CN117572814B - Multi-instrument automatic measurement and control method and system based on Internet of things - Google Patents
Multi-instrument automatic measurement and control method and system based on Internet of things Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/222—Studio circuitry; Studio devices; Studio equipment
- H04N5/262—Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
- H04N5/268—Signal distribution or switching
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
- H04N5/91—Television signal processing therefor
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- G05B2219/24036—Test signal generated by microprocessor, for all I-O tests
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Abstract
The invention discloses an automatic measurement and control method and system for multi-instrument equipment based on the Internet of things, and belongs to the technical field of the Internet of things. The method comprises the following steps: the console module configures a working mode, a power control parameter, an acquisition channel parameter and an image fault judging mode for the adapting unit; the adaptation unit collects a first video output by the DYT product to be tested, performs multiplexing on the first video to obtain a second video and outputs the second video to the coaxial interface, and meanwhile, the adaptation unit outputs the second video to the VGA interface; the console module uses a frame error image generation method to compare the video to be processed with the first video frame by frame to obtain a frame error image, calculates the total frame error rate, stores the frame error image together with the video to be processed by adding a time stamp, and generates a test report. The product testing system has the advantages of realizing automation of product testing, reducing misjudgment of human factors, improving product testing efficiency, and feeding back important nodes for optimizing product life cycles of products such as design, debugging, production, experiment and the like.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to an automatic measurement and control method and system for multi-instrument equipment based on the Internet of things.
Background
The development of the Internet, big data and cloud computing technology promotes the development of a test system to a more valuable direction, and the key point is automatic measurement and control of multi-instrument equipment based on the Internet of things. Most of the existing automatic test systems only change data from manual recording to electronic recording, often lack consideration of data informatization, cannot fully exert the value of test data, and scientifically and efficiently excavate and utilize the data, so that important nodes for optimizing the life cycle of products such as design, debugging, production, experiments and the like are fed back, and are the content of important research of a new-generation automatic test platform.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an automatic measurement and control method and system for multi-instrument equipment based on the Internet of things.
The aim of the invention is realized by the following technical scheme: the first aspect of the present invention provides: an automatic measurement and control method for multi-instrument equipment based on the Internet of things comprises the following steps:
S1: the console module configures a working mode, a power control parameter, an acquisition channel parameter and an image fault judging mode for the adapting unit;
S2: the adaptive unit provides an allocated power supply for the DYT product to be tested according to the working mode, the power supply control parameter, the acquisition channel parameter and the image fault judging mode, and meanwhile, the control console module sends a control instruction and a video to be processed to the DYT product to be tested through the adaptive unit, and the DYT product to be tested starts working and outputs a first video;
S3: the adaptation unit collects a first video output by the DYT product to be tested, performs multiplexing on the first video to obtain a second video and outputs the second video to the coaxial interface, and meanwhile, the adaptation unit outputs the second video to the VGA interface;
S4: the high-definition video recorder module stores the second video, the adaptation unit uses a fault judgment method to judge the fault of the second video according to the image judgment mode, if the fault occurs, the second video is marked as the fault video and the fault information is transmitted to the console module, the console module pops up a fault prompt window, and the user side opens the fault video through the fault prompt side;
s5: the console module uses a frame error image generation method to compare the video to be processed with the first video frame by frame to obtain a frame error image, calculates the total frame error rate, stores the frame error image together with the video to be processed by adding a time stamp, and generates a test report.
Preferably, the power control parameters include: a power supply output voltage upper and lower limit and a current upper limit; the acquisition channel parameters include: access channel configuration, image splicing setting and image uploading setting; the image fault judging mode comprises the following steps: fault category selection and fault sensitivity setting.
Preferably, the control instructions sent to the tested DYT product by the console module through the adapting unit are an RS422 control instruction and an RS232 control instruction.
Preferably, the first video output by each tested DYT product has N paths, the second video generated by the adapting unit also has N paths, the adapting unit selects 1 path from the N paths of second videos or combines the N paths of second videos into 1 path to output to the VGA interface, and N is less than or equal to 4.
Preferably, the high-definition video recorder module can make the second video to be displayed in picture; the console module can access the high definition video recorder module via the LAN.
Preferably, the adaptation unit detects the on-off signal output by the DYT product to be tested, reads the RS422 control instruction and the RS232 control instruction to obtain control instruction data, sends the control instruction data to the console module through the LAN, and the console module stores the control instruction data in the database and simultaneously analyzes the control instruction data and displays the control instruction data through the numerical value, the graph and the table; and the console module reads the numerical values of the temperature sensor and the acceleration sensor and performs a temperature test and a vibration test.
Preferably, the fault judging method includes the following steps:
S41: the adaptation unit judges whether the signal of the video effective signal pin of the video decoding chip is effective, if not, the signal indicates that no video signal fault exists, and if the signal effectively indicates that the video signal is normal, the complete second video image frame data packet is acquired frame by frame;
S42: if the video signal is normal, acquiring frame number information in a second video image frame data packet, recording, and making a difference between the current image frame number and the previous image frame number, and if the difference is greater than 1, indicating that a frame failure occurs;
S43: if the video signal is normal, acquiring resolution information in a data packet of a frame of a second video image, and if the resolution is not equal to the known resolution of the first video, the frame image is a resolution abnormal fault;
S44: if the video signal is normal, and the resolution information in the second video image frame data packet is normal resolution, acquiring the real size of the line data frame in the second video image frame data packet to obtain the real width of the image, accumulating the frame number of the line data frame in the second video image frame data packet to obtain the real height of the image, and if the width and the height of the resolution information are smaller than the real width and the height of the image, indicating that the frame image is a signal incomplete fault;
s45: if any one of the steps S41-S44 fails in the second video, marking the video as a failure video and transmitting failure information to the console module.
Preferably, the frame error image generating method includes the following steps:
S51: the control console module reads the video to be processed and the first video according to the frame as a unit to obtain two frames of images;
S52: the two frames of image data are extracted in an interlacing mode according to the image frames, so that the size of an image is compressed, and the identification rate of the error frame image is improved;
s53: the method comprises the steps of carrying out canny edge detection on two frames of images, judging whether edge positions of the two frames of images are coincident or not, and if not, determining that the frame of images are error frames;
s54: calculating deflection angles of the two frames of images by using a hough algorithm, judging whether the deflection angles of the two frames of images are consistent, and if not, determining that the frame of images are error frames;
S55: if the frame is identified as an error frame in the steps S51-S54, the original image frame of the first video is saved as an error frame image, a time stamp is added into the image, and meanwhile the total number of the error frames is accumulated and counted;
s56: and repeating the steps S51-S55, generating a frame error image, simultaneously storing the original video of the video to be processed until the detection of the video to be processed is completed, calculating the total frame error rate = the total number of error frames/the total number of video frames, and finally generating a test report according to the detection result.
Preferably, the number of the adapter units and the DYT products to be tested is plural.
A second aspect of the invention provides: an automatic measurement and control system of multi-instrument equipment based on the internet of things, which is used for realizing any of the automatic measurement and control methods of the multi-instrument equipment based on the internet of things, comprises the following steps: the system comprises a DYT product to be tested, an adaptation unit, a direct current power supply, a network switch, a high-definition video recorder module, a console module, a display, a temperature sensor, an acceleration sensor and a UPS; the UPS power supply is connected with the control console module, the display, the direct current power supply and the network switch, the temperature sensor and the acceleration sensor are connected with the control console module, the control console module is connected with the network switch and the display, the direct current power supply is connected with the adapting unit, the network switch is connected with the high-definition video recorder and the adapting unit, and the adapting unit is connected with the DYT product to be tested.
The beneficial effects of the invention are as follows:
1) Through the integrated control to many test equipment, realize the automation to the product test, reduce the erroneous judgement of human factor, promote product test efficiency.
2) The test data are collected, transmitted, stored and calculated, and through big data automatic analysis, the test data are scientifically and efficiently mined and utilized, and important nodes for optimizing the life cycle of the product such as design, debugging, production and experiment of the product are fed back.
Drawings
FIG. 1 is a flow chart of an automatic measurement and control method of multi-instrument equipment based on the Internet of things;
FIG. 2 is a block diagram of an automatic measurement and control system of multi-instrument equipment based on the Internet of things;
Fig. 3 is a diagram of a console module data transmission.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
The specific test process is shown in fig. 1, a command is issued by a ① cloud platform, a ② control console sends the cloud platform command to each instrument device, ③ tested products are electrified, ④ control consoles send images to the tested products, ⑤ control consoles receive images sent by the tested products, images sent by ⑥ control consoles and the received images are compared in the control consoles, and ⑦ control consoles upload image comparison results. And the testing process of the tested product is executed according to the sequence numbers, so that the automatic testing of the product is completed. The control console plays a vital role in the whole test system, is connected with the cloud platform, the test equipment and the product, and performs a key image comparison function in the control console. The control console generates image output with corresponding format according to the functional requirement of the tested product, and can also receive the corresponding format image to compare frame by frame. The console also has a storage function, can store the error code image, and can be orderly uploaded when the cloud platform invokes the error code image. The control console controls the instruments and equipment according to the communication protocol of the instruments and equipment, so that the whole test system can work orderly under the control of the control console.
Referring to fig. 1-3, a first aspect of the present invention provides: an automatic measurement and control method for multi-instrument equipment based on the Internet of things comprises the following steps:
S1: the console module configures a working mode, a power control parameter, an acquisition channel parameter and an image fault judging mode for the adapting unit;
S2: the adaptive unit provides an allocated power supply for the DYT product to be tested according to the working mode, the power supply control parameter, the acquisition channel parameter and the image fault judging mode, and meanwhile, the control console module sends a control instruction and a video to be processed to the DYT product to be tested through the adaptive unit, and the DYT product to be tested starts working and outputs a first video;
S3: the adaptation unit collects a first video output by the DYT product to be tested, performs multiplexing on the first video to obtain a second video and outputs the second video to the coaxial interface, and meanwhile, the adaptation unit outputs the second video to the VGA interface;
S4: the high-definition video recorder module stores the second video, the adaptation unit uses a fault judgment method to judge the fault of the second video according to the image judgment mode, if the fault occurs, the second video is marked as the fault video and the fault information is transmitted to the console module, the console module pops up a fault prompt window, and the user side opens the fault video through the fault prompt side;
s5: the console module uses a frame error image generation method to compare the video to be processed with the first video frame by frame to obtain a frame error image, calculates the total frame error rate, stores the frame error image together with the video to be processed by adding a time stamp, and generates a test report.
In some embodiments, the power control parameters include: a power supply output voltage upper and lower limit and a current upper limit; the acquisition channel parameters include: access channel configuration, image splicing setting and image uploading setting; the image fault judging mode comprises the following steps: fault category selection and fault sensitivity setting.
In some embodiments, the control instructions sent by the console module to the tested DYT product through the adapting unit are an RS422 control instruction and an RS232 control instruction.
In some embodiments, the first video output by each tested DYT product has N paths, the second video generated by the adapting unit also has N paths, and the adapting unit selects 1 path from the N paths of second videos or combines the N paths of second videos into 1 path to output to the VGA interface, where N is less than or equal to 4.
In some embodiments, the high definition video recorder module is capable of picture-in-picture display of the second video; the console module can access the high definition video recorder module via the LAN.
In some embodiments, the adaptation unit detects the on-off signal output by the measured DYT product, reads the RS422 control command and the RS232 control command to obtain control command data, sends the control command data to the console module through the LAN, and the console module stores the control command data in the database, and simultaneously analyzes the control command data and displays the control command data through the numerical value, the graph and the table; and the console module reads the numerical values of the temperature sensor and the acceleration sensor and performs a temperature test and a vibration test.
In some embodiments, the fault determination method includes the steps of:
S41: the adaptation unit judges whether the signal of the video effective signal pin of the video decoding chip is effective, if not, the signal indicates that no video signal fault exists, and if the signal effectively indicates that the video signal is normal, the complete second video image frame data packet is acquired frame by frame;
S42: if the video signal is normal, acquiring frame number information in a second video image frame data packet, recording, and making a difference between the current image frame number and the previous image frame number, and if the difference is greater than 1, indicating that a frame failure occurs;
S43: if the video signal is normal, acquiring resolution information in a data packet of a frame of a second video image, and if the resolution is not equal to the known resolution of the first video, the frame image is a resolution abnormal fault;
S44: if the video signal is normal, and the resolution information in the second video image frame data packet is normal resolution, acquiring the real size of the line data frame in the second video image frame data packet to obtain the real width of the image, accumulating the frame number of the line data frame in the second video image frame data packet to obtain the real height of the image, and if the width and the height of the resolution information are smaller than the real width and the height of the image, indicating that the frame image is a signal incomplete fault;
s45: if any one of the steps S41-S44 fails in the second video, marking the video as a failure video and transmitting failure information to the console module.
In some embodiments, the method for generating a frame error image includes the steps of:
S51: the control console module reads the video to be processed and the first video according to the frame as a unit to obtain two frames of images;
S52: the two frames of image data are extracted in an interlacing mode according to the image frames, so that the size of an image is compressed, and the identification rate of the error frame image is improved;
s53: the method comprises the steps of carrying out canny edge detection on two frames of images, judging whether edge positions of the two frames of images are coincident or not, and if not, determining that the frame of images are error frames;
s54: calculating deflection angles of the two frames of images by using a hough algorithm, judging whether the deflection angles of the two frames of images are consistent, and if not, determining that the frame of images are error frames;
S55: if the frame is identified as an error frame in the steps S51-S54, the original image frame of the first video is saved as an error frame image, a time stamp is added into the image, and meanwhile the total number of the error frames is accumulated and counted;
s56: and repeating the steps S51-S55, generating a frame error image, simultaneously storing the original video of the video to be processed until the detection of the video to be processed is completed, calculating the total frame error rate = the total number of error frames/the total number of video frames, and finally generating a test report according to the detection result.
In some embodiments, there are multiple adaptor units and DYT products tested.
The control console sets a working mode, power control parameters (such as upper and lower limits of output voltage and upper limits of current of each power supply), acquisition channel parameters (such as access channel configuration, image splicing setting, image uploading setting and the like), image fault judging modes (such as fault type selection and fault sensitivity setting) and the like for each adapting unit through the LAN; and after receiving the configuration data, the universal adapter terminal works according to a preset mode to provide the distributed power supply for the tested DYT product. Meanwhile, the control console sends RS422 and RS232 control instructions to the DYT to be tested through the universal adapter terminal, and the DYT starts to work; the adaptation unit collects the video output by the tested DYT product, and outputs a plurality of paths of video (not more than 4 paths) output by the DYT to the coaxial (PAL mode) interface in a multiplexing way, and simultaneously outputs the video to the VGA interface (4-1 output or 4-1 output) so as to display an adaptation picture when a display is externally connected; the 20 adaptation units output 20 paths of coaxial videos (PAL mode), the 20 paths of videos are stored by adopting a high-definition video recorder, and meanwhile, the high-definition video recorder also has a picture real-time display function, so that 20 paths of video pictures can be subjected to picture-in-picture display, a certain path of video needs to be switched out independently, and the video can be amplified and displayed independently; the console can also access the high-definition video recorder through LAN; meanwhile, the video unit carries out fault judgment on each path of video signal according to a preset image judging mode, if faults occur, the control console is informed, the control console pops up a fault prompt window, and a user can open the section of fault video playback (read the original image transmitted by the adapting unit) according to the prompt window; the universal adapter terminal also detects the on-off signal output by the DYT product to be tested, reads the data of RS422 and RS232 of the DYT product, forwards all the obtained data to the console through the LAN, and the console stores the data in the database and displays the data in the modes of numerical value, graph, table and the like after analyzing the data; the control console respectively reads the values of the temperature sensor and the acceleration sensor through the USB-RS485, so that the test state marks in the later temperature test and vibration test and the control mode reference in the test stage are facilitated; the control console produces a test report according to the test result and provides functions such as report inquiry and the like;
A second aspect of the invention provides: an automatic measurement and control system of multi-instrument equipment based on the internet of things, which is used for realizing any of the automatic measurement and control methods of the multi-instrument equipment based on the internet of things, comprises the following steps: the system comprises a DYT product to be tested, an adaptation unit, a direct current power supply, a network switch, a high-definition video recorder module, a console module, a display, a temperature sensor, an acceleration sensor and a UPS; the UPS power supply is connected with the control console module, the display, the direct current power supply and the network switch, the temperature sensor and the acceleration sensor are connected with the control console module, the control console module is connected with the network switch and the display, the direct current power supply is connected with the adapting unit, the network switch is connected with the high-definition video recorder and the adapting unit, and the adapting unit is connected with the DYT product to be tested.
As shown in fig. 2, the automatic measurement and control system of the multi-instrument equipment based on the internet of things consists of a console, a universal adapter terminal (comprising 20 adapter units), a display, a network switch, a high-definition video recorder, a direct-current power supply and an Uninterruptible Power Supply (UPS). The whole test system adopts a distributed architecture, the functions of 20 adaptation units in a universal adaptation terminal are completely consistent, the universal adaptation terminal is connected to a control console through an Ethernet, the control console configures the working parameters of the universal adaptation terminal, and each adaptation unit can be respectively connected with 1 DYT to be tested. The whole test system power supply provides uninterrupted power supply through UPS, and after the commercial power is abnormally powered off, the system can still continue to work for 5-10 min. The system is based on the internet of things to realize the interconnection of the tested product and the cloud platform, the cloud platform is connected with a plurality of control consoles through a network switch, and the control consoles are interconnected with testing equipment and the tested product through the network switch. Each control console is responsible for testing the image receiving and transmitting of the tested product, comparing each frame of image with the original image after receiving each frame of image, storing the frame error image, and calculating the overall frame error rate. And uploading the frame error image, the original image and the frame error rate to the cloud platform through a network. As shown in fig. 3, a plurality of controllers are connected to the cloud platform through a network. Each console is connected to 20 products under test. The test data of the tested product are stored in each control console, when the error frame image appears, the control console adds a time stamp on the error frame image and stores the error frame image together with the original image, thereby facilitating the comparison and analysis. The test data may be viewed at the consoles or on the cloud platform. And storing the final test result in the cloud platform according to the test time and the assembly batch.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (9)
1. A multi-instrument automatic measurement and control method based on the Internet of things is characterized in that: the method comprises the following steps:
S1: the console module configures a working mode, a power control parameter, an acquisition channel parameter and an image fault judging mode for the adapting unit;
S2: the adaptive unit provides an allocated power supply for the DYT product to be tested according to the working mode, the power supply control parameter, the acquisition channel parameter and the image fault judging mode, and meanwhile, the control console module sends a control instruction and a video to be processed to the DYT product to be tested through the adaptive unit, and the DYT product to be tested starts working and outputs a first video;
S3: the adaptation unit collects a first video output by the DYT product to be tested, performs multiplexing on the first video to obtain a second video and outputs the second video to the coaxial interface, and meanwhile, the adaptation unit outputs the second video to the VGA interface;
S4: the high-definition video recorder module stores the second video, the adaptation unit uses a fault judgment method to judge the fault of the second video according to the image judgment mode, if the fault occurs, the second video is marked as the fault video and the fault information is transmitted to the console module, the console module pops up a fault prompt window, and the user side opens the fault video through the fault prompt side;
S5: the control console module uses a frame error image generation method to compare the video to be processed with the first video frame by frame to obtain a frame error image, calculates the total frame error rate, stores the frame error image together with the video to be processed by adding a time stamp, and generates a test report;
the fault judging method comprises the following steps:
S41: the adaptation unit judges whether the signal of the video effective signal pin of the video decoding chip is effective, if not, the signal indicates that no video signal fault exists, and if the signal effectively indicates that the video signal is normal, the complete second video image frame data packet is acquired frame by frame;
S42: if the video signal is normal, acquiring frame number information in a second video image frame data packet, recording, and making a difference between the current image frame number and the previous image frame number, and if the difference is greater than 1, indicating that a frame failure occurs;
S43: if the video signal is normal, acquiring resolution information in a data packet of a frame of a second video image, and if the resolution is not equal to the known resolution of the first video, the frame image is a resolution abnormal fault;
S44: if the video signal is normal, and the resolution information in the second video image frame data packet is normal resolution, acquiring the real size of the line data frame in the second video image frame data packet to obtain the real width of the image, accumulating the frame number of the line data frame in the second video image frame data packet to obtain the real height of the image, and if the width and the height of the resolution information are smaller than the real width and the height of the image, indicating that the frame image is a signal incomplete fault;
s45: if any one of the steps S41-S44 fails in the second video, marking the video as a failure video and transmitting failure information to the console module.
2. The automatic measurement and control method for multi-instrument equipment based on the internet of things according to claim 1, wherein the method comprises the following steps: the power control parameters include: a power supply output voltage upper and lower limit and a current upper limit; the acquisition channel parameters include: access channel configuration, image splicing setting and image uploading setting; the image fault judging mode comprises the following steps: fault category selection and fault sensitivity setting.
3. The automatic measurement and control method for multi-instrument equipment based on the internet of things according to claim 1, wherein the method comprises the following steps: the control instructions sent to the DYT product to be tested by the control console module through the adapting unit are an RS422 control instruction and an RS232 control instruction.
4. The automatic measurement and control method for multi-instrument equipment based on the internet of things according to claim 1, wherein the method comprises the following steps: the first video output by each tested DYT product has N paths, the second video generated by the adapting unit also has N paths, the adapting unit selects 1 path from the N paths of second videos or combines the N paths of second videos into 1 path to output to the VGA interface, and N is less than or equal to 4.
5. The automatic measurement and control method for multi-instrument equipment based on the internet of things according to claim 1, wherein the method comprises the following steps: the high-definition video recorder module can be used for making the second video into picture-in-picture display; the console module can access the high definition video recorder module via the LAN.
6. The automatic measurement and control method for multi-instrument equipment based on the internet of things according to claim 3, wherein the method comprises the following steps of: the adaptation unit detects an on-off signal output by the DYT product to be detected, reads an RS422 control instruction and an RS232 control instruction to obtain control instruction data, sends the control instruction data to the control console module through the LAN, and the control console module stores the control instruction data in the database and simultaneously analyzes the control instruction data and displays the control instruction data through a numerical value, a graph and a table; and the console module reads the numerical values of the temperature sensor and the acceleration sensor and performs a temperature test and a vibration test.
7. The automatic measurement and control method for multi-instrument equipment based on the internet of things according to claim 1, wherein the method comprises the following steps: the frame error image generation method comprises the following steps:
S51: the control console module reads the video to be processed and the first video according to the frame as a unit to obtain two frames of images;
S52: the two frames of image data are extracted in an interlacing mode according to the image frames, so that the size of an image is compressed, and the identification rate of the error frame image is improved;
s53: the method comprises the steps of carrying out canny edge detection on two frames of images, judging whether edge positions of the two frames of images are coincident or not, and if not, determining that the frame of images are error frames;
s54: calculating deflection angles of the two frames of images by using a hough algorithm, judging whether the deflection angles of the two frames of images are consistent, and if not, determining that the frame of images are error frames;
S55: if the frame is identified as an error frame in the steps S51-S54, the original image frame of the first video is saved as an error frame image, a time stamp is added into the image, and meanwhile the total number of the error frames is accumulated and counted;
s56: and repeating the steps S51-S55, generating a frame error image, simultaneously storing the original video of the video to be processed until the detection of the video to be processed is completed, calculating the total frame error rate = the total number of error frames/the total number of video frames, and finally generating a test report according to the detection result.
8. The automatic measurement and control method for the multi-instrument equipment based on the internet of things according to any one of claims 1 to 7, wherein the method is characterized by comprising the following steps of: the number of the adaptive units and the DYT products to be tested is plural.
9. The utility model provides an automatic measurement and control system of multi-instrument equipment based on thing networking which characterized in that: the method for realizing the multi-instrument automatic measurement and control method based on the internet of things according to any one of claims 1-8, comprising the following steps: the system comprises a DYT product to be tested, an adaptation unit, a direct current power supply, a network switch, a high-definition video recorder module, a console module, a display, a temperature sensor, an acceleration sensor and a UPS; the UPS power supply is connected with the control console module, the display, the direct current power supply and the network switch, the temperature sensor and the acceleration sensor are connected with the control console module, the control console module is connected with the network switch and the display, the direct current power supply is connected with the adapting unit, the network switch is connected with the high-definition video recorder and the adapting unit, and the adapting unit is connected with the DYT product to be tested.
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