Inspection method and device, inspection robot and storage medium

Abstract

The application discloses an inspection method and device, an inspection robot and a storage medium, and relates to the technical field of intelligent inspection. The method is applied to an inspection robot, the inspection robot is provided with a double-six-degree-of-freedom flexible mechanical arm (including a first mechanical arm and a second mechanical arm) and a detachable camera, and the method comprises the following steps: determining an inspection path according to an inspection task, and performing inspection based on the inspection path; determining whether the current inspection position is an electrical equipment cabinet; if the current inspection position is not the electrical equipment cabinet, acquiring image data, temperature data and sound data of the current inspection position; determining an inspection result of the current inspection position according to the image data, the temperature data and the sound data; if the current inspection position is the electrical equipment cabinet, opening a cabinet door of the electrical equipment cabinet through the first mechanical arm; after the second mechanical arm is controlled to grab the detachable camera, collecting cabinet image data of the electrical equipment cabinet by using the detachable camera; and determining the inspection result of the current inspection position based on the cabinet image data.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of intelligent inspection technology, and in particular to an inspection method, device, inspection robot and storage medium. Background Technology

[0002] As the backbone of urban public transportation, the operational safety and reliability of urban rail transit directly affect the lives and property of passengers and social order. Therefore, conducting high-quality inspections of the interiors of rail transit vehicles is an indispensable part of ensuring operational safety.

[0003] However, current inspection methods for the interior of rail transit vehicles are still mainly based on manual inspection. This method relies primarily on the sensory experience and manual operation of maintenance personnel, which not only suffers from low efficiency and high cost, but also has the quality of inspection greatly affected by factors such as individual responsibility, experience level, and fatigue, making it prone to missed inspections and misjudgments.

[0004] Therefore, there is an urgent need to propose a new method to solve the above problems. Summary of the Invention

[0005] This invention provides an inspection method, device, inspection robot, and storage medium, which can improve inspection efficiency, reduce inspection costs, and enhance the accuracy of inspection results.

[0006] In a first aspect, embodiments of the present invention provide an inspection method applied to an inspection robot, the inspection robot being equipped with a dual six-degree-of-freedom flexible robotic arm and a detachable camera, the dual six-degree-of-freedom flexible robotic arm comprising a first robotic arm and a second robotic arm, the method comprising:

[0007] The inspection path is determined based on the inspection task, and the inspection is carried out based on the inspection path.

[0008] Determine if the current inspection location is an electrical equipment cabinet;

[0009] If the current inspection location is not an electrical equipment cabinet, then acquire the image data, temperature data, and sound data of the current inspection location; determine the inspection result of the current inspection location based on the image data, temperature data, and sound data;

[0010] If the current inspection location is an electrical equipment cabinet, the cabinet door of the electrical equipment cabinet is opened by the first robotic arm; after the second robotic arm grabs the detachable camera, the detachable camera is used to collect image data inside the electrical equipment cabinet; the inspection result of the current inspection location is determined based on the image data inside the cabinet.

[0011] The technical solution of this invention first determines the inspection path based on the inspection task and then performs the inspection based on the path. This not only ensures the adaptability and reliability of the inspection operation and the comprehensiveness and accuracy of the inspection coverage, but also effectively reduces the risk of missed inspections and improves inspection efficiency. Next, it determines whether the current inspection location is an electrical equipment cabinet, achieving accurate classification and identification of the inspection scenario (electrical equipment cabinet / non-electrical equipment cabinet). This provides a clear and reliable preliminary judgment basis for subsequently determining differentiated data collection schemes for different scenarios and the logic for judging inspection results, thereby ensuring the targeted, orderly, and efficient nature of the inspection process. If it is an electrical equipment cabinet, the cabinet door is opened by the first robotic arm; the second robotic arm is controlled to grab the detachable camera, and the detachable camera is used to collect image data inside the electrical equipment cabinet; the inspection result of the current inspection location is determined based on the image data inside the cabinet, realizing the fully automated operation of the electrical equipment cabinet inspection process. This not only significantly reduces the intensity of manual labor but also overcomes the technical limitation that it is difficult to detect blind spots inside the cabinet from a fixed perspective. Meanwhile, relying on targeted data collection within the cabinet and professional intelligent analysis, accurate identification and visual inspection of the equipment status within the cabinet are achieved, significantly improving the professionalism and accuracy of electrical equipment cabinet inspections. If the cabinet is not an electrical equipment cabinet, image, temperature, and sound data of the current inspection location are acquired. Based on these data, the inspection result for the current location is determined, avoiding complex mechanical operations such as opening doors in non-electrical equipment cabinet scenarios. This greatly simplifies the inspection process, shortens operation time, and thus balances inspection efficiency and coverage. Furthermore, the use of multi-source data fusion (image, temperature, and sound) enables more comprehensive capture of potential anomalies, reducing the risk of misjudgment based on a single data dimension and further improving the accuracy and reliability of inspection results. Therefore, the technical solution of this invention solves the problems of low inspection efficiency, high labor costs, and susceptibility to missed inspections and misjudgments in existing technologies.

[0012] Secondly, embodiments of the present invention also provide an inspection device applied to an inspection robot, wherein the inspection robot is equipped with a dual six-degree-of-freedom flexible robotic arm and a detachable camera, the dual six-degree-of-freedom flexible robotic arm comprising a first robotic arm and a second robotic arm, and the device comprising:

[0013] The path planning module is used to determine the inspection path according to the inspection task and to perform the inspection based on the inspection path.

[0014] The location determination module is used to determine whether the current inspection location is an electrical equipment cabinet;

[0015] A general inspection module is used to acquire image data, temperature data, and sound data of the current inspection location if the current inspection location is not an electrical equipment cabinet; and to determine the inspection result of the current inspection location based on the image data, temperature data, and sound data.

[0016] The specialized inspection module is used to open the cabinet door of an electrical equipment cabinet via the first robotic arm if the current inspection location is an electrical equipment cabinet; control the second robotic arm to grab the detachable camera and use the detachable camera to collect image data inside the electrical equipment cabinet; and determine the inspection result of the current inspection location based on the image data inside the cabinet.

[0017] Thirdly, embodiments of the present invention also provide an inspection robot, which includes: an electronic device, the electronic device including at least one processor; and a memory communicatively connected to the at least one processor;

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the inspection method described in any embodiment of the present invention.

[0019] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, implement the inspection method described in any embodiment of the present invention.

[0020] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the processor of the inspection device, or it may be packaged separately from the processor of the inspection device; this application does not impose any limitations on this.

[0021] The descriptions of the second, third, and fourth aspects in this application can be referenced to the detailed description of the first aspect; and the beneficial effects described in the second, third, and fourth aspects can be referenced to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0022] In this application, the name of the aforementioned inspection device does not limit the equipment or functional module itself. In actual implementation, these devices or functional modules may appear under other names. As long as the function of each device or functional module is similar to that of this application, it falls within the scope of the claims of this application and its equivalents.

[0023] These or other aspects of this application will become more readily apparent in the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1a A flowchart illustrating an inspection method provided in an embodiment of the present invention;

[0026] Figure 1b This is a schematic diagram of the structure of a dual six-degree-of-freedom flexible robotic arm provided in an embodiment of the present invention;

[0027] Figure 2 A flowchart illustrating another inspection method provided in an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the structure of an inspection device provided in an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the structure of an electronic device in an inspection robot provided by an embodiment of the present invention;

[0030] Figure label:

[0031] 10-First robotic arm; 20-Second robotic arm; 30-Robot platform; 101-First tool quick-change interface; 102-First end effector; 103-First flexible robotic arm; 201-Second tool quick-change interface; 202-Second end effector; 203-Second flexible robotic arm. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0033] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0034] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0035] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0036] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the 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 figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc. Moreover, without conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0037] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0038] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0039] Figure 1a This is a flowchart illustrating an inspection method provided by an embodiment of the present invention. This embodiment is applicable to situations requiring inspection of the interior of urban rail transit vehicles. The method is applied to an inspection robot equipped with a dual six-degree-of-freedom flexible robotic arm and a detachable camera. The dual six-degree-of-freedom flexible robotic arm includes a first robotic arm and a second robotic arm. The method can be executed by an inspection device, which can be implemented using software and / or hardware. For example, the device can be an electronic device within the inspection robot. (Reference) Figure 1a The inspection method in this embodiment specifically includes the following steps:

[0040] Step 110: Determine the inspection path based on the inspection task, and conduct the inspection based on the inspection path.

[0041] Specifically, an inspection task refers to a set of pre-set task instructions designed to guide the execution of inspections based on actual conditions or needs. This includes information such as inspection targets (e.g., equipment type, inspection area), inspection items (e.g., visual inspection, parameter collection), inspection standards (e.g., pass thresholds, data acquisition accuracy requirements), and inspection time requirements. An inspection path refers to the orderly movement route of the inspection robot from its starting position to each inspection location, planned according to the inspection task. An inspection robot is an automated inspection device integrating a mobile chassis, sensing modules, mechanical actuators, and a data processing unit. It can autonomously plan its path and move to the target inspection location according to a pre-set inspection task, and perform inspection operations such as data collection and equipment status detection through various onboard sensors and robotic arms. For example, the specific configuration of an inspection robot may include: a composite chassis combining four-wheel differential drive and omnidirectional wheels, LiDAR, ultrasonic sensors, infrared sensors, visible light cameras, infrared thermal imaging cameras, depth cameras, a circularly arranged microphone array, a vacuuming device, a rotating brush, a spraying device, a dual six-degree-of-freedom flexible robotic arm, a detachable camera, and a height-adjustable gimbal. A dual six-degree-of-freedom flexible robotic arm refers to a device mounted on an inspection robot, consisting of two independent six-degree-of-freedom robotic arms. It possesses multi-joint linkage, high-precision positioning, and flexible operation capabilities, adaptable to complex operational needs in various inspection scenarios (such as cabinet door opening, equipment grasping, and posture adjustment). For example: Figure 1b A schematic diagram of a dual six-degree-of-freedom flexible robotic arm is given, as follows: Figure 1b As shown, the dual six-degree-of-freedom flexible robotic arm includes a first robotic arm 10, a second robotic arm 20, and a robot platform 30. The first robotic arm 10 consists of a first flexible robotic arm 103, a first tool quick-change interface 101, and a first end effector 102. The second robotic arm 20 consists of a second flexible robotic arm 203, a second tool quick-change interface 201, and a second end effector 202. The robot platform 30 is an integrated mounting base and is the core load-bearing unit connecting the dual six-degree-of-freedom flexible robotic arm and the inspection robot body. It is used to realize the installation and fixation of the robotic arm, power supply, and data interaction.

[0042] In practice, the inspection task (including target carriage number, inspection type, inspection items, inspection points, equipment priority, etc.) issued by the central dispatch system or manually can be obtained first. Then, the internal environment of the urban rail transit vehicle is scanned in real time using sensing devices such as LiDAR, depth camera, and ultrasonic sensor. Based on a preset 3D base map of the vehicle, a dynamic environment map is generated by integrating real-time data such as the location of temporary obstacles and changes in passage width, clearly defining the boundaries of passable areas. Next, the initial position coordinates are calibrated by combining the positioning results from odometer, visual real-time localization, and map building. With the help of LiDAR and real-time localization and map building algorithms, a high-precision 2D / 3D environment map of the carriage is constructed or loaded to achieve real-time positioning within the carriage. Then, a dynamic path planning algorithm (such as the improved A* algorithm, D Lite algorithm, etc.) is used, combined with the inspection task, dynamic environment map, and reinforcement learning strategy, while referring to real-time sensor data and historical anomaly information, to plan a globally optimal path covering all areas to be inspected. Finally, the vehicle travels along the planned path to start the inspection process. The pre-set 3D base map of the vehicle refers to a digital 3D model of the static internal environment of urban rail transit vehicles, collected and constructed in advance using high-precision 3D scanning equipment (such as LiDAR, 3D structured light scanners, etc.) before the inspection task is performed. This map serves as the benchmark framework and prior knowledge base for the inspection robot's environmental cognition and does not change in real time with the task. The central dispatch system is an intelligent command and control platform that integrates task management, equipment control, data interaction, and status monitoring for urban rail transit vehicle inspection scenarios. It is the core command center for inspection robot clusters or individual devices.

[0043] In this embodiment, the above steps not only ensure the adaptability and reliability of the inspection operation and the comprehensiveness and accuracy of the inspection coverage, but also effectively reduce the risk of missed inspections and improve inspection efficiency.

[0044] Step 120: Determine whether the current inspection location is an electrical equipment cabinet.

[0045] If it is an electrical equipment cabinet, proceed to step 130; if it is not an electrical equipment cabinet, proceed to step 140.

[0046] Specifically, an electrical equipment cabinet refers to an enclosed cabinet that integrates and installs various electrical components, control modules, wiring terminals, and protection devices for urban rail transit vehicles.

[0047] In practice, after the inspection begins, it is determined whether the current inspection location is an electrical equipment cabinet. Specifically, this can be determined in the following two ways: (1) Coordinate matching determination: The positioning coordinates of the current location of the inspection robot are accurately matched with the reference coordinates stored in the electrical equipment cabinet database; (2) Visual recognition determination: The appearance feature information of the equipment at the current location is collected by a high-definition visible light camera and visual feature matching is performed with the preset electrical equipment cabinet structure template. Finally, the determination is made based on the matching result of any of the above methods: if the matching is successful, the current inspection location is determined to be an electrical equipment cabinet; if the matching fails, the current inspection location is determined not to be an electrical equipment cabinet.

[0048] If it is an electrical equipment cabinet, the cabinet door needs to be opened for internal inspection. In this case, the cabinet door can be opened by the first robotic arm; after controlling the second robotic arm to grab the detachable camera, the detachable camera is used to collect image data inside the electrical equipment cabinet; the inspection result of the current inspection location is determined based on the image data inside the cabinet; if it is not an electrical equipment cabinet (such as a seating area, connecting passage, general bulkhead, etc.), it is a routine inspection situation. In this case, image data, temperature data, and sound data of the current inspection location can be obtained; the inspection result of the current inspection location is determined based on the image data, temperature data, and sound data.

[0049] In this embodiment, the above steps enable accurate classification and identification of inspection scenarios (electrical equipment cabinets / non-electrical equipment cabinets), providing a clear and reliable basis for subsequent determination of differentiated data collection schemes for different scenarios and inspection result judgment logic, thereby ensuring the pertinence, orderliness and efficiency of the inspection process.

[0050] Step 130: Open the cabinet door of the electrical equipment cabinet using the first robotic arm; control the second robotic arm to grab the detachable camera and use the detachable camera to collect image data inside the electrical equipment cabinet; determine the inspection result of the current inspection position based on the image data inside the cabinet.

[0051] Specifically, the cabinet interior image data refers to image information captured by a detachable camera, reflecting the internal environment and component status of the electrical equipment cabinet. The first robotic arm refers to the mechanical actuator in a dual six-degree-of-freedom flexible robotic arm used to open and close the cabinet door. The second robotic arm refers to the mechanical actuator in the same dual six-degree-of-freedom flexible robotic arm used to grasp, carry, deploy, and retrieve the detachable camera. A detachable camera refers to an image acquisition device with independent image acquisition and processing capabilities, capable of detaching from the robot body and flexibly deployed and retrieved by the second robotic arm. The inspection result refers to the structured inspection conclusion of the current inspection location.

[0052] In practice, after determining that the current inspection location is an electrical equipment cabinet, the first robotic arm can be controlled to move along a preset trajectory (generated based on pre-stored cabinet door dimensions, opening methods, and mechanical parameters of similar electrical equipment cabinets), and the output force can be adjusted in real time to open the cabinet door flexibly to avoid deformation. Then, the second robotic arm is controlled to grab the detachable camera mounted on the robot body, and the robotic arm's movement path is planned according to the internal space structure and the distribution of the components to be inspected. The camera is then inserted into the electrical equipment cabinet by adjusting its posture. Next, the detachable camera is activated to collect image data of the electrical components (such as terminals, circuit breakers, and relays) inside the cabinet, obtaining internal image data. After the data collection is completed, the second robotic arm drives the detachable camera to reset and retrieves it to a dedicated storage location on the robot body. Simultaneously, the first robotic arm closes the cabinet door along a reverse trajectory, meaning the first robotic arm performs the actions in the completely opposite order and path to the movement trajectory it used when opening the cabinet door. Finally, the collected internal image data is input into a pre-trained electrical equipment cabinet inspection result judgment model to obtain the current inspection result of the electrical equipment cabinet. Among them, the electrical equipment cabinet inspection result judgment model refers to the model obtained by training a deep learning model (such as convolutional neural network, Transformer, etc.) on historical image data inside different electrical equipment cabinets and corresponding labeled inspection results (such as normal status, component overheating status, metal corrosion status, bolt loosening status, contaminant adhesion status, etc.).

[0053] In this embodiment, the above steps achieve fully automated operation of the electrical equipment cabinet inspection process. This not only significantly reduces the intensity of manual labor but also overcomes the technical limitations of fixed perspectives in detecting blind spots inside the cabinet. Simultaneously, relying on targeted cabinet data collection and professional intelligent analysis, accurate identification and visual inspection of the equipment status inside the cabinet are achieved, significantly improving the professionalism and accuracy of the electrical equipment cabinet inspection work.

[0054] Step 140: Obtain image data, temperature data, and sound data of the current inspection location; determine the inspection result of the current inspection location based on the image data, temperature data, and sound data.

[0055] Specifically, temperature data refers to the real-time temperature information of non-electrical equipment cabinet inspection locations, collected by temperature sensors. Sound data refers to the real-time acoustic information of non-electrical equipment cabinet inspection locations, collected by sound sensors.

[0056] In practice, after determining that the current inspection location is not an electrical equipment cabinet, image data of the current inspection location can be acquired through an onboard visible light camera; temperature data of the current inspection location can be collected through an onboard temperature sensor (such as an infrared temperature sensor); and sound data of the current inspection location can be collected through an onboard ring microphone array. Subsequently, the collected multi-source data, including images, temperature, and sound, are input into a pre-trained multimodal inspection determination model to obtain the inspection result of the current inspection location. The multimodal inspection determination model refers to a model trained on a deep learning model based on historical multi-source data (including historical image data, historical temperature data, and historical sound data) of non-electrical equipment cabinet inspection locations and corresponding labeled inspection results.

[0057] In this embodiment, the above steps avoid complex mechanical operations such as opening doors in non-electrical equipment cabinet scenarios, significantly simplifying the inspection process and shortening operation time, thus balancing inspection efficiency and coverage. Simultaneously, the use of multi-source data fusion methods involving images, temperature, and sound enables more comprehensive capture of potential anomalies, reducing the risk of misjudgment based on a single data dimension and further improving the accuracy and reliability of inspection results.

[0058] The inspection method provided in this invention first determines the inspection path based on the inspection task and then performs the inspection based on the path. This not only ensures the adaptability and reliability of the inspection operation and the comprehensiveness and accuracy of the inspection coverage, but also effectively reduces the risk of missed inspections and improves inspection efficiency. Next, it determines whether the current inspection location is an electrical equipment cabinet, achieving accurate classification and identification of the inspection scenario (electrical equipment cabinet / non-electrical equipment cabinet). This provides a clear and reliable preliminary judgment basis for subsequently determining differentiated data collection schemes for different scenarios and the logic for judging inspection results, thereby ensuring the targeted, orderly, and efficient nature of the inspection process. If it is an electrical equipment cabinet, the cabinet door is opened by a first robotic arm; after controlling a second robotic arm to grab a detachable camera, the detachable camera is used to collect image data inside the electrical equipment cabinet; based on the image data inside the cabinet, the inspection result of the current inspection location is determined, realizing fully automated operation of the electrical equipment cabinet inspection process. This not only significantly reduces the intensity of manual labor but also overcomes the technical limitation that a fixed perspective makes it difficult to detect blind spots inside the cabinet. Meanwhile, relying on targeted data collection within the cabinet and professional intelligent analysis, accurate identification and visual inspection of the equipment status within the cabinet are achieved, significantly improving the professionalism and accuracy of electrical equipment cabinet inspections. If the cabinet is not an electrical equipment cabinet, image, temperature, and sound data of the current inspection location are acquired. Based on these data, the inspection result for the current location is determined, avoiding complex mechanical operations such as opening doors in non-electrical equipment cabinet scenarios. This greatly simplifies the inspection process, shortens operation time, and thus balances inspection efficiency and coverage. Furthermore, the use of multi-source data fusion (image, temperature, and sound) enables more comprehensive capture of potential anomalies, reducing the risk of misjudgment based on a single data dimension and further improving the accuracy and reliability of inspection results. Therefore, the technical solution of this invention solves the problems of low inspection efficiency, high labor costs, and susceptibility to missed inspections and misjudgments in existing technologies.

[0059] Figure 2 This is a flowchart illustrating another inspection method provided by an embodiment of the present invention. This embodiment is a specific implementation based on the above embodiment. In this embodiment, the method may further include:

[0060] Step 210: Determine the inspection path based on the inspection task, and carry out the inspection based on the inspection path.

[0061] Step 211: Determine whether the current inspection location is an electrical equipment cabinet.

[0062] If it is an electrical equipment cabinet, proceed to step 213; if it is not an electrical equipment cabinet, proceed to step 212.

[0063] Step 212: Obtain image data, temperature data, and sound data of the current inspection location; determine the inspection result of the current inspection location based on the image data, temperature data, and sound data.

[0064] Furthermore, the inspection result of the current inspection location is determined based on image data, temperature data, and sound data, including: extracting features from the image data, temperature data, and sound data respectively to obtain image features, temperature features, and sound features; fusing the image features, temperature features, and sound features to obtain fused features; and determining the inspection result of the current inspection location based on the fused features.

[0065] Specifically, image features refer to key information extracted from image data at the inspection location that reflects the visual state, such as color features, texture features, and shape features. Temperature features refer to key information extracted from temperature data at the inspection location, such as regional temperature values, temperature distribution differences, and the location of high-temperature points. Sound features refer to key acoustic information extracted from sound data at the inspection location, such as frequency, amplitude, and waveform features. Fusion features refer to the comprehensive information obtained by integrating image features, temperature features, and sound features through methods such as splicing and weighting.

[0066] In the specific implementation, after obtaining image data, temperature data, and sound data, the first step is to perform spatiotemporal alignment and standardization preprocessing operations on the three types of data (such as removing outliers, normalizing image pixel values ​​to the [0,1] interval, and applying a high-pass filter to the sound signal to enhance high-frequency components). Then, convolutional neural networks (such as ResNet-50, ResNet-101, etc.) are used to extract features from the processed image data to obtain image features. Based on the processed temperature data, basic statistical features such as mean, variance, maximum, minimum, and median are calculated. At the same time, derived feature indicators such as the proportion of high-temperature points and the rate of change of temperature spatial gradient are calculated [where the proportion of high-temperature points is the proportion of data points with temperature values ​​exceeding a preset threshold (such as 45 degrees Celsius) to the total number of data points; the rate of change of temperature spatial gradient represents the temperature change amplitude of adjacent spatial units]. The basic statistical features and derived feature indicators are integrated to obtain the temperature features. The processed audio data undergoes a Fast Fourier Transform (FFT) to convert the time-domain signal into frequency-domain data. This frequency-domain data is then mapped to a Mel scale, and the Mel spectrum is extracted using a Mel filter bank. After performing a logarithmic operation on the Mel spectrum, the first 13 Mel frequency cepstral coefficients are calculated using a Discrete Cosine Transform (DCT). The first and second differences of these coefficients are then solved, and the 13 Mel frequency cepstral coefficients and their first and second differences are summarized to obtain the audio features. Next, a weighted splicing fusion method is used to fuse the image features, temperature features, and audio features to obtain the fused features. Finally, the fused features are input into a pre-trained inspection result determination model to obtain the inspection result for the current inspection location. The inspection result determination model refers to a model trained on a deep learning model based on different historical fused features and their corresponding manually labeled inspection results.

[0067] In this embodiment, the above steps enable the fusion analysis of multi-dimensional anomaly information to effectively reduce the risk of misjudgment caused by a single data source; at the same time, the cross-validation of multi-source information significantly improves the robustness and comprehensiveness of anomaly judgment, achieving more accurate and reliable inspection result judgment.

[0068] Furthermore, the inspection results for the current inspection location are determined based on image data, temperature data, and sound data, including: performing visual analysis and feature recognition processing on image data to obtain image detection results; performing threshold analysis and hotspot detection processing on temperature data to obtain temperature detection results; performing spectrum analysis and abnormal voiceprint recognition processing on sound data to obtain sound detection results; and determining the inspection results for the current inspection location based on the image detection results, temperature detection results, and sound detection results.

[0069] Specifically, the image detection result refers to the structured detection conclusion output after visual analysis and feature recognition of the image data. The temperature detection result refers to the structured detection conclusion output after threshold analysis and hotspot detection of the temperature data. The sound detection result refers to the structured detection conclusion output after spectral analysis and abnormal voiceprint recognition of the sound data. The inspection result in this step refers to the unified and final inspection conclusion output after integrating the image detection result, temperature detection result, and sound detection result.

[0070] In the specific implementation, the three types of data are first preprocessed uniformly (such as data format standardization, timestamp alignment, and removal of invalid data). Next, a convolutional neural network (such as ResNet-50 or YOLOv8) is used to extract deep features from the preprocessed image data to obtain image features. These image features are then input into a pre-trained image detection model to obtain the image detection results. The image detection model refers to a model obtained through supervised training of a deep learning model based on historical image features from different inspection scenarios and their corresponding manually labeled image detection results [such as normal state, contaminant adhesion state, and equipment failure state (such as component damage, missing parts, deformation, etc.)].

[0071] Then, a density clustering algorithm (such as DBSCAN) was used to perform spatial distribution analysis on the preprocessed temperature data to identify hotspot areas with significantly higher temperature values ​​than the surrounding areas. Next, the validity of the identified hotspot areas was verified using preset judgment criteria, which are as follows: (1) the average temperature of the hotspot area must be 15 degrees Celsius or more higher than the average temperature of the surrounding local background; (2) the clustered area must consist of 3 or more spatially adjacent continuous collection points. If the hotspot area meets both of the above criteria, the temperature detection result is determined to be abnormal, and the hotspot area that meets the conditions is included in the temperature detection result; if neither criterion is met, the temperature detection result is determined to be normal.

[0072] Subsequently, spectral analysis is performed on the preprocessed sound data (such as extracting Mel spectrum or Mel frequency cepstral coefficient features) to obtain voiceprint features. These features are then matched with a pre-defined database of typical fault sounds (using dynamic time warping or deep learning classifiers) to determine whether there are any known types of abnormal noises (such as friction, impact, arcing, etc.) in the voiceprint features. If an abnormal noise feature is matched, the sound detection result is determined to be abnormal, and the type of abnormal noise is clearly marked in the result; if no match is found, the sound detection result is determined to be normal.

[0073] Finally, the image detection results, temperature detection results, and sound detection results are summarized to obtain the inspection results for the current inspection location.

[0074] In this embodiment, the accuracy of the inspection results is effectively improved through the above steps.

[0075] Furthermore, after determining the inspection results of the current inspection location based on image data, temperature data, and sound data, the process also includes: determining whether the inspection results indicate a contaminant attachment state; if the inspection results indicate a contaminant attachment state, then determining a cleaning strategy based on image data if the contaminant attachment state is cleanable; and executing a cleaning operation according to the cleaning strategy.

[0076] Specifically, the contaminant adhesion state refers to the abnormal state in which the current inspection location (such as the surface of electrical equipment cabinets, pipes, sensor probes, etc.) is covered by external impurities (such as dust, oil stains, water stains, particulate matter, chemical residues, corrosive deposits, etc.). The cleanable state is a subcategory of the contaminant adhesion state, referring to a state where contaminants adhering to the surface of the inspected object can be removed by conventional cleaning methods such as wiping and blowing, and the cleaning process will not cause secondary damage to the material, structure, or operational performance of the inspected object. The cleaning strategy refers to a targeted cleaning execution plan based on image detection results. For example, a cleaning strategy may include cleaning methods (such as dry wiping, high-pressure airflow blowing, neutral detergent spraying, ultrasonic cleaning, etc.), cleaning tool selection (such as lint-free cloths, soft brushes, high-pressure air guns, dedicated cleaning nozzles, etc.), cleaning paths (such as from edge to center, from high-contamination areas to low-contamination areas), cleaning parameters (such as cleaning pressure, detergent dosage, cleaning time, operating distance, etc.), and safety protection requirements during the cleaning process (such as preventing cleaning fluid from entering the equipment interior, preventing electrostatic damage, etc.). Cleaning operations refer to specific decontamination behaviors performed according to a cleaning strategy. The inspection results in this step refer to the final inspection conclusions determined by integrating multi-source data such as images, temperature, and sound. Inspection results may include types such as normal status, contaminant adhesion status, equipment malfunction status (e.g., damage, overheating, abnormal noise), and abnormal environmental status (e.g., smoke, foreign object intrusion). Contaminant adhesion status is an independent abnormality category distinct from equipment functional malfunctions and requires separate assessment and handling.

[0077] In practice, after determining the inspection result of the current inspection location based on image data, temperature data, and sound data, it can be determined whether the inspection result indicates a contaminant attachment state. If the inspection result indicates a contaminant attachment state, the image data of the current inspection location is input into a pre-trained contaminant classification model to obtain the attribute information of the contaminants in the image data (such as type, range, attachment characteristics, thickness, etc.). Based on this attribute information, a matching query is performed in a preset cleanability rule base (this rule base contains the mapping relationship between contaminant type, attachment characteristics, thickness, and cleaning feasibility, such as "floating dust + thin layer thickness → cleanable", "corrosive adhesive contaminants → not cleanable", etc.) to determine whether the current contaminant is cleanable. Prepare cleaning conditions; if the matching result indicates that the current contaminant meets the cleaning conditions, the contaminant's attachment state is determined to be cleanable. At this point, a cleaning strategy can be determined based on image data and contaminant attribute information. Specifically, based on the contaminant type, a lookup is performed in a preset contaminant type-to-cleaning tool correspondence table to obtain the target cleaning tool (e.g., high-pressure air gun for dust contaminants, neutral detergent + lint-free cloth for oil contaminants, special cleaning cotton swabs for contaminants in crevices, etc.). Then, based on the contaminant type, a lookup is performed in a preset contaminant type-to-cleaning time correspondence table to obtain the target cleaning time (e.g., 3-5 minutes for thin-layer dust cleaning, 10-15 minutes for medium-thick layer oil stains, etc.). Afterwards, based on the target cleaning tool and target cleaning time, an automated cleaning operation is initiated. That is, the inspection robot is controlled to carry the target cleaning tool and complete the contaminant removal operation within the set time according to the planned path (a cleaning operation path generated based on the location coordinates, distribution range, and motion constraints of the inspection robot). If the inspection results do not indicate a contaminant attachment state or the contaminant attachment state is determined to be uncleanable, the inspection results will be sent to the staff's terminal (such as mobile phone, computer, etc.) so that the staff can take timely manual cleaning or professional treatment measures.

[0078] It should be noted that all the correspondence tables mentioned above were pre-configured based on actual application needs, equipment operating parameters, and historical inspection data.

[0079] In this embodiment, the above steps achieve a closed-loop linkage between inspection and cleaning, which can eliminate potential risks caused by pollutants in a timely manner. At the same time, by accurately identifying the cleanability of pollutants, secondary damage to equipment caused by blind cleaning is effectively avoided. Furthermore, targeted cleaning strategies not only improve the safety and accuracy of cleaning operations but also optimize cleaning efficiency and reduce equipment operation and maintenance costs.

[0080] Step 213: Open the cabinet door of the electrical equipment cabinet using the first robotic arm.

[0081] Optionally, the inspection robot is also equipped with a key tool assembly, which includes a key tool head.

[0082] Further, step 213 may specifically include: determining the attribute information of the electrical equipment cabinet based on the current inspection location; selecting a key tool head that matches the electrical equipment cabinet from the key tool components based on the attribute information to obtain a target key tool head; controlling the end effector of the first robotic arm to detachably connect to the docking interface of the target key tool head; and controlling the first robotic arm to drive the target key tool head to open the cabinet door of the electrical equipment cabinet.

[0083] Specifically, the key tool assembly refers to the set of key tools mounted on the inspection robot. Its core function is to provide compatible key tool support for opening electrical equipment cabinet doors. Its structure includes key tool heads of various specifications and models, as well as mounting bases (such as tool racks or slot-type storage structures) for supporting and positioning the tool heads, supporting rapid grasping and docking by the robotic arm. The key tool head is the core actuating component of the key tool assembly. It is a dedicated unlocking component adapted to the door lock structure of the electrical equipment cabinet. Its shape, tooth profile, interface size, and other parameters are matched one-to-one with the door locks of different types of electrical equipment cabinets (such as high-voltage switchgear, low-voltage distribution boxes, and control cabinets). It has replaceable and reusable characteristics and is equipped with a standardized docking interface adapted to the end effector of the robotic arm. The attribute information of an electrical equipment cabinet refers to structured data that characterizes the cabinet's identity and lock features. This includes cabinet model, cabinet number, lock type (e.g., plum blossom lock, cross lock, special-shaped lock), lock specifications (e.g., lock cylinder size, key interface shape), and installation location coordinates. This information is pre-stored in the inspection system's equipment database and can be retrieved based on the inspection location. The target key tool head refers to the key tool head selected from the key tool components that is compatible with the lock of the electrical equipment cabinet to be opened. The end effector refers to the functional component at the end of the first robotic arm. It is the core structure that enables interaction between the robotic arm and the key tool head, possessing clamping, positioning, and power transmission functions. Its interface design matches the key tool head's docking interface (e.g., snap-fit, magnetic, or threaded docking structure), allowing for quick and detachable connection and transmitting the torque or thrust required for unlocking. The docking interface refers to the standardized connection structure set on the key tool head and the end effector of the robotic arm. It is used to achieve precise positioning and stable connection between the two, ensuring that there is no relative displacement when the robotic arm drives the key tool head to perform the unlocking action. The interface type includes, but is not limited to, standardized snap-fit ​​interfaces, magnetic positioning interfaces, and polygonal transmission interfaces. The detachable connection refers to the non-fixed connection between the key tool head and the end effector of the robotic arm. It supports quick docking and locking when it is necessary to open the electrical equipment cabinet. After unlocking, it can automatically detach, so that the robotic arm can switch to grab other tool heads or perform other inspection tasks.

[0084] In practice, the coordinates of the current inspection location are matched with a pre-set equipment ledger database to obtain the attribute information of the electrical equipment cabinet. The equipment ledger database stores the attribute information of each electrical equipment cabinet, including cabinet model, region, lock type, lock cylinder specifications, door opening direction, and lock installation location coordinates. Then, based on this attribute information, a query is performed in the key tool component management database to filter out key tool head information matching the current electrical equipment cabinet lock, thus determining the target key tool head. Next, based on the storage location coordinates of the target key tool head, the end effector of the first robotic arm (i.e., the first end effector) is moved to the corresponding storage location, and the interface between the end effector and the target key tool head is detachably connected. Finally, according to the preset torque and rotation angle parameters corresponding to the lock type, the first robotic arm is controlled to drive the target key tool head to complete the opening operation of the electrical equipment cabinet door.

[0085] In this embodiment, the above steps enable automated and precise opening of the electrical equipment cabinet door. This method not only ensures accurate matching of the key and lock, preventing damage to the equipment due to improper manual operation, but also significantly improves inspection efficiency and reduces labor costs. Simultaneously, the automated opening process further enhances the safety of the operation, helps promote the standardization of maintenance work, and flexibly adapts to diverse inspection scenarios.

[0086] Step 214: After controlling the second robotic arm to grab the detachable camera, use the detachable camera to collect image data inside the electrical equipment cabinet.

[0087] Step 215: Determine the inspection result of the current inspection location based on the image data inside the cabinet.

[0088] Furthermore, before step 215, the method further includes: acquiring the internal temperature data and internal sound data of the electrical equipment cabinet; correspondingly, determining the inspection result of the current inspection location based on the internal image data, including: determining the inspection result of the current inspection location based on the internal image data, internal temperature data, and internal sound data.

[0089] Specifically, the cabinet temperature data refers to the temperature data obtained by the temperature sensor carried by the inspection robot after the electrical equipment cabinet door is opened. The cabinet sound data refers to the sound data collected by the acoustic sensor carried by the inspection robot after the electrical equipment cabinet door is opened.

[0090] In practice, before determining the inspection result of the current inspection location based on the cabinet's internal image data, the internal temperature and sound data of the electrical equipment cabinet are first collected using corresponding sensors. Then, the internal image, temperature, and sound data are simultaneously input into the cabinet inspection result determination model to obtain the inspection result for the current inspection location. The cabinet inspection result determination model refers to a model trained on a deep learning model using historical internal image, temperature, and sound data of different types of electrical equipment cabinets, along with corresponding actual inspection results, as training samples.

[0091] In this embodiment, the above steps effectively improve the accuracy of inspection results when the current inspection location is an electrical equipment cabinet.

[0092] Step 216: Determine whether the inspection results indicate that the pollutants are attached.

[0093] If the pollutant is attached, proceed to step 217; otherwise, proceed to step 218.

[0094] In practice, after determining the inspection result of the current inspection location based on the image data inside the cabinet, it can be determined whether the inspection result indicates a contaminant attachment state. If a contaminant attachment state is found, it means that there are dust, oil stains, water stains, or other deposits on the components inside the electrical equipment cabinet, which may affect the heat dissipation performance or sensor detection accuracy of the equipment. In this case, the second robotic arm can be controlled to grab cleaning tools and perform cleaning operations. If a contaminant attachment state is not found, it means that there are no deposits inside the electrical equipment cabinet that would affect the operation of the equipment. In this case, the cabinet door of the electrical equipment cabinet can be closed by the first robotic arm, and the inspection result can be sent to the operator's terminal.

[0095] In this embodiment, the above steps can accurately distinguish various types of anomalies, improve the pertinence of operation and maintenance work, reduce ineffective cleaning operations, and protect the delicate components of the equipment.

[0096] Optionally, the inspection robot is also equipped with cleaning tools.

[0097] Step 217: Control the second robotic arm to grab the cleaning tool and perform the cleaning operation.

[0098] Specifically, cleaning tools refer to specialized equipment mounted on inspection robots for removing contaminants. Cleaning tools may include miniature brushes, anti-static dust removal cloths, high-pressure air nozzles, etc.

[0099] In practice, after confirming that the inspection result indicates a contaminant attachment state, a cleaning tool matching the contaminant type can be retrieved from the cleaning tool storage compartment on the inspection robot itself. Secondly, based on real-time analysis of the contaminant image data, an adaptive cleaning strategy is generated, including the work path, intensity, and amount of cleaning medium used. Finally, the robotic arm is controlled to automatically execute the cleaning operation according to this strategy, and after completing the cleaning operation, the first robotic arm closes the door of the electrical equipment cabinet. The specific implementation methods for contaminant analysis and cleaning strategy generation are consistent with the aforementioned technical process and will not be elaborated here.

[0100] In this embodiment, the above steps not only improve cleaning efficiency and ensure cleaning quality, but also effectively reduce operation and maintenance costs and help extend the service life of the equipment.

[0101] Step 218: Close the cabinet door of the electrical equipment cabinet using the first robotic arm and send the inspection results to the staff's terminal.

[0102] Specifically, the staff terminal refers to the electronic device that receives data uploaded by the inspection robot, such as an industrial tablet, computer, or smartphone.

[0103] In practice, after confirming that the inspection result is not a state of contaminant adhesion, a preset motion control algorithm (such as A* algorithm, fast extended random tree algorithm, artificial potential field method, etc.) is used to automatically plan the optimal door closing path without collision based on the real-time position coordinates of the cabinet door collected by the vision sensor, the initial pose data of the first robotic arm, and the distribution information of obstacles around the electrical equipment cabinet. Then, the first robotic arm is controlled to move to the pre-set force point of the cabinet door according to the planned path to perform the cabinet door closing and locking operation. After the operation is completed, the key tool head on the end effector of the first robotic arm is controlled to reset to the original storage position, and the inspection result is sent to the terminal of the staff.

[0104] In this embodiment, the above steps ensure the safety and integrity of the inspection process and effectively maintain the stable operating environment of the equipment. On the other hand, they also enable staff to obtain and grasp the inspection results as soon as possible.

[0105] The inspection method provided in this invention first determines the inspection path based on the inspection task and then performs the inspection based on the path. This not only ensures the adaptability and reliability of the inspection operation and the comprehensiveness and accuracy of the inspection coverage, but also effectively reduces the risk of missed inspections and improves inspection efficiency. Next, it determines whether the current inspection location is an electrical equipment cabinet, achieving accurate classification and identification of the inspection scenario (electrical equipment cabinet / non-electrical equipment cabinet). This provides a clear and reliable preliminary judgment basis for subsequently determining differentiated data collection schemes for different scenarios and the logic for judging inspection results, thereby ensuring the targeted, orderly, and efficient nature of the inspection process. If it is not an electrical equipment cabinet, it acquires image data, temperature data, and sound data of the current inspection location. Based on the image data, temperature data, and sound data, it determines the inspection result of the current inspection location, avoiding complex mechanical operations such as opening doors in non-electrical equipment cabinet scenarios. This significantly simplifies the inspection process, shortens the operation time, and thus balances inspection efficiency and coverage. Meanwhile, the use of multi-source data fusion methods, including images, temperature, and sound, enables a more comprehensive capture of potential anomalies. This reduces the risk of misjudgment based on a single data dimension and further improves the accuracy and reliability of inspection results. For electrical equipment cabinets, the first robotic arm opens the cabinet door; the second robotic arm then grabs a detachable camera to collect image data from inside the cabinet; based on this image data, the inspection result for the current location is determined, achieving fully automated operation of the electrical equipment cabinet inspection process. This significantly reduces manual labor intensity and overcomes the technical limitations of fixed-viewpoint detection of blind spots inside the cabinet. Furthermore, relying on targeted data collection and professional intelligent analysis, accurate identification and visual inspection of the equipment status inside the cabinet are achieved, significantly improving the professionalism and accuracy of electrical equipment cabinet inspections. Next, it is determined whether the inspection result indicates contaminant adhesion. If contaminant adhesion is found, the second robotic arm grabs cleaning tools to perform cleaning operations, improving cleaning efficiency and ensuring cleaning quality while effectively reducing maintenance costs and helping to extend the equipment's lifespan. If the equipment is not in a contaminant-laden state, the first robotic arm closes the cabinet door of the electrical equipment cabinet and sends the inspection results to the operator's terminal. This ensures the safety and integrity of the inspection process, effectively maintaining a stable operating environment for the equipment. It also allows operators to obtain and understand the inspection results immediately. Therefore, the technical solution of this invention solves the problems of low inspection efficiency, high labor costs, and susceptibility to missed inspections and misjudgments in existing technologies.

[0106] Figure 3 This is a schematic diagram of an inspection device provided in an embodiment of the present invention. This device belongs to the same inventive concept as the inspection methods in the above embodiments. Details not described in detail in the embodiments of the inspection device can be found in the embodiments of the above inspection methods. Figure 3 As shown, the device includes:

[0107] like Figure 3 As shown, the device includes:

[0108] The path planning module 310 is used to determine the inspection path according to the inspection task and to perform inspection based on the inspection path.

[0109] The location determination module 320 is used to determine whether the current inspection location is an electrical equipment cabinet;

[0110] The general inspection module 330 is used to acquire image data, temperature data, and sound data of the current inspection location if the current inspection location is not an electrical equipment cabinet; and to determine the inspection result of the current inspection location based on the image data, temperature data, and sound data.

[0111] The special inspection module 340 is used to open the cabinet door of the electrical equipment cabinet by means of the first robotic arm if the current inspection location is an electrical equipment cabinet; control the second robotic arm to grab the detachable camera and use the detachable camera to collect the image data inside the electrical equipment cabinet; and determine the inspection result of the current inspection location based on the image data inside the cabinet.

[0112] Based on the above embodiments, the inspection robot is also equipped with cleaning tools, and the device further includes:

[0113] The first cleaning module is used to determine whether the inspection result of the current inspection position is a contaminant-attached state after determining the inspection result based on the image data inside the cabinet; if the inspection result is a contaminant-attached state, the second robotic arm is controlled to grab the cleaning tool to perform a cleaning operation; if the inspection result is not a contaminant-attached state, the cabinet door of the electrical equipment cabinet is closed by the first robotic arm, and the inspection result is sent to the staff's terminal.

[0114] Based on the above embodiments, the general inspection module 330 determines the inspection result of the current inspection location according to the image data, the temperature data, and the sound data, including:

[0115] Feature extraction is performed on the image data, temperature data, and sound data respectively to obtain image features, temperature features, and sound features; the image features, temperature features, and sound features are fused to obtain fused features; and the inspection result of the current inspection location is determined based on the fused features.

[0116] Based on the above embodiments, the inspection robot is also equipped with a key tool assembly, which includes a key tool head. The specialized inspection module 340 opens the cabinet door of the electrical equipment cabinet via the first robotic arm, including:

[0117] The attribute information of the electrical equipment cabinet is determined based on the current inspection location; a key tool head matching the electrical equipment cabinet is selected from the key tool assembly based on the attribute information to obtain a target key tool head; the end effector of the first robotic arm is detachably connected to the docking interface of the target key tool head; the first robotic arm is controlled to drive the target key tool head to open the cabinet door of the electrical equipment cabinet.

[0118] Based on the above embodiments, the device further includes:

[0119] The second cleaning module is used to determine whether the inspection result of the current inspection location is a state of contaminant adhesion after determining the inspection result based on the image data, the temperature data, and the sound data; if the inspection result is a state of contaminant adhesion, then if the contaminant adhesion state is a cleanable state, a cleaning strategy is determined based on the image data; and a cleaning operation is performed according to the cleaning strategy.

[0120] Based on the above embodiments, the general inspection module 330 determines the inspection result of the current inspection location according to the image data, the temperature data, and the sound data, including:

[0121] The image data is subjected to visual analysis and feature recognition processing to obtain image detection results; the temperature data is subjected to threshold analysis and hotspot detection processing to obtain temperature detection results; the sound data is subjected to spectrum analysis and abnormal voiceprint recognition processing to obtain sound detection results; and the inspection results of the current inspection location are determined based on the image detection results, the temperature detection results, and the sound detection results.

[0122] Based on the above embodiments, the device further includes:

[0123] The acquisition module is used to acquire the internal temperature data and internal sound data of the electrical equipment cabinet before determining the inspection result of the current inspection position based on the internal image data; correspondingly, the special inspection module 340 determines the inspection result of the current inspection position based on the internal image data, including: determining the inspection result of the current inspection position based on the internal image data, the internal temperature data and the internal sound data.

[0124] The inspection device provided in the embodiments of the present invention can execute the inspection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0125] It is worth noting that in the embodiments of the above-mentioned inspection device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0126] Figure 4 This is a schematic diagram of the structure of an electronic device in an inspection robot provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary electronic device 4 suitable for implementing embodiments of the present invention is shown. Figure 4 The electronic device 4 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0127] like Figure 4 As shown, electronic device 4 is represented in the form of a general-purpose computing electronic device. The components of electronic device 4 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0128] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0129] Electronic device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 4, including volatile and non-volatile media, removable and non-removable media.

[0130] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0131] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0132] Electronic device 4 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 4, and / or with any device that enables electronic device 4 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, electronic device 4 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 4 As shown, network adapter 20 communicates with other modules of electronic device 4 via bus 18. It should be understood that, although... Figure 4 Not shown, it can be combined with electronic device 4 to use other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0133] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28, such as implementing the inspection method provided in the embodiments of the present invention.

[0134] Of course, those skilled in the art will understand that the processor can also implement the technical solution of the inspection method provided in any embodiment of the present invention.

[0135] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements, for example, the inspection method provided in this invention.

[0136] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0137] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0138] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0139] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0141] Furthermore, the acquisition, storage, use, and processing of data in the technical solution of this invention all comply with relevant laws and regulations.

[0142] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. An inspection method, characterized in that, The method, applied to an inspection robot equipped with a dual six-degree-of-freedom flexible robotic arm and a detachable camera, comprises a first robotic arm and a second robotic arm, and includes: The inspection path is determined based on the inspection task, and the inspection is carried out based on the inspection path. Determine if the current inspection location is an electrical equipment cabinet; If the current inspection location is not an electrical equipment cabinet, then acquire the image data, temperature data, and sound data of the current inspection location; determine the inspection result of the current inspection location based on the image data, temperature data, and sound data; If the current inspection location is an electrical equipment cabinet, the cabinet door of the electrical equipment cabinet is opened by the first robotic arm; after the second robotic arm grabs the detachable camera, the detachable camera is used to collect image data inside the electrical equipment cabinet; the inspection result of the current inspection location is determined based on the image data inside the cabinet.

2. The method according to claim 1, characterized in that, The inspection robot is also equipped with cleaning tools. After determining the inspection result of the current inspection location based on the image data inside the cabinet, it also includes: Determine whether the inspection results indicate a state of contaminant adhesion; If the inspection result indicates that the contaminants are attached, then the second robotic arm is controlled to grab the cleaning tool and perform a cleaning operation. If the inspection result is not a state of contaminant adhesion, the cabinet door of the electrical equipment cabinet is closed by the first robotic arm, and the inspection result is sent to the staff's terminal.

3. The method according to claim 1, characterized in that, Determining the inspection result of the current inspection location based on the image data, temperature data, and sound data includes: Feature extraction is performed on the image data, temperature data, and sound data respectively to obtain image features, temperature features, and sound features. The image features, temperature features, and sound features are fused together to obtain fused features. The inspection result of the current inspection location is determined based on the fusion features.

4. The method according to claim 1, characterized in that, The inspection robot is also equipped with a key tool assembly, which includes a key tool head. The first robotic arm is used to open the cabinet door of the electrical equipment cabinet, including: The attribute information of the electrical equipment cabinet is determined based on the current inspection location; Based on the attribute information, a key tool head matching the electrical equipment cabinet is selected from the key tool components to obtain the target key tool head; The end effector of the first robotic arm is detachably connected to the docking interface of the target key tool head; Control the first robotic arm to drive the target key tool head to open the cabinet door of the electrical equipment cabinet.

5. The method according to claim 1, characterized in that, After determining the inspection result of the current inspection location based on the image data, the temperature data, and the sound data, the process further includes: Determine whether the inspection results indicate a state of contaminant adhesion; If the inspection result indicates that the contaminants are attached, and the contaminants are in a cleanable state, a cleaning strategy is determined based on the image data; and a cleaning operation is performed according to the cleaning strategy.

6. The method according to claim 1, characterized in that, Determining the inspection result of the current inspection location based on the image data, temperature data, and sound data includes: The image data is subjected to visual analysis and feature recognition processing to obtain image detection results; Threshold analysis and hotspot detection processing are performed on the temperature data to obtain the temperature detection results; The sound data is subjected to spectrum analysis and abnormal voiceprint recognition processing to obtain sound detection results; The inspection result for the current inspection location is determined based on the image detection result, the temperature detection result, and the sound detection result.

7. The method according to claim 1, characterized in that, Before determining the inspection result of the current inspection location based on the image data inside the cabinet, the process also includes: Acquire the internal temperature data and internal sound data of the electrical equipment cabinet; Accordingly, determining the inspection result of the current inspection location based on the image data inside the cabinet includes: The inspection result of the current inspection location is determined based on the image data, temperature data, and sound data inside the cabinet.

8. An inspection device, characterized in that, An application is made in inspection robots, the inspection robot being equipped with a dual six-degree-of-freedom flexible robotic arm and a detachable camera, the dual six-degree-of-freedom flexible robotic arm comprising a first robotic arm and a second robotic arm, the device comprising: The path planning module is used to determine the inspection path according to the inspection task and to perform the inspection based on the inspection path. The location determination module is used to determine whether the current inspection location is an electrical equipment cabinet; A general inspection module is used to acquire image data, temperature data, and sound data of the current inspection location if the current inspection location is not an electrical equipment cabinet; and to determine the inspection result of the current inspection location based on the image data, temperature data, and sound data. The specialized inspection module is used to open the cabinet door of an electrical equipment cabinet via the first robotic arm if the current inspection location is an electrical equipment cabinet; control the second robotic arm to grab the detachable camera and use the detachable camera to collect image data inside the electrical equipment cabinet; and determine the inspection result of the current inspection location based on the image data inside the cabinet.

9. An inspection robot, characterized in that, The inspection robot includes: an electronic device, the electronic device including at least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the inspection method according to any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the inspection method according to any one of claims 1-7.

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