CN117014646A - Vehicle-mounted video transmission supervision system based on artificial intelligence - Google Patents
Vehicle-mounted video transmission supervision system based on artificial intelligence Download PDFInfo
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
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- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
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- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/439—Processing of audio elementary streams
- H04N21/4394—Processing of audio elementary streams involving operations for analysing the audio stream, e.g. detecting features or characteristics in audio streams
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
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- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
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Abstract
The invention relates to the technical field of video transmission supervision, in particular to an artificial intelligence-based vehicle-mounted video transmission supervision system, which comprises a video transmission platform, a data acquisition unit, a front-end power supply analysis unit, a middle-end network analysis unit, an integrated transmission analysis unit, a video analysis unit, an early warning display unit and an optimization management unit, wherein the video transmission platform is connected with the data acquisition unit; according to the invention, the front end and the middle end of the vehicle-mounted video transmission are subjected to supervision analysis, namely, the power supply interruption risk supervision analysis is performed on the power supply data from the front end angle, the condition of video transmission interruption is avoided, the video transmission cartoon risk assessment analysis is performed on the transmission data from the middle end angle, so that the stability and the transmission efficiency of the vehicle-mounted video transmission are ensured, and the deep data integration influence analysis is performed in a mode of information feedback and data integration, so that the reasonable and targeted transmission management is performed according to the overall influence level of the front end and the middle end, and the supervision effect of the vehicle-mounted video transmission is improved.
Description
Technical Field
The invention relates to the technical field of video transmission supervision, in particular to an artificial intelligence-based vehicle-mounted video transmission supervision system.
Background
With the development of wireless network technology, wireless video monitoring is widely applied to field monitoring and emergency command work, the requirements of a plurality of fields such as police law enforcement, security guard, emergency command and the like on mobile video monitoring are more and more urgent, a monitoring center can remotely know the situation of a case field through a vehicle-mounted video monitoring system positioned on the field, and command and dispatch police strength are facilitated, so that a police car can be used as a command car for emergencies or group events, and can also carry out real-time video evidence collection on some current illegal criminal activities, and evidence materials are provided for striking such activities;
the vehicle running safety is always the pursued goal of people, the common method is that a vehicle-mounted video monitoring system is arranged in a vehicle to record image data of the vehicle in the running process, but the running state of the existing vehicle-mounted monitoring terminal cannot be monitored and early-warned, the vehicle-mounted video transmission process cannot be monitored, the stability of the vehicle-mounted video transmission is further influenced, the problems of transmission blocking and interruption exist, the reasonable and targeted transmission management cannot be carried out according to the overall influence condition of the front end and the middle end, the monitoring efficiency of the vehicle-mounted video transmission is reduced, and in addition, the displayed video of the vehicle-mounted video receiving end cannot be monitored and reasonably and pertinently optimized;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based vehicle-mounted video transmission supervision system to solve the technical defects, and the invention performs supervision analysis from the front end and the middle end of vehicle-mounted video transmission, namely performs power supply interruption risk supervision analysis from the front end to ensure the stability of video transmission of a vehicle-mounted monitoring terminal, avoids the condition of video transmission interruption, performs video transmission blocking risk assessment analysis from the middle end to judge whether the risk of blocking and interruption of a network is too high in the vehicle-mounted video transmission process or not so as to ensure the stability and the transmission efficiency of vehicle-mounted video transmission, and performs deep data integration influence analysis in an information feedback and data integration mode so as to perform reasonable and targeted transmission management according to the overall influence level of the front end and the middle end, thereby improving the supervision effect of vehicle-mounted video transmission.
The aim of the invention can be achieved by the following technical scheme: the vehicle-mounted video transmission supervision system based on artificial intelligence comprises a video transmission platform, a data acquisition unit, a front-end power supply analysis unit, a middle-end network analysis unit, an integrated transmission analysis unit, a video analysis unit, an early warning display unit and an optimization management unit;
when the video transmission platform generates a management command, the management command is sent to the data acquisition unit, the data acquisition unit immediately acquires power supply data of the vehicle-mounted monitoring terminal and transmission data of the transmission network after receiving the management command, the power supply data comprise resistance interference values and environment interference values of a vehicle-mounted monitoring terminal circuit, the transmission data comprise transmission speeds, transmission bandwidth values and transmission delay values, the power supply data and the transmission data are respectively sent to the front-end power supply analysis unit and the middle-end network analysis unit through the video transmission platform, the front-end power supply analysis unit immediately carries out power supply interruption risk supervision analysis on the power supply data after receiving the power supply data, sends an obtained normal signal to the video analysis unit and sends an obtained early warning signal to the early warning display unit;
the middle-end network analysis unit immediately carries out video transmission katon risk assessment analysis on the transmission data after receiving the transmission data, sends the obtained transmission risk abnormal constant to the integrated transmission analysis unit, sends the obtained stable signal to the video analysis unit, and sends the obtained risk signal to the early warning display unit;
the integrated transmission analysis unit immediately performs deep data integration influence analysis on the transmission risk abnormal constant after receiving the transmission risk abnormal constant, and sends the obtained primary influence signal, secondary influence signal and tertiary influence signal to the early warning display unit;
the video analysis unit immediately acquires audio data of a receiving end in vehicle-mounted video transmission after receiving the normal signal and the stable signal, the audio data comprises an alternating current sound decibel value and an image resolution, self-checking feedback supervision analysis is carried out on the audio data, and the obtained primary optimization signal, secondary optimization signal and tertiary optimization signal are sent to the optimization management unit.
Preferably, the power interruption risk supervision and analysis process of the front-end power supply analysis unit is as follows:
SS1: acquiring the duration of a period of time after the vehicle-mounted video is transmitted, marking the duration as a time threshold, acquiring a resistance interference value of a vehicle-mounted monitoring terminal circuit in the time threshold, wherein the resistance interference value represents a product value obtained by carrying out data normalization processing on a circuit resistance value and a part of the circuit reactive power exceeding a preset circuit reactive power threshold recorded and stored, comparing the resistance interference value with a preset resistance interference value threshold, and marking a part of the resistance interference value larger than the preset resistance interference value threshold as an influence risk value if the resistance interference value is larger than the preset resistance interference value threshold;
SS12: acquiring an environment interference value of a vehicle-mounted monitoring terminal in a time threshold, wherein the environment interference value refers to a product value obtained by carrying out data normalization processing on a part of the humidity value of the environment in the vehicle-mounted monitoring terminal in the time threshold exceeding a stored preset humidity value threshold and an electromagnetic influence value, and the electromagnetic influence value represents a ratio of the part of the electromagnetic interference decibel value exceeding the stored preset electromagnetic interference decibel value threshold to the electromagnetic interference decibel value;
SS13: comparing the influence risk value and the environment interference value with a preset influence risk value threshold value and a preset environment interference value threshold value which are recorded and stored in the influence risk value and the environment interference value:
if the influence risk value is smaller than a preset influence risk value threshold value and the environment interference value is smaller than a preset environment interference value threshold value, generating a normal signal;
and if the influence risk value is greater than or equal to a preset influence risk value threshold or the environment interference value is greater than or equal to a preset environment interference value threshold, generating an early warning signal.
Preferably, the video transmission katon risk assessment analysis process of the middle-end network analysis unit is as follows:
s1: dividing a time threshold into i sub-time nodes, wherein i is a natural number larger than zero, acquiring the transmission speed, the transmission bandwidth value and the transmission delay value of a transmission network in each sub-time node, comparing the transmission speed, the transmission bandwidth value and the transmission delay value with a stored preset transmission speed threshold, a preset transmission bandwidth value threshold and a preset transmission delay value threshold, and if the transmission speed is smaller than the preset transmission speed threshold, the transmission bandwidth value is smaller than the preset transmission bandwidth value threshold and the transmission delay value is larger than the preset transmission delay value threshold, marking the part of the transmission speed smaller than the preset transmission speed threshold as a transmission influence value, marking the part of the transmission bandwidth value smaller than the preset transmission bandwidth value threshold as a bandwidth influence value, marking the part of the transmission delay value larger than the preset transmission delay value threshold as a delay interference value, and further marking the transmission influence value, the bandwidth influence value and the delay interference value in each sub-time node as CYi, KYi and YRi respectively;
s12: obtaining a transmission abnormal risk assessment coefficient Hi according to a formula, constructing a set A of the transmission abnormal risk assessment coefficient Hi, obtaining a difference value between two connected subsets in the set A, marking the difference value as a transmission risk floating value, comparing the transmission risk floating value with a stored preset transmission risk floating value threshold, if the transmission risk floating value is larger than the preset transmission risk floating value threshold, marking the total number of the transmission risk floating values corresponding to the transmission risk floating value larger than the preset transmission risk floating value threshold as a transmission risk abnormal constant, and comparing the transmission risk abnormal constant with a preset transmission risk abnormal constant threshold recorded in the transmission risk abnormal constant and stored in the transmission risk abnormal constant threshold:
if the transmission risk abnormal constant is smaller than a preset transmission risk abnormal constant threshold, generating a stable signal;
and if the transmission risk abnormal constant is greater than or equal to a preset transmission risk abnormal constant threshold value, generating a risk signal.
Preferably, the deep data integration influence analysis process of the integrated transmission analysis unit is as follows:
the method comprises the steps of calling an influence risk value and an environment interference value from a front-end power supply analysis unit, respectively marking the influence risk value and the environment interference value as YF and HR, and simultaneously obtaining a transmission risk abnormal constant CF within a time threshold;
according to the formulaObtaining comprehensive influence evaluation coefficients, wherein f1, f2 and f3 are respectively preset weight factor coefficients of an influence risk value, an environment interference value and a transmission risk abnormal constant, f1, f2 and f3 are positive numbers larger than zero, f4 is a preset correction factor coefficient, the value is 2.442, Z is the comprehensive influence evaluation coefficient, and the comprehensive influence evaluation coefficient Z is compared with a preset comprehensive influence evaluation coefficient interval recorded and stored in the comprehensive influence evaluation coefficient Z:
if the comprehensive influence evaluation coefficient Z is larger than the maximum value in the preset comprehensive influence evaluation coefficient interval, generating a primary influence signal;
if the comprehensive influence evaluation coefficient Z belongs to a preset comprehensive influence evaluation coefficient interval, generating a secondary influence signal;
and if the comprehensive influence evaluation coefficient Z is smaller than the minimum value in the preset comprehensive influence evaluation coefficient interval, generating a three-level influence signal.
Preferably, the self-checking feedback supervision and analysis process of the video analysis unit is as follows:
acquiring an alternating current sound decibel value and an image resolution of a vehicle-mounted video receiving end in a time threshold, comparing the alternating current sound decibel value and the image resolution with a stored preset alternating current sound decibel value threshold and a preset image resolution threshold, and if the alternating current sound decibel value is larger than the preset alternating current sound decibel value threshold and the image resolution is larger than the preset image resolution threshold, respectively marking a part of the alternating current sound decibel value larger than the preset alternating current sound decibel value threshold and a part of the image resolution larger than the preset image resolution threshold as an audio effect influence value and a display influence value, respectively marking the parts as YZ and XZ, and simultaneously, adjusting a comprehensive influence evaluation coefficient Z from an integrated transmission analysis unit;
according to the formulaObtaining a display risk evaluation coefficient, wherein alpha, beta and epsilon are respectively preset influence factor coefficients of an audio effect influence value, a display influence value and a comprehensive influence evaluation coefficient, alpha, beta and epsilon are positive numbers larger than zero, T is the display risk evaluation coefficient, and the display risk evaluation coefficient T is compared with a preset display risk evaluation coefficient threshold value recorded and stored in the display risk evaluation coefficient T:
if the display risk assessment coefficient T is smaller than or equal to a preset display risk assessment coefficient threshold value, no signal is generated;
if the display risk assessment coefficient T is larger than a preset display risk assessment coefficient threshold value, generating a feedback instruction.
Preferably, when the video analysis unit generates the feedback instruction, the in-depth optimization evaluation analysis is immediately performed on the display risk evaluation coefficient T, and the specific in-depth optimization evaluation analysis process is as follows:
the method comprises the steps of obtaining a part with a display risk assessment coefficient T larger than a preset display risk assessment coefficient threshold value, marking the part with the display risk assessment coefficient T larger than the preset display risk assessment coefficient threshold value as a quality influence value, and comparing the quality influence value with a preset quality influence value interval recorded and stored in the quality influence value and the quality influence value interval:
if the quality influence value is larger than the maximum value in the preset quality influence value interval, generating a first-level optimization signal;
if the quality influence value belongs to a preset quality influence value interval, generating a secondary optimization signal;
and if the quality influence value is smaller than the minimum value in the preset quality influence value interval, generating a three-level optimization signal.
The beneficial effects of the invention are as follows:
(1) According to the invention, the front end and the middle end of the vehicle-mounted video transmission are subjected to supervision analysis, namely, the power supply interruption risk supervision analysis is performed on the power supply data from the front end angle so as to ensure the stability of the video transmission of the vehicle-mounted monitoring terminal, avoid the condition of video transmission interruption, and the video transmission jam risk evaluation analysis is performed on the transmission data from the middle end angle so as to judge whether the risk of the network jam and interruption is too high in the vehicle-mounted video transmission process or not, so as to ensure the stability and the transmission efficiency of the vehicle-mounted video transmission, and the deep data integration influence analysis is performed in a mode of information feedback and data integration so as to perform reasonable and targeted transmission management according to the overall influence level of the front end and the middle end, so as to improve the supervision effect of the vehicle-mounted video transmission;
(2) According to the invention, the audio data of the receiving end in the vehicle-mounted video transmission are collected, and the self-checking feedback supervision analysis is carried out, so that the display video of the vehicle-mounted video receiving end can be reasonably and pertinently optimized in time, and the stability and the effectiveness of the display video of the vehicle-mounted video receiving end can be improved.
Drawings
The invention is further described below with reference to the accompanying drawings;
FIG. 1 is a flow chart of the system of the present invention;
fig. 2 is a partial analysis reference diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
referring to fig. 1 to 2, the invention discloses an artificial intelligence-based vehicle-mounted video transmission supervision system, which comprises a video transmission platform, a data acquisition unit, a front-end power supply analysis unit, a middle-end network analysis unit, an integrated transmission analysis unit, a video analysis unit, an early warning display unit and an optimization management unit, wherein the video transmission platform is in bidirectional communication connection with the data acquisition unit, the video transmission platform is in unidirectional communication connection with the front-end power supply analysis unit and the middle-end network analysis unit, the front-end power supply analysis unit and the middle-end network analysis unit are in unidirectional communication connection with the integrated transmission analysis unit, the video analysis unit and the early warning display unit, the integrated transmission analysis unit is in unidirectional communication connection with the early warning display unit, and the video analysis unit is in unidirectional communication connection with the optimization management unit;
the video transmission platform is used for carrying out control analysis on the transmission of the vehicle-mounted video, when the video transmission platform generates a management command, the management command is sent to the data acquisition unit, the data acquisition unit immediately acquires power supply data of the vehicle-mounted monitoring terminal and transmission data of the transmission network after receiving the management command, the power supply data comprise resistance interference values and environment interference values of a line of the vehicle-mounted monitoring terminal, the transmission data comprise transmission speed, transmission bandwidth values and transmission delay values, the power supply data and the transmission data are respectively sent to the front-end power supply analysis unit and the middle-end network analysis unit through the video transmission platform, the front-end power supply analysis unit immediately carries out power supply interruption risk supervision analysis on the power supply data after receiving the power supply data so as to ensure the stability of the transmission video of the vehicle-mounted monitoring terminal, the condition of video transmission interruption is avoided, and the specific power supply interruption risk supervision analysis process is as follows:
acquiring the duration of a period of time after the vehicle-mounted video is transmitted, marking the duration as a time threshold, acquiring a resistance interference value of a vehicle-mounted monitoring terminal circuit in the time threshold, wherein the resistance interference value represents a product value obtained by carrying out data normalization processing on a circuit resistance value and a part of the circuit reactive power exceeding a preset circuit reactive power threshold recorded and stored, comparing the resistance interference value with the stored preset resistance interference value threshold, and if the resistance interference value is larger than the preset resistance interference value threshold, marking a part of the resistance interference value larger than the preset resistance interference value threshold as an influence risk value, wherein the influence risk value is an influence parameter reflecting the operation of the vehicle-mounted monitoring terminal;
acquiring an environment interference value of a vehicle-mounted monitoring terminal in a time threshold, wherein the environment interference value refers to a product value obtained by carrying out data normalization processing on a part of the humidity value of the environment in the vehicle-mounted monitoring terminal in the time threshold exceeding a stored preset humidity value threshold and an electromagnetic influence value, the electromagnetic influence value represents a ratio of the part of the electromagnetic interference decibel value exceeding the stored preset electromagnetic interference decibel value threshold to the electromagnetic interference decibel value, and the larger the value of the environment interference value is, the larger the video transmission abnormal risk of the vehicle-mounted monitoring terminal is;
comparing the influence risk value and the environment interference value with a preset influence risk value threshold value and a preset environment interference value threshold value which are recorded and stored in the influence risk value and the environment interference value:
if the influence risk value is smaller than a preset influence risk value threshold and the environment interference value is smaller than a preset environment interference value threshold, generating a normal signal and sending the normal signal to a video analysis unit;
if the influence risk value is greater than or equal to a preset influence risk value threshold value or the environment interference value is greater than or equal to a preset environment interference value threshold value, generating an early warning signal and sending the early warning signal to an early warning display unit, wherein the early warning display unit immediately makes a preset early warning operation corresponding to the early warning signal after receiving the early warning signal, so that the vehicle-mounted monitoring terminal is maintained and managed in time, and the stability of vehicle-mounted video transmission is improved;
the middle-end network analysis unit immediately carries out video transmission cartoon risk assessment analysis on the transmission data after receiving the transmission data so as to judge whether the risk of the cartoon and interruption of the network in the vehicle-mounted video transmission process is too high or not, so as to ensure the stability and the transmission efficiency of the vehicle-mounted video transmission, and the specific video transmission cartoon risk assessment analysis process is as follows:
dividing a time threshold into i sub-time nodes, wherein i is a natural number larger than zero, acquiring the transmission speed, the transmission bandwidth value and the transmission delay value of a transmission network in each sub-time node, comparing the transmission speed, the transmission bandwidth value and the transmission delay value with a stored preset transmission speed threshold, a preset transmission bandwidth value threshold and a preset transmission delay value threshold, and if the transmission speed is smaller than the preset transmission speed threshold, the transmission bandwidth value is smaller than the preset transmission bandwidth value threshold and the transmission delay value is larger than the preset transmission delay value threshold, marking the part of the transmission speed smaller than the preset transmission speed threshold as a transmission influence value, marking the part of the transmission bandwidth value smaller than the preset transmission bandwidth value threshold as a bandwidth influence value, marking the part of the transmission delay value larger than the preset transmission delay value threshold as a delay interference value, and further marking the transmission influence value, the bandwidth influence value and the delay interference value in each sub-time node as CYi, KYi and YRi respectively;
according to the formulaObtaining transmission abnormality risk assessment coefficients, wherein a1, a2 and a3 are preset scale factor coefficients of a transmission influence value, a bandwidth influence value and a delay interference value respectively, the scale factor coefficients are used for correcting deviation of various parameters in a formula calculation process, so that calculation results are more accurate, a1, a2 and a3 are positive numbers larger than zero, a4 is a preset fault tolerance factor coefficient, a value is 1.668, hi is a transmission abnormality risk assessment coefficient of each sub-time node, a set A of the transmission abnormality risk assessment coefficients Hi is constructed, a difference value between two sub-sets connected in the set A is obtained, the difference value is marked as a transmission risk floating value, the transmission risk floating value is compared with a stored preset transmission risk floating value threshold, if the transmission risk floating value is larger than the preset transmission risk floating value, the total number of the transmission floating value corresponding to the transmission risk floating value larger than the preset transmission risk floating value threshold is marked as a transmission abnormal constant, and the transmission abnormal risk constant is sent to the set AIntegrating a transmission analysis unit;
comparing the transmission risk abnormal constant with a preset transmission risk abnormal constant threshold value recorded and stored in the transmission risk abnormal constant to analyze:
if the transmission risk abnormal constant is smaller than a preset transmission risk abnormal constant threshold, generating a stable signal and sending the stable signal to a video analysis unit;
if the transmission risk abnormal constant is greater than or equal to a preset transmission risk abnormal constant threshold, a risk signal is generated and sent to an early warning display unit, and after the early warning display unit receives the risk signal, preset early warning operation corresponding to the risk signal is immediately carried out, so that a management person is reminded to optimize the vehicle-mounted video transmission network, the risk degree of blocking and interruption in the vehicle-mounted video transmission process is reduced, and the stability and the transmission efficiency of vehicle-mounted video transmission are ensured.
Example 2:
the integrated transmission analysis unit immediately performs deep data integration influence analysis on the transmission risk abnormal constants after receiving the transmission risk abnormal constants so as to judge the power supply data of the vehicle-mounted monitoring terminal and combine the influence level of the transmission data in the vehicle-mounted video transmission process on the vehicle-mounted video transmission, and further performs reasonable and targeted transmission management according to different influence levels so as to improve the safety of the vehicle-mounted video transmission, wherein the specific deep data integration influence analysis process is as follows:
the method comprises the steps of calling an influence risk value and an environment interference value from a front-end power supply analysis unit, respectively marking the influence risk value and the environment interference value as YF and HR, and simultaneously obtaining a transmission risk abnormal value within a time threshold, and marking the influence risk value and the environment interference value as CF;
according to the formulaObtaining comprehensive influence evaluation coefficients, wherein f1, f2 and f3 are preset weight factor coefficients of an influence risk value, an environment interference value and a transmission risk abnormal constant respectively, f1, f2 and f3 are positive numbers larger than zero, f4 is a preset correction factor coefficient, the value is 2.442, and Z is the comprehensive influence evaluationThe coefficient is compared with a preset comprehensive influence evaluation coefficient interval which is recorded and stored in the comprehensive influence evaluation coefficient Z:
if the comprehensive influence evaluation coefficient Z is larger than the maximum value in the preset comprehensive influence evaluation coefficient interval, generating a primary influence signal;
if the comprehensive influence evaluation coefficient Z belongs to a preset comprehensive influence evaluation coefficient interval, generating a secondary influence signal;
if the comprehensive influence evaluation coefficient Z is smaller than the minimum value in the preset comprehensive influence evaluation coefficient interval, generating a three-level influence signal, wherein the influence degrees corresponding to the first-level influence signal, the second-level influence signal and the three-level influence signal are sequentially reduced, sending the first-level influence signal, the second-level influence signal and the three-level influence signal to an early warning display unit, and immediately making preset early warning operation corresponding to the first-level influence signal, the second-level influence signal and the three-level influence signal after the early warning display unit receives the first-level influence signal, the second-level influence signal and the three-level influence signal, so that the front end and the middle end of the vehicle-mounted video transmission can be managed reasonably and pertinently in time, the safety and the transmission efficiency of the vehicle-mounted video transmission can be improved, and the stability of the vehicle-mounted video transmission can be improved;
the video analysis unit immediately collects audio data of a receiving end in vehicle-mounted video transmission after receiving normal signals and stable signals, the audio data comprises alternating current sound decibel values and image resolution, and self-checking feedback supervision analysis is carried out on the audio data to judge whether the display quality of the vehicle-mounted monitoring receiving end is qualified or not, so that the display video of the vehicle-mounted video receiving end can be optimized reasonably and pertinently in time, and the specific self-checking feedback supervision analysis process is as follows:
acquiring an alternating current sound decibel value and an image resolution of a vehicle-mounted video receiving end in a time threshold, comparing the alternating current sound decibel value and the image resolution with a stored preset alternating current sound decibel value threshold and a preset image resolution threshold, and if the alternating current sound decibel value is larger than the preset alternating current sound decibel value threshold and the image resolution is larger than the preset image resolution threshold, respectively marking a part of the alternating current sound decibel value larger than the preset alternating current sound decibel value threshold and a part of the image resolution larger than the preset image resolution threshold as an audio effect influence value and a display influence value, respectively marking the parts as YZ and XZ, and simultaneously, adjusting a comprehensive influence evaluation coefficient Z from an integrated transmission analysis unit;
according to the formulaObtaining a display risk evaluation coefficient, wherein alpha, beta and epsilon are respectively preset influence factor coefficients of an audio effect influence value, a display influence value and a comprehensive influence evaluation coefficient, alpha, beta and epsilon are positive numbers larger than zero, T is the display risk evaluation coefficient, and the display risk evaluation coefficient T is compared with a preset display risk evaluation coefficient threshold value recorded and stored in the display risk evaluation coefficient T:
if the display risk assessment coefficient T is smaller than or equal to a preset display risk assessment coefficient threshold value, no signal is generated;
if the display risk assessment coefficient T is larger than a preset display risk assessment coefficient threshold value, generating a feedback instruction, and immediately performing deep optimization assessment analysis on the display risk assessment coefficient T when the feedback instruction is generated so as to ensure the display quality and the display safety of the vehicle-mounted video receiving end, wherein the specific deep optimization assessment analysis process is as follows:
the method comprises the steps of obtaining a part with a display risk assessment coefficient T larger than a preset display risk assessment coefficient threshold value, marking the part with the display risk assessment coefficient T larger than the preset display risk assessment coefficient threshold value as a quality influence value, and comparing the quality influence value with a preset quality influence value interval recorded and stored in the quality influence value and the quality influence value interval:
if the quality influence value is larger than the maximum value in the preset quality influence value interval, generating a first-level optimization signal;
if the quality influence value belongs to a preset quality influence value interval, generating a secondary optimization signal;
if the quality influence value is smaller than the minimum value in the preset quality influence value interval, generating a three-level optimization signal, wherein the optimization degree corresponding to the first-level optimization signal, the second-level optimization signal and the three-level optimization signal is sequentially reduced, and the first-level optimization signal, the second-level optimization signal and the three-level optimization signal are sent to an optimization management unit;
in summary, the monitoring analysis is performed from the front end and the middle end of the vehicle-mounted video transmission, that is, the power supply interruption risk monitoring analysis is performed on the power supply data from the front end, so as to ensure the stability of the video transmission of the vehicle-mounted monitoring terminal, avoid the condition of video transmission interruption, and perform video transmission jam risk assessment analysis on the transmission data from the middle end, so as to judge whether the risk of the jam and interruption of the network in the vehicle-mounted video transmission process is too high, ensure the stability and the transmission efficiency of the vehicle-mounted video transmission, and perform deep data integration influence analysis in a mode of information feedback and data integration, so that the reasonable and targeted transmission management is performed according to the overall influence level of the front end and the middle end, so as to improve the monitoring effect of the vehicle-mounted video transmission, and in addition, the self-checking feedback monitoring analysis is performed through collecting the audio data of the receiving end in the vehicle-mounted video transmission, so as to perform reasonable and targeted optimization processing on the display video of the vehicle-mounted video receiving end, thereby being beneficial to improve the stability and effectiveness of the display video of the vehicle-mounted video receiving end.
The size of the threshold is set for ease of comparison, and regarding the size of the threshold, the number of cardinalities is set for each set of sample data depending on how many sample data are and the person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
The above formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and coefficients in the formulas are set by a person skilled in the art according to practical situations, and the above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art is within the technical scope of the present invention, and the technical scheme and the inventive concept according to the present invention are equivalent to or changed and are all covered in the protection scope of the present invention.
Claims (6)
1. The vehicle-mounted video transmission supervision system based on the artificial intelligence is characterized by comprising a video transmission platform, a data acquisition unit, a front-end power supply analysis unit, a middle-end network analysis unit, an integrated transmission analysis unit, a video analysis unit, an early warning display unit and an optimization management unit;
when the video transmission platform generates a management command, the management command is sent to the data acquisition unit, the data acquisition unit immediately acquires power supply data of the vehicle-mounted monitoring terminal and transmission data of the transmission network after receiving the management command, the power supply data comprise resistance interference values and environment interference values of a vehicle-mounted monitoring terminal circuit, the transmission data comprise transmission speeds, transmission bandwidth values and transmission delay values, the power supply data and the transmission data are respectively sent to the front-end power supply analysis unit and the middle-end network analysis unit through the video transmission platform, the front-end power supply analysis unit immediately carries out power supply interruption risk supervision analysis on the power supply data after receiving the power supply data, sends an obtained normal signal to the video analysis unit and sends an obtained early warning signal to the early warning display unit;
the middle-end network analysis unit immediately carries out video transmission katon risk assessment analysis on the transmission data after receiving the transmission data, sends the obtained transmission risk abnormal constant to the integrated transmission analysis unit, sends the obtained stable signal to the video analysis unit, and sends the obtained risk signal to the early warning display unit;
the integrated transmission analysis unit immediately performs deep data integration influence analysis on the transmission risk abnormal constant after receiving the transmission risk abnormal constant, and sends the obtained primary influence signal, secondary influence signal and tertiary influence signal to the early warning display unit;
the video analysis unit immediately acquires audio data of a receiving end in vehicle-mounted video transmission after receiving the normal signal and the stable signal, the audio data comprises an alternating current sound decibel value and an image resolution, self-checking feedback supervision analysis is carried out on the audio data, and the obtained primary optimization signal, secondary optimization signal and tertiary optimization signal are sent to the optimization management unit.
2. The vehicle-mounted video transmission supervision system based on artificial intelligence according to claim 1, wherein the power interruption risk supervision analysis process of the front-end power supply analysis unit is as follows:
SS1: acquiring the duration of a period of time after the vehicle-mounted video is transmitted, marking the duration as a time threshold, acquiring a resistance interference value of a vehicle-mounted monitoring terminal circuit in the time threshold, wherein the resistance interference value represents a product value obtained by carrying out data normalization processing on a circuit resistance value and a part of the circuit reactive power exceeding a preset circuit reactive power threshold recorded and stored, comparing the resistance interference value with a preset resistance interference value threshold, and marking a part of the resistance interference value larger than the preset resistance interference value threshold as an influence risk value if the resistance interference value is larger than the preset resistance interference value threshold;
SS12: acquiring an environment interference value of a vehicle-mounted monitoring terminal in a time threshold, wherein the environment interference value refers to a product value obtained by carrying out data normalization processing on a part of the humidity value of the environment in the vehicle-mounted monitoring terminal in the time threshold exceeding a stored preset humidity value threshold and an electromagnetic influence value, and the electromagnetic influence value represents a ratio of the part of the electromagnetic interference decibel value exceeding the stored preset electromagnetic interference decibel value threshold to the electromagnetic interference decibel value;
SS13: comparing the influence risk value and the environment interference value with a preset influence risk value threshold value and a preset environment interference value threshold value which are recorded and stored in the influence risk value and the environment interference value:
if the influence risk value is smaller than a preset influence risk value threshold value and the environment interference value is smaller than a preset environment interference value threshold value, generating a normal signal;
and if the influence risk value is greater than or equal to a preset influence risk value threshold or the environment interference value is greater than or equal to a preset environment interference value threshold, generating an early warning signal.
3. The vehicle-mounted video transmission supervision system based on artificial intelligence according to claim 1, wherein the video transmission katon risk assessment analysis process of the middle-end network analysis unit is as follows:
s1: dividing a time threshold into i sub-time nodes, wherein i is a natural number larger than zero, acquiring the transmission speed, the transmission bandwidth value and the transmission delay value of a transmission network in each sub-time node, comparing the transmission speed, the transmission bandwidth value and the transmission delay value with a stored preset transmission speed threshold, a preset transmission bandwidth value threshold and a preset transmission delay value threshold, and if the transmission speed is smaller than the preset transmission speed threshold, the transmission bandwidth value is smaller than the preset transmission bandwidth value threshold and the transmission delay value is larger than the preset transmission delay value threshold, marking the part of the transmission speed smaller than the preset transmission speed threshold as a transmission influence value, marking the part of the transmission bandwidth value smaller than the preset transmission bandwidth value threshold as a bandwidth influence value, marking the part of the transmission delay value larger than the preset transmission delay value threshold as a delay interference value, and further marking the transmission influence value, the bandwidth influence value and the delay interference value in each sub-time node as CYi, KYi and YRi respectively;
s12: obtaining a transmission abnormal risk assessment coefficient Hi according to a formula, constructing a set A of the transmission abnormal risk assessment coefficient Hi, obtaining a difference value between two connected subsets in the set A, marking the difference value as a transmission risk floating value, comparing the transmission risk floating value with a stored preset transmission risk floating value threshold, if the transmission risk floating value is larger than the preset transmission risk floating value threshold, marking the total number of the transmission risk floating values corresponding to the transmission risk floating value larger than the preset transmission risk floating value threshold as a transmission risk abnormal constant, and comparing the transmission risk abnormal constant with a preset transmission risk abnormal constant threshold recorded in the transmission risk abnormal constant and stored in the transmission risk abnormal constant threshold:
if the transmission risk abnormal constant is smaller than a preset transmission risk abnormal constant threshold, generating a stable signal;
and if the transmission risk abnormal constant is greater than or equal to a preset transmission risk abnormal constant threshold value, generating a risk signal.
4. The vehicle-mounted video transmission supervision system based on artificial intelligence according to claim 1, wherein the in-depth data integration influence analysis process of the integrated transmission analysis unit is as follows:
the method comprises the steps of calling an influence risk value and an environment interference value from a front-end power supply analysis unit, respectively marking the influence risk value and the environment interference value as YF and HR, and simultaneously obtaining a transmission risk abnormal constant CF within a time threshold;
according to the formulaObtaining comprehensive influence evaluation coefficients, wherein f1, f2 and f3 are respectively preset weight factor coefficients of an influence risk value, an environment interference value and a transmission risk abnormal constant, f1, f2 and f3 are positive numbers larger than zero, f4 is a preset correction factor coefficient, the value is 2.442, Z is the comprehensive influence evaluation coefficient, and the comprehensive influence evaluation coefficient Z is compared with a preset comprehensive influence evaluation coefficient interval recorded and stored in the comprehensive influence evaluation coefficient Z:
if the comprehensive influence evaluation coefficient Z is larger than the maximum value in the preset comprehensive influence evaluation coefficient interval, generating a primary influence signal;
if the comprehensive influence evaluation coefficient Z belongs to a preset comprehensive influence evaluation coefficient interval, generating a secondary influence signal;
and if the comprehensive influence evaluation coefficient Z is smaller than the minimum value in the preset comprehensive influence evaluation coefficient interval, generating a three-level influence signal.
5. The vehicle-mounted video transmission supervision system based on artificial intelligence according to claim 1, wherein the self-checking feedback supervision analysis process of the video analysis unit is as follows:
acquiring an alternating current sound decibel value and an image resolution of a vehicle-mounted video receiving end in a time threshold, comparing the alternating current sound decibel value and the image resolution with a stored preset alternating current sound decibel value threshold and a preset image resolution threshold, and if the alternating current sound decibel value is larger than the preset alternating current sound decibel value threshold and the image resolution is larger than the preset image resolution threshold, respectively marking a part of the alternating current sound decibel value larger than the preset alternating current sound decibel value threshold and a part of the image resolution larger than the preset image resolution threshold as an audio effect influence value and a display influence value, respectively marking the parts as YZ and XZ, and simultaneously, adjusting a comprehensive influence evaluation coefficient Z from an integrated transmission analysis unit;
according to the formulaObtaining a display risk evaluation coefficient, wherein alpha, beta and epsilon are respectively preset influence factor coefficients of an audio effect influence value, a display influence value and a comprehensive influence evaluation coefficient, alpha, beta and epsilon are positive numbers larger than zero, T is the display risk evaluation coefficient, and the display risk evaluation coefficient T is compared with a preset display risk evaluation coefficient threshold value recorded and stored in the display risk evaluation coefficient T:
if the display risk assessment coefficient T is smaller than or equal to a preset display risk assessment coefficient threshold value, no signal is generated;
if the display risk assessment coefficient T is larger than a preset display risk assessment coefficient threshold value, generating a feedback instruction.
6. The system of claim 5, wherein the video analysis unit immediately performs a deep optimization evaluation analysis on the risk evaluation coefficient T when generating the feedback instruction, and the specific deep optimization evaluation analysis process is as follows:
the method comprises the steps of obtaining a part with a display risk assessment coefficient T larger than a preset display risk assessment coefficient threshold value, marking the part with the display risk assessment coefficient T larger than the preset display risk assessment coefficient threshold value as a quality influence value, and comparing the quality influence value with a preset quality influence value interval recorded and stored in the quality influence value and the quality influence value interval:
if the quality influence value is larger than the maximum value in the preset quality influence value interval, generating a first-level optimization signal;
if the quality influence value belongs to a preset quality influence value interval, generating a secondary optimization signal;
and if the quality influence value is smaller than the minimum value in the preset quality influence value interval, generating a three-level optimization signal.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117596386A (en) * | 2023-12-06 | 2024-02-23 | 中云数科(广州)信息科技有限公司 | Intelligent building safety monitoring system |
| CN117877225A (en) * | 2024-03-11 | 2024-04-12 | 厦门美契信息技术有限公司 | Traffic track monitoring and early warning system based on Internet of things and spatial distribution |
| CN118540723A (en) * | 2024-06-12 | 2024-08-23 | 泰州市鸿宝消防器材有限公司 | Help caller back field receiving device with image transmission function |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117596386A (en) * | 2023-12-06 | 2024-02-23 | 中云数科(广州)信息科技有限公司 | Intelligent building safety monitoring system |
| CN117596386B (en) * | 2023-12-06 | 2024-05-24 | 中云数科(广州)信息科技有限公司 | Intelligent building safety monitoring system |
| CN117877225A (en) * | 2024-03-11 | 2024-04-12 | 厦门美契信息技术有限公司 | Traffic track monitoring and early warning system based on Internet of things and spatial distribution |
| CN117877225B (en) * | 2024-03-11 | 2024-06-07 | 厦门美契信息技术有限公司 | Traffic track monitoring and early warning system based on Internet of things and spatial distribution |
| CN118540723A (en) * | 2024-06-12 | 2024-08-23 | 泰州市鸿宝消防器材有限公司 | Help caller back field receiving device with image transmission function |
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