Smart grid test detection instrument and meter system and detection data collection method

Abstract

The present application relates to the technical field of data acquisition, and particularly relates to a smart power grid test detection instrument and meter internet of things system and a detection data acquisition method, which comprises: a type identification module, which is used for identifying the type of a power test detection instrument and meter; a data acquisition gateway, a middleware system and an internet of things management platform, which are used for completing the detection data acquisition task of a communication type fixed device or a cross-domain communication portable device; an intelligent image detection and identification module, which is used for completing the detection data acquisition task of a non-communication type power test detection instrument and meter or a non-cross-domain communication type portable device by using a target detection and identification model and an image analysis processing method; and a digital comprehensive service platform, which is used for acquiring the real-time state of a power device. The present application realizes efficient and accurate real-time transmission of detection data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data acquisition, in particular to a smart power grid test detection instrument and meter system and a detection data acquisition method. BACKGROUND

[0002] In the process of power grid construction, the index measurement and state monitoring of power equipment play a crucial role, they are not only the core to ensure the safety of power production and improve the operation and maintenance efficiency, but also the cornerstone of building a new smart grid system. However, due to the diversification of test detection instruments and meters, the traditional power equipment index data acquisition highly depends on manual operation, and the detection personnel need to manually record the detection data related to the index data in the detection process, which causes the problem that the detection data cannot be transmitted in time, resulting in low timeliness of detection data, transmission delay, and even unable to meet the demand of real-time monitoring and rapid response of power system to power equipment. SUMMARY

[0003] Therefore, the technical problem to be solved by the present application is to overcome the problem that manual recording of power equipment detection data in the prior art results in low data timeliness, transmission delay, and even unable to meet the demand of real-time monitoring and rapid response of power system to power equipment.

[0004] To solve the above technical problems, the present application provides a smart power grid test detection instrument and meter system, comprising:

[0005] A type identification module is used to identify the type of power test detection instrument and meter, wherein the type of power test detection instrument and meter includes a communication type power test detection instrument and meter and a non-communication type power test detection instrument and meter, the communication type power test detection instrument and meter includes a communication type portable device and a communication type fixed device, and the communication type portable device includes a non-cross-domain communication type portable device and a cross-domain communication type portable device.

[0006] A data acquisition gateway is in communication connection with the type identification module, used to receive data packets in the communication type fixed device or the cross-domain communication portable device, identify the source protocol of the data packet by using a pre-defined protocol identifier, parse the data packet according to the source protocol rule of the data packet to obtain parsed data, and transmit the parsed data to a middleware system.

[0007] The middleware system is in communication connection with the data acquisition gateway; is used for receiving and analyzing data, reformatting the analyzed data according to the format requirements of the target protocol, obtaining the analyzed data after format conversion; performing data conversion on the analyzed data after format conversion based on the mapping rules between the source protocol semantics and the target protocol semantics, obtaining the first target data; reorganizing the target data according to the data structure of the target protocol, obtaining new first target data; adding a header and metadata to the new first target data according to the data packet format of the target protocol, and encapsulating into a first target data packet; and sending the first target data packet to the Internet of Things management platform;

[0008] The Internet of Things management platform is in communication connection with the middleware system; is used for receiving and storing the first target data packet, pre-processing the first target data packet to obtain the pre-processed first target data packet, and completing the detection data acquisition task of the communication type fixed device or the cross-domain communication portable device;

[0009] The intelligent image detection and recognition module is in communication connection with the type identification module; is used for receiving a background image containing a non-communication type power test detection instrument or a non-cross-domain communication type portable device, detecting the background image by using a target detection and recognition model to obtain a device image of the non-communication type power test detection instrument or the non-cross-domain communication type portable device; identifying and analyzing the numerical information in the device image by using an image analysis processing method to obtain second target data, completing the detection data acquisition task of the non-communication type power test detection instrument or the non-cross-domain communication type portable device; and transmitting the second target data to the digital comprehensive service platform;

[0010] The digital comprehensive service platform is in communication connection with the Internet of Things management platform and the intelligent image detection and recognition module; is used for obtaining the real-time state of the corresponding power equipment according to the pre-processed first target data packet; and obtaining the real-time state of the corresponding power equipment according to the second target data.

[0011] Preferably, when the non-communication type power test detection instrument is a non-communication type pointer instrument, the device image of the non-communication type pointer instrument is obtained, the numerical information in the device image is identified and analyzed by using an image analysis processing method to obtain second target data, which includes:

[0012] The trained U-Net model is used to segment the input device image of the non-communication type pointer instrument to obtain a segmented image corresponding to the device image of the non-communication type pointer instrument as a target image;

[0013] The numerical information in the target image is identified by using an OCR technology to obtain the device scale content; wherein the numerical information is the scale information in the non-communication type pointer instrument;

[0014] Acquire the pointer position in the target image, and obtain second target data in combination with the device scale content.

[0015] Preferably, the trained U-Net model is composed of an encoder and a decoder.

[0016] The encoder is configured to extract device image feature information.

[0017] The decoder is configured to output a segmentation image corresponding to the device image as the target image.

[0018] Preferably, the device scale content is obtained by recognizing numerical information in the target image using an OCR technique, and includes:

[0019] The target image is subjected to image preprocessing using the OCR technique to obtain a preprocessed target image.

[0020] The device scale region is subjected to row segmentation and character segmentation to obtain various scale characters.

[0021] The numerical values of the scale characters are reassembled according to the arrangement order of the scale characters in the preprocessed target image to obtain the device scale content.

[0022] Preferably, the image preprocessing includes tilt correction, blur removal, and image enhancement.

[0023] Preferably, the pointer position in the target image is acquired using an edge detection algorithm or a Hough transform method.

[0024] Preferably, the source protocol semantics include source protocol commands, source protocol responses, and source protocol states.

[0025] Preferably, the first target data is obtained by performing field renaming and data type conversion on the parsed data after format conversion based on a mapping rule between the source protocol semantics and the target protocol semantics.

[0026] Preferably, the target detection and recognition model is a trained YOLOv7 model.

[0027] The application further provides a smart power grid test detection instrument and meter detection data acquisition method, which is applied to the smart power grid test detection instrument and meter object system and includes:

[0028] The type recognition module is used to acquire the type of the power test detection instrument;

[0029] If the type of the power test detection instrument is a communication type fixed device or a cross-domain communication portable device, a data acquisition gateway is used to receive the data packet in the communication type fixed device or the cross-domain communication portable device, and the data packet is parsed according to the source protocol rule of the data packet to obtain parsed data; a middleware system is used to receive the parsed data, reformat the parsed data according to the format requirement of the target protocol to obtain the parsed data after format conversion; the parsed data after format conversion is subjected to data conversion operation based on the mapping rule between the source protocol semantics and the target protocol semantics to obtain first target data; the target data is reorganized according to the data structure of the target protocol to obtain new first target data; the new first target data is added with a header and metadata according to the data packet format of the target protocol, and is encapsulated into a first target data packet; the first target data packet is sent to the Internet of Things management platform; the Internet of Things management platform is used to receive and store the first target data packet, pre-process the first target data packet to obtain the first target data packet after pre-processing, and complete the detection data acquisition task of the communication type fixed device or the cross-domain communication portable device;

[0030] If the power test detection instrument is a non-communication type power test detection instrument or a non-cross-domain communication type portable device, an intelligent image detection and recognition module is used to receive a background image containing the non-communication type power test detection instrument or the non-cross-domain communication type portable device, a target detection and recognition model is used to detect the background image to obtain a device image of the non-communication type power test detection instrument or the non-cross-domain communication type portable device; an image analysis processing method is used to identify and analyze the numerical information in the device image to obtain second target data, and complete the detection data acquisition task of the non-communication type power test detection instrument or the non-cross-domain communication type portable device.

[0031] The above technical scheme of the present application has the following beneficial effects compared with the prior art:

[0032] The smart power grid test detection instrument and internet of things system divides power test detection instruments and meters into different types, and identifies the type of the power test detection instrument and meter through a type identification module; according to different types, a combination of a data acquisition gateway, a middleware system and an internet of things management platform or an intelligent image detection and identification module is used to execute different detection data acquisition mechanisms; if the type identified by the type identification module is a communication type fixed device or a cross-domain communication portable device, the combination of the data acquisition gateway, the middleware system and the internet of things management platform is used to realize real-time acquisition of the detection data of this type; if the type identified by the type identification module is a non-communication type power test detection instrument or a non-cross-domain communication type portable device, the intelligent image detection and identification module is used to realize real-time acquisition of the detection data of this type by using target detection and identification technology, which improves the accuracy of numerical recognition, effectively processes complex layout, and increases the application support of OCR technology in the numerical recognition scene; finally, the acquired detection data is transmitted to a digital comprehensive service platform to obtain the real-time state of the power equipment corresponding to the detection data; the whole system realizes efficient and accurate real-time transmission of detection data, which helps to improve the intelligentization and automation level of detection data acquisition of multiple types of test detection instruments and meters, reduces the labor and time cost, improves the safety and reliability of detection data acquisition of power test detection instruments and meters, and enables power test detection instruments and meters to realize efficient and seamless data interaction and integration with power equipment, improving the timeliness of real-time state detection of power equipment. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to make the content of the present application more easily understood, the present application will be further described in detail below according to specific embodiments of the present application and in conjunction with the drawings, in which:

[0034] Figure 1 is a structural diagram of a smart power grid test detection instrument and internet of things system provided by the present application;

[0035] Figure 2 is a schematic diagram of power test detection instrument and meter type division;

[0036] Figure 3 is a flowchart of the OCR technology for recognizing numerical information. DETAILED DESCRIPTION

[0037] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting to the present application.

[0038] Referring to Figure 1 , Figure 1A structure diagram of a smart grid test detection instrument and meter object system provided by the application; specifically includes:

[0039] A type identification module is configured to identify the power test detection instrument and meter type; wherein the power test detection instrument and meter type includes a communication type power test detection instrument and meter and a non-communication type power test detection instrument and meter; the communication type power test detection instrument and meter includes a communication type portable device and a communication type fixed device; the communication type portable device includes a non-cross-domain communication type portable device and a cross-domain communication type portable device; wherein the specific power test detection instrument and meter type division is as shown in Figure 2

[0040] According to Figure 2 , first, according to the communication ability of the equipment, the equipment is divided into two types: communication type equipment and non-communication type equipment;

[0041] The non-communication type equipment, such as the direct current resistance tester HDBZ-20, does not have data communication function because it is purchased too early, so it cannot directly exchange data, and the real-time monitoring and recording of the detection data of such equipment is realized by seeking external solutions, and the deep learning technology with high accuracy, universality and high adaptability to the detection task becomes an ideal solution;

[0042] The data communication ability of the communication type equipment is the basis for the automatic recording of the test detection equipment detection data, however, the communication environment requirements of different communication type test detection equipment are different; for such equipment, further comprehensive investigation of its application scene and communication environment requirement is needed, that is, according to the application scene, the communication type equipment is subdivided into portable equipment and fixed equipment;

[0043] ​The portable device needs to be carried to the detection site to perform test detection tasks; some portable devices, such as vibration analyzer QN-F4Pro and MOA resistance current detector LCM500, can only support wired connection or short-distance wireless data communication, and the device cannot establish a data transmission channel with the upper computer in the detection site. Even if they have data communication capability, they cannot transmit data; therefore, for such data communication supporting portable devices, further according to their data communication environment, they are divided into cross-domain devices and non-cross-domain devices; the cross-domain device can establish a data exchange channel with the upper computer by virtue of its internet access capability or its long-distance wireless data communication capability (4G / 5G), and then transmit data; and the non-cross-domain device needs to adopt the same solution as the non-communication device, and realizes the recording of its test detection data by means of target detection and recognition technology; the non-cross-domain portable device is a digital display device (analog pointer device usually does not have data communication capability), so it is not further subdivided, and the numerical value information in the device can also be recognized by using OCR technology combined with edge detection or contour detection;

[0044] The fixed device is deployed in the power system environment for a long time; the fixed communication device mainly concerns its data communication mode, including computer communication, wireless communication and wired communication, due to its stable data communication environment;

[0045] The data acquisition gateway is in communication connection with the type recognition module; it is used for receiving data packets in the communication fixed device or the cross-domain communication portable device; the source protocol of the data packet is identified by using a predefined protocol identifier; the data packet is parsed according to the source protocol rule of the data packet, the key information and data load are extracted, and parsed data are obtained; the parsed data are transmitted to the middleware system;

[0046] The middleware system is in communication connection with the data acquisition gateway; it is used for receiving parsed data, performing reformatting operation on the parsed data according to the format requirement of the target protocol, obtaining format-converted parsed data; performing data conversion operation on the format-converted parsed data based on the mapping rule between the source protocol semantics and the target protocol semantics, obtaining first target data; reorganizing the target data according to the data structure of the target protocol, obtaining new first target data; adding a header and metadata to the new first target data according to the data packet format of the target protocol, and encapsulating into a first target data packet; sending the first target data packet to the IOT management platform; wherein, the source protocol semantics includes source protocol command, source protocol response and source protocol state; the target protocol semantics includes target protocol command, target protocol response and target protocol command; the data conversion operation is field renaming and data type conversion operation;

[0047] The Internet of Things management platform is in communication connection with the middleware system, and is used for receiving and storing the first target data packet, pre-processing the first target data packet to obtain a pre-processed first target data packet, and completing a detection data collection task of the communication type fixed device or the cross-domain communication portable device;

[0048] The intelligent image detection and recognition module is in communication connection with the type recognition module, and is used for receiving a background image containing the non-communication type power test detection instrument or the non-cross-domain communication type portable device, detecting the background image by using a target detection and recognition model to obtain a device image of the non-communication type power test detection instrument or the non-cross-domain communication type portable device, recognizing and analyzing numerical information in the device image by using an image analysis processing method to obtain second target data, completing a detection data collection task of the non-communication type power test detection instrument or the non-cross-domain communication type portable device, and transmitting the second target data to the digitalized comprehensive service platform;

[0049] The target detection and recognition model is a trained YOLOv7 model, which can significantly improve the precision and efficiency of dial scale detection;

[0050] When the non-communication type power test detection instrument is a non-communication type pointer instrument, the device image of the non-communication type pointer instrument is obtained, the numerical information in the device image is recognized and analyzed by using the image analysis processing method to obtain the second target data, and the second target data includes:

[0051] The trained U-Net model is used for segmentation processing of the input non-communication type pointer instrument equipment image, and a segmentation image corresponding to the non-communication type pointer instrument equipment image is obtained as a target image; wherein the trained U-Net model is composed of an encoder and a decoder; the encoder is used to reduce the equipment image information from a high-dimensional space to a low-dimensional space, so as to extract the equipment image feature information; wherein the input equipment image at this time is the dial image corresponding to the non-communication type pointer instrument; the equipment image feature information at each stage in the encoder is transmitted to the corresponding stage of the decoder through a jump connection; the decoder is used to map the low-dimensional feature information back to the original equipment image, so as to output the segmentation image corresponding to the equipment image as the target image; the U-Net model can segment the equipment image obtained by the YOLOv7 model, accurately locate the pointer and the dial scale, and lay a foundation for automatic reading extraction; the U-Net introduces a jump connection to help the network learn more detailed feature information, and the feature information at each stage of the encoder is transmitted to the corresponding stage of the decoder through a jump connection; in this way, the decoder can use the detailed information obtained from the encoder to better restore the segmentation result of the original equipment image, effectively solve the problem of loss of image detail information in the down-sampling process of the encoder, and improve the image segmentation accuracy of the pointer and the dial scale; at the same time, the jump connection fuses features at different levels, so that the network can use information from different levels to improve the accuracy of image segmentation;

[0052] The OCR technology is used to recognize the numerical information in the target image, and the equipment scale content is obtained, wherein the numerical information is the scale information in the non-communication type pointer instrument; the deep learning model and the OCR technology are used to realize real-time acquisition of the detection data of this type, improve the accuracy of dial scale recognition, effectively process complex layout, and increase the application support of the OCR technology in the dial scale recognition scene; the specific process is as shown in Figure 3 The specific process is as shown in

[0053] The OCR technology is used to recognize the numerical information in the target image, and the equipment scale content is obtained, wherein the numerical information is the scale information in the non-communication type pointer instrument; the deep learning model and the OCR technology are used to realize real-time acquisition of the detection data of this type, improve the accuracy of dial scale recognition, effectively process complex layout, and increase the application support of the OCR technology in the dial scale recognition scene; the specific process is as shown in

[0054] The OCR technology is used to recognize the numerical information in the target image, and the equipment scale content is obtained, wherein the numerical information is the scale information in the non-communication type pointer instrument; the deep learning model and the OCR technology are used to realize real-time acquisition of the detection data of this type, improve the accuracy of dial scale recognition, effectively process complex layout, and increase the application support of the OCR technology in the dial scale recognition scene; the specific process is as shown in

[0055] According to the arrangement order of the scales in the preprocessed target image, a layout restoration operation is performed to reassemble the numerical values of each scale character to obtain the device scale content; wherein the entire OCR technology process not only improves the accuracy of the dial scale recognition, but also effectively processes complex layout, making the OCR technology perform well in the dial scale recognition scenario;

[0056] The edge detection algorithm or Hough transform method is used to obtain the pointer position in the target image, and the second target data is obtained by combining the device scale content;

[0057] The digital comprehensive service platform is in communication connection with the Internet of Things management platform and the intelligent image detection and recognition module; the real-time state of the corresponding power equipment is obtained according to the preprocessed first target data packet; and the real-time state of the corresponding power equipment is obtained according to the second target data.

[0058] In summary, when collecting the detection data of the communication type fixed equipment or the cross-domain communication portable equipment, the data collection gateway and the preset collection mechanism in the middleware system are used to develop the data collection middleware system and the data collection gateway, so as to shield the differences in the hardware type, interface and protocol of the perception layer in an intelligent manner, form the intelligent data processing and interconnection of the business layer, and help to realize the intelligent and automatic collection of the detection data of the communication type fixed equipment or the cross-domain communication portable equipment, and realize the efficient and accurate real-time transmission of the detection data of the communication type fixed equipment or the cross-domain communication portable equipment.

[0059] When collecting the detection data of the non-communication type power test detection instrument or the non-cross-domain communication type portable equipment, the preset collection mechanism in the intelligent image detection and recognition module is used, which helps to realize the intelligent and automatic collection of the detection data of the non-communication type power test detection instrument or the non-cross-domain communication type portable equipment, and realize the efficient and accurate real-time transmission of the detection data of the non-communication type power test detection instrument or the non-cross-domain communication type portable equipment.

[0060] By dividing the types of test detection instruments into those with data communication capability and those without data communication capability (communication type and non-communication type), and respectively acquiring multiple data sources based on the preset collection mechanism in the data collection gateway and the middleware system and the preset collection mechanism in the intelligent image detection and recognition module, a multi-source heterogeneous detection data collection method is constructed, which helps to intelligently and automatically collect data on multiple types of test detection instruments, and realizes the efficient and accurate real-time transmission of the detection data of the test detection instruments without data communication capability.

[0061] The application further provides a smart power grid test detection instrument detection data collection method applied to the smart power grid test detection instrument Internet of Things system.

[0062] The type identification module is used to obtain the type of the power test detection instrument;

[0063] If the type of the power test detection instrument is a communication type fixed device or a cross-domain communication portable device, a data acquisition gateway is used to receive a data packet in the communication type fixed device or the cross-domain communication portable device, analyze the data packet according to a source protocol rule of the data packet, and obtain parsed data; a middleware system is used to receive the parsed data, reformat the parsed data according to a format requirement of a target protocol, and obtain the parsed data after format conversion; a data conversion operation is performed on the parsed data after format conversion based on a mapping rule between a source protocol semantic and a target protocol semantic, and first target data is obtained; the target data is reorganized according to a data structure of the target protocol, and new first target data is obtained; a header and metadata are added to the new first target data according to a data packet format of the target protocol, and the new first target data is encapsulated into a first target data packet; the first target data packet is sent to an Internet of Things management platform; the Internet of Things management platform is used to receive and store the first target data packet, pre-process the first target data packet, obtain the first target data packet after pre-processing, and complete a detection data acquisition task for the communication type fixed device or the cross-domain communication portable device;

[0064] If the power test detection instrument is a non-communication type power test detection instrument or a non-cross-domain communication type portable device, an intelligent image detection and recognition module is used to receive a background image containing the non-communication type power test detection instrument or the non-cross-domain communication type portable device, detect the background image by using a target detection and recognition model, obtain a device image of the non-communication type power test detection instrument or the non-cross-domain communication type portable device, and identify and analyze numerical information in the device image by using an image analysis processing method to obtain second target data and complete an acquisition task for detection data of the non-communication type power test detection instrument or the non-cross-domain communication type portable device.

[0065] Compared with the prior art, the present application has the following advantages:

[0066] By dividing the type of the test detection instrument into a device with data communication capability or a device without data communication capability (communication type and non-communication type), and collecting data through a pre-constructed data acquisition mechanism according to the type of the instrument, the problem that the detection personnel needs to record data by hand on site when obtaining the detection result due to the diversification of the type of the test detection instrument in the prior art, and the problem that the data cannot be transmitted in real time are solved, and efficient and accurate real-time transmission of the detection data is achieved.

[0067] By dividing the types of test detection instruments into those with data communication capability and those without data communication capability, and respectively acquiring multiple data sources through the preset acquisition mechanism in the data acquisition gateway and middleware system and the preset deep learning model in the intelligent image detection and recognition module, a multi-source heterogeneous detection data acquisition method is constructed, which is helpful for the intelligent and automated acquisition of data on multiple types of test detection instruments, and realizes efficient and accurate real-time transmission of detection data of test detection instruments without data communication capability.

[0068] When the acquisition is performed based on the acquisition mechanism of the deep learning model preset in the intelligent image detection and recognition module, for the equipment with a scale such as a meter dial (non-communication pointer instrument) without data communication capability, the acquired image is input to the preset deep learning model, and the reading on the equipment is extracted by using the OCR technology, the scale and the number on the equipment are recognized and converted into readable text, automatic data acquisition and analysis are realized, the whole process not only improves the accuracy of dial scale recognition, but also effectively processes complex layout, so that the OCR technology performs well in the dial scale recognition scene.

[0069] Obviously, the above embodiments are only examples for clearly illustrating, and are not limited to the embodiments. For those skilled in the art, other different forms of changes or variations can be made on the basis of the above description. Here, all the embodiments are not required to be exhausted, and the obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A smart grid testing and inspection instrument IoT system, characterized in that, include: A type identification module is used to identify the type of power testing and inspection instruments; wherein, the power testing and inspection instrument types include communication type power testing and inspection instruments and non-communication type power testing and inspection instruments; the communication type power testing and inspection instruments include communication type portable devices and communication type fixed devices; the communication type portable devices include non-cross-domain communication type portable devices and cross-domain communication type portable devices; The data acquisition gateway communicates with the type identification module; it receives data packets from fixed communication devices or portable cross-domain communication devices; it identifies the source protocol of the data packets using predefined protocol identifiers; it parses the data packets according to the source protocol rules to obtain parsed data; and it transmits the parsed data to the middleware system. The middleware system communicates with the data acquisition gateway; it receives and parses data, reformats the parsed data according to the format requirements of the target protocol, and obtains format-converted parsed data; based on the mapping rules between the source protocol semantics and the target protocol semantics, it performs data conversion on the format-converted parsed data to obtain the first target data; according to the data structure of the target protocol, it reorganizes the target data to obtain new first target data; according to the data packet format of the target protocol, it adds headers and metadata to the new first target data and encapsulates it into a first target data packet; and sends the first target data packet to the IoT management platform. The IoT management platform communicates with the middleware system; it is used to receive and store the first target data packet, preprocess the first target data packet to obtain the preprocessed first target data packet, and complete the task of collecting detection data of communication-type fixed devices or cross-domain communication portable devices. The intelligent image detection and recognition module, which communicates with the type recognition module, receives background images containing non-communication power testing instruments or non-cross-domain communication portable devices. It then uses a target detection and recognition model to detect the background image, acquiring the device image of the non-communication power testing instruments or non-cross-domain communication portable devices. Using image analysis and processing methods, it identifies and analyzes the numerical information in the device image to obtain second target data, completing the task of collecting detection data from the non-communication power testing instruments or non-cross-domain communication portable devices. Finally, it transmits the second target data to the digital integrated service platform. The digital integrated service platform communicates with the IoT management platform and the intelligent image detection and recognition module; it is used to obtain the real-time status of the corresponding power equipment based on the preprocessed first target data packet; and to obtain the real-time status of the corresponding power equipment based on the second target data.

2. The smart grid testing and inspection instrument IoT system according to claim 1, characterized in that, When the non-communication type power testing instrument is a non-communication type pointer instrument, the equipment image of the non-communication pointer instrument is acquired, and image analysis and processing methods are used to identify and analyze the numerical information in the equipment image to obtain the second target data, including: Using the trained U-Net model, the device image of the input non-communication pointer instrument is segmented to obtain the segmented image corresponding to the device image of the non-communication pointer instrument as the target image; Using OCR technology, numerical information in the target image is identified to obtain the scale content of the device; wherein, the numerical information is the scale information of a non-communication type pointer instrument; The pointer position in the target image is obtained, and combined with the device scale content, the second target data is obtained.

3. The smart grid testing and inspection instrument IoT system according to claim 2, characterized in that, The trained U-Net model consists of an encoder and a decoder. The encoder is used to extract device image feature information; wherein, the device image feature information of each stage in the encoder is passed to the corresponding stage of the decoder through a skip connection; The decoder is used to output a segmented image corresponding to the device image as the target image.

4. The smart grid testing and inspection instrument IoT system according to claim 2, characterized in that, The method of using OCR technology to identify numerical information in the target image and obtain the device scale content includes: Using OCR technology, image preprocessing is performed on the target image to obtain the preprocessed target image; layout analysis is performed on the preprocessed target image to identify the device scale area and background area in the preprocessed target image, and the device scale area is marked. The device's scale area is segmented into rows and characters to obtain individual scale characters; a deep learning model is then used to identify the value of each scale character. According to the order of the scales in the preprocessed target image, the layout restoration operation is performed to reassemble the values ​​of each scale character to obtain the device scale content.

5. The smart grid testing and inspection instrument IoT system according to claim 4, characterized in that, The image preprocessing operations include tilt correction, blur removal, and image enhancement.

6. The smart grid testing and inspection instrument IoT system according to claim 2, characterized in that, The pointer position in the target image can be obtained using edge detection algorithms or Hough transform methods.

7. The smart grid testing and inspection instrument IoT system according to claim 1, characterized in that, The source protocol semantics include source protocol commands, source protocol responses, and source protocol status; The target protocol semantics include target protocol commands, target protocol responses, and target protocol commands.

8. The smart grid testing and inspection instrument IoT system according to claim 1, characterized in that, Based on the mapping rules between the source protocol semantics and the target protocol semantics, the parsed data after format conversion is renamed and its data type is converted to obtain the first target data.

9. The smart grid testing and inspection instrument IoT system according to claim 1, characterized in that, The target detection and recognition model is a pre-trained YOLOv7 model.

10. A method for acquiring test data from smart grid testing instruments, characterized in that, An IoT system for smart grid testing and inspection instruments as described in any one of claims 1-9, comprising: The type recognition module is used to obtain the type of power testing and inspection instruments; If the power testing and inspection instrument is a communication-type fixed device or a cross-domain communication portable device, then a data acquisition gateway is used to receive data packets from the communication-type fixed device or the cross-domain communication portable device, and the data packets are parsed according to the source protocol rules to obtain parsed data. A middleware system is used to receive the parsed data, and according to the format requirements of the target protocol, the parsed data is reformatted to obtain format-converted parsed data. Based on the mapping rules between the source protocol semantics and the target protocol semantics, the format-converted parsed data is converted to obtain the first target data. According to the data structure of the target protocol, the target data is reorganized to obtain new first target data. According to the data packet format of the target protocol, a header and metadata are added to the new first target data, and it is encapsulated into a first target data packet. The first target data packet is sent to the IoT management platform. The IoT management platform receives and stores the first target data packet, preprocesses the first target data packet to obtain the preprocessed first target data packet, and completes the data acquisition task for the communication-type fixed device or the cross-domain communication portable device. If the power testing and inspection instrument is a non-communication type or a non-cross-domain communication portable device, then the intelligent image detection and recognition module receives a background image containing the non-communication type or non-cross-domain communication portable device. The target detection and recognition model is then used to detect the background image, obtaining the device image. Finally, image analysis and processing methods are used to identify and analyze the numerical information in the device image to obtain the second target data, thus completing the task of collecting testing data from the non-communication type or non-cross-domain communication portable device.

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