Intelligent power grid test detection instrument and meter internet-of-things system and detection data acquisition method

Abstract

The invention relates to the technical field of data acquisition, in particular to a smart power grid test detection instrument instrumentation Internet of Things system and a detection data acquisition method, and the system comprises a type recognition module which is used for recognizing the type of a power test detection instrument; the data acquisition gateway, the middleware system and the internet-of-things management platform are used for completing a detection data acquisition task of communication type fixed equipment or cross-domain communication portable equipment; the intelligent image detection and recognition module is used for completing a task of collecting detection data of a non-communication type electric power test detection instrument or a non-cross-domain communication type portable device by using a target detection and recognition model and an image analysis and processing method; and the digital comprehensive service platform is used for acquiring the real-time state of the power equipment. According to the invention, efficient and accurate real-time transmission of detection data is realized.

Description

Technical Field

[0001] The present invention relates to the field of data acquisition technology, and in particular to a smart grid test and detection instrument Internet of Things system and a detection data acquisition method. Background Art

[0002] In the process of power grid construction, the index measurement and status monitoring of power equipment play a vital role. They are not only the core of ensuring the safety of power production and improving the efficiency of operation and maintenance, but also the cornerstone of building a new smart grid system. However, due to the diversity of test and detection instrument types, the traditional power equipment index data collection is highly dependent on manual operation. During the detection process, the test personnel need to handwrite the test data related to the index data. There is a problem that the test data cannot be transmitted in real time, resulting in low timeliness of the test data and transmission delays, so that it cannot meet the power system's needs for real-time monitoring and rapid response of power equipment. Summary of the invention

[0003] To this end, the technical problem to be solved by the present invention is to overcome the problem in the prior art of manually recording power equipment detection data, which leads to low data timeliness and transmission delay, and thus cannot meet the power system's needs for real-time monitoring and rapid response of power equipment.

[0004] In order to solve the above technical problems, the present invention provides a smart grid test and detection instrumentation IoT system, comprising:

[0005] A type identification module, used to identify the type of electric power test and detection instrument; wherein the electric power test and detection instrument type includes communication type electric power test and detection instrument and non-communication type electric power test and detection instrument; the communication type electric power test and detection instrument includes communication type portable equipment and communication type fixed equipment; the communication type portable equipment includes non-cross-domain communication type portable equipment and cross-domain communication type portable equipment;

[0006] The data acquisition gateway is connected to the type identification module for communication; it is used to receive data packets from communication type fixed devices or cross-domain communication portable devices; it uses a predefined protocol identifier to identify the source protocol of the data packet; it parses the data packet according to the source protocol rules of the data packet to obtain parsed data; it transmits the parsed data to the middleware system;

[0007] The middleware system is connected to the data acquisition gateway for communication; it is used to receive the parsed data, reformat the parsed data according to the format requirements of the target protocol, and obtain the parsed data after format conversion; based on the mapping rules between the semantics of the source protocol and the semantics of the target protocol, perform data conversion operations on the parsed data after format conversion to obtain the first target data; according to the data structure of the target protocol, reorganize the target data to obtain new first target data; according to the data packet format of the target protocol, add a header and metadata to the new first target data, and encapsulate it into a first target data packet; send the first target data packet to the IoT management platform;

[0008] The IoT management platform is connected to the middleware system in communication; it is used to receive and store the first target data packet, pre-process the first target data packet, obtain the pre-processed first target data packet, and complete the detection data collection task for the communication type fixed device or the cross-domain communication portable device;

[0009] An intelligent image detection and recognition module is communicatively connected with the type recognition module; it is used to receive a background image containing non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices, detect the background image using a target detection and recognition model, and obtain a device image of the non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices; use an image analysis and processing method to identify and analyze numerical information in the device image to obtain second target data, and complete the task of collecting detection data of the non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices; transmit the second target data to a digital integrated service platform;

[0010] The digital integrated service platform is connected to the Internet of Things management platform and the intelligent image detection and recognition module; it is used to obtain the real-time status of the corresponding power equipment according to the preprocessed first target data packet; and to obtain the real-time status of the corresponding power equipment according to the second target data.

[0011] According to claim 1, a smart grid test and detection instrument IoT system is characterized in that, when the non-communication type power test and detection instrument is a non-communication type pointer instrument, a device image of the non-communication type pointer instrument is obtained, and the numerical information in the device image is identified and analyzed by an image analysis and processing method, and the second target data is obtained, including:

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

[0013] Using OCR technology, the numerical information in the target image is identified to obtain the scale content of the device; wherein the numerical information is the scale information in the non-communication pointer instrument;

[0014] The pointer position in the target image is obtained, and the second target data is obtained by combining the device scale content.

[0015] Preferably, the trained U-Net model consists of an encoder and a decoder;

[0016] The encoder is used to extract device image feature information; wherein the device image feature information of each stage in the encoder is transmitted to the corresponding stage of the decoder through a jump connection;

[0017] The decoder is used to output the segmented image corresponding to the device image as the target image.

[0018] Preferably, the using of OCR technology to identify numerical information in the target image and obtain device scale content includes:

[0019] Using OCR technology, perform image preprocessing on the target image to obtain a preprocessed target image; perform layout analysis on the preprocessed target image to identify the device scale area and background area in the preprocessed target image, and mark the device scale area;

[0020] Perform line segmentation and character segmentation operations on the device scale area to obtain each scale character; use the deep learning model to identify the value of each scale character;

[0021] According to the scale arrangement order in the preprocessed target image, the layout restoration operation is performed, and the value of each scale character is reconnected to obtain the device scale content.

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

[0023] Preferably, the position of the pointer 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 status; the target protocol semantics include target protocol commands, target protocol responses and target protocol commands.

[0025] Preferably, based on a mapping rule between source protocol semantics and target protocol semantics, field renaming and data type conversion operations are performed on the parsed data after format conversion to obtain the first target data.

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

[0027] The present invention also provides a method for collecting data from smart grid test and detection instruments, which is characterized by being applied to the above-mentioned smart grid test and detection instrument Internet of Things system, comprising:

[0028] Use the type identification module to obtain the type of power test and detection instruments;

[0029] If the type of the electric power test and detection instrument is a communication-type fixed device or a cross-domain communication portable device, a data acquisition gateway is used to receive data packets in the communication-type fixed device or the cross-domain communication portable device, and the data packets are parsed according to the source protocol rules of the data packets to obtain parsed data; the middleware system is used to receive the parsed data, and the parsed data is reformatted according to the format requirements of the target protocol to obtain the parsed data after format conversion; based on the mapping rules between the source protocol semantics and the target protocol semantics, a data conversion operation is performed on the parsed data after format conversion 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 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, and the first target data packet is preprocessed to obtain the preprocessed first target data packet, thereby completing the detection data collection task for the communication-type fixed device or the cross-domain communication portable device;

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

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

[0032] The smart grid test and detection instrument and meter IoT system described in the present invention divides electric power test and detection instruments and meters into different types, and identifies the types of electric power test and detection instruments and meters through a type recognition module; according to different types, different detection data collection mechanisms are executed by using a combination of a data acquisition gateway, a middleware system and an IoT management platform or an intelligent image detection and recognition module; if the type identified by the type recognition module is a communication type fixed device or a cross-domain communication portable device, the combination of a data acquisition gateway, a middleware system and an IoT management platform is used to realize real-time collection of detection data of this type; if the type identified by the type recognition module is a non-communication type electric power test and detection instrument and meter or a non-cross-domain communication type portable device, the intelligent image detection and recognition module is used to adopt target detection The detection and recognition technology is used to realize the real-time collection of this type of detection data, improve the accuracy of numerical recognition, effectively handle complex layouts, and increase the application support of OCR technology in numerical recognition scenarios; finally, the collected detection data is transmitted to the digital integrated service platform to obtain the real-time status of the power equipment corresponding to the detection data; the entire system realizes efficient and accurate real-time transmission of detection data, which helps to improve the intelligence and automation level of detection data collection on various types of test and detection instruments, while reducing manpower and time costs, and improving the security and reliability of detection data collection of power test and detection instruments, so that power test and detection instruments can achieve efficient and seamless data interaction and integration with power equipment, and improve the timeliness of real-time status detection of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 It is a structural diagram of a smart grid test and detection instrumentation IoT system provided by the present invention;

[0035] Figure 2 It is a schematic diagram of the classification of power test and detection instruments;

[0036] Figure 3 It is a flow chart of OCR technology recognizing numerical information. DETAILED DESCRIPTION

[0037] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0038] Reference Figure 1 As shown, Figure 1This is a structural diagram of a smart grid test and detection instrumentation IoT system provided by the present invention; specifically includes:

[0039] A type identification module is used to identify the type of power test and detection instruments; wherein the power test and detection instruments include communication-type power test and detection instruments and non-communication-type power test and detection instruments; the communication-type power test and detection 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; wherein the specific types of power test and detection instruments are divided as follows Figure 2 As shown;

[0040] Among them, according to Figure 2 It can be seen that, first of all, based on the communication capabilities of the devices, the devices are divided into two categories: communication devices and non-communication devices;

[0041] Non-communication equipment, such as the DC resistance tester HDBZ-20, does not have data communication capabilities because it was purchased too early. Therefore, direct data exchange is not possible. To achieve real-time monitoring and recording of test data from such equipment, external solutions are sought. Deep learning technology, with its high accuracy, versatility, and high adaptability to test tasks, has become an ideal solution.

[0042] The data communication capability of communication equipment is the basis for the automatic collection and recording of test data of test equipment. However, the communication environment requirements of different communication test equipment are different. For such equipment, its application scenarios and communication environment requirements are further comprehensively examined, that is, according to the application scenarios, communication equipment is subdivided into portable equipment and fixed equipment;

[0043] The portable devices need to be carried to the testing site to perform test and detection tasks; some portable devices, such as the vibration analyzer QN-F4Pro and the MOA resistive current detector LCM500, can only support wired connection or short-distance wireless data communication. The devices cannot establish a data transmission channel with the host computer at the testing site, even if they have data communication capabilities. Therefore, for such portable devices that support data communication, they are further divided into cross-domain devices and non-cross-domain devices according to their data communication environment; the cross-domain devices can establish a data exchange channel with the host computer by virtue of their Internet access capabilities or their long-distance wireless data communication capabilities (4G / 5G), and then transmit data; while the non-cross-domain devices need to adopt the same solution as non-communication devices, and realize the collection and recording of their test and detection data by using target detection and recognition technology; non-cross-domain portable devices are all digital display devices (pointer devices usually do not have data communication capabilities), so they are not further subdivided, and OCR technology can also be used in combination with edge detection or contour detection to identify the numerical information in the device;

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

[0045] The data acquisition gateway is connected to the type identification module for communication; it is used to receive data packets from communication-type fixed devices or cross-domain communication portable devices; it uses a predefined protocol identifier to identify the source protocol of the data packet; it parses the data packet according to the source protocol rules of the data packet, extracts key information and data load, and obtains parsed data; and it transmits the parsed data to the middleware system;

[0046] The middleware system is communicatively connected with the data acquisition gateway; it is used to receive parsed data, reformat the parsed data according to the format requirements of the target protocol, and obtain the parsed data after format conversion; based on the mapping rules between the source protocol semantics and the target protocol semantics, perform data conversion operations on the parsed data after format conversion to obtain the first target data; according to the data structure of the target protocol, reorganize the target data to obtain new first target data; according to the data packet format of the target protocol, add a header and metadata to the new first target data, and encapsulate it into a first target data packet; send the first target data packet to the IoT management platform; wherein, the source protocol semantics includes source protocol commands, source protocol responses, and source protocol status; the target protocol semantics includes target protocol commands, target protocol responses, and target protocol commands; the data conversion operations are field renaming and data type conversion operations;

[0047] The IoT management platform is connected to the middleware system in communication; it is used to receive and store the first target data packet, pre-process the first target data packet, obtain the pre-processed first target data packet, and complete the detection data collection task for the communication type fixed device or the cross-domain communication portable device;

[0048] An intelligent image detection and recognition module is communicatively connected with the type recognition module; it is used to receive a background image containing non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices, detect the background image using a target detection and recognition model, and obtain a device image of the non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices; use an image analysis and processing method to identify and analyze numerical information in the device image to obtain second target data, and complete the task of collecting detection data of the non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices; transmit the second target data to a digital integrated service platform;

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

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

[0051] The trained U-Net model is used to segment the input device image of the non-communication pointer instrument, and the segmented image corresponding to the device image of the non-communication pointer instrument is obtained as the target image; wherein the trained U-Net model is composed of an encoder and a decoder; the encoder is used to reduce the dimension of the device image information from a high-dimensional space to a low-dimensional space, thereby extracting the device image feature information; wherein the input device image at this time is the dial image corresponding to the non-communication pointer instrument; the device image feature information of 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 device image, thereby outputting the segmented image corresponding to the device image as the target image; using U-N et model can segment the device image obtained by the YOLOv7 model, accurately locate the pointer and dial scale, and lay the foundation for automatic reading extraction; U-Net introduces skip connections to help the network learn more detailed feature information. At each stage of the encoder, the feature information of the stage is passed to the corresponding stage of the decoder through a skip connection; in this way, the decoder can use the detailed information obtained from the encoder to better restore the segmentation result of the original device image, effectively solving the problem of loss of image detail information during the encoder downsampling process, thereby improving the image segmentation accuracy of the pointer and dial scale; at the same time, the skip connection fuses features at different levels, allowing the network to use information from different levels, thereby improving the accuracy of image segmentation;

[0052] Use OCR technology to identify the numerical information in the target image and obtain the equipment scale content, where the numerical information is the scale information in the non-communication pointer instrument; use deep learning model and OCR technology to realize the real-time collection of this type of detection data, improve the accuracy of dial scale recognition, effectively handle complex layout, and increase the application support of OCR technology in dial scale recognition scenarios; the specific process is as follows Figure 3 As shown, including:

[0053] Using OCR technology, the target image is subjected to image preprocessing to obtain a preprocessed target image; the preprocessed target image is subjected to layout analysis to identify the device scale area and the background area in the preprocessed target image, and the device scale area is marked; wherein the image preprocessing operation includes tilt correction, blur removal and image enhancement operations;

[0054] Perform line segmentation and character segmentation operations on the device scale area to obtain each scale character; use the deep learning model to capture the character features of the scale on the device, compare them with the preset character library, and identify the value of each scale character;

[0055] According to the scale arrangement order in the pre-processed target image, the layout restoration operation is performed to reassemble the values ​​of each scale character to obtain the device scale content; the entire OCR technology process not only improves the accuracy of dial scale recognition, but also effectively handles complex layouts, making OCR technology perform well in dial scale recognition scenarios;

[0056] Using edge detection algorithm or Hough transform method, the pointer position in the target image is obtained, and combined with the device scale content, the second target data is obtained;

[0057] The digital integrated service platform is connected to the Internet of Things management platform and the intelligent image detection and recognition module; it is used to obtain the real-time status of the corresponding power equipment according to the preprocessed first target data packet; and to obtain the real-time status of the corresponding power equipment according to the second target data.

[0058] In summary, when collecting detection data of communication-type fixed equipment or cross-domain communication portable equipment, the data collection gateway and the collection mechanism preset in the middleware system are adopted, and the data collection middleware system and the data collection gateway are developed, so as to intelligently shield the differences in hardware types, interfaces, protocols, etc. at the perception layer, and form intelligent data processing and interconnection at the business layer, which helps to realize the intelligent and automated collection of detection data of communication-type fixed equipment or cross-domain communication portable equipment, and realize efficient and accurate real-time transmission of detection data of communication-type fixed equipment or cross-domain communication portable equipment.

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

[0060] By dividing the types of test and detection instruments into those with data communication capabilities and those without data communication capabilities (communication type and non-communication type), and respectively acquiring multiple data sources through the collection mechanisms preset in the data acquisition gateway and middleware system and the collection mechanisms preset in the intelligent image detection and recognition module, a method for collecting multi-source heterogeneous detection data is constructed, which facilitates the intelligent and automated collection of data on multiple types of test and detection instruments, and realizes efficient and accurate real-time transmission of detection data of test and detection instruments without data communication capabilities.

[0061] The present invention also provides a method for collecting data of a smart grid test and detection instrument, which is applied to the above-mentioned smart grid test and detection instrument IoT system, including:

[0062] Use the type identification module to obtain the type of power test and detection instruments;

[0063] If the type of the electric power test and detection instrument is a communication-type fixed device or a cross-domain communication portable device, a data acquisition gateway is used to receive data packets in the communication-type fixed device or the cross-domain communication portable device, and the data packets are parsed according to the source protocol rules of the data packets to obtain parsed data; the middleware system is used to receive the parsed data, and the parsed data is reformatted according to the format requirements of the target protocol to obtain the parsed data after format conversion; based on the mapping rules between the source protocol semantics and the target protocol semantics, a data conversion operation is performed on the parsed data after format conversion 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 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, and the first target data packet is preprocessed to obtain the preprocessed first target data packet, thereby completing the detection data collection task for the communication-type fixed device or the cross-domain communication portable device;

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

[0065] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0066] By classifying the types of test and detection instruments into devices with data communication capabilities or devices without data communication capabilities (communication type and non-communication type), and collecting data through a pre-built data collection mechanism based on the types of instruments, the problem in the prior art that due to the diversity of test and detection instruments, test personnel need to record data by hand on site when obtaining test results, resulting in the inability to transmit data instantly, is solved, thereby achieving efficient and accurate real-time transmission of test data.

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

[0068] When collecting data based on the collection mechanism of the preset deep learning model in the intelligent image detection and recognition module, when processing devices with scales and no data communication capabilities (non-communication pointer instruments) such as instrument dials, the acquired image is input into the preset deep learning model, and the OCR technology is used to extract the readings on the device, and the scales and numbers on the device are recognized and converted into readable text to achieve automated data collection and analysis. The whole process not only improves the accuracy of dial scale recognition, but also can effectively handle complex layouts, making OCR technology perform well in dial scale recognition scenarios.

[0069] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A smart grid test and detection instrumentation Internet of Things system, characterized in that: include: A type identification module, used to identify the type of electric power test and detection instrument; wherein the electric power test and detection instrument type includes communication type electric power test and detection instrument and non-communication type electric power test and detection instrument; the communication type electric power test and detection instrument includes communication type portable equipment and communication type fixed equipment; the communication type portable equipment includes non-cross-domain communication type portable equipment and cross-domain communication type portable equipment; The data acquisition gateway is connected to the type identification module for communication; it is used to receive data packets from communication-type fixed devices or cross-domain communication portable devices; it uses a predefined protocol identifier to identify the source protocol of the data packet; it parses the data packet according to the source protocol rules of the data packet to obtain parsed data; and it transmits the parsed data to the middleware system; The middleware system is connected to the data acquisition gateway for communication; it is used to receive the parsed data, reformat the parsed data according to the format requirements of the target protocol, and obtain the parsed data after format conversion; based on the mapping rules between the semantics of the source protocol and the semantics of the target protocol, perform data conversion operations on the parsed data after format conversion to obtain the first target data; according to the data structure of the target protocol, reorganize the target data to obtain new first target data; according to the data packet format of the target protocol, add a header and metadata to the new first target data, and encapsulate it into a first target data packet; send the first target data packet to the IoT management platform; The IoT management platform is connected to the middleware system in communication; it is used to receive and store the first target data packet, pre-process the first target data packet, obtain the pre-processed first target data packet, and complete the detection data collection task for the communication type fixed device or the cross-domain communication portable device; An intelligent image detection and recognition module is communicatively connected with the type recognition module; it is used to receive a background image containing non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices, detect the background image using a target detection and recognition model, and obtain a device image of the non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices; use an image analysis and processing method to identify and analyze numerical information in the device image to obtain second target data, and complete the task of collecting detection data of the non-communication type electric power test and detection instruments or non-cross-domain communication type portable devices; transmit the second target data to a digital integrated service platform; The digital integrated service platform is connected to the Internet of Things management platform and the intelligent image detection and recognition module; it is used to obtain the real-time status of the corresponding power equipment according to the preprocessed first target data packet; and to obtain the real-time status of the corresponding power equipment according to the second target data.

2. According to claim 1, a smart grid test and detection instrumentation Internet of Things system is characterized in that: When the non-communication type electric power test and detection instrument is a non-communication type pointer instrument, a device image of the non-communication type pointer instrument is obtained, and the numerical information in the device image is identified and analyzed by using an image analysis and processing method, and the second target data obtained includes: The trained U-Net model is used to segment the input device image of the non-communication pointer instrument, and the segmented image corresponding to the device image of the non-communication pointer instrument is obtained as the target image; Using OCR technology, the numerical information in the target image is identified to obtain the scale content of the device; wherein the numerical information is the scale information in the non-communication pointer instrument; The pointer position in the target image is obtained, and the second target data is obtained by combining the device scale content.

3. A smart grid test and detection instrumentation 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 transmitted to the corresponding stage of the decoder through a jump connection; The decoder is used to output the segmented image corresponding to the device image as the target image.

4. The smart grid test and detection instrument IoT system according to claim 2, characterized in that: The OCR technology is used to identify the numerical information in the target image and obtain the device scale content, including: Using OCR technology, perform image preprocessing on the target image to obtain a preprocessed target image; perform layout analysis on the preprocessed target image to identify the device scale area and background area in the preprocessed target image, and mark the device scale area; Perform line segmentation and character segmentation operations on the device scale area to obtain each scale character; use the deep learning model to identify the value of each scale character; According to the scale arrangement order in the preprocessed target image, the layout restoration operation is performed, and the value of each scale character is reconnected to obtain the device scale content.

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

6. The smart grid test and detection instrument IoT system according to claim 2, characterized in that: The pointer position in the target image is obtained using edge detection algorithm or Hough transform method.

7. The smart grid test and detection 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 test and detection 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 first target data is obtained after performing field renaming and data type conversion operations on the parsed data after format conversion.

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

10. A method for collecting data from smart grid test instruments, characterized in that: A smart grid test and detection instrument IoT system as claimed in any one of claims 1 to 9, comprising: Use the type identification module to obtain the type of power test and detection instruments; If the type of the electric power test and detection instrument is a communication-type fixed device or a cross-domain communication portable device, a data acquisition gateway is used to receive data packets in the communication-type fixed device or the cross-domain communication portable device, and the data packets are parsed according to the source protocol rules of the data packets to obtain parsed data; the middleware system is used to receive the parsed data, and the parsed data is reformatted according to the format requirements of the target protocol to obtain the parsed data after format conversion; based on the mapping rules between the source protocol semantics and the target protocol semantics, a data conversion operation is performed on the parsed data after format conversion 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 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, and the first target data packet is preprocessed to obtain the preprocessed first target data packet, thereby completing the detection data collection task for the communication-type fixed device or the cross-domain communication portable device; If the electric power test and detection instrument is a non-communication type electric power test and detection instrument or a non-cross-domain communication type portable device, the intelligent image detection and recognition module is used to receive a background image containing the non-communication type electric power test and detection instrument or the non-cross-domain communication type portable device, and the target detection and recognition model is used to detect the background image to obtain the device image of the non-communication type electric power test and detection instrument or the non-cross-domain communication type portable device; the image analysis and processing method is used to identify and analyze the numerical information in the device image to obtain the second target data, thereby completing the task of collecting the detection data of the non-communication type electric power test and detection instrument or the non-cross-domain communication type portable device.

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