Image recognition-based cable joint temperature anomaly detection device, method and equipment

Abstract

The application discloses an image recognition-based cable joint temperature anomaly detection device, method and equipment, and belongs to the technical field of Internet of Things.The device comprises the following: a visible light camera and an infrared camera, which acquire visible light images and infrared images at a cable joint; an identification module, which identifies the number information and temperature information of the cable joint in the visible light images and infrared images; a fitting module, which fits the visible light images and infrared images to obtain a fitting image of the cable joint, and adds the number information and temperature information; a temperature anomaly identification module, which inputs the fitting image into a pre-trained anomaly cause analysis model and determines at least one cause leading to the anomaly when the temperature information is higher than a set temperature threshold; and an alarm module, which correlates and displays the fitting image and the cause leading to the anomaly.The technical scheme can accurately acquire the temperature of the cable joint in real time, efficiently determine the anomaly cause, reduce the occurrence of false positives, and improve the work efficiency of maintenance personnel.

Description

Technical Field

[0001] This application belongs to the field of power equipment technology, specifically relating to a cable joint temperature anomaly detection device, method and equipment based on image recognition. Background Technology

[0002] With the steady and rapid growth of my country's economy, the demand for wires and cables has also shown a strong momentum. Wires and cables account for a significant proportion of the national economy and are indispensable in all economic activities and social life. The generation, transmission, and application of electricity all require wires and cables as connections. Among them, the temperature of the cable head is a critical indicator for cable operation. In actual operation, the cable head often heats up before a fault occurs. If not handled in time, insulation breakdown may occur, causing short circuits, tripping, and other power supply faults. In severe cases, it can even lead to fires. Therefore, it is essential to monitor and regulate the temperature of the cable head in a timely manner to ensure the normal operation of the cable.

[0003] The current method for measuring cable head temperature involves deploying wireless temperature sensors at monitoring nodes to collect temperature values ​​at the cable joints. These values ​​are then transmitted to a host computer via wireless communication technology for analysis and processing. If the temperature value exceeds a preset threshold, the host computer will send a command to trigger an alarm.

[0004] However, current wireless temperature sensors are battery-powered and require periodic replacement, increasing the workload for maintenance personnel. Furthermore, these sensors are typically externally mounted, resulting in poor fit with the cable and lower measurement accuracy. This can lead to false alarms or failure to detect abnormally high temperatures, thus reducing cable lifespan. Therefore, how to accurately measure cable head temperature in real time and promptly detect anomalies, reduce false alarms, further ensure normal cable operation, and extend cable lifespan is a pressing issue that needs to be addressed in this field. Summary of the Invention

[0005] This application provides an image recognition-based cable joint temperature anomaly detection device, method, and equipment. The aim is to address the problem of low accuracy in existing cable joint temperature measurements, which may lead to false alarms or failure to detect abnormally high temperatures, thus reducing cable lifespan. The image recognition-based cable joint temperature anomaly detection device can accurately acquire cable joint temperature information in real time and determine whether an anomaly has occurred and its cause, allowing for timely handling by personnel. This reduces false alarms to a certain extent and extends cable lifespan.

[0006] In a first aspect, embodiments of this application provide a cable joint temperature anomaly detection device based on image recognition, the device comprising:

[0007] A visible light camera is used to acquire visible light images of the cable connector.

[0008] An infrared camera is used to acquire infrared images of the cable connector.

[0009] The identification module is used to identify the cable connector number information included in the visible light image; and to obtain the temperature information at the cable connector based on the infrared image;

[0010] The fitting module is used to fit the visible light image and the infrared image to obtain a fitted image of the cable connector, and to append the number information and the temperature information to the fitted image;

[0011] A temperature anomaly identification module is used to input the fitted image into a pre-trained anomaly cause analysis model when the temperature information is higher than a set temperature threshold, so as to determine at least one cause of the anomaly through the anomaly cause analysis model.

[0012] The alarm module is used to correlate and display the fitted image with the cause of the anomaly.

[0013] Furthermore, the temperature anomaly detection module is specifically used for:

[0014] If the temperature information is higher than a set temperature threshold, the fitted image is divided into blocks according to a preset area.

[0015] Identify the highest and average temperatures of each segment after division and assign them to the fitted image;

[0016] The fitted image with block temperature assignments is input into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly.

[0017] Furthermore, the causes of the abnormality include: abnormal material, loose contact, increased wiring resistance, electrochemical reaction of wiring, and loose wrapping;

[0018] The anomaly cause analysis model is obtained by training a preset number of samples based on the temperature information reflected under the conditions of each cause of the anomaly.

[0019] Furthermore, the device also includes:

[0020] The connector location determination module is used to read the number information of the cable connector and determine the installation location of the cable connector based on a pre-stored lookup table of number information and installation location.

[0021] Furthermore, the connector position determination module is also used for:

[0022] If the numbering information of the cable connector cannot be determined or the numbering information does not exist in the lookup table, obtain the device ID of the visible light camera and / or the infrared camera;

[0023] The installation location of the cable connector is determined based on the device ID.

[0024] Secondly, embodiments of this application provide a method for detecting abnormal temperature at cable joints based on image recognition, the method comprising:

[0025] Acquire visible light images of the cable connector using a visible light camera;

[0026] Infrared images of the cable connector are obtained using an infrared camera.

[0027] The identification module identifies the cable connector number information included in the visible light image; and obtains the temperature information at the cable connector based on the infrared image.

[0028] The visible light image and the infrared image are fitted by the fitting module to obtain a fitted image of the cable connector, and the number information and the temperature information are added to the fitted image.

[0029] When the temperature information is higher than a set temperature threshold, the temperature anomaly identification module inputs the fitted image into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly.

[0030] The alarm module displays the correlation between the fitted image and the cause of the anomaly.

[0031] Furthermore, if the temperature information is higher than a set temperature threshold, the fitted image is input into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly, including:

[0032] If the temperature information is higher than a set temperature threshold, the fitted image is divided into blocks according to a preset area.

[0033] Identify the highest and average temperatures of each segment after division and assign them to the fitted image;

[0034] The fitted image with block temperature assignments is input into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly.

[0035] Furthermore, the causes of the abnormality include: abnormal material, loose contact, increased wiring resistance, electrochemical reaction of wiring, and loose wrapping;

[0036] The anomaly cause analysis model is obtained by training a preset number of samples based on the temperature information reflected under the conditions of each cause of the anomaly.

[0037] Furthermore, after correlating and displaying the fitted image and the cause of the anomaly, the method further includes:

[0038] The connector location determination module reads the cable connector's serial number information and determines the cable connector's installation location based on a pre-stored lookup table of serial numbers and installation locations.

[0039] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0040] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0041] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0042] In this embodiment, a visible light camera is used to acquire a visible light image of the cable joint; an infrared camera is used to acquire an infrared image of the cable joint; an identification module is used to identify the cable joint number information included in the visible light image; and to acquire the temperature information of the cable joint based on the infrared image; a fitting module is used to fit the visible light image and the infrared image to obtain a fitted image of the cable joint, and to append the number information and the temperature information to the fitted image; a temperature anomaly identification module is used to input the fitted image into a pre-trained anomaly cause analysis model when the temperature information is higher than a set temperature threshold, so as to determine at least one cause of the anomaly through the anomaly cause analysis model; and an alarm module is used to correlate and display the fitted image and the cause of the anomaly. Through the above-described image recognition-based cable joint temperature anomaly detection device, the temperature information of the cable joint can be acquired accurately in real time, and the presence and cause of the anomaly can be determined in real time based on this temperature information, so that staff can handle the anomaly in a timely manner. This reduces the occurrence of false alarms to a certain extent and can extend the service life of the cable. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the structure of the cable joint temperature anomaly detection device based on image recognition provided in Embodiment 1 of this application;

[0044] Figure 2 This is a schematic diagram of the structure of the cable joint temperature anomaly detection device based on image recognition provided in Embodiment 2 of this application;

[0045] Figure 3 This is a flowchart illustrating the image recognition-based cable joint temperature anomaly detection method provided in Embodiment 3 of this application;

[0046] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0048] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0049] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0050] The following description, in conjunction with the accompanying drawings, details the image recognition-based cable joint temperature anomaly detection device, method, and equipment provided in this application through specific embodiments and application scenarios.

[0051] Example 1

[0052] Figure 1 This is a schematic diagram of the cable joint temperature anomaly detection device based on image recognition provided in Embodiment 1 of this application. Figure 1 As shown, it specifically includes the following:

[0053] Visible light camera 101 is used to acquire visible light images of the cable joint;

[0054] Infrared camera 102 is used to acquire infrared images of the cable joint.

[0055] The identification module 103 is used to identify the cable connector number information included in the visible light image; and to obtain the temperature information at the cable connector based on the infrared image;

[0056] The fitting module 104 is used to fit the visible light image and the infrared image to obtain a fitted image of the cable joint, and to append the number information and the temperature information to the fitted image;

[0057] The temperature anomaly identification module 105 is used to input the fitted image into a pre-trained anomaly cause analysis model when the temperature information is higher than a set temperature threshold, so as to determine at least one cause of the anomaly through the anomaly cause analysis model.

[0058] The alarm module 106 is used to correlate and display the fitted image with the cause of the anomaly.

[0059] Firstly, this solution can be used in scenarios involving the measurement of cable head temperature and the analysis and display of abnormal temperature causes. Specifically, a temperature measuring device can measure the cable head temperature, and a control terminal can analyze and display the causes of abnormal cable head temperatures. The temperature measuring device can include visible light cameras and infrared cameras, and the control terminal can be a smart terminal device, such as a laptop, desktop computer, or tablet computer, or an IoT platform.

[0060] Based on the above usage scenarios, it is understood that the executing entity of this application may be a terminal device that integrates the measurement of cable head temperature and the analysis and display of the causes of abnormal cable head temperature, without further limitations here.

[0061] The visible light camera can be a color camera, the same camera used to take color photographs. In this solution, the color camera is used to capture color images of the cable connectors, which are then used by smart terminal devices or IoT platforms to identify the cable connector's serial number and to measure its temperature.

[0062] A cable can be a device for transmitting electrical energy or signals, typically composed of several or groups of conductors. Types include power cables, control cables, compensating cables, shielded cables, high-temperature cables, computer cables, signal cables, coaxial cables, fire-resistant cables, marine cables, mining cables, and aluminum alloy cables. A cable joint, also known as a cable termination, is a connection point between sections of a cable after it has been laid, ensuring a continuous line. Cable joints are located in the middle of a cable line and are called intermediate joints, while those at the ends of the line are called terminal joints. Cable joints are used to lock and secure incoming and outgoing cables, providing waterproofing, dustproofing, and vibration protection.

[0063] The visible light image can be a color image of the cable connector captured by a color camera. Furthermore, capturing a color image of the cable connector using a color camera can be the process of acquiring a visible light image of the cable connector.

[0064] An infrared camera, specifically an infrared temperature measurement camera, is a camera that uses infrared light to create an image. Besides using ordinary light, it can also form an image based on the heat of infrared rays. In this solution, the infrared camera is used to capture images including the cable joint and its temperature. These images are then used by smart terminal devices or IoT platforms to perform cable joint temperature detection based on the captured images and visible light images of the cable joint taken by a visible light camera.

[0065] An infrared image can be an image captured by an infrared camera that includes the cable joint and its temperature. Furthermore, capturing an image containing the cable joint and its temperature using an infrared camera can be the process of obtaining an infrared image of the cable joint.

[0066] The numbering information can be a unique number for each cable joint. To distinguish different cable joints, each cable joint can be assigned a different number so that its location can be determined based on the number when an abnormal temperature occurs. Specifically, the numbering can include numbers, letters, and words. In this solution, numbers can be used to number the cable joints from one end to the other along the cable laying direction. For example, the first cable joint along the cable laying direction is numbered 1001, the second cable joint is numbered 1002, and so on, to obtain the subsequent cable joint numbers.

[0067] Temperature information can be the temperature of the cable joint. Since infrared images can reflect the temperature value of each pixel in the image, the highest temperature at the cable joint in this image can be used as the temperature information so that it can be used to determine whether the temperature of the cable joint is abnormal.

[0068] Once a visible light camera captures a visible light image of a cable connector, it can transmit this image to a smart terminal device or IoT platform via wireless communication technology. Wireless communication refers to long-distance transmission between multiple nodes without the transmission of signals through conductors or cables; radios and other wireless communication methods can be used. When the smart terminal device or IoT platform receives this visible light image, it identifies the numbering information within the image based on a template library. For example, when using numbers as the cable connector's numbering information, since the numbers are simple characters, a dataset can be downloaded online beforehand. Then, the smart terminal or IoT platform reads the visible light image captured by the camera and performs grayscale and binarization on the image (since the image itself is stored as numbers, the binarized image only has two values: 0 and 255). After binarizing the image, the characters in the image are segmented horizontally and vertically to obtain individual digit images. These images are then resized to match the template size (generally the largest size in the template). Next, the image to be matched is subtracted from one of the 10 templates (templates for numbers 0-10) by subtracting the corresponding pixel values ​​of the two images. The absolute values ​​of all differences are summed. The template with the smallest sum of absolute values ​​is the best match, and thus the character is identified. After all characters are identified, they are combined to obtain the cable connector number information. This is the entire process of identifying cable connector number information in a visible light image.

[0069] Once the infrared camera captures an infrared image of the cable joint, it can transmit this image to a smart terminal device or IoT platform via wireless communication technology. Since the infrared image contains the temperature value of each pixel, the smart terminal device or IoT platform can read the highest temperature value at the cable joint as the temperature information. The above describes the process of obtaining temperature information from an infrared image of the cable joint.

[0070] The fitted image can be a combination of a visible light image and an infrared image. Since a visible light image can clearly show the outline and number of the cable joint, and an infrared image can show the temperature information of the cable joint, a combination of a visible light image and an infrared image can be used to obtain an image that includes the outline, number, and temperature information of the cable joint.

[0071] After receiving visible light and infrared images, smart terminal devices or IoT platforms can input them into image processing software. Since color images are captured using the three primary colors of light (red, green, and blue), and these primary colors are represented in image processing software as channels, specifically RGB (red, green, blue) three channels, where the red channel displays red information, the green channel displays green information, and the blue channel displays blue information, the color image is represented as an RGB 3-channel image in image processing software. Furthermore, the single infrared channel of an infrared image can be directly expanded into a 3-channel RGB image, where each pixel has the same RGB value—essentially a grayscale image with three channels. This image is then semi-transparently fused with the color image to obtain a fitted image. The above describes the process of obtaining a fitted image of a cable connector.

[0072] After obtaining the fitted image, PhotoCap (a batch processing tool for digital cameras) can be used to add Exif (Exchangeable Image File Format) information to it. Specifically, after importing the fitted image in standard mode into PhotoCap's editing area, you can enter the cable connector number and temperature information in the "Exif Object Property Settings" dialog box to append this information to the fitted image. The Exif format is specifically designed for digital camera photos. This format can record the attribute information of digital photos. PhotoCap has powerful batch processing functions commonly used by digital camera users, such as adding dates, text, borders, adding Exif data, and changing filenames.

[0073] The set temperature threshold can be the temperature at which an anomaly occurs at the cable joint. For example, if an anomaly occurs in the cable when the temperature at the cable joint exceeds 60°C, the set temperature threshold can be set to 60°C. When the temperature exceeds this set temperature threshold, the next step will be to input the fitted image into the pre-trained anomaly cause analysis model.

[0074] Anomaly cause analysis models can be anomaly attribution models, which are big data models that analyze information to identify the causes of anomalies. In this solution, the anomaly attribution model can be pre-trained. This involves inputting anomaly indicators into a data modeling tool and setting up the anomaly attribution model using methods such as funnel attribution, internal attribution, and external attribution. This achieves the purpose of training the anomaly cause analysis model. Funnel attribution seeks causes from the lower levels of the people or things that caused the anomaly; internal attribution directly looks for causes from the people or things that caused the anomaly; the rest fall under the category of external attribution. The number of anomaly indicators can be determined using the tenfold rule. The tenfold rule states that the model typically needs ten times more data than its degrees of freedom. Here, degrees of freedom can be parameters affecting the model output, or attributes of data points, i.e., columns in the dataset. The goal of the tenfold rule is to offset the changes brought about by these combined parameters to the model input, allowing us to quickly estimate the amount of data and ensure the project continues to run. For example, if the parameters in this model are number information, temperature information, fitted images, and anomaly causes, then the number of anomaly indicators should be at least forty.

[0075] The causes of abnormalities can include issues such as manufacturing process problems, mechanical damage, and dense installation.

[0076] Once a smart terminal device or IoT platform obtains the fitted image, and detects that the temperature exceeds a set threshold, it will invoke the anomaly cause analysis model and input the fitted image into this model. The anomaly cause analysis model will then determine the cause of the anomaly by comprehensively analyzing the fitted image, temperature information, and identification information.

[0077] Once the cause of the anomaly is determined, the anomaly cause analysis model will display the fitted image and the cause of the anomaly in a correlated manner through the client, allowing staff to promptly repair the cable joint based on this information. Specifically, the correlated display format can be: fitted image - cause of anomaly.

[0078] Optionally, based on the above technical solution, the device further includes:

[0079] The connector location determination module is used to read the number information of the cable connector and determine the installation location of the cable connector based on a pre-stored lookup table of number information and installation location.

[0080] The installation location can be the geographical coordinates of the cable connector, which can be expressed as (latitude, longitude). For example, the geographical coordinates of the cable connector installation are (30°N 120°E), indicating that the cable connector is installed at 30 degrees north latitude and 120 degrees east longitude.

[0081] After the cable connector is installed, a database table (Table 1) can be created on a smart terminal or IoT platform to store the cable connector number information, installation location, and fitted image. This database table (Table 1) is a lookup table of the pre-stored number information and installation location.

[0082] Once an anomaly is identified at a cable joint, its location needs to be pinpointed so that maintenance personnel can access it for repairs. The smart terminal or IoT platform uses an Exif information viewer to examine the Exif information of the fitted image of the malfunctioning cable joint and retrieves its identification number. After obtaining the cable joint number, the smart terminal or IoT platform retrieves a pre-stored lookup table of identification numbers and installation locations. By searching this table based on the identification number, the corresponding installation location can be determined.

[0083] In this solution, by pre-storing the numbering information and installation location in a lookup table, the corresponding installation location can be quickly determined when the cable connector numbering information is read, so that staff can quickly carry out maintenance based on this location, which improves the work efficiency of maintenance personnel to a certain extent.

[0084] Based on the above technical solution, optionally, the joint position determination module is further used for:

[0085] If the numbering information of the cable connector cannot be determined or the numbering information does not exist in the lookup table, obtain the device ID of the visible light camera and / or the infrared camera;

[0086] The installation location of the cable connector is determined based on the device ID.

[0087] In this solution, each cable connector has a corresponding visible light camera and an infrared camera, each with its own device number, i.e., device ID. Specifically, the device ID can be a combination of letters and numbers; for example, the visible light camera device ID could be a001, and the infrared camera device ID could be b001. Correspondingly, the corresponding cable connector number could be 1001. After the cable connectors are installed, a database table (Table 2) can be pre-established on a smart terminal or IoT platform to store the visible light camera device ID, the infrared camera device ID, the fitted image, and the installation location.

[0088] When the cable connector number cannot be determined or the number is not found in the lookup table, the smart terminal or IoT platform transmits the "read device ID command" to the visible light camera and infrared camera that captured the image of the cable connector via wireless communication technology. After receiving this command, the visible light camera and infrared camera read the device ID from their internal storage unit and transmit it to the smart terminal or IoT platform via wireless communication technology, thus achieving the purpose of obtaining the device ID of the visible light camera and infrared camera.

[0089] After receiving the device IDs of the visible light camera and the infrared camera, the smart terminal or IoT platform automatically calls database table 2 and can determine the installation location of the corresponding cable connector by querying the ID.

[0090] This solution uses the device IDs of visible light and infrared cameras that capture visible light and infrared images of the cable joints to determine their installation location. This allows for location determination even at night or when the cable joint number is unclear and unidentifiable, enabling maintenance personnel to perform appropriate repairs based on the location. Essentially, it provides a backup plan, improving the efficiency of determining cable joint locations and allowing maintenance personnel to handle cable joint malfunctions more effectively.

[0091] The technical solution provided in this embodiment includes a visible light camera for acquiring visible light images of the cable joint; an infrared camera for acquiring infrared images of the cable joint; an identification module for identifying the cable joint number information included in the visible light image; and acquiring temperature information of the cable joint based on the infrared image; a fitting module for fitting the visible light image and the infrared image to obtain a fitted image of the cable joint, and appending the number information and the temperature information to the fitted image; a temperature anomaly identification module for inputting the fitted image into a pre-trained anomaly cause analysis model when the temperature information is higher than a set temperature threshold, so as to determine at least one cause of the anomaly through the anomaly cause analysis model; and an alarm module for displaying the fitted image and the cause of the anomaly in association. Through the above-mentioned image recognition-based cable joint temperature anomaly detection device, the temperature information of the cable joint can be acquired accurately in real time, and the presence and cause of anomalies can be determined in real time based on this temperature information, so that staff can handle anomalies promptly. This reduces the occurrence of false alarms to a certain extent and can extend the service life of the cable.

[0092] The image recognition-based cable connector temperature anomaly detection device in this application embodiment can be a device, or it can be a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0093] The image recognition-based cable joint temperature anomaly detection device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0094] Example 2

[0095] Figure 2 This is a schematic diagram of the image recognition-based cable joint temperature anomaly detection device provided in Embodiment 2 of this application. Figure 2 As shown, it specifically includes the following:

[0096] The temperature anomaly detection module 105 is specifically used for:

[0097] If the temperature information is higher than a set temperature threshold, the fitted image is divided into blocks according to a preset area.

[0098] Identify the highest and average temperatures of each segment after division and assign them to the fitted image;

[0099] The fitted image with block temperature assignments is input into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly.

[0100] In this scheme, the preset area can be set according to preset rules. For example, the preset area of ​​the cable joint can be defined as the three parts: the cable joint portion, the middle portion, and the end portion. Accordingly, the cable joint can be divided into three blocks according to these three preset areas. Finally, the fitted image can be divided into three blocks according to the three preset areas to complete the block division of the fitted image according to the preset area.

[0101] Since the fitted image is a combination of a visible light image and an infrared image, the infrared image reflects the temperature value corresponding to all pixels. The highest temperature can be the highest temperature value among all pixels in the current block. The average temperature can be the ratio of the sum of the temperature values ​​of all pixels in the current block to the number of pixels. The calculation formula is as follows:

[0102] Average temperature = (temperature of pixel 1 + temperature of pixel 2 + ... + temperature of pixel n) ÷ number of pixels

[0103] When a smart terminal or IoT platform detects that the temperature of a cable joint in a fitted image is higher than a set temperature threshold, it automatically divides the fitted image into blocks according to a preset area. Then, it reads the highest temperature of each block and uses the average temperature calculation formula to calculate the average temperature of the current block, thus completing the process of identifying the highest and average temperatures of each block after division.

[0104] Once the highest and average temperatures of each block are identified, the smart terminal or IoT platform can import the fitted image in standard mode in the PhotoCap editing area. Then, in the "Exif Object Attribute Setting" dialog box, the highest and average temperatures of each block of the cable connector can be entered to assign values ​​to the fitted image.

[0105] After identifying the highest and average temperatures of each block, the fitted image, along with its assigned number and temperature values ​​for each block, can be input into a pre-trained anomaly cause analysis model. This model then analyzes and identifies at least one cause of the anomaly. Since the number of parameters changes, the amount of data required for training the anomaly cause analysis model will also change according to the tenfold rule. For example, if the fitted image is divided into three modules, the parameters could be: number information, temperature information for block 1, temperature information for block 2, temperature information for block 3, the fitted image, and the cause of the anomaly. In this case, the anomaly cause analysis model would require sixty data points for training.

[0106] The technical solution provided in this embodiment improves the accuracy of determining the cause of anomalies by dividing the fitted image into blocks according to a preset area and then measuring the highest and average temperatures of each block. Simultaneously, it allows for more precise location of the cable joint where the anomaly occurred.

[0107] Based on the above technical solution, optionally, the causes of the abnormality include: abnormal material, loose contact, increased wiring resistance, electrochemical reaction of wiring, and loose wrapping;

[0108] The anomaly cause analysis model is obtained by training a preset number of samples based on the temperature information reflected under the conditions of each cause of the anomaly.

[0109] Material abnormalities can be caused by quality issues with the cable joint materials, leading to abnormal cable joint temperatures. For example, when the insulation material of the cable joint is polyvinyl chloride (PVC), there may be quality problems such as excessive impurities, substandard thermal weight loss, air bubbles in the extruded layer, and difficulty in plasticization. These issues can cause the cable to overheat during operation, and further, the cable joint temperature may become abnormal.

[0110] Loose contact can be caused by poor joint manufacturing technology or loose crimping, resulting in excessive contact resistance at the joint. This can also cause the cable to overheat, and further, the cable joint temperature is prone to abnormalities.

[0111] When the wiring resistance increases while the voltage remains constant, the current increases, and the heat generated when the current flows through the cable increases. Furthermore, the cable will generate heat during operation, making the cable joint temperature prone to abnormalities.

[0112] Electrochemical reactions can be chemical reactions falling under the category of electrochemistry. Electrochemical reactions are often accompanied by electrode reactions involving hydrogen, oxygen, and chlorine evolution on the electrode surface. These evolved gases are adsorbed onto the electrode surface as bubbles, resulting in a reduction in the electrode's active area, uneven microscopic distribution of surface potential and current density, and electrode polarization. When a large number of bubbles are adsorbed on the electrode surface, a gas film forms, causing electrode passivation and deactivation. The evolved gases also disperse in the electrolyte as bubbles, making the electrolyte a gas-liquid mixture and reducing the actual conductivity. To maintain a constant power transmission capacity, the voltage needs to be increased, inevitably increasing energy consumption. Furthermore, cables generate heat during operation, making cable joint temperatures prone to abnormalities.

[0113] When the cable is loose, it can cause excessive contact resistance at the joint, which can lead to overheating of the cable and further cause abnormal temperature at the cable joint.

[0114] When the cause of the anomaly is different, the temperature information of the cable joint anomaly may also be different. For example, when the cause of the anomaly is increased wiring resistance, the abnormal temperature range of the cable joint may be 120℃~150℃, and the abnormal temperature information is a temperature value within this range. When the cause of the anomaly is material abnormality, the abnormal temperature range of the cable joint may be 70℃~80℃, and the abnormal temperature information is a temperature value within this range. Therefore, when training the anomaly cause analysis model, the anomaly cause in the parameters may be refined into specific causes. After dividing the fitted image into three blocks, the refined parameters may include the number information, temperature information of block 1, temperature information of block 2, temperature information of block 3, fitted image, anomaly cause - material abnormality, anomaly cause - loose contact, anomaly cause - increased wiring resistance, anomaly cause - wiring electrochemical reaction, and anomaly cause - loose wrapping. Correspondingly, the preset number of samples is set to one hundred according to the ten-fold rule.

[0115] This solution refines the causes of abnormal cable joint temperatures, thereby expanding the preset number of training parameters when training the anomaly cause analysis model. This makes the model's analysis results more accurate, enabling maintenance personnel to more quickly determine the appropriate solutions based on the cause of the anomaly, thus improving maintenance efficiency. Simultaneously, faster troubleshooting of cable joint faults can, to some extent, increase the cable's normal operating time and extend its lifespan.

[0116] Example 3

[0117] Figure 3 This is a flowchart illustrating the image recognition-based cable joint temperature anomaly detection method provided in Embodiment 3 of this application. Figure 3 As shown, the specific steps include the following:

[0118] S301, acquires a visible light image of the cable connector using a visible light camera;

[0119] S302, acquires infrared images of the cable connector using an infrared camera;

[0120] S303, the identification module identifies the cable connector number information included in the visible light image; and obtains the temperature information at the cable connector based on the infrared image;

[0121] S304, The visible light image and the infrared image are fitted by the fitting module to obtain a fitted image of the cable connector, and the number information and the temperature information are added to the fitted image;

[0122] S305, when the temperature information is higher than a set temperature threshold, the temperature anomaly identification module inputs the fitted image into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly through the anomaly cause analysis model.

[0123] S306, The alarm module displays the correlation between the fitted image and the cause of the anomaly.

[0124] Furthermore, if the temperature information is higher than a set temperature threshold, the fitted image is input into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly, including:

[0125] If the temperature information is higher than a set temperature threshold, the fitted image is divided into blocks according to a preset area.

[0126] Identify the highest and average temperatures of each segment after division and assign them to the fitted image;

[0127] The fitted image with block temperature assignments is input into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly.

[0128] Furthermore, the causes of the abnormality include: abnormal material, loose contact, increased wiring resistance, electrochemical reaction of wiring, and loose wrapping;

[0129] The anomaly cause analysis model is obtained by training a preset number of samples based on the temperature information reflected under the conditions of each cause of the anomaly.

[0130] Furthermore, after correlating and displaying the fitted image and the cause of the anomaly, the method further includes:

[0131] The connector location determination module reads the cable connector's serial number information and determines the cable connector's installation location based on a pre-stored lookup table of serial numbers and installation locations.

[0132] In this embodiment, a visible light image of the cable joint is acquired using a visible light camera; an infrared image of the cable joint is acquired using an infrared camera; a recognition module identifies the cable joint number information included in the visible light image; and temperature information of the cable joint is acquired based on the infrared image; a fitting module fits the visible light image and the infrared image to obtain a fitted image of the cable joint, and the number information and temperature information are appended to the fitted image; a temperature anomaly identification module, when the temperature information is higher than a set temperature threshold, inputs the fitted image into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly; and an alarm module displays the fitted image and the cause of the anomaly in association. This image recognition-based cable joint temperature anomaly detection method can accurately acquire cable joint temperature information in real time and determine whether an anomaly has occurred and its cause based on this temperature information, allowing staff to handle anomalies promptly. This reduces false alarms to some extent and extends cable lifespan.

[0133] The method for detecting abnormal temperature of cable joints based on image recognition provided in this embodiment corresponds to the device provided in the above embodiments and has a corresponding execution process and beneficial effects, which will not be described in detail here.

[0134] Example 4

[0135] like Figure 4 As shown, this application embodiment also provides an electronic device 400, including a processor 401, a memory 402, and a program or instructions stored in the memory 402 and executable on the processor 401. When the program or instructions are executed by the processor 401, they implement the various processes of the above-described embodiment of the cable joint temperature anomaly detection device based on image recognition and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0136] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0137] Example 5

[0138] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiment of the cable joint temperature anomaly detection device based on image recognition and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0139] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0140] Example 6

[0141] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described embodiment of the cable joint temperature anomaly detection device based on image recognition, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0142] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0143] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0144] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0145] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0146] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. An image recognition-based cable joint temperature anomaly detection device, characterized by, The device includes: A visible light camera is used to acquire visible light images of the cable connector. An infrared camera is used to acquire infrared images of the cable connector. The identification module is used to identify the cable connector number information included in the visible light image; and to obtain the temperature information at the cable connector based on the infrared image; The fitting module is used to fit the visible light image and the infrared image to obtain a fitted image of the cable connector, and to append the number information and the temperature information to the fitted image; A temperature anomaly identification module is used to input the fitted image into a pre-trained anomaly cause analysis model when the temperature information is higher than a set temperature threshold, so as to determine at least one cause of the anomaly through the anomaly cause analysis model. The alarm module is used to correlate and display the fitted image with the cause of the anomaly; The temperature anomaly detection module is specifically used for: When the temperature information is higher than a set temperature threshold, the fitted image is divided into blocks according to a preset area; the highest temperature and average temperature of each block after division are identified and assigned to the fitted image; the fitted image with the block temperature assignment is input into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly through the anomaly cause analysis model. The causes of the anomalies include: abnormal materials, loose contact, increased wiring resistance, electrochemical reaction of wiring, and loose packaging. The anomaly cause analysis model is obtained by collecting a preset number of samples and training based on the temperature information reflected under the conditions of the causes of the anomalies. The anomaly cause analysis model determines the cause of the anomaly by comprehensively judging the fitted image, block temperature information, and number information.

2. The image recognition-based cable joint temperature abnormality detection apparatus according to claim 1, characterized by, The device further includes: The connector location determination module is used to read the number information of the cable connector and determine the installation location of the cable connector based on a pre-stored lookup table of number information and installation location.

3. The cable joint temperature anomaly detection device based on image recognition according to claim 2, characterized in that, The connector position determination module is also used for: If the numbering information of the cable connector cannot be determined or the numbering information does not exist in the lookup table, obtain the device ID of the visible light camera and / or the infrared camera; The installation location of the cable connector is determined based on the device ID.

4. A method for detecting abnormal temperature at cable joints based on image recognition, characterized in that, The method includes: Acquire visible light images of the cable connector using a visible light camera; Infrared images of the cable connector are obtained using an infrared camera. The identification module identifies the cable connector number information included in the visible light image; and obtains the temperature information at the cable connector based on the infrared image. The visible light image and the infrared image are fitted by the fitting module to obtain a fitted image of the cable connector, and the number information and the temperature information are added to the fitted image. When the temperature information is higher than a set temperature threshold, the temperature anomaly identification module inputs the fitted image into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly. The alarm module displays the correlation between the fitted image and the cause of the anomaly. When the temperature information is higher than a set temperature threshold, the fitted image is input into a pre-trained anomaly cause analysis model to determine at least one cause of the anomaly. This includes: dividing the fitted image into blocks according to a preset area when the temperature information is higher than the set temperature threshold; identifying the highest and average temperatures of each block after division and assigning them to the fitted image; and inputting the fitted image with the assigned block temperatures into the pre-trained anomaly cause analysis model to determine at least one cause of the anomaly. The causes of the anomalies include: abnormal materials, loose contact, increased wiring resistance, electrochemical reaction of wiring, and loose packaging. The anomaly cause analysis model is obtained by collecting a preset number of samples and training based on the temperature information reflected under the conditions of the causes of the anomalies. The anomaly cause analysis model determines the cause of the anomaly by comprehensively judging the fitted image, block temperature information, and number information.

5. The method for detecting abnormal temperature at cable joints based on image recognition according to claim 4, characterized in that, After correlating and displaying the fitted image and the cause of the anomaly, the method further includes: The connector location determination module reads the cable connector's serial number information and determines the cable connector's installation location based on a pre-stored lookup table of serial numbers and installation locations.

6. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the image recognition-based cable joint temperature anomaly detection method as described in any one of claims 4-5.

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