A data acquisition system and method for offline computers

Abstract

The application discloses a kind of data acquisition system and method for offline computer, it is related to industrial internet of things technical field, the system includes: offline computer, display, camera and industrial computer;Display is connected with offline computer by first data line, for showing the data to be collected in offline computer;Camera is connected with industrial computer by second data line, for shooting high-definition display picture according to the shooting parameter configured by industrial computer, and the first image obtained by shooting is transmitted to industrial computer;Industrial computer is used to receive first image, whether the time interval of current time from last time executing optical character recognition operation reaches first preset threshold value is judged, if reaching first preset threshold value, first image is segmented according to preset rule to obtain segmentation area, optical character recognition is carried out to each segmentation area, and the optical character recognition result and segmentation area parameter information of each segmentation area are recorded correspondingly.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) technology, and in particular to a data acquisition system and method for offline computers. Background Technology

[0002] Smart power plants, as a model of digital transformation in the energy industry, have achieved a leapfrog development from traditional operating models to highly automated and intelligent ones by integrating next-generation information technologies such as the Internet of Things. Modern smart power plants deploy various smart sensors that can collect parameters such as temperature, pressure, and vibration of equipment in real time. The data collected by these smart sensors is often directly stored in the management information area. According to regulations, power monitoring systems should implement zoned protection, with protection areas divided into production control areas (which can be divided into Security Zone I and Security Zone II) and management information areas (which can be divided into Security Zone III and Security Zone IV) according to their security levels from high to low. Under normal circumstances, the computers in the management information area are strictly physically or logically isolated from the external internet; that is, the computers in the management information area are in an offline state, and any form of direct network connection is prohibited.

[0003] After smart sensors are installed on-site, equipment manufacturers need to ensure operational stability through long-term debugging, requiring real-time retrieval of sensor data stored in offline computers. Similarly, companies providing technical supervision services to smart power plants also need to access sensor data stored in offline computers to optimize technical supervision, data analysis, decision support, and cross-system business collaboration. However, the offline nature of management information area computers makes data export a pain point in the industry, and neither of the existing data acquisition methods can balance efficiency, cost, and security.

[0004] Currently, the main technical solutions for acquiring smart sensor data from offline computers in the management information area are as follows: Method 1: Manual Data Recording and Media Transfer. Operators periodically go to the computer room in the management information area to manually record data by observing the computer monitors, or copy the data from the offline computer using secure removable storage media such as dedicated USB flash drives or CDs, and then transfer it to a computer with internet access for transmission. The disadvantages of this method are as follows: (1) Low work efficiency: It requires dedicated human resources and the operation process is cumbersome.

[0005] (2) Security risks exist: mobile storage media may become a carrier for the spread of viruses and Trojans, violating network security regulations.

[0006] (3) Unable to automate: The entire process relies on manual intervention and cannot meet the needs of high-frequency, automated data collection.

[0007] Method 2: Establish a dedicated network line for data transmission. The disadvantages of this method are as follows: (1) The cost of setting up a dedicated network line is relatively high.

[0008] (2) The network leased line may be connected to the outside Internet, making the power plant's information management area vulnerable to hacker attacks.

[0009] (3) Poor scalability: Each dedicated line is only suitable for data transmission of a fixed power plant and cannot flexibly cope with data transmission of multiple power plants. Summary of the Invention

[0010] The purpose of this invention is to provide a data acquisition system and method for offline computers, which can solve at least one of the above-mentioned problems in the prior art.

[0011] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a data acquisition system for offline computers, including: an offline computer, a display, a camera, and an industrial computer; The display is connected to the offline computer via a first data cable and is used to display the data to be collected in the offline computer; The camera is connected to the industrial computer via a second data cable and is used to capture images of the display screen according to the shooting parameters configured on the industrial computer, and transmit the captured first image to the industrial computer. The industrial computer, upon receiving the first image, determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold. If the first preset threshold is reached, the first image is segmented into multiple segmented regions according to preset rules. Optical character recognition is performed on each segmented region, and the optical character recognition results and segmented region parameter information of each segmented region are recorded accordingly.

[0012] Optionally, the shooting parameters include: shooting time interval and camera warm-up time.

[0013] Optionally, the industrial computer is also used for: If the time interval between the current moment and the last optical character recognition operation does not reach the first preset threshold, then the received first image is compared with the second image from the last optical character recognition. If the comparison result shows that the first image has a significant change compared to the second image, then the steps of segmenting the first image to obtain multiple segmentation regions, performing optical character recognition on each segmentation region, and recording the optical character recognition results and segmentation region parameter information for each segmentation region are performed. If the comparison result shows that the first image is not significantly different from the second image, the camera is triggered to continue capturing the display screen.

[0014] Optionally, when the industrial computer performs a similarity comparison between the received first image and the second image from the previous optical character recognition, it specifically uses the following methods: The first image is segmented according to the preset rules to obtain multiple first segmentation regions; For each first segmentation region, determine a second segmentation region in the second image that matches the first segmentation region; Calculate the pixel values ​​of corresponding pixels in the first segmented region and the second segmented region, and filter out the differing pixels; The proportion of differing pixels is determined based on the total number of all differing pixels in the first image. If the proportion of the difference pixels is greater than a preset difference threshold, it is determined that the first image has a significant change compared to the second image; If the percentage of the difference pixels is less than or equal to a preset difference threshold, it is determined that the first image has no significant change compared to the second image.

[0015] Optionally, the preset rules include: each segmented region contains a variable parameter; the parameter information of the segmented region includes: the segmented region name named after the included variable parameter, the segmented region coordinates, and the segmentation order.

[0016] Optionally, the display, camera, and industrial computer are housed in the same chassis; the chassis includes opposing, openable and closable cabinet doors. The industrial computer is housed inside the chassis, and the display is fixed to an openable cabinet door on one side of the chassis by a fixing device. The camera is mounted on the openable cabinet door on the other side of the chassis via a fixing device; When the opposing openable cabinet door is closed, the monitor, camera, and external light are isolated by the chassis.

[0017] Optionally, the industrial computer is also used for: Each received frame of image is saved according to a pre-configured image quality level; Monitor the storage duration of optical character recognition results and segmentation region parameter information in historical records. When the preset retention time is reached, delete the optical character recognition results and segmentation region parameter information. Monitor the amount of data stored in the folder used to store optical character recognition results and parameter information of segmented regions. If the amount of data stored reaches a preset storage limit, clean up the data stored in the folder.

[0018] Optionally, the system further includes: a data receiver and a router; The industrial computer sends the optical character recognition results and parameter information of each segmented region to the data receiving end via the dedicated router.

[0019] This invention also provides a data acquisition method for offline computers, applicable to a system including an offline computer, a display, a camera, and an industrial computer. The method includes: The offline computer transmits the data to be collected to the display. The camera captures images of the display screen according to the shooting parameters configured by the industrial computer, and transmits the captured first image to the industrial computer. After receiving the first image, the industrial computer determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold. If the first preset threshold is reached, the first image is segmented into multiple segmented regions according to preset rules. Optical character recognition is performed on each segmented region, and the optical character recognition results and segmented region parameter information of each segmented region are recorded accordingly.

[0020] Optionally, after the industrial computer receives the first image and determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold, the method further includes: If the time interval between the current moment and the last optical character recognition operation does not reach the first preset threshold, then the received first image is compared with the second image from the last optical character recognition operation for similarity. If the comparison result shows that the first image has a significant change compared to the second image, then the steps of segmenting the first image to obtain multiple segmentation regions, performing optical character recognition on each segmentation region, and recording the optical character recognition results and segmentation region parameter information for each segmentation region are performed. If the comparison result shows that the first image is not significantly different from the second image, the camera is triggered to continue capturing the display screen.

[0021] This invention provides a data acquisition system for offline computers. Firstly, it eliminates the need for dedicated network lines; when data needs to be collected from multiple power plants, only a specific chassis with a monitor and camera needs to be installed at each plant, offering good scalability and low cost. Secondly, its non-intrusive software design and non-direct network connection architecture do not alter the original network topology of the power plants, preventing implicit connections between isolated networks and external networks, ensuring high security and reliability, and meeting the requirements for isolated environments. Thirdly, image segmentation configuration improves the structure of text recognition results, eliminating the need for manual sorting of disordered data and improving processing efficiency. In summary, the system provided in this application is low-cost, highly efficient, secure, and reliable. Through hardware integration and software co-design, it solves the problem of efficient data acquisition for offline computers. Attached Figure Description

[0022] Figure 1 This is a structural block diagram illustrating a data acquisition system for offline computers according to an embodiment of this application; Figure 2 This is a schematic diagram showing the structure between the chassis, camera, and industrial computer; Figure 3 This is a schematic diagram illustrating the image segmentation process of an embodiment of this application; Figure 4 This is a schematic diagram of the software workflow of a data acquisition system for offline computers; Figure 5 This is a flowchart illustrating the steps of a data acquisition method for an offline computer according to an embodiment of this application. Detailed Implementation

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] This invention provides a data acquisition method and system for offline computers. This system is low-cost, highly efficient, and secure. Through hardware integration and software co-design, it solves the problem of efficient data acquisition for offline computers. Specifically, it comprises two parts: hardware and software. The following detailed description, in conjunction with the accompanying drawings, through specific embodiments and application scenarios, illustrates the data acquisition scheme for offline computers provided in this application.

[0025] As attached Figure 1 As shown, the data acquisition system for offline computers in this application embodiment includes: an offline computer 101, a display 102, a camera 103, and an industrial computer 104. The display 102 is connected to the offline computer 101 via a first data cable and is used to display the data to be collected in the offline computer 101; the camera 103 is connected to the industrial computer 104 via a second data cable and is used to capture the display screen according to the shooting parameters configured in the industrial computer and transmit the captured first image to the industrial computer 104.

[0026] The first data cable can be a High Definition Multimedia Interface Cable (HDMI), and the second data cable can be a Universal Serial Bus data cable (USB).

[0027] The industrial computer 104, upon receiving the first image, determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold. If the first preset threshold is reached, the first image is segmented into multiple segmented regions according to preset rules. Optical character recognition is performed on each segmented region, and the optical character recognition results and segmentation region parameter information of each segmented region are recorded accordingly.

[0028] The first preset threshold can be flexibly set by those skilled in the art, and no specific restrictions are imposed on it in the embodiments of this application.

[0029] In an optional embodiment, the data acquisition system for offline computers further includes a data receiver 106 and a router 105. The industrial computer sends the optical character recognition results and segmentation parameter information of each segmented region to the data receiver via the router, so that the data receiver can perform further analysis on the data.

[0030] A feasible hardware structure for a data acquisition system oriented towards offline computers may include: an offline computer 101, a monitor 102, a camera 103, an industrial computer 104, a router 105, an HDMI data cable, a USB data cable, a network cable, a monitor power cord, and a chassis. The connection relationships and structural design of each component are as follows: Figure 1 As shown.

[0031] The offline computer 101 is connected to the monitor 102 via an HDMI cable to transmit the data to be collected to the monitor 102; the camera 103 is connected to the industrial computer 104 via a USB cable to transmit the captured images from the monitor to the industrial computer 104 for optical character recognition; the industrial computer 104 is connected to the router 105 via a network cable to achieve stable wired communication; the router 105 transmits the optical character recognition results and the photos captured by the camera to the data receiving terminal 106, such as a manufacturer's debugging platform or a technical supervision system, via the Internet.

[0032] In one optional embodiment, the display 102, camera 103, and industrial computer 104 are housed within the same chassis. The chassis includes opposing, closable cabinet doors. The industrial computer 104 is housed inside the chassis, and the display 102 is fixed to one of the closable cabinet doors via a fixing device. The camera 103 is fixed to the other closable cabinet door via a fixing device. When the opposing cabinet doors are closed, the chassis isolates the display 102 and camera 103 from external light. When both cabinet doors are closed, the chassis effectively isolates the camera 103 from external light sources, thereby ensuring the quality of the images captured by the camera 103. Because the camera and display are fixed within a closed chassis, the impact of the external environment on the quality of the images captured by the camera 103 is reduced.

[0033] The structural diagram between the chassis, camera 103, and industrial computer 104 is shown below. Figure 2 As shown. In actual implementation, the connection method and positional relationship between the chassis, camera 103, and industrial computer 104 can be specifically set as follows: The chassis has a cubic structure with two opposing, openable cabinet doors. One door secures a monitor 102 to the chassis with four screws. The monitor 102 is equipped with a power cord 107 and an HDMI cable 108. The HDMI cable 108 is used to connect to an isolated network computer. The back of the cabinet door has a power interface 109 and an HDMI interface 110 for the monitor 102. The other door secures a camera 103 to the chassis with two screws. The camera 103 is equipped with a USB data cable 111 for connecting to and communicating with an industrial computer 104. The back of the cabinet door has a corresponding USB interface 112. The distance between the center of the camera 103 and the center of the monitor 102 is greater than the minimum focusing distance of the camera 103, ensuring clear and blur-free images.

[0034] In one alternative embodiment, the shooting parameters of the camera 103 may include, but are not limited to, shooting time interval and camera warm-up time.

[0035] In an optional embodiment, the industrial computer 104 is further configured to: If the time interval between the current moment and the last optical character recognition operation does not reach the first preset threshold, then the received first image is compared with the second image from the last optical character recognition operation for similarity. If the comparison result shows that the first image has a significant change compared to the second image, then the process of segmenting the first image to obtain multiple segmented regions, performing optical character recognition on each segmented region, and recording the optical character recognition results and segmented region parameter information for each segmented region is executed; if the comparison result shows that the first image has no significant change compared to the second image, then the camera 103 is triggered to continue capturing the display screen.

[0036] More preferably, when the industrial computer 104 compares the similarity of the received first image with the second image from the previous optical character recognition, it specifically performs the following steps: segmenting the first image according to a preset rule to obtain multiple first segmentation regions; for each first segmentation region, determining a second segmentation region in the second image that matches the first segmentation region; calculating the pixel values ​​of corresponding pixels in the first and second segmentation regions, and filtering out differing pixels; determining the proportion of differing pixels based on the total number of differing pixels in the first image; determining that the first image has a significant change compared to the second image when the proportion of differing pixels is greater than a preset difference threshold; and determining that the first image has no significant change compared to the second image when the proportion of differing pixels is less than or equal to the preset difference threshold.

[0037] In an optional embodiment, when the first image is segmented into multiple segmented regions according to a preset rule, the preset rule may include, but is not limited to: each segmented region contains a variable parameter; the parameter information of the segmented region includes: the name of the segmented region named after the included variable parameter, the coordinates of the segmented region, and the segmentation order.

[0038] In an optional embodiment, the industrial computer 104 is further configured to: save each received frame image according to a pre-configured image quality level; monitor the storage duration of historical optical character recognition results and segmentation region parameter information, and delete the optical character recognition results and segmentation region parameter information when the preset retention duration is reached; monitor the amount of stored data in the folder used to store the optical character recognition results and segmentation region parameter information, and clean up the data stored in the folder when the amount of stored data reaches a preset storage amount.

[0039] The data acquisition system for offline computers provided by this invention specifically includes two parts: hardware and software. The software is optical character recognition and camera control software deployed on an industrial computer, and for example, the software is divided into four functional modules: Functional Module 1: Camera Information and Control and Image Recognition Segmentation Region Configuration Module.

[0040] After the software starts, it automatically scans the serial port information of the industrial computer, identifies the connected high-definition cameras, and supports opening, closing, previewing and adjusting the parameters of the cameras, including but not limited to exposure and focal length.

[0041] The data display locations on the smart platforms of different power plants are inconsistent, exhibiting multi-column layouts, mixed text and images, complex tables, and inconsistent font sizes. Optical character recognition (OCR) cannot determine the logical relationships between text blocks. Directly performing OCR on the captured smart platform pages results in data structure chaos, hindering subsequent data processing. This module supports custom image segmentation regions (segmentation region size thresholds: minimum 10×10 pixels, maximum 500×500 pixels). Each segmentation region can contain only one variable data, such as temperature or pressure parameters, and the variable name after character recognition for each segmentation region can be customized. During OCR, the segmentation regions are recognized independently according to a preset order, avoiding the data format disorder caused by traditional "left-to-right, top-to-bottom" recognition, resulting in a fixed structured format that facilitates subsequent data processing and application. The segmented regions of the image have corresponding parameter information, including coordinates, segmentation order, and segmentation region name. Segmentation region coordinates refer to the coordinates of the four vertices of the segmented region with the top-left corner of the original image as the origin; segmentation order refers to the sequential order of the segmented regions; and segmentation region name refers to the parameter name of the data within the segmented region. These parameters can all be saved in a fixed format to a specified JSON file. After the image segmentation parameters are saved, subsequent images will be segmented using the parameters from this JSON file. The image segmentation process is shown in Figure 3.

[0042] Functional Module 2: Core Parameter Configuration Module Configure the camera photo interval: 1-300 seconds (default 5 seconds), which defines the time interval between two camera shots.

[0043] Configure camera warm-up time: 0.5-5 seconds (default 1 second) to ensure the camera is fully focused after being activated, thus improving shooting quality.

[0044] Configure and save image quality: Supports three settings: high, medium, and low (corresponding file sizes ≥2MB, 1-2MB, and ≤1MB).

[0045] Configuration file management parameters: file retention time (configurable from 1 to 90 days), folder storage size limit (configurable from 10 to 100 GB), automatic cleanup when exceeding the threshold.

[0046] Configure automatic operation settings: The software will start automatically on startup and will automatically open and run after the industrial computer restarts due to a power outage.

[0047] Functional module 3: File path configuration module.

[0048] It supports customizable save paths for captured images and optical character recognition results, allowing users to choose between local disks on industrial computers and shared network folders for convenient local data backup and traceability. Shared folders refer to folders that smart sensor manufacturers or companies providing technical supervision services to power plants can access and download captured images and optical character recognition results at any time via the internet.

[0049] Functional module 4: Log display and optical character recognition result preview module.

[0050] The log displays the real-time system operating status (such as camera connection status, number of recognitions, data transmission results, etc.) and abnormal information (such as recognition failure, network interruption, etc.), and saves the log in TXT file format.

[0051] Recognition Result Preview: Real-time display of structured data after each optical character recognition, supporting comparison with the original captured image.

[0052] The software workflow of a data acquisition system for offline computers includes the following steps: Step 1: Start the system: The offline computer runs the power plant smart platform and displays the smart platform on the monitor (the smart platform refers to the software that fully utilizes the cloud service platform to display the data collected by various smart sensors in the power plant in real time on the basis of digitalization and informatization); the industrial computer starts the optical character recognition and camera control software, completes the camera connection, segmentation area configuration and core parameter settings, the camera captures images and completes the first optical character recognition and image segmentation, and then proceeds to Step 2; Step 2, Timed Shooting: The camera takes pictures of the monitor screen at preset intervals (default 5 seconds). Before shooting, a warm-up delay (default 5 seconds) is allowed to ensure clear focus, and then Step 3 is executed; Step 3: File Cleanup: Check the folder storage status. If the storage size limit is reached or the files exceed the retention time, automatically clean up expired files, and then proceed to Step 4. Step 4: Time since last optical character recognition: Compare the current time with the time since the last optical character recognition to see if it exceeds the first preset threshold. If it does, proceed to step 5; otherwise, proceed to step 5.

[0053] Step 5, Image Change Detection: Compare the pixel differences in the segmented regions between the currently captured image and the previously saved image to determine if the data has been updated; if the image has no significant changes (not reaching the difference threshold), do not perform storage and recognition, and return to step 2; if the image has significant changes (reaching the difference threshold), proceed to step 6. Step 6, Data Processing: Perform image segmentation on the image and store the currently captured image. Perform optical character recognition on each segmented region to generate structured data. Step 7: Repeat steps 2-7 until the user manually terminates the operation.

[0054] The image change detection step compares the pixel changes of each segmented region in adjacent photographs. Let the image widths of the two images to be compared be... The height is pixel coordinates are ,in , Each pixel is composed of the brightness values ​​of three channels: red (R), green (G), and blue (B). The pixel values ​​of the two contrasting images are denoted as follows:

[0055] in, I1(i,j) represents the complete set of red, green, and blue pixels at pixel coordinates (i,j) in the first image, and R1(i,j), G1(i,j), and B1(i,j) represent the brightness values ​​of the red, green, and blue pixels at pixel coordinates (i,j) in the first image, respectively. I2(i,j) represents the complete set of red, green, and blue pixels at pixel coordinates (i,j) in the second image, and R2(i,j), G2(i,j), and B2(i,j) represent the brightness values ​​of the red, green, and blue pixels at pixel coordinates (i,j) in the second image, respectively. The brightness value of each channel is between 0 and 255, therefore... , where k represents the k-th image, k=1,2.

[0056] Define the difference value of a single pixel Quantize two images in The degree of pixel difference at a location is calculated using the following formula:

[0057] Set the pixel difference threshold to ( (This means that there is no difference only if the pixel values ​​are exactly the same). when When this condition is met, the pixel is determined to be a difference pixel. The formula for calculating difference pixels is as follows.

[0058]

[0059] N represents the number of pixels that differ between the two images, the function The value is 1 if the condition is met, and 0 otherwise. Let the percentage of differing pixels be... ,but Let the overall difference threshold be... The final determination formula is:

[0060] Because the camera is fixed inside a closed enclosure, there is no shaking or external light interference. Changes in the captured image are only caused by changes in the data on the monitor, ensuring the accuracy of the algorithm's judgment.

[0061] This application provides a data acquisition system for offline computers. Firstly, it eliminates the need for dedicated network lines; when data needs to be collected from multiple power plants, only a specific chassis with a monitor and camera needs to be installed at each plant, offering good scalability and low cost. Secondly, its non-intrusive software design and non-direct network connection architecture do not alter the original network topology of the power plants, preventing hidden connections between isolated networks and external networks, ensuring high security and reliability, and meeting the requirements for isolated environments. Thirdly, image segmentation configuration improves the structure of text recognition results, eliminating the need for manual sorting of disordered data and improving processing efficiency. In summary, the system provided in this application is low-cost, highly efficient, secure, and reliable. Through hardware integration and software co-design, it solves the challenge of efficient data acquisition for offline computers.

[0062] This application also provides a data acquisition method for offline computers, as shown in the attached figure. Figure 5 As shown, the data acquisition method for offline computers in this application includes the following steps: Step 501: The offline computer transmits the data to be collected to the monitor for display.

[0063] The data acquisition method for offline computers provided in this application is applied to a system that includes an offline computer, a display, a camera, and an industrial computer.

[0064] Step 502: The camera captures the screen image according to the shooting parameters configured on the industrial computer, and transmits the captured first image to the industrial computer.

[0065] Step 503: After receiving the first image, the industrial computer determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold. If it reaches the first preset threshold, the first image is segmented into multiple segmented regions according to preset rules. Optical character recognition is performed on each segmented region, and the optical character recognition results and segmented region parameter information of each segmented region are recorded accordingly.

[0066] In one optional embodiment, after receiving the first image, the industrial computer determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold, and then may further perform the following steps: If the time interval between the current moment and the last optical character recognition operation does not reach a first preset threshold, the received first image is compared with the second image from the last optical character recognition. If the comparison result shows that the first image has a significant change compared to the second image, the first image is segmented to obtain multiple segmented regions, optical character recognition is performed on each segmented region, and the optical character recognition result and segmented region parameter information of each segmented region are recorded accordingly. If the comparison result shows that the first image has no significant change compared to the second image, the camera is triggered to continue capturing the display screen.

[0067] It should be noted that the industrial computer can execute the software portion of the data acquisition system for offline computers described above. For the specific operations related to the industrial computer's control of the camera and processing of the received images, please refer to the aforementioned detailed explanation of the software portion, which will not be repeated here.

[0068] The data acquisition system for offline computers may also include a data receiver and a dedicated router. After step 503, the industrial computer sends the optical character recognition results and segmentation parameter information of each segmented region to the data receiver via the router. The data receiver performs further analysis based on the received optical character recognition results and segmentation parameter information of each segmented region.

[0069] The embodiments provided in this application Figure 1 The data acquisition system shown is designed for offline computers and can achieve... Figure 5 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0070] Optionally, embodiments of this application also provide an electronic device, including a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the processes in the data acquisition method for offline computers executed by the industrial computer described above, and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0071] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0072] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data acquisition system for offline computers, characterized in that, include: Offline computers, monitors, cameras, and industrial computers; The display is connected to the offline computer via a first data cable and is used to display the data to be collected in the offline computer; The camera is connected to the industrial computer via a second data cable and is used to capture images of the display screen according to the shooting parameters configured on the industrial computer, and transmit the captured first image to the industrial computer. The industrial computer, upon receiving the first image, determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold. If the first preset threshold is reached, the first image is segmented into multiple segmented regions according to preset rules. Optical character recognition is performed on each segmented region, and the optical character recognition results and segmented region parameter information of each segmented region are recorded accordingly.

2. The data acquisition system for offline computers according to claim 1, characterized in that, The shooting parameters include: shooting time interval and camera warm-up time.

3. The data acquisition system for offline computers according to claim 1, characterized in that, The industrial computer is also used for: If the time interval between the current moment and the last optical character recognition operation does not reach the first preset threshold, then the received first image is compared with the second image from the last optical character recognition. If the comparison result shows that the first image has a significant change compared to the second image, then the steps of segmenting the first image to obtain multiple segmentation regions, performing optical character recognition on each segmentation region, and recording the optical character recognition results and segmentation region parameter information for each segmentation region are performed. If the comparison result shows that the first image is not significantly different from the second image, the camera is triggered to continue capturing the display screen.

4. The data acquisition system for offline computers according to claim 3, characterized in that, When the industrial computer performs a similarity comparison between the received first image and the second image from the previous optical character recognition, it is specifically used for: The first image is segmented according to the preset rules to obtain multiple first segmentation regions; For each first segmentation region, determine a second segmentation region in the second image that matches the first segmentation region; Calculate the pixel values ​​of corresponding pixels in the first segmented region and the second segmented region, and filter out the differing pixels; The proportion of differing pixels is determined based on the total number of all differing pixels in the first image. If the proportion of the difference pixels is greater than a preset difference threshold, it is determined that the first image has a significant change compared to the second image; If the percentage of the difference pixels is less than or equal to a preset difference threshold, it is determined that the first image has no significant change compared to the second image.

5. The data acquisition system for offline computers according to claim 1, characterized in that, The preset rules include: each segmented region contains a variable parameter; the parameter information of the segmented region includes: the segmented region name named after the included variable parameter, the segmented region coordinates, and the segmentation order.

6. The data acquisition system for offline computers according to claim 1, characterized in that, The display, camera, and industrial computer are housed in the same chassis; the chassis includes opposing, openable and closable cabinet doors. The industrial computer is housed inside the chassis, and the display is fixed to an openable cabinet door on one side of the chassis by a fixing device. The camera is mounted on the openable cabinet door on the other side of the chassis via a fixing device; When the opposing openable cabinet door is closed, the monitor, camera, and external light are isolated by the chassis.

7. The data acquisition system for offline computers according to claim 1, characterized in that, The industrial computer is also used for: Each received frame of image is saved according to a pre-configured image quality level; Monitor the storage duration of optical character recognition results and segmentation region parameter information in historical records. When the preset retention time is reached, delete the optical character recognition results and segmentation region parameter information. Monitor the amount of data stored in the folder used to store optical character recognition results and parameter information of segmented regions. If the amount of data stored reaches a preset storage limit, clean up the data stored in the folder.

8. The data acquisition system for offline computers according to claim 1, characterized in that, The system also includes: a data receiver and a router; The industrial computer sends the optical character recognition results and parameter information of each segmented region to the data receiving end through the router.

9. A data acquisition method for offline computers, characterized in that, Applied to a system comprising an offline computer, a monitor, a camera, and an industrial computer, the method includes: The offline computer transmits the data to be collected to the display. The camera captures images of the display screen according to the shooting parameters configured by the industrial computer, and transmits the captured first image to the industrial computer. After receiving the first image, the industrial computer determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold. If the first preset threshold is reached, the first image is segmented into multiple segmented regions according to preset rules. Optical character recognition is performed on each segmented region, and the optical character recognition results and segmented region parameter information of each segmented region are recorded accordingly.

10. The data acquisition method for offline computers according to claim 9, characterized in that, After the industrial computer receives the first image, and determines whether the time interval between the current moment and the last optical character recognition operation reaches a first preset threshold, the method further includes: If the time interval between the current moment and the last optical character recognition operation does not reach the first preset threshold, then the received first image is compared with the second image from the last optical character recognition. If the comparison result shows that the first image has a significant change compared to the second image, then the steps of segmenting the first image to obtain multiple segmentation regions, performing optical character recognition on each segmentation region, and recording the optical character recognition results and segmentation region parameter information for each segmentation region are performed. If the comparison result shows that the first image is not significantly different from the second image, the camera is triggered to continue capturing the display screen.

Images