monitoring and data analysis management platform according to claim 6, characterized in that: The deformation analysis module is used to obtain image data after the light spot is projected onto the surface of the equipment or pipeline, and perform real-time image processing and deformation analysis based on the computer vision algorithm image data to determine the corrosion area and the corresponding corrosion information. Specifically, the steps include: Preprocessing the image data, wherein the preprocessing includes denoising and contrast enhancement; Perform illumination normalization on the preprocessed image data; According to the specific shape of the light spot in the image, different deformation analysis methods are selected to analyze and calculate the image data to obtain corrosion information; For the dot light spot, analysis and calculation are performed based on the displacement of the dots and the change of the dot spacing; For grid spots, analysis and calculation are performed based on the displacement of intersection points and geometric fitting; For the stripe light spot, analysis and calculation are performed based on the change of stripe angle and spacing.

8. The municipal pipe network intelligent monitoring and data analysis management platform according to claim 1 is characterized in that: The corrosion information specifically includes the type of corrosion, the area of ​​corrosion, and the degree of corrosion.

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