Heating line detection method, apparatus, system, and non-transitory storage medium

Abstract

The application discloses a heat supply pipeline detection method, device and system and a nonvolatile storage medium. The method comprises the following steps: acquiring infrared sensing data forwarded by an infrared thermal imaging detection terminal through a communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging detection terminal in an area where a heat supply pipeline to be detected is located; determining abnormal data in the infrared sensing data; determining a fault type corresponding to the abnormal data according to position information of the infrared thermal imaging detection terminal; and generating a re-inspection instruction or a maintenance instruction according to fault position information of the fault in the case where the fault type is determined to be a fault of the heat supply pipeline to be detected. The application solves the technical problem that the heat supply pipeline is prone to misjudgment and missed detection and low in efficiency during inspection due to the manual inspection mode in the related art.

Description

Technical Field

[0001] This application relates to the field of cloud computing, and more specifically, to a method, apparatus, system, and non-volatile storage medium for detecting heating pipelines. Background Technology

[0002] In related technologies, the commonly used technical means for inspecting heating pipelines is manual inspection. The problem with this method is that it can only rely on the experience of the staff to judge whether there is a fault, and the data collected by various testing equipment cannot be summarized and analyzed in real time, which leads to problems such as misjudgment, omission, and low efficiency when inspecting heating pipelines.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a heating pipeline inspection method, apparatus, system, and non-volatile storage medium to at least solve the technical problems of easy misjudgment and missed detection and low efficiency when inspecting heating pipelines due to the use of manual inspection in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for detecting heating pipelines is provided, comprising: acquiring infrared sensing data forwarded by an infrared thermal imaging detection terminal through a communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be detected is located; determining abnormal data in the infrared sensing data; determining the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal; and, if the fault type is determined to be a fault in the heating pipeline to be detected, generating a re-inspection instruction or a maintenance instruction based on the fault location information.

[0006] Optionally, the step of determining the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal includes: determining the target cable in the area where the heating pipeline to be inspected is located based on the location information; acquiring the cable temperature data of the target cable through a cable monitoring system, wherein the cable monitoring system includes: distributed temperature measuring optical fiber laid in the target cable, and a centralized monitoring server; determining the fault type corresponding to the abnormal data as a cable fault when the cable temperature data is abnormal; and determining the fault type corresponding to the abnormal data as a fault in the heating pipeline to be inspected when the cable temperature data is normal.

[0007] Optionally, when the fault type is determined to be a fault in the heating pipeline to be inspected, before generating a re-inspection instruction or maintenance instruction based on the fault location information, the heating pipeline inspection method further includes: converting infrared sensing data into a digital grid format; performing wavelet transform filtering on the infrared sensing data after conversion to digital grid format; determining the position information of edge points corresponding to abnormal data in the infrared sensing data using wavelet edge extraction; and determining the fault location information based on the position information of the edge points.

[0008] Optionally, the step of performing wavelet transform filtering on the infrared sensing data after conversion to digital grid format includes: determining a preset threshold, wherein the preset threshold is used to distinguish between noise data and valid data in the infrared sensing data; and performing wavelet transform filtering on the infrared sensing data after conversion to digital grid format according to the preset threshold to eliminate noise data in the infrared sensing data.

[0009] Optionally, the step of determining abnormal data in the infrared sensing data includes: inputting the infrared sensing data into the heating pipeline abnormal data detection model, and obtaining the abnormal detection results output by the heating pipeline abnormal data detection model, wherein the abnormal detection results include abnormal data.

[0010] Optionally, the heating pipeline anomaly data detection model is trained in the following way: acquiring historical operating data of the heating pipeline, wherein the historical operating data includes at least one of the following: the temperature of the heating pipeline, the pressure of the heating pipeline, and the flow rate of the heating pipeline; extracting feature operating data from the historical operating data according to preset rules; dividing the feature operating data into a training dataset, a validation dataset, and a test dataset, and training a preset convolutional neural network model based on the training dataset, the validation dataset, and the test dataset to obtain the heating pipeline anomaly data detection model.

[0011] Optionally, the step of determining abnormal data in the infrared sensing data includes: determining the temperature range corresponding to the heating pipeline under test during normal operation; and determining abnormal temperature points in the infrared sensing data based on the temperature range, wherein the abnormal temperature points are abnormal data.

[0012] Optionally, the step of generating a re-inspection instruction or maintenance instruction based on the fault location information includes: determining the inspection object information corresponding to the fault location information; determining the preset fault handling plan corresponding to the fault type; and generating and sending the re-inspection instruction or maintenance instruction to the target inspection object corresponding to the inspection object information based on the preset fault handling plan and the fault location information.

[0013] According to another aspect of the embodiments of this application, a heating pipeline inspection system is also provided, including: an infrared thermal imaging inspection terminal, a cloud server, and a communication network. The cloud server is used to acquire infrared sensing data forwarded by the infrared thermal imaging inspection terminal through the communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging inspection terminal in the area where the heating pipeline to be inspected is located; determine abnormal data in the infrared sensing data; determine the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging inspection terminal; and, if the fault type is determined to be a fault in the heating pipeline to be inspected, generate a re-inspection instruction or a maintenance instruction based on the fault location information. The infrared thermal imaging inspection terminal includes: a handheld infrared thermal imaging inspection terminal and an automatic inspection infrared thermal imaging inspection terminal.

[0014] Optionally, the infrared thermal imaging detection terminal is equipped with a communication module, a satellite positioning module, an open-source operating system, and a unified cloud desktop transmission protocol.

[0015] According to another aspect of the embodiments of this application, a heating pipeline detection device is also provided, comprising: a first processing module, configured to acquire infrared sensing data forwarded by an infrared thermal imaging detection terminal through a communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be detected is located; a second processing module, configured to determine abnormal data in the infrared sensing data; a third processing module, configured to determine the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal; and a fourth processing module, configured to generate a re-inspection instruction or a maintenance instruction based on the fault location information of the fault when the fault type is determined to be a fault in the heating pipeline to be detected.

[0016] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, wherein a program is stored in the non-volatile storage medium, and the program controls the device where the non-volatile storage medium is located to execute a heating pipeline detection method when it runs.

[0017] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program executes a heating pipeline detection method during runtime.

[0018] In this embodiment, infrared sensing data forwarded by an infrared thermal imaging detection terminal via a communication network is acquired. This infrared sensing data is collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be inspected is located. Abnormal data within the infrared sensing data is identified. The fault type corresponding to the abnormal data is determined based on the location information of the infrared thermal imaging detection terminal. If the fault type is determined to be a fault in the heating pipeline to be inspected, a re-inspection command or maintenance command is generated based on the fault location information. By acquiring the data collected by the detection terminal from the cloud server and identifying the abnormal data, and then determining the fault type based on the abnormal data and location information, the purpose of automatically determining whether the heating pipeline is faulty is achieved. This improves the accuracy and efficiency of heating pipeline fault detection, and solves the technical problem of low efficiency and misjudgment when inspecting heating pipelines due to manual inspection methods in related technologies. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 This is a schematic diagram of a heating pipeline detection system provided according to an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of the structure of a cloud server and an infrared thermal imaging detection terminal according to an embodiment of this application;

[0022] Figure 3 This is a schematic flowchart of a heating pipeline inspection method provided according to an embodiment of this application;

[0023] Figure 4 This is a schematic flowchart of a heating pipeline inspection process provided according to an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of the structure of a heating pipeline detection device according to an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] To better understand the embodiments of this application, the technical terms involved in the embodiments of this application are explained below:

[0029] Cloud-network-device collaboration: This comes from China Telecom's cloud-network convergence strategy. The network is the foundation, the cloud is the core, and the terminal is the source of user operations and data collection. Through cloud-network-device collaboration, comprehensive information services such as data collection, application loading, terminal management, and network collaboration can be provided on demand by users according to usage scenarios.

[0030] Cloud computing services refer to obtaining required services on demand and in a scalable manner through the network; they also refer to computing resources and data storage services provided via the internet. It allows users to use various software applications and data storage services without needing to purchase, install, and maintain hardware equipment. It enables users to access and use computing resources and data storage services anytime, anywhere. Cloud services include public cloud services, private cloud services, and hybrid cloud services.

[0031] 5G network: A 5G network refers to a cellular wireless communication network built using fifth-generation mobile communication technology. It enables faster data transmission, lower latency, and more stable network connections. 5G NR defines three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC).

[0032] Infrared thermal imaging: Infrared radiation is an electromagnetic wave with a frequency between microwaves and visible light, ranging from 0.3 THz to 400 THz. This corresponds to wavelengths between 1 mm and 750 nm in a vacuum, and is non-visible light with a frequency lower than red light. Infrared thermal imaging equipment utilizes the physical properties of infrared radiation to measure the infrared radiation of a target object. Through photoelectric conversion and signal processing, it converts the thermal distribution data of the target object into video images. Infrared thermal imaging is an emerging technology widely used in military, medical, industrial inspection, and automotive imaging fields.

[0033] Heating networks are an important urban infrastructure, but routine inspections and leaks in heating pipelines have always been pain points for urban heating companies. Leaks in heating pipelines not only waste water resources, increase water treatment costs and electricity consumption, accelerate equipment aging, and raise heating costs for companies, making it impossible to guarantee normal heating for users, but also pose huge safety hazards, causing road collapses, loss of life and property, and causing great harm.

[0034] Traditionally, routine maintenance and leak detection of heating pipelines are mainly carried out manually, with maintenance engineers using handheld infrared detectors to check for leaks in the heating network.

[0035] Current methods for detecting urban heating pipe networks in related technologies mainly suffer from the following problems:

[0036] 1. Urban heating pipe networks lack cloud-network-terminal collaborative systems and intelligent detection methods.

[0037] 2. Urban heating networks typically use stand-alone infrared thermal imaging detectors, and the front-end equipment lacks the capability for smart household networking.

[0038] 1) There is a lack of real-time data connection and interaction between stand-alone equipment and the heating operation center, the collected data cannot be effectively processed in real time, and the detection response efficiency is low;

[0039] 2) The operation center cloud lacks control over the front-end testing equipment, has no connection protocol or equipment management capabilities, and cannot upgrade the equipment testing capabilities. Front-end testing depends on the equipment itself, and cloud-side capabilities and edge-side capabilities cannot be coordinated.

[0040] 3. Routine inspections of heating pipelines rely heavily on manual experience, leading to misjudgments, omissions, and low efficiency.

[0041] To address the aforementioned issues, this application provides relevant solutions, which are detailed below.

[0042] According to an embodiment of this application, a method embodiment for detecting heating pipelines is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0043] The method embodiments provided in this application can be executed in a mobile terminal, computer terminal, or similar computing device. For example, in... Figure 1 This is performed in the heating pipeline inspection system shown. For example... Figure 1 As shown, the system includes: an infrared thermal imaging detection terminal 10, a communication network 12, and a cloud server 14. The cloud server 14 is used to acquire infrared sensing data forwarded by the infrared thermal imaging detection terminal 10 through the communication network 12. The infrared sensing data is data collected by the infrared thermal imaging detection terminal 10 in the area where the heating pipeline to be inspected is located. It identifies abnormal data in the infrared sensing data; determines the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal 10; and, if the fault type is determined to be a fault in the heating pipeline to be inspected, generates a re-inspection command or a maintenance command based on the fault location information. The infrared thermal imaging detection terminal 10 includes: a handheld infrared thermal imaging detection terminal 10 and an automatic inspection infrared thermal imaging detection terminal 10.

[0044] The aforementioned communication network 12 can be a terrestrial communication network 12 or a satellite communication network 12, such as a 5G cellular communication network 12. The communication network 12 can provide a network link for the front-end detection equipment, providing Embb high bandwidth capabilities, real-time bidirectional data transmission, and OTA upgrade links.

[0045] The aforementioned infrared thermal imaging detection terminal 10 is used for infrared sensing data acquisition. Furthermore, in some embodiments of this application, such as... Figure 2 As shown, the infrared thermal imaging detection terminal 10 is equipped with a communication module, a satellite positioning module, an open-source operating system, and a unified cloud desktop transmission protocol. The communication module in the infrared thermal imaging detection terminal 10 can be a 5G communication module, the positioning module can be a satellite positioning and navigation module, and the system is an open-source OS.

[0046] In the heating pipeline detection system provided in this application embodiment, various AI computing power models used to generate detection data are uniformly scheduled and configured in the cloud, with hardware and software decoupling, and can be configured as needed. The data collected by the terminal detection is connected to the cloud server 14 in real time through the communication network 12 and the application.

[0047] Under the above operating environment, this application provides a method for detecting heating pipelines, such as... Figure 3 As shown, the method includes the following steps:

[0048] Step S302: Obtain infrared sensing data forwarded by the infrared thermal imaging detection terminal through the communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be detected is located.

[0049] In the technical solution provided in step S302, the infrared thermal imaging detection terminal can connect to the network and locate its working position. Then it interacts with the cloud center and uses the 5G network and cloud desktop transmission protocol to call the detection application and thermal imaging AI algorithm in the cloud center to automatically generate detection data and send it back to the cloud center.

[0050] Step S304: Identify abnormal data in the infrared sensing data;

[0051] In the technical solution provided in step S304, the step of determining abnormal data in the infrared sensing data includes: inputting the infrared sensing data into the heating pipeline abnormal data detection model, and obtaining the abnormal detection result output by the heating pipeline abnormal data detection model, wherein the abnormal detection result includes abnormal data.

[0052] In some embodiments of this application, the heating pipeline abnormal data detection model is trained in the following manner: acquiring historical operating data of the heating pipeline, wherein the historical operating data includes at least one of the following: the temperature of the heating pipeline, the pressure of the heating pipeline, and the flow rate of the heating pipeline; extracting feature operating data from the historical operating data according to preset rules; dividing the feature operating data into a training dataset, a validation dataset, and a test dataset, and training a preset convolutional neural network model based on the training dataset, the validation dataset, and the test dataset to obtain the heating pipeline abnormal data detection model.

[0053] Specifically, the following methods can be used to construct an anomaly detection model for heating pipelines:

[0054] The first step is to collect relevant data from the heating pipeline, such as temperature, pressure, and flow rate, and to preprocess this data, including data cleaning, noise reduction, and normalization, in order to facilitate subsequent algorithm analysis.

[0055] The second step involves selecting and constructing appropriate features based on the characteristics and actual needs of the heating pipeline for use in subsequent algorithms. For example, statistical features and frequency domain features can be extracted from time-series data based on temperature and pressure.

[0056] The third step is to divide the dataset into training, validation, and test sets. Typically, 70% of the data is used as the training set, 15% as the validation set, and 15% as the test set. A CNN model is then built using a deep learning framework (such as TensorFlow or PyTorch). A CNN model usually consists of multiple convolutional layers, pooling layers, and fully connected layers. Convolutional layers extract features, pooling layers reduce feature dimensionality, and fully connected layers output the final prediction. The CNN model is trained using the training set, updating the model's weights and biases through backpropagation to make the model's predictions closer to the true labels. The performance of the trained model is evaluated using the validation set. Based on the model's performance on the validation set, hyperparameters such as the learning rate, kernel size, and pooling window size are adjusted.

[0057] The fourth step involves using the trained model to predict and analyze new data. This allows for real-time monitoring of the heating pipeline's operational status, prediction of fault risks, and identification of potential problems. For example, detecting and analyzing temperature anomalies can reveal issues such as leaks or blockages in the heating pipeline.

[0058] Fifth, when a fault risk or potential problem is predicted in the heating pipeline, the system can promptly issue an early warning to notify relevant personnel to handle the situation. Simultaneously, based on the prediction results, corresponding measures are formulated, such as pipeline inspection, maintenance, or replacement, to prevent the fault from occurring or escalating further.

[0059] The sixth step is to optimize and update the algorithm model based on feedback and data from practical applications to improve prediction accuracy and reliability. For example, new data can be continuously collected to update the model, feature extraction methods can be improved, or new machine learning or deep learning algorithms can be tried to adapt to different heating pipeline conditions and environmental changes.

[0060] As an optional implementation, the step of determining abnormal data in infrared sensing data includes: determining the temperature range corresponding to the heating pipeline under test during normal operation; determining abnormal temperature points in the infrared sensing data based on the temperature range, wherein the abnormal temperature points are abnormal data.

[0061] Specifically, the temperature of the heating pipeline being tested reflects the pipeline's infrared radiation energy, and the specific calculation formula is as follows:

[0062] P=δζT 4

[0063] In the above formula, P represents radiant energy, expressed in W / cm². 2 δ is the Boltzmann constant, with units of 5.673 × 10⁻¹² W / (cm²). 2·K4); ζ - emissivity of a common object; T - thermodynamic temperature of the object's surface, in K.

[0064] In addition, the material heat conduction models for heating pipelines made of different materials are as follows:

[0065]

[0066] In the above formula, t is time, in minutes; α is the thermal conductivity, in meters. 2 / s; λ is the thermal conductivity, in W / (cm·K); ρ is the density, in kg / m³. 2 C represents specific heat capacity, measured in J / (kg·K).

[0067] As an optional implementation, an infrared thermal imaging detection terminal can detect the infrared radiation energy of the heating pipeline based on the two formulas mentioned above, thereby determining the pipeline temperature. Furthermore, according to the "Design Code for Urban Heating Pipeline Networks" (CJJ34-2010), urban heating systems generally have a design pressure of less than or equal to 2.5 MPa and a design temperature of less than or equal to 200℃ for hot water; and a design pressure of less than or equal to 1.6 MPa and a design temperature of less than or equal to 350℃ for steam. For hot water heating networks using power plants or large regional boiler rooms as heat sources, the design supply water temperature can be taken as 110℃~150℃, and the return water temperature should not exceed 70℃. When a leak occurs in a heating pipeline, it will cause a certain temperature difference between the ground surface above the leak point and the surrounding environment. The temperature and infrared images displayed by an infrared thermal imager are used to locate the temperature anomaly point, and then other auxiliary methods are used for confirmation to determine whether it is caused by a leak. By collecting a large amount of temperature anomaly data and images and eliminating false anomalies, infrared imagers can be used to detect leaks in heating pipelines.

[0068] Step S306: Determine the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal;

[0069] In the technical solution provided in step S306, the step of determining the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal includes: determining the target cable in the area where the heating pipeline to be detected is located based on the location information, and obtaining the cable temperature data of the target cable through a cable monitoring system, wherein the cable monitoring system includes: distributed temperature measuring optical fiber laid in the target cable, and a centralized monitoring server; in the case of abnormal cable temperature data, determining the fault type corresponding to the abnormal data as a cable fault; in the case of normal cable temperature data, determining the fault type corresponding to the abnormal data as a fault in the heating pipeline to be detected.

[0070] Specifically, since infrared imagers use thermal imaging principles for monitoring, they are easily affected by the external environment during detection. For example, when there is an intersection between the cable laying area and the heating pipe laying area, a cable fault in the intersection area can also cause heat generation. Therefore, when an abnormal temperature point is detected, the real-time location of the infrared imager is determined; the cable laying information at that real-time location is obtained from the cloud; and if there is an overlapping area between the cable and the heating pipe at that real-time location, it is determined whether the abnormality is caused by a cable abnormality or a heating pipe abnormality.

[0071] As an optional implementation, a cloud server and a cable monitoring system can communicate to obtain cable temperature data. The cable monitoring system includes at least: distributed temperature-sensing optical fibers laid on the high-voltage cable to sense real-time temperature changes in the power cable and transmit the power cable temperature data to a centralized monitoring computer; the centralized monitoring computer determines whether the power cable is faulty based on the temperature data. If a cable fault is determined, the cause of the current anomaly is determined not to be a thermal pipeline malfunction. If the cable is determined to be fault-free, the cause of the current anomaly is determined to be a thermal pipeline malfunction.

[0072] In some embodiments of this application, before generating a re-inspection instruction or maintenance instruction based on the fault location information when the fault type is determined to be a fault in the heating pipeline to be inspected, the heating pipeline inspection method further includes: converting infrared sensing data into a digital grid format; performing wavelet transform filtering on the infrared sensing data after conversion to digital grid format; determining the position information of edge points corresponding to abnormal data in the infrared sensing data using wavelet edge extraction; and determining the fault location information based on the position information of the edge points.

[0073] As an optional implementation, the step of performing wavelet transform filtering on the infrared sensing data after conversion to digital grid format includes: determining a preset threshold, wherein the preset threshold is used to distinguish between noise data and valid data in the infrared sensing data; and performing wavelet transform filtering on the infrared sensing data after conversion to digital grid format according to the preset threshold to eliminate noise data in the infrared sensing data.

[0074] Specifically, due to noise and other factors, the results produced by infrared thermal imaging do not form clear boundaries, making it impossible to determine the precise location of anomalies. Therefore, noise reduction is necessary. For example, the thermal imaging index reflectance results (orthophotos) are converted into a digital grid format, that is, a point-to-point temperature value format in coordinates, and then gridded. Then, wavelet transform algorithms are used for frequency filtering to process stripe and band interference from different directions and angles. The filter factor is adjusted according to the degree of stripe interference to remove it without affecting the original data as much as possible.

[0075] In some embodiments of this application, the wavelet transform-based denoising method places the noisy signal on a two-dimensional plane and performs time-division and frequency-division processing using the distinct characteristics of the signal and noise. Theoretically, this method can not only achieve a high signal-to-noise ratio but also maintain good resolution. Threshold selection is the most important step in discrete wavelet denoising. The wavelet threshold δ plays a decisive role in the denoising process; if the threshold is too small, the wavelet coefficients will contain too much noise after applying the threshold, failing to achieve the denoising effect; conversely, if the threshold is too large, useful components will be removed, causing distortion. As an optional implementation, a uniform threshold method can be used to distinguish between noise and effective components.

[0076] Edge extraction is performed on the wavelet-denoised image using wavelet edge extraction. Line fitting and coordinate extraction are performed on the extracted edge points to obtain the coordinate information of each edge point. The image edge position can be obtained by calculating the local maxima of the gradient vector of the infrared signal, thus completing the edge extraction.

[0077] Step S308: If the fault type is determined to be a fault in the heating pipeline to be inspected, a re-inspection instruction or a maintenance instruction is generated based on the fault location information.

[0078] In the technical solution provided in step S308, the step of generating a re-inspection instruction or maintenance instruction based on the fault location information includes: determining the inspection object information corresponding to the fault location information and determining the preset fault handling plan corresponding to the fault type; generating and sending the re-inspection instruction or maintenance instruction to the target inspection object corresponding to the inspection object information based on the preset fault handling plan and the fault location information.

[0079] Specifically, when issuing inspection tasks, different types of inspection tasks can be issued to manual inspection teams and automated inspection robots. For manual inspection teams, target inspection points can be directly issued, while for automated inspection robots, navigation paths and target inspection points can be issued.

[0080] In some embodiments of this application, a method such as... is also provided. Figure 4 The inspection process shown includes the following steps:

[0081] Step S402: The cloud server sends inspection tasks to handheld inspection devices or automated inspection robots.

[0082] Step S404: Configure the thermal imaging AI algorithm and detection application used by the handheld inspection device or the automatic inspection robot on the cloud server;

[0083] Step S406: Obtain the detection data of the detection points uploaded by the handheld detection device or the automatic inspection robot through the communication network;

[0084] Step S408: Determine whether the detection point is faulty based on the detection data.

[0085] Specifically, after a fault is identified, a maintenance work order can be issued through the official website. After confirming that no fault has occurred, a new inspection task can be assigned.

[0086] By acquiring infrared sensing data forwarded by an infrared thermal imaging detection terminal through a communication network—data collected by the terminal in the area of ​​the heating pipeline to be inspected—abnormal data is identified within the infrared sensing data. The fault type corresponding to the abnormal data is determined based on the location information of the infrared thermal imaging detection terminal. If the fault type is determined to be a fault in the heating pipeline to be inspected, a re-inspection or maintenance command is generated based on the fault location information. This method, achieved by having a cloud server acquire the data collected by the detection terminal and identify abnormal data, and then determining the fault type based on the abnormal data and location information, automatically determines whether the heating pipeline is faulty. This improves the accuracy and efficiency of heating pipeline fault detection, and solves the technical problem of low efficiency and misjudgment caused by manual inspection methods in related technologies.

[0087] This application provides a heating pipeline detection device. Figure 5 This is a schematic diagram of the device. (For example...) Figure 5 As shown, the device includes: a first processing module 50, used to acquire infrared sensing data forwarded by the infrared thermal imaging detection terminal through a communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be inspected is located; a second processing module 52, used to determine abnormal data in the infrared sensing data; a third processing module 54, used to determine the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal; and a fourth processing module 56, used to generate a re-inspection command or a maintenance command based on the fault location information when the fault type is determined to be a fault in the heating pipeline to be inspected.

[0088] In some embodiments of this application, the step of the second processing module 52 in determining abnormal data in the infrared sensing data includes: inputting the infrared sensing data into the heating pipeline abnormal data detection model, and obtaining the abnormal detection result output by the heating pipeline abnormal data detection model, wherein the abnormal detection result includes abnormal data.

[0089] In some embodiments of this application, the heating pipeline abnormal data detection model is trained in the following manner: acquiring historical operating data of the heating pipeline, wherein the historical operating data includes at least one of the following: the temperature of the heating pipeline, the pressure of the heating pipeline, and the flow rate of the heating pipeline; extracting feature operating data from the historical operating data according to preset rules; dividing the feature operating data into a training dataset, a validation dataset, and a test dataset, and training a preset convolutional neural network model based on the training dataset, the validation dataset, and the test dataset to obtain the heating pipeline abnormal data detection model.

[0090] In some embodiments of this application, the step of the second processing module 52 in determining abnormal data in the infrared sensing data includes: determining the temperature range corresponding to the heating pipeline to be detected during normal operation; and determining abnormal temperature points in the infrared sensing data based on the temperature range, wherein the abnormal temperature points are abnormal data.

[0091] In some embodiments of this application, the step of the third processing module 54 determining the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal includes: determining the target cable in the area where the heating pipeline to be detected is located based on the location information; acquiring the cable temperature data of the target cable through a cable monitoring system, wherein the cable monitoring system includes: a distributed temperature measuring optical fiber laid in the target cable and a centralized monitoring server; determining the fault type corresponding to the abnormal data as a cable fault when the cable temperature data is abnormal; and determining the fault type corresponding to the abnormal data as a fault in the heating pipeline to be detected when the cable temperature data is normal.

[0092] In some embodiments of this application, before generating a re-inspection instruction or maintenance instruction based on the fault location information when the fault type is determined to be a fault in the heating pipeline to be inspected, the heating pipeline detection device is further configured to: convert the infrared sensing data into a digital grid format and perform wavelet transform filtering on the infrared sensing data after conversion to digital grid format; determine the position information of the edge points corresponding to the abnormal data in the infrared sensing data by using wavelet edge extraction; and determine the fault location information based on the position information of the edge points.

[0093] In some embodiments of this application, the step of the heating pipeline detection device performing wavelet transform filtering on the infrared sensing data converted to digital grid format includes determining a preset threshold, wherein the preset threshold is used to distinguish between noise data and valid data in the infrared sensing data; and performing wavelet transform filtering on the infrared sensing data converted to digital grid format according to the preset threshold to eliminate noise data in the infrared sensing data.

[0094] It should be noted that each module in the above-mentioned heating pipeline detection device can be a program module (for example, a set of program instructions to implement a certain function) or a hardware module. For the latter, it can be manifested in the following forms, but is not limited to them: each of the above modules is manifested as a processor, or the functions of each of the above modules are implemented by a processor.

[0095] This application provides a non-volatile storage medium. The non-volatile storage medium stores a program that, when executed, performs the following heating pipeline detection method: acquiring infrared sensing data forwarded by an infrared thermal imaging detection terminal via a communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be detected is located; identifying abnormal data in the infrared sensing data; determining the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal; and, if the fault type is determined to be a fault in the heating pipeline to be detected, generating a re-inspection instruction or a maintenance instruction based on the fault location information.

[0096] Figure 6 A schematic diagram of the hardware structure of an electronic device for implementing a heating pipeline inspection method is shown. This electronic device can be a computer terminal or a mobile device. Figure 6 As shown, the electronic device 60 may include one or more processors 602 (shown as 602a, 602b, ..., 602n in the figure) (processor 602 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 604 for storing data, and a transmission module 606 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 6 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, electronic device 60 may also include... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0097] It should be noted that the aforementioned one or more processors 602 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element of the electronic device 60. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0098] The memory 604 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the heating pipeline detection method in this embodiment. The processor 602 executes various functional applications and data processing by running the software programs and modules stored in the memory 604, thereby realizing the heating pipeline detection method of the aforementioned application. The memory 604 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 604 may further include memory remotely located relative to the processor 602, and these remote memories can be connected to the electronic device 60 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0099] The transmission device 606 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 60. In one example, the transmission device 606 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 606 may be a Radio Frequency (RF) module for wireless communication with the Internet.

[0100] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows a user to interact with the user interface of the electronic device 60.

[0101] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0103] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0105] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0106] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for detecting heating pipelines, characterized in that, include: The infrared sensing data is acquired by the infrared thermal imaging detection terminal through the communication network, wherein the infrared sensing data is the data collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be detected is located. Identify abnormal data in the infrared sensing data; The fault type corresponding to the abnormal data is determined based on the location information of the infrared thermal imaging detection terminal; If the fault type is determined to be a fault in the heating pipeline to be inspected, a re-inspection instruction or a maintenance instruction is generated based on the fault location information. Determining the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal includes: The target cable in the area where the heating pipeline to be detected is located is determined based on the location information. The cable temperature data of the target cable is obtained through a cable monitoring system, wherein the cable monitoring system includes: distributed temperature-measuring optical fibers laid in the target cable, and a centralized monitoring server; In the event of abnormal cable temperature data, the fault type corresponding to the abnormal data is determined to be a cable fault. If the cable temperature data is normal, the fault type corresponding to the abnormal data is determined to be a fault in the heating pipeline under test.

2. The heating pipeline inspection method according to claim 1, characterized in that, Before the step of generating a re-inspection command or maintenance command based on the fault location information when the fault type is determined to be a fault in the heating pipeline to be inspected, the heating pipeline inspection method further includes: The infrared sensing data is converted into a digital grid format; The infrared sensing data, after being converted to digital grid format, is subjected to wavelet transform filtering. The position information of the edge points corresponding to the abnormal data in the infrared sensing data is determined by wavelet edge extraction. The fault location information is determined based on the position information of the edge points.

3. The heating pipeline inspection method according to claim 2, characterized in that, The step of performing wavelet transform filtering on the infrared sensing data after it has been converted to digital grid format includes: A preset threshold is determined, wherein the preset threshold is used to distinguish between noise data and valid data in the infrared sensing data; The infrared sensing data, after being converted to digital grid format, is subjected to wavelet transform filtering based on the preset threshold to eliminate the noise data in the infrared sensing data.

4. The heating pipeline inspection method according to claim 1, characterized in that, The step of determining abnormal data in the infrared sensing data includes: The infrared sensing data is input into the heating pipeline abnormal data detection model, and the abnormal detection result output by the heating pipeline abnormal data detection model is obtained, wherein the abnormal detection result includes the abnormal data.

5. The heating pipeline inspection method according to claim 4, characterized in that, The abnormal data detection model for the heating pipeline is trained in the following way: Obtain historical operating data of the heating pipeline, wherein the historical operating data includes at least one of the following: the temperature of the heating pipeline, the pressure of the heating pipeline, and the flow rate of the heating pipeline; Featured work data is extracted from the historical work data according to preset rules; The feature working data is divided into a training dataset, a validation dataset, and a test dataset. A preset convolutional neural network model is trained based on the training dataset, the validation dataset, and the test dataset to obtain the heating pipeline abnormal data detection model.

6. The heating pipeline inspection method according to claim 1, characterized in that, The step of determining abnormal data in the infrared sensing data includes: Determine the temperature range of the heating pipeline under test during normal operation; Based on the temperature range, abnormal temperature points are determined in the infrared sensing data, wherein the abnormal temperature points are the abnormal data.

7. The heating pipeline inspection method according to claim 1, characterized in that, The step of generating a re-inspection instruction or maintenance instruction based on the fault location information includes: Determine the inspection object information corresponding to the fault location information; Determine the preset fault handling plan corresponding to the fault type; Based on the preset fault handling plan and the fault location information, the re-inspection instruction or the maintenance instruction is generated and sent to the target inspection object corresponding to the inspection object information.

8. A heating pipeline detection system, characterized in that, Infrared thermal imaging detection terminal, cloud server, communication network, among which... The cloud server is used to acquire infrared sensing data forwarded by the infrared thermal imaging detection terminal through the communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be detected is located; identify abnormal data in the infrared sensing data; determine the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal; and, if the fault type is determined to be a fault in the heating pipeline to be detected, generate a re-inspection command or a maintenance command based on the fault location information. The infrared thermal imaging detection terminal includes: a handheld infrared thermal imaging detection terminal and an automatic inspection infrared thermal imaging detection terminal. Determining the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal includes: The target cable in the area where the heating pipeline to be detected is located is determined based on the location information. The cable temperature data of the target cable is obtained through a cable monitoring system, wherein the cable monitoring system includes: distributed temperature-measuring optical fibers laid in the target cable, and a centralized monitoring server; In the event of abnormal cable temperature data, the fault type corresponding to the abnormal data is determined to be a cable fault. If the cable temperature data is normal, the fault type corresponding to the abnormal data is determined to be a fault in the heating pipeline under test.

9. The heating pipeline detection system according to claim 8, characterized in that, The infrared thermal imaging detection terminal is equipped with a communication module, a satellite positioning module, an open-source operating system, and a unified cloud desktop transmission protocol.

10. A heating pipeline detection device, characterized in that, include: The first processing module is used to acquire infrared sensing data forwarded by the infrared thermal imaging detection terminal through the communication network, wherein the infrared sensing data is data collected by the infrared thermal imaging detection terminal in the area where the heating pipeline to be detected is located. The second processing module is used to determine abnormal data in the infrared sensing data; The third processing module is used to determine the fault type corresponding to the abnormal data based on the location information of the infrared thermal imaging detection terminal. The fourth processing module is used to generate a re-inspection instruction or a maintenance instruction based on the fault location information when it is determined that the fault type is a fault in the heating pipeline to be inspected. The third processing module is further configured to: determine the target cable in the area where the heating pipeline to be inspected is located based on the location information; acquire cable temperature data of the target cable through a cable monitoring system, wherein the cable monitoring system includes: distributed temperature-measuring optical fibers laid in the target cable, and a centralized monitoring server; determine the fault type corresponding to the abnormal cable temperature data as a cable fault when the cable temperature data is abnormal; and determine the fault type corresponding to the abnormal cable temperature data as a fault in the heating pipeline to be inspected when the cable temperature data is normal.

11. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a program, wherein when the program is executed, it controls the device containing the non-volatile storage medium to perform the heating pipeline detection method according to any one of claims 1 to 7.

12. An electronic device, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the heating pipeline detection method according to any one of claims 1 to 7.

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