Handheld mobile remote cooperation imager based on Internet of Things
By using a handheld mobile remote collaborative imager based on the Internet of Things in substation equipment failure prediction, combined with the fusion processing of image and sound data, the problems of low patrol efficiency and lag in fault detection in the prior art are solved, and earlier and more accurate fault detection and prevention are achieved.
Patent Information
- Application Number
- CN202510208270.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-25
- Publication Date
- 2025-06-27
AI Technical Summary
The prior art has problems such as low patrol efficiency, easy missed inspection, and lag in fault detection in substation equipment failure prediction, and traditional methods are difficult to achieve fault prevention.
Using a handheld mobile remote collaborative imager based on the Internet of Things, the image data and sound data of the detection object are obtained through the image acquisition module and the sound acquisition module, and combined with the data fusion unit and the beam output unit, fault detection based on sound characteristics is realized.
It can detect latent faults earlier, improve the accuracy and efficiency of fault detection, reduce missed and missed detection, and achieve fault prevention.
Smart Images
Figure CN120223832A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of substation equipment fault prediction, and more specifically, to a handheld mobile remote collaboration imager based on the Internet of Things. Background Art
[0002] The equipment inspection work in substations is of great significance for maintaining the reliable operation of the equipment in substations and ensuring the reliability of the power system. With the continuous increase in electricity consumption in China, it has brought huge pressure to the operation and maintenance work of substations. With the development and upgrade of inspection technologies, most substations have basically realized an intelligent remote inspection system that combines machines, inspection robots, and online monitoring systems, and uses technologies such as visible light recognition and infrared thermal imaging to remotely inspect the operating status, equipment defects, equipment failures, etc. of power equipment.
[0003] Currently, many traditional industrial equipment operation and maintenance methods, such as the application of infrared and thermosensitive monitoring methods, provide a reference basis for monitoring the operating status of equipment, reduce the workload of manual inspections, and improve the efficiency of equipment status monitoring and the reference basis for evaluation. However, the above monitoring methods have the following disadvantages: (1) The sensors need to be installed in contact; for the existing sensor solutions, the installation positions are not flexible, and they need to be closely attached to the equipment (such as vibration sensors), which will affect the health of the equipment itself; (2) The inspection efficiency is low and it is easy to miss inspections; relying too much on manual regular inspections, the efficiency is low, the cost is high, and it is easy to miss inspections and misinspections. Currently, solutions such as infrared imagers (monitoring temperature, but only some faults will have temperature changes) and vibration sensors are applied, and there will be missed inspections; (3) There is a lag in fault discovery; only after a fault occurs, it is known that there is a fault, and the response is not timely; currently, the detection methods such as infrared and thermosensitive applied are used for fault location after the fault occurs and cannot play a role in fault prevention, and the value is limited. Moreover, the transformer physical parameters on which the traditional methods rely also have corresponding defects in the fault detection task. Specifically, the insulation performance parameters are difficult to realize online monitoring, the temperature parameters are difficult to be widely applied due to the high price of the equipment, and the vibration parameters are not suitable for the detection of high-field-strength positions because they need to measure the vibration source in contact.
[0004] On the other hand, with the development of artificial intelligence technology, it has provided unprecedented opportunities for the intelligent diagnosis of power grid main equipment based on acoustic characteristics. The signal recognition mode based on deep learning is more suitable for solving complex non-linear problems in power equipment than the traditional information processing mode, and can flexibly and efficiently process a large amount of complex information in terms of expression and modeling capabilities, providing more accurate and intelligent judgment results. However, the current existing models are difficult to be applied to the fault prediction of substation equipment due to the lack of training samples. Summary of the Invention
[0005] The present invention provides a handheld mobile remote collaboration imager based on the Internet of Things, which can overcome certain or some defects of the prior art.
[0006] A handheld mobile remote collaboration imager based on the Internet of Things according to the present invention includes a device body, and a reference plane is constructed at the device body; provided at the device body is an image acquisition module for acquiring image data of a detection object; wherein, the imaging plane of the image acquisition module is parallel to the reference plane; a sound acquisition module for acquiring sound data of the detection object; wherein, the sound acquisition module includes a microphone array composed of a plurality of microphone units, and the microphone array has at least 2 transverse microphone units distributed at equal intervals laterally away from the image acquisition module along the reference plane and at least 2 longitudinal microphone units distributed at equal intervals longitudinally away from the image acquisition module along the reference plane; a ranging module for acquiring distance data between the detection object and the reference plane; an attitude acquisition module for acquiring deflection angle data of the reference plane; a processing module including a data fusion unit and a beam output unit; wherein, the data fusion unit is used for fusing the image data and the sound data to obtain fusion data, and the beam output unit is used for obtaining beam data based on the sound data.
[0007] The handheld mobile remote collaboration imager provided by the present invention, by setting the image acquisition module and the sound acquisition module, can acquire the image data and the sound data at the area where the detection object is located, and can provide the original data for the subsequent fault detection based on the sound characteristics. By identifying the faults based on the sound characteristics, the latent faults can be discovered earlier.
[0008] Preferably, the sound data includes the angle between the sound source propagation direction and the transverse direction of the reference plane and the angle between the sound source propagation direction and the longitudinal direction of the reference plane , and the deflection angle data of the reference plane includes the angle between the transverse direction of the reference plane and the horizontal direction and the angle between the longitudinal direction of the reference plane and the vertical direction ; specifically, the data fusion unit is used for obtaining the camera coordinates of the sound source in the camera coordinate system based on the angle , the angle , the angle , the angle and the distance data L ; wherein, ; ; ; Based on the transformation matrix between the camera coordinate system and the image coordinate system, obtain the coordinates of the sound source in the image coordinate system ; Based on the coordinates of the sound source in the image coordinate system and the sound intensity distribution at the sound source, complete the acquisition of the fused data.
[0009] Based on the above, it is possible to achieve the positioning of the sound source in the image data based on the propagation angle and distance information of the sound source during data acquisition, so that the acquisition of the fused data can be better achieved.
[0010] Preferably, the angle between the propagation direction of the sound source and the transverse direction of the reference plane is obtained based on the at least 2 transverse microphone units. Specifically, ; where m is the total number of transverse microphone units, is the time when the first transverse microphone unit in the direction away from the image acquisition module collects the acoustic wave data at the detection object, is the time when the i-th transverse microphone unit in the direction away from the image acquisition module collects the acoustic wave data at the detection object, and d is the array pitch of the at least 2 transverse microphone units.
[0011] Based on the above, through the construction of the at least 2 transverse microphone units, the propagation angle data of the sound source propagation direction relative to the transverse direction of the reference plane can be better achieved.
[0012] Preferably, the angle between the propagation direction of the sound source and the longitudinal direction of the reference plane is obtained based on the at least 2 longitudinal microphone units. Specifically, ; where m is the total number of longitudinal microphone units, is the time when the first longitudinal microphone unit in the direction away from the image acquisition module collects the acoustic wave data at the detection object, is the time when the j-th longitudinal microphone unit in the direction away from the image acquisition module collects the acoustic wave data at the detection object, and p is the array pitch of the at least 2 longitudinal microphone units.
[0013] Based on the above, through the construction of the at least 2 longitudinal microphone units, the propagation angle data of the sound source propagation direction relative to the longitudinal direction of the reference plane can be better achieved.
[0014] Preferably, the sound data of the detection object includes the acoustic wave signals collected by each microphone unit. The beam output unit is specifically used for Obtain the original beam data based on the minimum variance distortionless response algorithm ; Based on the distance data between the detection object and the reference plane, compensate the intensity of the original beam data, and then obtain the beam data ; Among them, ; Among them, is the reference value obtained by transformation through the transformation matrix between the image coordinate system and the world coordinate system based on the coordinates of the sound source in the image coordinate system .
[0015] Based on the above, the finally obtained beam data can be closer to the sound wave distribution at the surface area of the detection object, which is conducive to subsequent data processing
[0016] Preferably, a communication module is also provided on the device body, and the communication module is used to realize the long-distance transmission of the fusion data and the beam data. Thus, the long-distance communication of the collected data can be better realized, which is conducive to the subsequent processing of relevant data based on the upper computer
[0017] Preferably, a power supply module is also provided on the device body, and the power supply module is used to supply power to the device body. Therefore, the device body can be used in the outdoor environment
[0018] Preferably, a display module is also provided on the device body, and the display module is used to display the fusion data. Therefore, it is beneficial for the user to aim and orient during the data acquisition action
[0019] Preferably, an environmental monitoring module is also provided on the device body, and the environmental monitoring module is used to collect environmental temperature data, environmental humidity data and environmental air pressure data, and the environmental temperature data, environmental humidity data and environmental air pressure data are used to correct the sound wave transmission speed. Thus, the accuracy of data acquisition can be further improved Brief Description of the Drawings
[0020] Figure 1 It is a block diagram schematic diagram of a hand-held mobile remote collaboration imager based on the Internet of Things in Embodiment 1 Figure 2 It is a schematic diagram of the prediction model adopted by a substation main transformer fault prediction method based on voiceprint features in Embodiment 1 Detailed Embodiments
[0021] To further understand the content of the present invention, the present invention will be described in detail in combination with embodiments. It should be understood that the embodiments are only for explaining the present invention and not for limiting it
[0022] Embodiment 1 For the inspection of core equipment such as transformers and reactors in a substation, currently it mainly relies on manual "listening to sounds to identify abnormalities". This method has high requirements for personnel experience and low accuracy, especially being restricted by the audible sound frequency range of the human ear.
[0023] Based on this, this embodiment provides a handheld mobile remote collaboration imager based on the Internet of Things, as Figure 1 shown. It includes a device body, and a reference plane is constructed at the device body; there is provided at the device body an image acquisition module for acquiring image data of a detection object; wherein, the imaging plane of the image acquisition module is parallel to the reference plane; a sound acquisition module for acquiring sound data of the detection object; wherein, the sound acquisition module includes a microphone array composed of multiple microphone units, and the microphone array has at least 2 transverse microphone units distributed at equal distances laterally away from the image acquisition module along the reference plane and at least 2 longitudinal microphone units distributed at equal distances longitudinally away from the image acquisition module along the reference plane; a ranging module for acquiring distance data between the detection object and the reference plane; an attitude acquisition module for acquiring the deflection angle data of the reference plane; a processing module including a data fusion unit and a beam output unit; wherein, the data fusion unit is used for fusing the image data and the sound data to obtain fusion data, and the beam output unit is used for obtaining beam data based on the sound data.
[0024] The handheld mobile remote collaboration imager provided by the present invention, by setting the image acquisition module and the sound acquisition module, can acquire the image data and sound data of the area where the detection object is located, and can provide the original data for subsequent fault detection based on sound characteristics. By identifying faults based on sound characteristics, latent faults can be discovered earlier.
[0025] In this embodiment, the sound data includes the angle between the sound source propagation direction and the transverse direction of the reference plane and the angle between the sound source propagation direction and the longitudinal direction of the reference plane , and the deflection angle data of the reference plane includes the angle between the transverse direction of the reference plane and the horizontal direction and the angle between the longitudinal direction of the reference plane and the vertical direction ; specifically, the data fusion unit is used for based on the angle , the angle , the angle , the angle and the distance data L, to obtain the camera coordinates of the sound source in the camera coordinate system; wherein, ; ; ; Based on the transformation matrix between the camera coordinate system and the image coordinate system, obtain the coordinates of the sound source in the image coordinate system ; Based on the coordinates of the sound source in the image coordinate system and the sound intensity distribution at the sound source, complete the acquisition of the fused data.
[0026] Based on the above, it is possible to achieve the positioning of the sound source in the image data based on the propagation angle and distance information of the sound source during data acquisition, so that the acquisition of the fused data can be better achieved.
[0027] It can be understood that in this embodiment, the alignment of the sound intensity data and the image data can be achieved by finding the center coordinates of the sound source so as to be able to obtain the fused data more accurately.
[0028] In this embodiment, the angle between the propagation direction of the sound source and the transverse direction of the reference plane is obtained based on the at least 2 transverse microphone units. Specifically, ; where m is the total number of transverse microphone units, is the time when the first transverse microphone unit in the direction away from the image acquisition module collects the sound wave data at the detection object, is the time when the i-th transverse microphone unit in the direction away from the image acquisition module collects the sound wave data at the detection object, d is the array pitch of the at least 2 transverse microphone units, and c is the sound wave transmission speed.
[0029] Based on the above, through the construction of the at least 2 transverse microphone units, the propagation angle data of the sound source propagation direction relative to the transverse direction of the reference plane can be better achieved.
[0030] In this embodiment, the angle between the propagation direction of the sound source and the longitudinal direction of the reference plane is obtained based on the at least 2 longitudinal microphone units. Specifically, ; where m is the total number of longitudinal microphone units, is the time when the first longitudinal microphone unit in the direction away from the image acquisition module collects the sound wave data at the detection object, At the moment when the j-th longitudinal microphone unit in the direction away from the image acquisition module collects the acoustic wave data at the detection object, p is the array pitch of the at least two longitudinal microphone units, and c is the acoustic wave transmission speed.
[0031] Based on the above, through the construction of the at least two longitudinal microphone units, it is possible to preferably realize the longitudinal propagation angle data of the sound source propagation direction relative to the reference plane.
[0032] In this embodiment, the sound data of the detection object includes the acoustic wave signals collected by each microphone unit. The beam output unit is specifically used for, Obtaining the original beam data based on the minimum variance distortionless response algorithm ; Compensating the intensity of the original beam data based on the distance data between the detection object and the reference plane, and then obtaining the beam data ; Among them, ; Among them, is the reference value obtained by transformation through the transformation matrix between the image coordinate system and the world coordinate system based on the coordinates of the sound source in the image coordinate system ,
[0033] Based on the above, the finally obtained beam data can be closer to the acoustic wave distribution at the surface area of the detection object, which is conducive to subsequent data processing.
[0034] In this embodiment, a communication module is further provided on the device body. The communication module is used to realize the long-distance transmission of the fusion data and the beam data. Thus, it is possible to preferably realize the long-distance communication of the collected data, which is conducive to the subsequent processing of relevant data based on the upper computer.
[0035] In this embodiment, a power supply module is further provided on the device body. The power supply module is used to supply power to the device body. Therefore, it is possible to realize the use of the device body in the outdoor environment.
[0036] In this embodiment, a display module is further provided on the device body. The display module is used to display the fusion data. Therefore, it is conducive to the aiming and orientation of the user during the data collection operation.
[0037] In this embodiment, an environmental monitoring module is further provided on the device body. The environmental monitoring module is used to collect environmental temperature data, environmental humidity data, and environmental air pressure data. The environmental temperature data, environmental humidity data, and environmental air pressure data are used to correct the acoustic wave transmission speed. Thus, it is possible to further improve the accuracy of data collection.
[0038] Embodiment 2 For equipment such as transformers in a substation, during its main life cycle, it is in a normal state for the vast majority of the time. Therefore, abnormal data is very rare. This data imbalance makes it difficult to use traditional methods such as voiceprint classification to judge the state of transformers.
[0039] Based on the data collected by the imager in Embodiment 1, this embodiment provides a method for predicting faults in the main transformer of a substation based on voiceprint features, which includes, Obtain the image data, fusion data, and beam data of the detection object; among them, the fusion data includes the image data of the detection object and the sound intensity distribution data at the detection object; Construct a first input based on the image data and the fusion data; Construct a second input based on the beam data; Process the first input based on the first model to obtain a first feature vector; Process the second input based on the second model to obtain a second feature vector; Concatenate the first feature vector and the second feature vector to obtain a feature vector; Process the feature vector based on the third model to obtain a fault prediction output.
[0040] The method of the present invention can utilize the working states implied by the image data and the beam data to realize fault prediction for equipment such as the main transformer of a substation. It can make relatively full use of image features and voiceprint features to realize fault prediction with a small sample size, and is especially suitable for fault prediction under the imbalance of sample data.
[0041] The construction of the first input based on the image data and the fusion data includes, Identify the detection object in the image data and construct a recognition frame; among them, the recognition frame is the smallest circumscribed rectangle that completely contains the detection object; Obtain the image of the area covered by the recognition frame and convert it into the grayscale image; Reconstruct the grayscale based on the sound intensity distribution data at the image of the area covered by the recognition frame to obtain the first input.
[0042] Based on the above, it is possible to preferably eliminate irrelevant image features, especially retain the image features of the area where the detection object is located. By integrating the sound intensity distribution data into the image data, it is possible to preferably introduce the acoustic wave distribution information at the detection object, and introduce the difference information of the vibration parts of the detection object in different working states into the overall prediction method, so that it is possible to better realize the prediction of abnormalities.
[0043] The reconstruction of the grayscale based on the sound intensity distribution data at the image of the area covered by the recognition frame to obtain the first input includes, Obtain the average grayscale value of the grayscale image ; where , N is the total number of pixel points in the grayscale image, is the sum of the grayscale values of all pixel points in the grayscale image; Obtain the grayscale compensation value at each pixel point of the grayscale image ; where , S is the sound intensity at the corresponding pixel point, is the maximum sound intensity in the image area covered by the recognition frame, is the minimum sound intensity in the image area covered by the recognition frame; Subtract the corresponding grayscale compensation value from the grayscale value at each pixel point in the grayscale image to obtain the first input.
[0044] Based on the above, it is possible to preferably introduce sound intensity distribution data into the image data.
[0045] The construction of the second input based on the beam data includes Extract the main frequency eigenvalue, acoustic vibration entropy eigenvalue, 50Hz odd and even harmonic ratio eigenvalue, fundamental frequency ratio eigenvalue and high and low frequency eigenvalue in the beam data to construct a voiceprint feature sequence as the second input.
[0046] Based on the above, it is possible to obtain the voiceprint features with a typical normal distribution in the beam data to construct the second input. Based on this method, in particular, it is possible to mine the standard voiceprint feature distribution information from the normal data, making the entire prediction method particularly sensitive to abnormal data, and achieving an improvement in the prediction accuracy of the prediction model under the imbalance of sample data.
[0047] The first model uses a convolutional neural network, which has a convolutional layer, an activation layer and a pooling layer; the second model uses a neural network, which has an input layer and a hidden layer; the third model includes a fully connected layer and an output layer; the first feature vector and the second feature vector are concatenated into a feature vector at the fully connected layer and output to the output layer after weighted calculation by the fully connected layer. The output layer is used to obtain the probability of the fault category based on the output of the fully connected layer to complete the fault prediction output. Thus, it is possible to preferably realize the separate processing of different types of data and the fusion processing of data from different sources.
[0048] The parameters of the convolutional layer, activation layer and pooling layer in the first model, the parameters of the hidden layer in the second model and the parameters of the fully connected layer in the third model are all obtained by separate training. This enables the use of richer sample data to train different models, which is beneficial to the improvement of model accuracy.
[0049] When obtaining the parameters of the convolutional layer, activation layer, and pooling layer in the first model, it includes: Construct a first sample set with the abnormal category as the label; wherein, the construction method of each sample data in the first sample set is the same as that of the first input; Adopt a convolutional neural network as the initial model; Train the initial model, and use the relevant parameters in the trained initial model as the parameters of the convolutional layer, activation layer, and pooling layer in the first model.
[0050] Based on this, it is possible to better realize the acquisition of relevant data in the first model.
[0051] When obtaining the hidden layer parameters in the second model, it includes: Construct a second sample set with the abnormal category as the label; wherein, the construction method of each sample data in the second sample set is the same as that of the second input; Adopt a neural network as the initial model; Train the initial model, and use the relevant parameters in the trained initial model as the hidden layer parameters in the second model.
[0052] Based on this, it is possible to better realize the acquisition of relevant data in the second model.
[0053] When obtaining the fully connected layer parameters in the third model, it includes: Construct a third sample set with the abnormal category as the label; wherein, each sample data in the third sample set includes the first data obtained based on the construction method of the first input and the second data obtained based on the construction method of the second input; Substitute the obtained parameters of the convolutional layer, activation layer, and pooling layer in the first model into the first model, and substitute the obtained hidden layer parameters in the second model into the second model; Use the outputs of the first model and the second model for the third sample set as the input of the third model, and train the third model to obtain the fully connected layer parameters in the third model.
[0054] Based on this, it is possible to better realize the acquisition of relevant data in the third model.
[0055] The output layer in the third model adopts the softmax function. Based on this, it is possible to better realize the prediction of multi-category anomalies.
[0056] It is easy to understand that those skilled in the art can combine, split, reorganize, etc. the embodiments of the present application based on one or several embodiments provided by the present application to obtain other embodiments, and these embodiments do not exceed the protection scope of the present application.
[0057] The above has schematically described the present invention and its implementation manners. This description is not restrictive, and what is shown in the embodiments is only part of the implementation manners of the present invention. The actual structure is not limited thereto. Therefore, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, creatively design structural manners and embodiments similar to the technical solution, they shall fall within the protection scope of the present invention.
Claims
1. A handheld mobile remote collaborative imaging device based on the Internet of Things, comprising a device body, a reference plane being constructed at the device body; and a device body being provided with: The image acquisition module is used to obtain the image data of the detection object; wherein, The imaging plane of the image acquisition module is parallel to the reference plane; A sound collection module, used to obtain sound data of the detection object; wherein the sound collection module includes a microphone array composed of a plurality of microphone units, the microphone array having at least two lateral microphone units distributed equidistantly away from the image collection module along the reference plane and at least two longitudinal microphone units distributed equidistantly away from the image collection module along the reference plane; A distance measurement module is used to obtain the distance data between the detection object and the reference plane; The posture acquisition module is used to obtain the deflection angle data of the reference plane; The processing module includes a data fusion unit and a beam output unit; wherein the data fusion unit is used to fuse the image data and the sound data to obtain fusion data, and the beam output unit is used to obtain beam data based on the sound data.
2. The handheld mobile remote collaborative imaging device based on the Internet of Things according to claim 1, characterized in that: The sound data includes the angle between the direction of sound source propagation and the horizontal direction of the reference plane. The angle between the sound source propagation direction and the longitudinal direction of the reference plane The deflection angle data of the reference plane includes the angle between the lateral direction and the horizontal direction of the reference plane. The angle between the longitudinal direction and the vertical direction of the reference plane ; The data fusion unit is specifically used for: Based on the angle , Angle , Angle , Angle And distance data L, get the camera coordinates of the sound source in the camera coordinate system ;in, ； ； ； Based on the transformation matrix between the camera coordinate system and the image coordinate system, obtain the coordinates of the sound source in the image coordinate system ; Based on the coordinates of the sound source in the image coordinate system And the sound intensity distribution at the sound source to complete the acquisition of fusion data.
3. The handheld mobile remote collaborative imaging device based on the Internet of Things according to claim 2, characterized in that: The angle between the direction of sound source propagation and the horizontal direction of the reference plane Based on the at least two lateral microphone units, specifically, ； Where m is the total number of lateral microphone units, is the moment when the first lateral microphone unit in the direction away from the image acquisition module collects the sound wave data at the detection object, is the moment when the i-th lateral microphone unit in the direction away from the image acquisition module collects the sound wave data at the detection object, and d is the array spacing of the at least two lateral microphone units.
4. The handheld mobile remote collaborative imaging device based on the Internet of Things according to claim 3, characterized in that: The angle between the sound source propagation direction and the longitudinal direction of the reference plane Based on the at least two longitudinal microphone units, specifically, ； Where m is the total number of vertical microphone units, is the moment when the first longitudinal microphone unit in the direction away from the image acquisition module collects the sound wave data at the detection object, is the moment when the jth longitudinal microphone unit in the direction away from the image acquisition module collects the sound wave data at the detection object, and p is the array spacing of the at least two longitudinal microphone units.
5. The handheld mobile remote collaborative imaging device based on the Internet of Things according to claim 1, characterized in that: The sound data of the detected object includes the sound wave signal collected by each microphone unit, and the beam output unit is specifically used to: Obtaining raw beam data based on the minimum variance distortion-free response algorithm ; Based on the distance data between the detection object and the reference plane, the intensity of the original beam data is compensated to obtain the beam data ; in, ; in, is the coordinate of the sound source in the image coordinate system , through the transformation matrix between the image coordinate system and the world coordinate system, the reference value is obtained after transformation.
6. The handheld mobile remote collaborative imaging device based on the Internet of Things according to claim 5, characterized in that: A communication module is also provided at the device body, and the communication module is used to realize long-distance transmission of fusion data and beam data.
7. The handheld mobile remote collaborative imaging device based on the Internet of Things according to claim 6, characterized in that: The device body is also provided with a power supply module, which is used to supply power to the device body.
8. The handheld mobile remote collaborative imaging device based on the Internet of Things according to claim 7, characterized in that: The device body is also provided with a display module, which is used to display the fused data.
9. The handheld mobile remote collaborative imaging device based on the Internet of Things according to claim 7, characterized in that: The device body is also provided with an environmental monitoring module, which is used to collect environmental temperature data, environmental humidity data and environmental air pressure data. The environmental temperature data, environmental humidity data and environmental air pressure data are used to correct the sound wave transmission speed.