CN113411573A - Power grid monitoring system detection method and device, computer equipment and medium - Google Patents
Power grid monitoring system detection method and device, computer equipment and medium Download PDFInfo
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
- H04N17/004—Diagnosis, testing or measuring for television systems or their details for digital television systems
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention discloses a power grid monitoring system detection method, a device, computer equipment and a medium, wherein the method comprises the following steps: logging in the terminal equipment to be detected by adopting a preset protocol; acquiring a login response message fed back by the terminal equipment to be detected, and determining the online state of the terminal equipment to be detected according to the login response message; acquiring the video image data collected by the terminal equipment to be detected; and carrying out picture quality detection on the video image data based on a preset detection algorithm, and determining an image quality detection report of the terminal equipment. The embodiment of the invention logs in the monitoring terminal by running the detection program, realizes the automatic detection of the online state and the imaging quality of the terminal equipment, is beneficial to improving the detection efficiency and the accuracy of the monitoring equipment and reducing the manual inspection cost.
Description
Technical Field
The invention relates to the technical field of power grid monitoring, in particular to a power grid monitoring system detection method, a power grid monitoring system detection device, computer equipment and a medium.
Background
With the development of intelligent construction of an electric power system, a power grid video monitoring system is widely applied, the video monitoring coverage rate of a transformer substation reaches more than 95%, all-weather video and image monitoring on an overhead transmission line can be realized, and the burden of inspection operation of the transmission line is greatly reduced.
In a power grid video monitoring system, technologies such as video/image acquisition, transmission, compression, software decoding and the like are mainly integrated, and the quality of a video source provided by video/image acquisition equipment is a key factor influencing the monitoring performance of the power grid video monitoring system.
At present, the video/image acquisition equipment manufacturers of the power grid video monitoring system are various, the quality of the equipment is uneven, and along with the increase of the service life of the equipment, the parts of the equipment are continuously aged, so that the following sampling quality problems can be caused: online rate, communication protocol stability, video image quality anomalies, and the like. Due to the fact that the number of terminal devices is large, time and labor are consumed by manual inspection, and certain protocol problems cannot be analyzed manually, and the video monitoring result of the transformer substation is influenced.
Disclosure of Invention
The invention provides a power grid monitoring system detection method, a power grid monitoring system detection device, computer equipment and a medium, which are used for automatically detecting the online state and the image quality of terminal equipment of a video monitoring system and improving the equipment detection efficiency.
In a first aspect, an embodiment of the present invention provides a method for detecting a power grid monitoring system, where the power grid monitoring system is connected to a terminal device, and the method includes the following steps:
logging in the terminal equipment to be detected by adopting a preset protocol;
acquiring a login response message fed back by the terminal equipment to be detected, and determining the online state of the terminal equipment to be detected according to the login response message;
acquiring the video image data collected by the terminal equipment to be detected;
and carrying out picture quality detection on the video image data based on a preset detection algorithm, and determining an image quality detection report of the terminal equipment.
Optionally, the acquiring the video image data acquired by the terminal device to be detected includes the following steps: periodically sending a video retrieval instruction to the terminal equipment to be detected based on a preset equipment list; acquiring video stream data returned by the terminal equipment to be detected; and unpacking and decoding the video stream data to obtain the video image data.
Optionally, the picture quality detecting includes: brightness anomaly detection, sharpness anomaly detection, snow interference detection, color cast detection, signal loss detection, or image occlusion detection.
Optionally, the detecting the picture quality of the video image data based on a preset detection algorithm includes the following steps: carrying out graying processing on any target frame picture in the video image data to obtain a grayscale image; acquiring a mean value and a variance value of the gray level image; acquiring a preset brightness threshold; and determining an image brightness detection result of the target frame picture according to the mean value, the variance value and the preset brightness threshold value.
Optionally, the detecting the picture quality of the video image data based on a preset detection algorithm includes the following steps: carrying out graying processing on any target frame picture in the video image data to obtain a grayscale image; acquiring a preset snowflake point brightness threshold value; carrying out binarization processing on the gray-scale image based on the snow point brightness threshold value to obtain a first binarization foreground image; calculating the proportion of white pixel points in the first binary foreground image, and determining an initial snowflake interference detection result according to the calculation result; acquiring an image checking instruction; and correcting the initial snowflake interference detection result according to the image checking instruction to obtain a final snowflake interference detection result.
Optionally, the detecting the picture quality of the video image data based on a preset detection algorithm includes the following steps: carrying out edge detection on any target frame picture in the video image data to obtain an edge image; acquiring the edge image, and performing binarization processing to obtain a second binarization foreground image; carrying out connected region detection on the second binarization foreground image, and determining the maximum connected region area according to the detection result; and determining a camera shielding detection result and a signal loss detection result according to the area of the maximum communication area.
Optionally, the detecting the picture quality of the video image data based on a preset detection algorithm includes the following steps: performing convolution filtering processing on any target frame picture in the video image data to obtain a fuzzy picture; acquiring a standard difference value of the fuzzy graph; and determining the definition abnormity detection result of the target frame picture according to the standard difference value.
In a second aspect, an embodiment of the present invention further provides a detection apparatus for a power grid monitoring system, where the power grid monitoring system is connected to a terminal device, and the detection apparatus includes: the login protocol sending module is used for logging in the terminal equipment to be detected by adopting a preset protocol; the online state detection module is used for acquiring a login response message fed back by the terminal equipment to be detected and determining the online state of the terminal equipment to be detected according to the login response message; the picture sampling module is used for acquiring the video image data acquired by the terminal equipment to be detected; and the picture detection module is used for carrying out picture quality detection on the video image data based on a preset detection algorithm and determining a picture quality detection report of the terminal equipment.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the power grid monitoring system detection method.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for detecting a power grid monitoring system is implemented.
The power grid monitoring system detection device, the computer equipment and the medium provided by the embodiment of the invention execute a power grid monitoring system detection method, the method adopts a preset protocol to reversely log in a terminal equipment to be detected of a monitoring system, determines the online state of the terminal equipment to be detected according to a login response message fed back by the terminal equipment to be detected, acquires video image data collected by the terminal equipment to be detected, detects the picture quality of the video image data based on a preset detection algorithm, and determines an image quality detection report of the terminal equipment.
Drawings
Fig. 1 is a flowchart of a power grid monitoring system detection method according to an embodiment of the present invention;
fig. 2 is a flowchart of another power grid monitoring system detection method according to an embodiment of the present invention;
fig. 3 is a flowchart of a picture quality detection method according to an embodiment of the present invention;
fig. 4 is a flowchart of another picture quality detection method according to an embodiment of the present invention;
fig. 5 is a flowchart of another picture quality detection method according to an embodiment of the present invention;
fig. 6 is a flowchart of another picture quality detection method according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a detection device of a power grid monitoring system according to a second embodiment of the present invention;
fig. 8 is a schematic structural diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a power grid monitoring system detection method according to an embodiment of the present invention, where the present embodiment is applicable to an application scenario for automatically monitoring a terminal device accessing to a power grid video monitoring system, and the method may be executed by a specific function module and a detection program, and the detection program may be stored in a server or an intelligent device.
As shown in fig. 1, the method for detecting the power grid monitoring system specifically includes the following steps:
step S1: and logging in the terminal equipment to be detected by adopting a preset protocol.
The terminal device to be detected may be a video monitoring terminal device accessed to a power grid video monitoring system or a substation processing Unit (RPU), and typically, the video monitoring terminal device may include a camera, a network camera, an infrared thermal imager, an image sensor, or the like.
Optionally, the preset protocol may be a southern power grid internal video monitoring protocol, which is referred to as a PG protocol for short, and the terminal device to be detected is provided with a login interface supporting the PG protocol.
In this step, a login message may be sent to the terminal device to be detected by running the device detection program, and the login message is encapsulated by the PG protocol.
Step S2: and obtaining a login response message fed back by the terminal equipment to be detected, and determining the online state of the terminal equipment to be detected according to the login response message.
In this step, the terminal device to be detected sends a login response message to the device detection program, and the login response message is encapsulated by using the PG protocol.
Optionally, the device detection program may store the login status code by establishing a custom device standard library, periodically detect the terminal device to be detected accessing the network video monitoring system by using the thread pool, receive the login response message, analyze the login response message fed back by the terminal device to be detected to obtain the response status code, and determine whether the login is successful from the response status code.
If the login is successful, the equipment detection program judges that the terminal equipment to be detected is online, and the subsequent step S3 is continuously executed; if the login fails, the device detection program saves the current online status detection result, for example, the Identity Document (ID) of the terminal device to be detected, the offline time of the device, and the reason of the failure of the terminal login (e.g., network failure or terminal device abnormality) may be stored in a storage log format.
Step S3: and acquiring video image data acquired by the terminal equipment to be detected.
In this step, the video image data may be sent to the server side running the detection program in a video stream manner.
Step S4: and carrying out picture quality detection on the video image data based on a preset detection algorithm, and determining an image quality detection report of the terminal equipment.
The image quality detection report can adopt a digital table mode, stores the ID number of the terminal device to be detected, quality detection items and quality detection results, and is used for judging the imaging quality of the terminal device.
In this step, the target of the picture quality detection is a single-frame picture, which can perform quality detection on one or more frames of pictures in the video stream, and generate an image quality detection report according to the detection result.
Optionally, the preset detection algorithm may include one or more combinations of image processing methods such as an image edge detection operator, a mean standard deviation algorithm, an interpolation expansion algorithm, an inverse binarization processing algorithm, or black hole filling.
Specifically, in the operation process of the power grid video monitoring system, an operator can operate an equipment detection program at a server or an intelligent equipment end, the equipment detection program sends a login message to any terminal equipment to be detected, which is accessed to the power grid video monitoring system, the login message can be packaged by adopting a PG protocol, and the terminal equipment to be detected is reversely logged in through the PG protocol. The equipment detection program receives a login response message returned by the terminal equipment to be detected, analyzes the login response message to obtain a response state code of the current terminal equipment to be detected, wherein the response state code comprises a login success state code and a login unsuccessful state code, and if the response state code is the login unsuccessful state code, the equipment detection program judges that the current terminal equipment to be detected is offline and stores a current online state detection result; and if the response status code is a login success status code, the equipment detection program judges that the current terminal equipment to be detected is online.
If the terminal equipment to be detected is online, the equipment detection program acquires the video stream of the current terminal equipment to be detected, processes the video stream to obtain a plurality of frames of pictures, can randomly select a preset number of frames of pictures, performs image processing and quality detection on the selected pictures based on a preset detection algorithm, and stores a detection result by adopting a quality detection report format, so that the problems of high quality monitoring cost and low efficiency of the power grid video monitoring system are solved, the online state and the image quality of the terminal equipment of the video monitoring system are automatically detected, the detection efficiency and the accuracy of the equipment are improved, the manual inspection cost of the system is reduced, and the monitoring effect of the power grid video monitoring system is favorably improved.
Optionally, fig. 2 is a flowchart of another power grid monitoring system detection method provided in an embodiment of the present invention, and on the basis of fig. 1, a specific implementation manner for acquiring video image data is exemplarily provided, so as to achieve an object of acquiring a terminal image online.
Referring to fig. 2, in the step S3, acquiring video image data collected by the terminal device to be detected specifically includes the following steps:
step S301: and periodically sending a video retrieval instruction to the terminal equipment to be detected based on the preset equipment list.
The preset equipment list can be used for storing the ID number, the model number, the equipment serial number and the like of the terminal equipment accessed to the same power grid video monitoring system.
In this step, the video retrieval instruction may be encapsulated using the PG protocol.
Step S302: and acquiring video stream data returned by the terminal equipment to be detected.
In this step, the video stream data may be encapsulated using the PG protocol.
Step S303: and unpacking and decoding the video stream data to obtain video image data.
Specifically, when polling is performed on the terminal device accessing the system, the device detection program may sequentially and cyclically send a video retrieval instruction to the terminal device to be detected according to the device serial number 1, the device serial number 2, and the device serial number … … in the preset device list, acquire video stream data returned by the terminal device to be detected in real time through the video stream-taking interface, perform unpacking and decoding operations on the video stream data to obtain 25 frames of pictures per second, select at least part of pictures from the decoded pictures as final video image data, implement automatic terminal device imaging acquisition, and facilitate improvement of device detection efficiency.
Optionally, the picture quality detection may include: brightness anomaly detection, sharpness anomaly detection, snow interference detection, color cast detection, signal loss detection, or image occlusion detection.
Specifically, a target frame picture can be selected from the video image data, picture quality detection is performed on the target frame picture, and whether the terminal device to be detected is in an abnormal operation state or not is judged according to a quality detection result.
Hereinafter, various image quality detection methods will be described in detail with reference to the following embodiments and the accompanying drawings.
Optionally, fig. 3 is a flowchart of a picture quality detection method according to an embodiment of the present invention, and this embodiment exemplarily shows a specific implementation of performing luminance anomaly detection on a video image, but not limiting a picture luminance anomaly detection manner.
Referring to fig. 3, the detecting of the abnormal brightness of the video image data based on the preset detection algorithm in step S4 includes the following steps:
step S431: and carrying out graying processing on any target frame picture in the video image data to obtain a grayscale image.
Step S432: and acquiring a mean value M1 and a variance value U1 of the gray-scale map.
Step S433: and acquiring a preset brightness threshold value.
The preset brightness threshold value can be set as a reference point according to the picture brightness mean value, and can include a preset brightness upper threshold value and a preset brightness lower threshold value.
Step S434: and determining an image brightness detection result of the target frame picture according to the mean value, the variance value and the preset brightness threshold value.
Specifically, when picture quality detection is carried out, an equipment detection program is operated, one or more decoded pictures are randomly selected as target frame pictures, graying processing is carried out on the target frame pictures, the ratio of the gray mean value M1 to the gray variance value U1 is calculated to obtain a brightness ratio, if the brightness ratio is higher than a preset brightness upper limit threshold, the equipment detection program judges that the current imaging picture is too bright, and the picture over-bright result is stored; if the ratio is lower than a preset brightness lower limit threshold, the equipment detection program judges that the current imaging picture is too dark, and stores the picture too dark result; if the brightness ratio is higher than the preset brightness upper limit threshold, the equipment detection program judges that the brightness of the current imaging picture is normal, and the brightness detection result is stored. Therefore, the automatic detection of the imaging brightness of the terminal equipment can be realized through a simple algorithm, and the detection efficiency is favorably improved.
Optionally, fig. 4 is a flowchart of another picture quality detection method provided in an embodiment of the present invention, and this embodiment exemplarily shows a specific implementation of performing snowflake interference detection on a video image, but not limiting a snowflake interference detection manner.
Referring to fig. 4, the snow interference detection on the video image data based on the preset detection algorithm in the step S4 includes the following steps:
step S441: and carrying out graying processing on any target frame picture in the video image data to obtain a grayscale image.
In this step, the same graying scheme as that of step S431 can be adopted for the graying process.
Step S442: and acquiring a preset snowflake point brightness threshold value.
Step S443: and carrying out binarization processing on the gray level image based on the snowflake point brightness threshold value to obtain a first binarization foreground image.
Step S444: and calculating the proportion of the white pixel points in the first binary foreground image, and determining an initial snowflake interference detection result according to the calculation result.
Step S445: and acquiring an image checking instruction.
Step S446: and correcting the initial snowflake interference detection result according to the image checking instruction to obtain a final snowflake interference detection result.
Specifically, when picture quality detection is carried out, an equipment detection program is operated, one or more frames of decoded pictures are randomly selected as target frame pictures, graying processing is carried out on the target frame pictures to obtain a gray map, pixel points with the pixel point brightness smaller than a preset snowflake point brightness threshold value in the gray map are processed into black pixel points, pixel points with the pixel point brightness larger than or equal to the preset snowflake point brightness threshold value in the gray map are processed into white pixel points to obtain a first binary foreground map, the proportion of the white pixel points in the first binary foreground map is calculated, and if the proportion value is higher than a preset proportion threshold value, the equipment detection program judges that snowflake interference exists and sends snowflake interference early warning; and if the proportion value is lower than the preset proportion threshold value, judging that no snowflake interference exists by the equipment detection program.
Further, after receiving the early warning of snow interference, the monitoring personnel manually recheck, manually judge whether snow points exist by observing video image data sent back by the terminal equipment to be detected, if the snow points exist by manual judgment, the monitoring personnel issue a check instruction with correct check result to the equipment detection program, and the equipment detection program does not need to adjust the initial snow interference detection result and related threshold values; if the snowflake is artificially determined to be absent, the monitoring personnel issues a check instruction with a wrong check result to the equipment detection program, the equipment detection program updates the related threshold (for example, a preset snowflake point brightness threshold and/or a preset proportion threshold) of the snowflake interference detection, and the snowflake interference detection is executed again by adopting the updated threshold until the artificial rechecking is correct.
The embodiment of the invention simplifies the snowflake recognition algorithm by carrying out binarization processing on the gray level image, is beneficial to improving the image detection efficiency, simplifies the algorithm complexity and reduces the performance requirements of software and hardware.
Optionally, fig. 5 is a flowchart of another picture quality detection method according to an embodiment of the present invention.
The method for detecting the picture quality of the video image data based on the preset detection algorithm comprises the following steps:
step S451: and carrying out edge detection on any target frame picture in the video image data to obtain an edge image.
Step S452: and acquiring an edge image, and performing binarization processing to obtain a second binarization foreground image.
Step S453: and detecting a connected region of the second binarization foreground image, and determining the maximum connected region area according to the detection result.
Step S454: and determining a camera shielding detection result and a signal loss detection result according to the area of the maximum communication area.
Specifically, when performing picture quality detection, an equipment detection program is operated, one or more decoded pictures are randomly selected as target frame pictures, the equipment detection program can adopt a canny edge detection operator to perform edge detection on the target frame pictures to obtain an edge map, binarization processing is performed on the edge map, pixel points with the pixel point brightness lower than a preset binarization threshold value are processed into black pixel points, pixel points with the pixel point brightness higher than or equal to the preset binarization threshold value are processed into white pixel points to obtain a second binarization foreground map, in the second binarization foreground map, black parts are grouped into a foreground, the rest parts are used as a background, connected region detection is performed on the foreground to obtain a maximum connected region area, the ratio of the maximum connected region area to the black value area of the target frame picture area is calculated, and the ratio of the black value area is the shielding rate of the camera, and if the area ratio of the black value is larger than the preset black screen upper limit threshold value, judging that the video signal is lost by the equipment detection program, and storing a camera shielding detection result and a video signal loss detection result. Therefore, the invention realizes the detection of the shielding rate and the loss of the video signal through the detection of the communication area, and is beneficial to improving the detection efficiency.
Optionally, fig. 6 is a flowchart of another picture quality detection method according to an embodiment of the present invention.
The method for detecting the picture quality of the video image data based on the preset detection algorithm comprises the following steps:
step S461: and carrying out convolution filtering processing on any target frame picture in the video image data to obtain a fuzzy picture.
Step S462: and acquiring a standard deviation value of the fuzzy graph.
Step S462: and determining the definition abnormity detection result of the target frame picture according to the standard difference value.
Specifically, when picture quality detection is performed, an equipment detection program is operated, one or more decoded pictures are randomly selected as target frame pictures, the equipment detection program can adopt a laplacian operator to perform convolution filtering processing on the target frame pictures, a convolution kernel can be 3 × 3, the convolution kernel is used for sliding on the target frame pictures, pixel values on picture points are multiplied by numerical values on the corresponding convolution kernels, all multiplied values are added to serve as pixel values of middle pixel points of the convolution kernel, all the pictures are finally slid, laplacian standard deviations of image blocks are calculated, standard deviation values are determined according to the laplacian standard deviations, and if the standard deviation values are larger than a preset standard deviation threshold (for example, 30), the equipment detection program judges that images are fuzzy, and the definition detection results are saved.
Optionally, the method for detecting the picture quality of the video image data based on the preset detection algorithm further includes the following steps: acquiring any target frame picture in video image data; calculating the average chroma value of the target frame picture by adopting a Lab color space; calculating a color cast factor according to the average value of the chroma; and determining a color cast detection result according to the chrominance factor.
Specifically, the target frame picture can be converted from an RGB color space to an Lab color space, a channel image a and a channel image b are extracted from the Lab color space, the ratio of the chromaticity average value Ms of the channel image a to the chromaticity center distance L is calculated to serve as a color cast factor K, if the color cast factor K is smaller than a preset color cast factor threshold value, it is judged that the picture has color cast, and a color cast abnormal result is stored; otherwise, the picture is not subjected to color cast, so that color cast detection is realized, and automatic analysis of imaging quality of the terminal equipment is realized according to a color cast detection result.
In summary, the PG protocol is adopted to log in the terminal device, the imaging data of the terminal device is collected, brightness anomaly detection, definition anomaly detection, snowflake interference detection, color cast detection, signal loss detection or image occlusion detection are performed, whether the terminal device is abnormal or not is judged according to the detection result, the online state and the image quality of the terminal device of the video monitoring system are automatically detected, missing detection is avoided, the device detection efficiency and accuracy are improved, the cost of manual detection of the system is reduced, and the monitoring effect of the power grid video monitoring system is favorably improved.
Example two
The power grid monitoring system detection device provided by the embodiment of the invention can execute the power grid monitoring system detection method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 7 is a schematic structural diagram of a detection device of a power grid monitoring system according to a second embodiment of the present invention.
As shown in fig. 7, the power grid monitoring system detection apparatus 00 includes: the system comprises a login protocol sending module 101, an online state detection module 102, a picture sampling module 103 and a picture detection module 104, wherein the login protocol sending module 101 is used for logging in the terminal equipment to be detected 01 by adopting a preset protocol; the online state detection module 102 is configured to obtain a login response message fed back by the terminal device to be detected, and determine an online state of the terminal device to be detected according to the login response message; the picture sampling module 103 is used for acquiring video image data acquired by the terminal equipment to be detected; the picture detection module 104 is configured to perform picture quality detection on the video image data based on a preset detection algorithm, and determine an image quality detection report of the terminal device.
Alternatively, the power grid monitoring system detection apparatus 00 may be a smart device or a server storing a specific device detection program.
Optionally, the picture sampling module 103 is configured to periodically send a video retrieval instruction to the terminal device to be detected based on the preset device list, obtain video stream data returned by the terminal device to be detected, and perform unpacking and decoding operations on the video stream data to obtain video image data.
Optionally, the picture quality detection comprises: brightness anomaly detection, sharpness anomaly detection, snow interference detection, color cast detection, signal loss detection, or image occlusion detection.
Optionally, the picture detection module 104 includes an image brightness detection unit, configured to perform graying processing on any target frame picture in the video image data to obtain a grayscale image, and obtain a mean value and a variance value of the grayscale image; and obtaining a preset brightness threshold value, and determining an image brightness detection result of the target frame picture according to the mean value, the variance value and the preset brightness threshold value.
Optionally, the picture detection module 104 includes a snowflake interference detection unit, configured to perform graying processing on any target frame picture in the video image data to obtain a grayscale image, obtain a preset snowflake point brightness threshold, and perform binarization processing on the grayscale image based on the snowflake point brightness threshold to obtain a first binarized foreground image; calculating the proportion of white pixel points in the first binary foreground image, and determining an initial snowflake interference detection result according to the calculation result; acquiring an image checking instruction; and correcting the initial snowflake interference detection result according to the image checking instruction to obtain a final snowflake interference detection result.
Optionally, the picture detection module 104 includes a signal occlusion and loss detection unit, configured to perform edge detection on any target frame picture in the video image data to obtain an edge map; acquiring an edge image, and performing binarization processing to obtain a second binarization foreground image; detecting a connected region of the second binary foreground image, and determining the maximum connected region area according to the detection result; and determining a camera shielding detection result and a signal loss detection result according to the area of the maximum communication area.
Optionally, the picture detection module 104 includes a sharpness detection unit, configured to perform convolution filtering on any target frame picture in the video image data to obtain a blur picture, obtain a standard difference value of the blur picture, and determine a sharpness abnormality detection result of the target frame picture according to the standard difference value.
The power grid monitoring system detection device provided by the embodiment of the invention executes a power grid monitoring system detection method, and the method comprises the steps of reversely logging in a terminal device to be detected of a monitoring system by adopting a preset protocol, determining the online state of the terminal device to be detected according to a login response message fed back by the terminal device to be detected, acquiring video image data collected by the terminal device to be detected, carrying out picture quality detection on the video image data based on a preset detection algorithm, and determining an image quality detection report of the terminal device.
EXAMPLE III
Fig. 8 is a schematic structural diagram of a computer device according to a third embodiment of the present invention. FIG. 8 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in fig. 8 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present invention.
As shown in FIG. 8, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors 16, a memory 28, a bus 18 connecting the various system components (including the memory 28 and the processors 16), and a computer program stored on the memory 28 and executable on the processors, the processor 16 implementing the grid monitoring system detection method described above when executing the computer program.
The memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 8, and commonly referred to as a "hard drive"). Although not shown in FIG. 8, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processor 16 executes programs stored in the memory 28 to execute various functional applications and data processing, for example, to implement the power grid monitoring system detection method provided by the embodiment of the present invention.
Example four
The fourth embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored thereon, and when the computer program is executed by a processor, the method for detecting a power grid monitoring system is implemented, where the method for detecting a power grid monitoring system includes: logging in the terminal equipment to be detected by adopting a preset protocol; acquiring a login response message fed back by the terminal equipment to be detected, and determining the online state of the terminal equipment to be detected according to the login response message; acquiring video image data collected by terminal equipment to be detected; and carrying out picture quality detection on the video image data based on a preset detection algorithm, and determining an image quality detection report of the terminal equipment.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A power grid monitoring system detection method is characterized by comprising the following steps of:
logging in the terminal equipment to be detected by adopting a preset protocol;
acquiring a login response message fed back by the terminal equipment to be detected, and determining the online state of the terminal equipment to be detected according to the login response message;
acquiring video image data acquired by the terminal equipment to be detected;
and carrying out picture quality detection on the video image data based on a preset detection algorithm, and determining an image quality detection report of the terminal equipment.
2. The power grid monitoring system detection method according to claim 1, wherein the obtaining of the video image data collected by the terminal device to be detected comprises the following steps:
periodically sending a video retrieval instruction to the terminal equipment to be detected based on a preset equipment list;
acquiring video stream data returned by the terminal equipment to be detected;
and unpacking and decoding the video stream data to obtain the video image data.
3. The power grid monitoring system detection method according to claim 1, wherein the picture quality detection comprises: brightness anomaly detection, sharpness anomaly detection, snow interference detection, color cast detection, signal loss detection, or image occlusion detection.
4. The power grid monitoring system detection method according to claim 1, wherein the picture quality detection of the video image data based on a preset detection algorithm comprises the following steps:
carrying out graying processing on any target frame picture in the video image data to obtain a grayscale image;
acquiring a mean value and a variance value of the gray level image;
acquiring a preset brightness threshold;
and determining an image brightness detection result of the target frame picture according to the mean value, the variance value and the preset brightness threshold value.
5. The power grid monitoring system detection method according to claim 1, wherein the picture quality detection of the video image data based on a preset detection algorithm comprises the following steps:
carrying out graying processing on any target frame picture in the video image data to obtain a grayscale image;
acquiring a preset snowflake point brightness threshold value;
carrying out binarization processing on the gray-scale image based on the snow point brightness threshold value to obtain a first binarization foreground image;
calculating the proportion of white pixel points in the first binary foreground image, and determining an initial snowflake interference detection result according to the calculation result;
acquiring an image checking instruction;
and correcting the initial snowflake interference detection result according to the image checking instruction to obtain a final snowflake interference detection result.
6. The power grid monitoring system detection method according to claim 1, wherein the picture quality detection of the video image data based on a preset detection algorithm comprises the following steps:
carrying out edge detection on any target frame picture in the video image data to obtain an edge image;
acquiring the edge image, and performing binarization processing to obtain a second binarization foreground image;
carrying out connected region detection on the second binarization foreground image, and determining the maximum connected region area according to the detection result;
and determining a camera shielding detection result and a signal loss detection result according to the area of the maximum communication area.
7. The grid monitoring system detection method according to claim 1,
performing convolution filtering processing on any target frame picture in the video image data to obtain a fuzzy picture;
acquiring a standard difference value of the fuzzy graph;
and determining the definition abnormity detection result of the target frame picture according to the standard difference value.
8. The utility model provides a power grid monitoring system detection device, power grid monitoring system connects terminal equipment which characterized in that, detection device includes:
the login protocol sending module is used for logging in the terminal equipment to be detected by adopting a preset protocol;
the online state detection module is used for acquiring a login response message fed back by the terminal equipment to be detected and determining the online state of the terminal equipment to be detected according to the login response message;
the image sampling module is used for acquiring video image data acquired by the terminal equipment to be detected;
and the picture detection module is used for carrying out picture quality detection on the video image data based on a preset detection algorithm and determining a picture quality detection report of the terminal equipment.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements a grid monitoring system detection method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a grid monitoring system detection method according to any one of claims 1 to 7.
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