CN115649080A - Vehicle driving safety intelligent monitoring system based on image recognition - Google Patents
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Abstract
The invention belongs to the field of vehicle safety monitoring, relates to a data processing technology, and is used for solving the problem that the existing vehicle running safety intelligent monitoring system has low accuracy of a state detection result of an automobile engine, in particular to a vehicle running safety intelligent monitoring system based on image recognition, which comprises an intelligent monitoring platform, wherein the intelligent monitoring platform is in communication connection with an operation monitoring module, an environment analysis module, an early warning module, a vehicle speed matching module and a storage module; the operation monitoring module is used for monitoring and analyzing the operation state of the vehicle engine: marking a vehicle engine as a monitoring object, and dividing the continuous running time of the vehicle into a plurality of monitoring time periods; the running monitoring module can monitor and analyze the running state of the vehicle engine, and the running state of the engine is judged whether to run normally or not by combining the current running state and the aging degree of the engine, so that the accuracy of the monitoring result of the engine is improved, and the driving safety is further ensured.
Description
Technical Field
The invention belongs to the field of vehicle safety monitoring, relates to a data processing technology, and particularly relates to an intelligent vehicle driving safety monitoring system based on image recognition.
Background
The automobile engine is a device for providing power for an automobile, is the heart of the automobile, determines the dynamic property, the economical efficiency, the stability and the environmental protection property of the automobile, and can be divided into a diesel engine, a gasoline engine, an electric automobile motor, hybrid power and the like according to different power sources.
The running state of an automobile engine affects the running safety of the whole vehicle, and an existing intelligent monitoring system for the running safety of the vehicle generally monitors and analyzes the running state of the automobile engine through running parameters of the automobile engine, however, the running state of the automobile engine is interfered by external influence factors, so that the running state of the automobile engine cannot be evaluated by adopting a unified standard aiming at a changeable external environment, the accuracy of a state detection result of the existing intelligent monitoring system for the running safety of the vehicle on the automobile engine is low, and further the running safety of the vehicle is low.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide an intelligent monitoring system for vehicle running safety based on image recognition, which is used for solving the problem that the existing intelligent monitoring system for vehicle running safety is not high in accuracy of a state detection result of an automobile engine;
the technical problems to be solved by the invention are as follows: how to provide a vehicle driving safety intelligent monitoring system which can monitor and analyze the running state of an automobile engine by combining external influence factors.
The purpose of the invention can be realized by the following technical scheme:
an intelligent monitoring system for vehicle running safety based on image recognition comprises an intelligent monitoring platform, wherein the intelligent monitoring platform is in communication connection with an operation monitoring module, an environment analysis module, an early warning module, a vehicle speed matching module and a storage module;
the operation monitoring module is used for monitoring and analyzing the operation state of the vehicle engine: marking a vehicle engine as a monitoring object, dividing the continuous operation time of a vehicle into a plurality of monitoring time periods, acquiring vibration data ZD, temperature data WD and use data SY of the monitoring object in the monitoring time periods, carrying out numerical calculation on the vibration data ZD, the temperature data WD and the use data SY of the monitoring object in the monitoring time periods to obtain an operation coefficient YX of the monitoring object in the monitoring time periods, and judging whether the operation state of the monitoring object in the monitoring time periods meets requirements or not according to the numerical value of the operation coefficient YX;
the environment analysis module is used for monitoring and analyzing the running environment of the vehicle and acquiring the ring shadow coefficient HY in the monitoring time period, marking the monitoring time period as a normal time period or an abnormal time period according to the numerical value of the ring shadow coefficient HY, and sending the ring shadow coefficient of the abnormal time period to the vehicle speed matching module;
the vehicle speed matching module is used for matching and analyzing the running speed of the vehicle.
As a preferred embodiment of the present invention, the vibration data ZD of the monitoring object is a maximum value of a vibration frequency of the monitoring object in the monitoring period, the temperature data WD of the monitoring object is a maximum value of a temperature of the monitoring object in the monitoring period, and the acquisition process of the usage data SY of the monitoring object includes: the method comprises the following steps of shooting an image of a monitored object, amplifying the shot image into a pixel grid image, carrying out gray scale conversion on the pixel grid image to obtain a gray scale value of a pixel grid, obtaining a gray scale range through a storage module, and judging whether the gray scale value of the pixel grid is located in the gray scale range: if yes, marking the corresponding pixel grid as a corroded pixel grid; if not, marking the corresponding pixel grid as a normal pixel grid; the ratio of the number of erosion pixel bins to the number of all pixel bins in the pixel bin image is marked as usage data SY.
As a preferred embodiment of the present invention, the specific process of determining whether the operating state of the monitored object in the monitoring period meets the requirement includes: acquiring an operation threshold YXmax through a storage module, and comparing the operation coefficient YX with the operation threshold:
if the operation coefficient YX is smaller than the operation threshold YXmax, judging that the operation state of the monitored object in the monitoring period meets the requirement;
if the operation coefficient YX is larger than or equal to the operation threshold YXmax, the operation state of the monitored object in the monitoring period is judged to be not met, the operation monitoring module sends an abnormal early warning signal to the intelligent monitoring platform, the intelligent monitoring platform sends the abnormal early warning signal to the early warning module after receiving the abnormal early warning signal, and the early warning module carries out abnormal early warning voice broadcast reminding after receiving the abnormal early warning signal.
As a preferred embodiment of the present invention, the process of acquiring the ring shadow coefficient HY includes: the method comprises the steps of obtaining air temperature data KW of vehicle driving in a monitoring period, rainfall data YL and fog data WQ, wherein the air temperature data KW of the monitoring period is the maximum temperature value of external air of the vehicle in the monitoring period, the rainfall data YL of the monitoring period is the total rainfall amount in the monitoring period, the fog data WQ of the monitoring period is the maximum fog concentration value of the external air of the vehicle in the monitoring period, and the ambient shadow coefficient HY of the monitoring period is obtained by carrying out numerical calculation on the air temperature data KW of the vehicle driving in the monitoring period, the rainfall data YL and the fog data WQ.
As a preferred embodiment of the present invention, the process of marking the monitoring period as a normal period or an abnormal period includes: acquiring a ring shadow threshold HYmax through a storage module, and comparing the ring shadow coefficient HY with the ring shadow threshold HYmax:
if the ambient shadow coefficient HY is less than the ambient shadow threshold HYmax, judging that the driving environment in the monitoring time period meets the requirement, and marking the corresponding monitoring time period as a normal time period;
if the surround image coefficient is larger than or equal to the surround image threshold value HYmax, judging that the driving environment in the monitoring period does not meet the requirements, marking the corresponding monitoring period as an abnormal period, acquiring the surround image interval and the speed data corresponding to the surround image coefficient through the storage module, sending a speed reduction signal to the intelligent monitoring platform by the environment analysis module, sending the speed reduction signal to the early warning module by the intelligent monitoring platform after receiving the speed reduction signal, and carrying out speed reduction voice broadcast reminding and speed data voice broadcast reminding after receiving the speed reduction signal by the early warning module.
As a preferred embodiment of the present invention, the specific process of the vehicle speed matching module performing matching analysis on the vehicle running speed includes: the method comprises the steps of forming an annular image range by the received maximum value and the minimum value of the annular image coefficient in the abnormal time period, dividing the annular image range into a plurality of annular image intervals, obtaining the running coefficient of the abnormal time period in the annular image intervals, obtaining the speed data of a vehicle in the abnormal time period with the minimum value of the running coefficient in the annular image intervals, matching the speed data with the annular image intervals, and sending the speed data to a storage module for storage, wherein the speed data comprise the maximum value, the minimum value and the average value of the running speed of the vehicle in the abnormal time period.
As a preferred embodiment of the present invention, the working method of the intelligent monitoring system for vehicle driving safety based on image recognition comprises the following steps:
the method comprises the following steps: monitoring and analyzing the running state of the vehicle engine, marking the vehicle engine as a monitoring object, dividing the continuous running time of the vehicle into a plurality of monitoring time periods, acquiring the running coefficient of the monitoring object in the monitoring time periods, and judging whether the running state of the monitoring object in the monitoring time periods is qualified or not according to the numerical value of the running coefficient;
step two: monitoring and analyzing the vehicle running environment, acquiring an ambient image coefficient of vehicle running in a monitoring time period, judging whether the vehicle running environment in the monitoring time period meets the requirement or not according to the numerical value of the ambient image coefficient, marking the monitoring time period as a normal time period or an abnormal time period, and sending the ambient image coefficient of the abnormal time period to a vehicle speed matching module;
step three: and matching and analyzing the running speed of the vehicle, dividing the ring shadow range into a plurality of ring shadow intervals, and matching the ring shadow intervals with the speed data through the running coefficients of abnormal time periods in the ring shadow intervals.
The invention has the following beneficial effects:
1. the running state of the vehicle engine can be monitored and analyzed through the running monitoring module, the engine state data of each running period is processed in a time-sharing monitoring mode to obtain a running coefficient, and whether the engine state runs normally or not is judged by combining the current running state and the aging degree of the engine, so that the accuracy of the monitoring result of the engine is improved, and the running safety is further ensured;
2. the external influence factors when the vehicle runs can be monitored and analyzed through the environment analysis module, the ring shadow coefficient is obtained through numerical calculation of external environment parameters of the vehicle, whether the external influence factors are normal or not is judged through the ring shadow coefficient, and the external influence factors are fed back in time when abnormal, so that a driver is reminded, and the accident probability is reduced;
3. the vehicle speed matching module can be used for matching and analyzing the vehicle running speed, matching the speed data corresponding to the abnormal time interval with the minimum running coefficient in the ring shadow interval with the ring shadow interval, and calling the corresponding speed data in the storage module for matching when the ring shadow coefficient does not meet the requirement after matching is finished, so that the safest running speed is recommended for a driver, and the accident occurrence probability is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in figure 1, the intelligent monitoring system for vehicle running safety based on image recognition comprises an intelligent monitoring platform, wherein the intelligent monitoring platform is in communication connection with an operation monitoring module, an environment analysis module, an early warning module, a vehicle speed matching module and a storage module.
The operation monitoring module is used for monitoring and analyzing the operation state of the vehicle engine: marking a vehicle engine as a monitoring object, dividing the continuous operation time of a vehicle into a plurality of monitoring time periods, acquiring vibration data ZD, temperature data WD and use data SY of the monitoring object in the monitoring time periods, directly acquiring the vibration data by a mechanical vibration sensor, converting engineering vibration parameters into mechanical signals by the mechanical vibration sensor, amplifying the mechanical signals by a mechanical system, and measuring and recording the mechanical signals, wherein common instruments comprise a lever-type vibration meter and a Geiger vibration meter; the temperature data is directly obtained by a temperature sensor, the temperature sensor refers to a sensor which can sense the temperature and convert the temperature into a usable output signal, the temperature sensor is the core part of a temperature measuring instrument, the temperature sensor has various varieties, can be divided into a contact type and a non-contact type according to the measuring mode, and is divided into a thermal resistor and a thermocouple according to the characteristics of sensor materials and electronic elements; the vibration data ZD of the monitored object is the maximum value of the vibration frequency of the monitored object in the monitoring period, the temperature data WD of the monitored object is the maximum value of the temperature of the monitored object in the monitoring period, and the acquisition process of the usage data SY of the monitored object comprises the following steps: the method comprises the steps of shooting an image of a monitored object, amplifying the shot image into a pixel grid image, and carrying out gray level transformation on the pixel grid image to obtain gray level values of pixel grids, wherein the gray level transformation refers to a method for changing the gray level value of each pixel in a source image point by point according to a certain target condition, and aims to improve the image quality and enable the display effect of the image to be clearer; acquiring a gray scale range through the storage module, wherein the gray scale range is (220, 235), and judging whether the gray scale value of the pixel grid is within the gray scale range: if yes, marking the corresponding pixel grid as a corroded pixel grid; if not, marking the corresponding pixel grid asA normal pixel grid; marking the ratio of the number of the corroded pixel grids to the number of all the pixel grids in the pixel grid image as use data; by the formula
Obtaining an operation coefficient YX of the monitored object in the monitoring time period, wherein the operation coefficient is a numerical value reflecting the operating state of the monitored object in the monitoring time period, and the larger the numerical value of the operation coefficient is, the worse the operating state of the monitored object in the monitoring time period is; wherein alpha 1, alpha 2, alpha 3 and alpha 4 are all proportionality coefficients, alpha 4 is more than alpha 3 and more than alpha 2 and more than alpha 1, and SC is the accumulated time length of single use of the automobile engine; acquiring an operation threshold YXmax through a storage module, and comparing the operation coefficient YX with the operation threshold: if the operation coefficient YX is smaller than the operation threshold YXmax, judging that the operation state of the monitored object in the monitoring period meets the requirement; if the operation coefficient YX is larger than or equal to the operation threshold YXmax, judging that the operation state of the monitored object in the monitoring period does not meet the requirement, sending an abnormal early warning signal to the intelligent monitoring platform by the operation monitoring module, sending the abnormal early warning signal to the early warning module by the intelligent monitoring platform after receiving the abnormal early warning signal, and carrying out abnormal early warning voice broadcast reminding by the early warning module after receiving the abnormal early warning signal; the running state of the vehicle engine is monitored and analyzed, the engine state data of each running time period is processed in a time-sharing monitoring mode to obtain a running coefficient, and whether the engine state normally runs or not is judged by combining the current running state and the aging degree of the engine, so that the accuracy of the monitoring result of the engine is improved, and the running safety is further ensured.
The environment analysis module is used for monitoring and analyzing the running environment of the vehicle: acquiring air temperature data KW, rainfall data YL and fog data WQ of vehicle running in a monitoring time period, wherein the air temperature data is directly acquired by a temperature sensor, the air temperature data KW in the monitoring time period is the maximum temperature of air outside the vehicle in the monitoring time period, and the air temperature has certain influence on the performance of an engine, because the air temperature can influence the air inlet temperature, the air inlet temperature can influence the performance of the engine, if the air inlet temperature is too high, the engine can be possibly subjected to knocking phenomenon, the engine can be damaged by knocking, and the power of the engine can be reduced; the rainfall data YL of the monitoring period is the total rainfall amount in the monitoring period, the fog data WQ of the monitoring period is the maximum fog concentration of the external air of the vehicle in the monitoring period, the ring shadow coefficient HY of the monitoring period is obtained through a formula HY = beta 1 KW + beta 2 YL + beta 3 WQ, the ring shadow coefficient is a numerical value reflecting the influence degree of the external environment on the running state of the monitored object, and the larger the numerical value of the ring shadow coefficient is, the larger the influence degree of the external environment on the running state of the monitored object is; wherein beta 1, beta 2 and beta 3 are proportionality coefficients, and beta 1 is more than beta 2 and more than beta 3 is more than 1; acquiring a ring shadow threshold HYmax through a storage module, and comparing the ring shadow coefficient HY with the ring shadow threshold HYmax: if the ambient shadow coefficient HY is less than the ambient shadow threshold HYmax, judging that the driving environment in the monitoring time period meets the requirement, and marking the corresponding monitoring time period as a normal time period; if the ring shadow coefficient is larger than or equal to a ring shadow threshold value HYmax, judging that the driving environment in the monitoring period does not meet the requirement, marking the corresponding monitoring period as an abnormal period, acquiring the ring shadow area and the speed data corresponding to the ring shadow coefficient through a storage module, sending a deceleration signal to an intelligent monitoring platform by an environment analysis module, sending the deceleration signal to an early warning module by the intelligent monitoring platform after receiving the deceleration signal, and carrying out deceleration voice broadcast reminding and speed data voice broadcast reminding after receiving the deceleration signal by the early warning module; sending the ring shadow coefficients of the abnormal time periods to a vehicle speed matching module; monitoring analysis is carried out to the external influence factor when the vehicle traveles, obtains the surround shadow coefficient through carrying out numerical calculation to vehicle external environment parameter, and then judges whether external influence factor is normal through the surround shadow coefficient, in time feeds back when external influence factor is unusual, reminds navigating mate, reduces accident probability.
The vehicle speed matching module is used for matching and analyzing the vehicle running speed: the method comprises the steps that a ring shadow range is formed by the maximum value and the minimum value of a received ring shadow coefficient of an abnormal time interval, the ring shadow range is divided into a plurality of ring shadow intervals, the running coefficient of the abnormal time interval in the ring shadow intervals is obtained, the speed data of a vehicle in the abnormal time interval with the minimum value of the running coefficient in the ring shadow intervals are obtained, the speed data comprise the running speed maximum value, the running speed minimum value and the running speed average value of the vehicle in the abnormal time interval, and the speed data are matched with the ring shadow intervals and sent to a storage module for storage; the method comprises the steps of carrying out matching analysis on the running speed of the vehicle, matching speed data corresponding to the abnormal time interval with the minimum running coefficient in the ring shadow interval with the ring shadow interval, and calling corresponding speed data in a storage module for matching when the ring shadow coefficient does not meet requirements after matching is finished, so that the safest running speed is recommended for a driver, and the accident occurrence probability is reduced.
Example two
As shown in fig. 2, an intelligent monitoring method for vehicle driving safety based on image recognition includes the following steps:
the method comprises the following steps: monitoring and analyzing the running state of the vehicle engine, marking the vehicle engine as a monitoring object, dividing the continuous running time of the vehicle into a plurality of monitoring time periods, acquiring the running coefficient of the monitoring object in the monitoring time periods, judging whether the running state of the monitoring object in the monitoring time periods is qualified or not according to the numerical value of the running coefficient, and judging whether the engine runs normally or not according to the current running state and the aging degree of the engine, so that the accuracy of the monitoring result of the engine is improved, and the driving safety is further ensured;
step two: monitoring and analyzing the vehicle running environment, acquiring a ring shadow coefficient of vehicle running in a monitoring time period, judging whether the vehicle running environment in the monitoring time period meets the requirement or not according to the numerical value of the ring shadow coefficient, marking the monitoring time period as a normal time period or an abnormal time period, sending the ring shadow coefficient in the abnormal time period to a vehicle speed matching module, feeding back in time when external influence factors are abnormal, reminding a driver, and reducing the accident probability;
step three: the method comprises the steps of carrying out matching analysis on the running speed of a vehicle, dividing a ring shadow range into a plurality of ring shadow intervals, matching the ring shadow intervals with speed data through the running coefficients of abnormal time periods in the ring shadow intervals, and calling the corresponding speed data in a storage module for matching, so that the safest running speed is recommended for a driver, and the accident occurrence probability is reduced.
An intelligent monitoring system for vehicle running safety based on image recognition is characterized in that when the system works, the running state of a vehicle engine is monitored and analyzed, the vehicle engine is marked as a monitoring object, the continuous running time of a vehicle is divided into a plurality of monitoring periods, the running coefficient of the monitoring object in the monitoring periods is obtained, and whether the running state of the monitoring object in the monitoring periods is qualified or not is judged according to the numerical value of the running coefficient; monitoring and analyzing the running environment of the vehicle to obtain the ring shadow coefficient of the running of the vehicle in the monitoring period, judging whether the running environment of the vehicle in the monitoring period meets the requirement or not according to the numerical value of the ring shadow coefficient, marking the monitoring period as a normal period or an abnormal period, and sending the ring shadow coefficient of the abnormal period to a vehicle speed matching module; and matching and analyzing the running speed of the vehicle, dividing the ring shadow range into a plurality of ring shadow intervals, and matching the ring shadow intervals with the speed data through the running coefficients of abnormal time periods in the ring shadow intervals.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: the formula HY = β 1 × kw + β 2 × yl + β 3 × wq; acquiring multiple groups of sample data by a person skilled in the art and setting a corresponding ring shadow coefficient for each group of sample data; substituting the set ring shadow coefficient and the acquired sample data into a formula, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of beta 1, beta 2 and beta 3 which are 5.37, 2.64 and 2.15 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and regarding the size of the coefficient, the size depends on the number of sample data and a corresponding ring shadow coefficient is preliminarily set for each group of sample data by a person skilled in the art; the proportional relation between the parameters and the quantized numerical values is not affected, for example, the ring shadow coefficient is in direct proportion to the numerical value of the air temperature data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. An intelligent monitoring system for vehicle running safety based on image recognition comprises an intelligent monitoring platform, and is characterized in that the intelligent monitoring platform is in communication connection with an operation monitoring module, an environment analysis module, an early warning module, a vehicle speed matching module and a storage module;
the operation monitoring module is used for monitoring and analyzing the operation state of the vehicle engine: marking an engine of a vehicle as a monitoring object, dividing the continuous operation time of the vehicle into a plurality of monitoring periods, acquiring vibration data ZD, temperature data WD and use data SY of the monitoring object in the monitoring periods, carrying out numerical calculation on the vibration data ZD, the temperature data WD and the use data SY of the monitoring object in the monitoring periods to obtain an operation coefficient YX of the monitoring object in the monitoring periods, and judging whether the operation state of the monitoring object in the monitoring periods meets the requirement or not according to the numerical value of the operation coefficient YX;
the environment analysis module is used for monitoring and analyzing the running environment of the vehicle, acquiring the ring shadow coefficient HY in a monitoring time period, marking the monitoring time period as a normal time period or an abnormal time period according to the numerical value of the ring shadow coefficient HY, and sending the ring shadow coefficient of the abnormal time period to the vehicle speed matching module;
the vehicle speed matching module is used for matching and analyzing the vehicle running speed.
2. The intelligent monitoring system for vehicle driving safety based on image recognition as claimed in claim 1, wherein the vibration data ZD of the monitored object is the maximum value of the vibration frequency of the monitored object in the monitoring period, the temperature data WD of the monitored object is the maximum value of the temperature of the monitored object in the monitoring period, and the obtaining process of the usage data SY of the monitored object comprises: the method comprises the following steps of shooting an image of a monitored object, amplifying the shot image into a pixel grid image, carrying out gray scale conversion on the pixel grid image to obtain a gray scale value of a pixel grid, obtaining a gray scale range through a storage module, and judging whether the gray scale value of the pixel grid is located in the gray scale range: if yes, marking the corresponding pixel grid as a corroded pixel grid; if not, marking the corresponding pixel grid as a normal pixel grid; the ratio of the number of erosion pixel bins to the number of all pixel bins in the pixel bin image is marked as usage data SY.
3. The intelligent monitoring system for vehicle driving safety based on image recognition as claimed in claim 1, wherein the specific process of determining whether the running state of the monitored object in the monitoring period meets the requirement comprises: acquiring an operation threshold YXmax through a storage module, and comparing the operation coefficient YX with the operation threshold:
if the operation coefficient YX is smaller than the operation threshold YXmax, judging that the operation state of the monitored object in the monitoring period meets the requirement;
if the operation coefficient YX is larger than or equal to the operation threshold YXmax, judging that the operation state of the monitored object in the monitoring period does not meet the requirement, sending an abnormal early warning signal to the intelligent monitoring platform by the operation monitoring module, sending the abnormal early warning signal to the early warning module by the intelligent monitoring platform after receiving the abnormal early warning signal, and carrying out abnormal early warning voice broadcast reminding after receiving the abnormal early warning signal by the early warning module.
4. The intelligent monitoring system for vehicle driving safety based on image recognition as claimed in claim 1, wherein the process of obtaining the environmental coefficient HY comprises: acquiring air temperature data KW, rainfall data YL and fog data WQ of vehicle running in a monitoring period, wherein the air temperature data KW of the monitoring period is the maximum temperature value of external air of the vehicle in the monitoring period, the rainfall data YL of the monitoring period is the total amount of rainfall in the monitoring period, the fog data WQ of the monitoring period is the maximum fog concentration value of the external air of the vehicle in the monitoring period, and the environmental shadow coefficient HY of the monitoring period is obtained by carrying out numerical calculation on the air temperature data KW, the rainfall data YL and the fog data WQ of the vehicle running in the monitoring period.
5. The intelligent monitoring system for vehicle driving safety based on image recognition as claimed in claim 4, wherein the process of marking the monitoring period as a normal period or an abnormal period comprises: acquiring a ring shadow threshold HYmax through a storage module, and comparing the ring shadow coefficient HY with the ring shadow threshold HYmax:
if the ambient shadow coefficient HY is less than the ambient shadow threshold HYmax, judging that the driving environment in the monitoring time period meets the requirement, and marking the corresponding monitoring time period as a normal time period;
if the ring shadow coefficient is greater than or equal to a ring shadow threshold value HYmax, then judge that the driving environment in the monitoring period is unsatisfied with the requirement, mark the monitoring period that corresponds as abnormal period, acquire the ring shadow interval and the speed data that correspond with the ring shadow coefficient through storage module, environmental analysis module sends the deceleration signal to intelligent monitoring platform, the intelligent monitoring platform receives after the deceleration signal with deceleration signal transmission to early warning module, early warning module carries out deceleration voice broadcast after receiving the deceleration signal and reminds and speed data voice broadcast reminds.
6. The intelligent monitoring system for vehicle driving safety based on image recognition as claimed in claim 4, wherein the specific process of the vehicle speed matching module for matching and analyzing the vehicle driving speed comprises: the method comprises the steps of forming a ring shadow range by the maximum value and the minimum value of the received ring shadow coefficients in the abnormal time period, dividing the ring shadow range into a plurality of ring shadow intervals, obtaining the running coefficients of the abnormal time period in the ring shadow intervals, obtaining the speed data of a vehicle in the abnormal time period with the minimum value of the running coefficients in the ring shadow intervals, matching the speed data with the ring shadow intervals, and sending the speed data to a storage module for storage, wherein the speed data comprises the maximum value, the minimum value and the average value of the running speeds of the vehicle in the abnormal time period.
7. The intelligent monitoring system for vehicle driving safety based on image recognition according to any one of claims 1-6, characterized in that the working method of the intelligent monitoring system for vehicle driving safety based on image recognition comprises the following steps:
the method comprises the following steps: monitoring and analyzing the running state of the vehicle engine, marking the vehicle engine as a monitoring object, dividing the continuous running time of the vehicle into a plurality of monitoring time periods, acquiring the running coefficient of the monitoring object in the monitoring time periods, and judging whether the running state of the monitoring object in the monitoring time periods is qualified or not according to the numerical value of the running coefficient;
step two: monitoring and analyzing the vehicle running environment, acquiring an ambient image coefficient of vehicle running in a monitoring time period, judging whether the vehicle running environment in the monitoring time period meets the requirement or not according to the numerical value of the ambient image coefficient, marking the monitoring time period as a normal time period or an abnormal time period, and sending the ambient image coefficient of the abnormal time period to a vehicle speed matching module;
step three: and matching and analyzing the running speed of the vehicle, dividing the ring shadow range into a plurality of ring shadow intervals, and matching the ring shadow intervals with the speed data through the running coefficients of abnormal time periods in the ring shadow intervals.
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