CN112288986A - Electric automobile charging safety monitoring and early warning system - Google Patents
Electric automobile charging safety monitoring and early warning system Download PDFInfo
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Abstract
The invention provides a charging safety monitoring and early warning system for an electric automobile, which comprises a data acquisition module, a data transmission module, a monitoring module and an alarm module, wherein the data acquisition module is used for acquiring charging safety information of the electric automobile; the data acquisition module is used for acquiring monitoring data of the electric automobile during charging and sending the monitoring data to the data transmission module; the data transmission module is used for transmitting the monitoring data to the monitoring module; the monitoring module is used for judging whether a fire accident occurs according to the monitoring data; the alarm module is used for giving an alarm to the person on duty when a fire accident happens. Through the monitoring data that acquire electric automobile's the charging process, and according to whether there is the emergence of fire incident monitoring data judgement, realized whole control to electric automobile charging process, can be when the fire incident takes place, the very first time carries out the early warning suggestion to the personnel on duty for the personnel on duty can in time handle the fire incident when electric automobile charges.
Description
Technical Field
The invention relates to the field of monitoring, in particular to a charging safety monitoring and early warning system for an electric automobile.
Background
With the development of technology, more and more electric vehicles are used by people. The charging time of the electric automobile is long, so that many people leave after being plugged in. The electric automobile is often unattended in the charging process, so that when fire accidents happen to the electric automobile due to the reasons of short circuit of a battery and the like, people cannot timely deal with the fire accidents due to the fact that the fire accidents cannot be found timely.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a charging safety monitoring and early warning system for an electric vehicle, which includes a data acquisition module, a data transmission module, a monitoring module and an alarm module;
the data acquisition module is used for acquiring monitoring data of the electric automobile during charging and sending the monitoring data to the data transmission module;
the data transmission module is used for transmitting the monitoring data to the monitoring module;
the monitoring module is used for judging whether a fire accident occurs according to the monitoring data;
the alarm module is used for giving an alarm to the person on duty when a fire accident happens.
Preferably, the data acquisition module comprises a temperature acquisition unit and a video acquisition unit;
the temperature acquisition unit is used for acquiring the temperature of the electric automobile during charging;
the video acquisition unit is used for acquiring a monitoring video when the electric automobile is charged.
Preferably, the data transmission module comprises a near wireless communication unit and a wired communication unit;
the wireless communication unit comprises a ZigBee communication device, a wireless cellular network communication device and a WiFi communication device;
the wired communication unit comprises an optical fiber communication device which is used for connecting a communication optical fiber provided by a network operator with the monitoring module.
Preferably, the monitoring module comprises a receiving unit, a storage unit, a judging unit and a sending unit;
the receiving unit is used for receiving the monitoring data sent by the data transmission module and transmitting the monitoring data to the storage unit;
the storage unit is used for storing the monitoring data;
the judging unit is used for extracting the monitoring data from the storage unit, judging whether a fire accident occurs according to the monitoring data and sending a judgment result to the sending unit;
and the sending unit is used for transmitting the judgment result to an alarm module.
Preferably, the alarm module is configured to identify the determination result, and determine whether an alarm prompt needs to be given to an attendant according to the determination result;
if the judgment result is that a fire accident occurs, the alarm module gives an alarm prompt to an attendant;
and if the judgment result shows that no fire accident occurs, the alarm module does not give an alarm prompt to the person on duty.
Preferably, the judging whether a fire accident occurs includes:
judging whether the temperature of the electric automobile during charging exceeds a set temperature threshold value or not and judging whether flame is generated in a monitoring video or not according to the monitoring video;
recording the duration that the temperature of the electric automobile exceeds a set temperature threshold value when the electric automobile is charged as T1, if T1 is larger than Tthre1, judging that a fire accident occurs, and the Tthre1 is a set first time threshold value;
and recording the continuous existence time of the flame in the monitoring video as T2, judging that a fire accident occurs if T2 is greater than Tthre2, wherein Tthre2 is a set second time threshold.
Compared with the prior art, the invention has the advantages that:
according to the invention, the monitoring data in the charging process of the electric automobile is obtained, and whether a fire accident occurs is judged according to the monitoring data, so that the whole process monitoring of the charging process of the electric automobile is realized, and early warning prompt can be carried out on an on-duty person at the first time when the fire accident occurs, so that the on-duty person can timely handle the fire accident during the charging of the electric automobile.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a diagram of an exemplary embodiment of a charging safety monitoring and warning system for an electric vehicle according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The invention provides an electric vehicle charging safety monitoring and early warning system, which comprises a data acquisition module, a data transmission module, a monitoring module and an alarm module, wherein the data acquisition module is used for acquiring charging safety information of an electric vehicle;
the data acquisition module is used for acquiring monitoring data of the electric automobile during charging and sending the monitoring data to the data transmission module;
the data transmission module is used for transmitting the monitoring data to the monitoring module;
the monitoring module is used for judging whether a fire accident occurs according to the monitoring data;
the alarm module is used for giving an alarm to the person on duty when a fire accident happens.
Through the monitoring data that acquire electric automobile's the charging process, and according to whether there is the emergence of fire incident monitoring data judgement, realized whole control to electric automobile charging process, can be when the fire incident takes place, the very first time carries out the early warning suggestion to the personnel on duty for the personnel on duty can in time handle the fire incident when electric automobile charges.
In one embodiment, the data acquisition module comprises a temperature acquisition unit and a video acquisition unit;
the temperature acquisition unit is used for acquiring the temperature of the electric automobile during charging;
the video acquisition unit is used for acquiring a monitoring video when the electric automobile is charged.
In one embodiment, the data transmission module includes a near wireless communication unit and a wired communication unit;
the wireless communication unit comprises a ZigBee communication device, a wireless cellular network communication device and a WiFi communication device;
the wired communication unit comprises an optical fiber communication device which is used for connecting a communication optical fiber provided by a network operator with the monitoring module.
In one embodiment, the monitoring module comprises a receiving unit, a storage unit, a judging unit and a sending unit;
the receiving unit is used for receiving the monitoring data sent by the data transmission module and transmitting the monitoring data to the storage unit;
the storage unit is used for storing the monitoring data;
the judging unit is used for extracting the monitoring data from the storage unit, judging whether a fire accident occurs according to the monitoring data and sending a judgment result to the sending unit;
and the sending unit is used for transmitting the judgment result to an alarm module.
In one embodiment, the alarm module is configured to identify the determination result, and determine whether an alarm prompt needs to be given to an attendant according to the determination result;
if the judgment result is that a fire accident occurs, the alarm module gives an alarm prompt to an attendant;
and if the judgment result shows that no fire accident occurs, the alarm module does not give an alarm prompt to the person on duty.
In one embodiment, determining whether a fire incident has occurred includes:
judging whether the temperature of the electric automobile during charging exceeds a set temperature threshold value or not and judging whether flame is generated in a monitoring video or not according to the monitoring video;
recording the duration that the temperature of the electric automobile exceeds a set temperature threshold value when the electric automobile is charged as T1, if T1 is larger than Tthre1, judging that a fire accident occurs, and the Tthre1 is a set first time threshold value;
and recording the continuous existence time of the flame in the monitoring video as T2, judging that a fire accident occurs if T2 is greater than Tthre2, wherein Tthre2 is a set second time threshold.
When judging whether a fire occurs, the temperature at a certain moment is not only judged to exceed a set temperature threshold value, because the temperature generated by the cigarette end when a driver smokes near the electric vehicle is easily judged to be the temperature of the electric vehicle, so that misjudgment is caused, and in addition, the temperature sensor is easily interfered by the erroneously obtained temperature, for example, the temperature sensor suddenly has a data acquisition error, so that the numerical value is larger, and the misjudgment is also easily caused. In addition, the monitoring video is used as a basis for judging whether a fire disaster occurs, safety monitoring of the charging process of the electric automobile is achieved through two different monitoring modes, and the problem that the charging process of the electric automobile cannot be correctly monitored when a single monitoring mode fails is solved.
In one embodiment, the determination unit determines whether there is a flame by:
sampling frame images in a monitoring video to obtain frame images to be detected;
carrying out image enhancement processing on the frame image to be detected to obtain an enhanced image;
performing flame pre-detection on the enhanced image, judging whether suspected flames exist in the enhanced image, if so, further processing the enhanced image, and if not, judging that no flames exist;
further processing the enhanced image, comprising:
determining an area in the enhanced image where the suspected flame exists;
and identifying the flame in the area with the suspected flame, and judging whether the flame exists or not.
In one embodiment, sampling frame images in a surveillance video includes:
the time interval of sampling is determined as follows:
if the suspected flame exists in the frame image to be detected during flame pre-detection, updating the sampling time interval by adopting the following formula:
if no suspected flame exists, updating the sampling time interval by adopting the following formula:
ct2where ct denotes the time interval of the last determined sample, ct × 22Indicating the time interval of the updated sample if ct2Is greater than or equal to a set sampling interval threshold value cthre2If yes, use the cthre2As the updated time interval of the samples.
In the present application, the time interval for sampling the frame images in the surveillance video is not a fixed time interval, but a variable adaptive time interval. Specifically, if no suspected flame is detected all the time, the sampling time interval is continuously expanded, and the maximum sampling time interval is known, so that the problems of high operation pressure and high energy consumption of the system caused by the fact that the system detects frame images in the monitoring video all the time can be effectively solved. If the existence of suspected flame is detected last time, even if the electric automobile does not necessarily have flame, the time for next sampling is shortened, and therefore the problem that fire accidents cannot be found timely due to false detection of the system can be avoided.
In one embodiment, the image enhancement processing on the frame image to be detected includes:
the frame number of the frame image to be detected in the monitoring video is marked as n, the frame image of the (n-1) th frame and the frame image of the (n + 1) th frame are selected, and the frame image to be detected is enhanced:
G1(x,y)＝B1(x,y)＝R1(x,y)
in the formula, qmi represents taking a minimum function, taking the minimum value of three parameters in parentheses, and R (x, y), G (x, y) and B (x, y) represent the channel values of the red component, the green component and the blue component of the frame image to be detected in the RGB color space, respectively; (x, y) denotes the position of the pixel, R1(x,y)、G1(x, y) and B1(x, y) respectively representing channel values of a red component, a green component and a blue component of the first intermediate image in an RGB color space, wherein the channel values are obtained after the frame to be detected is preliminarily enhanced;
weighting the frame image to be detected by using the frame images of the (n-1) th frame and the (n + 1) th frame:
R2(x,y)＝a1×Rn-1(x,y)+a2×R(x,y)+a3×Rn+1(x,y)
G2(x,y)＝a1×Gn-1(x,y)+a2×G(x,y)+a3×Gn+1(x,y)
B2(x,y)＝a1×Bn-1(x,y)+a2×B(x,y)+a3×Bn+1(x,y)
in the formula, R2(x,y)、G2(x,y)、B2(x, y) respectively represent channel values of a red component, a green component, and a blue component in the RGB color space of the second intermediate image obtained after the weighting processing; a is1、a2And a3Representing a set weight parameter, Rn-1(x,y)、Gn-1(x,y)、Bn-1(x, y) channel values of a red component, a green component, and a blue component in the RGB color space of the frame image of the n-1 th frame, respectively; rn+1(x,y)、Gn+1(x,y)、Bn+1(x, y) represent channel values of red, green and blue components in the RGB color space of the frame image of the (n + 1) th frame, respectively；
Subtracting the frame image of the (n-1) th frame from the first intermediate image to obtain a third intermediate image; subtracting the frame image of the (n + 1) th frame from the second intermediate image to obtain a fourth intermediate image;
the enhanced image is obtained using the following formula:
fz(x,y)＝b1×f3(x,y)+b2×f4(x,y)
wherein fz (x, y) represents an enhanced image, b1And b2To a set proportionality factor, b1+b2＝1，f3(x, y) and f4(x, y) denote the third intermediate image and the fourth intermediate image, respectively.
In the process of acquiring the third intermediate image and the fourth intermediate image, the pixel values of the pixel points belonging to the non-change region are inhibited, the pixel values of the pixel points belonging to the change region are enhanced, if the pixel points belonging to the flame part exist in the frame image to be detected, the pixel values of the pixel points belonging to the flame part are changed compared with the image of the previous frame if the pixel points in the frame image to be detected, so that the enhancement of the pixel points is facilitated, and whether the area suspected of flame exists or not is conveniently and quickly distinguished subsequently. And the final enhanced image is obtained by adding the third intermediate image and the fourth intermediate image, so that the problems that the enhancement is not comprehensive enough and the enhancement is missed easily caused by single image subtraction can be solved.
In one embodiment, the performing flame pre-detection on the enhanced image and determining whether a suspected flame exists in the enhanced image includes:
carrying out graying processing on the enhanced image to obtain a grayed image;
and (3) carrying out moving target detection by using a ViBe moving target detection algorithm gray image, and if a moving target exists in the gray image, judging that suspected flames exist in the enhanced image.
In one embodiment, determining a region in the enhanced image where a suspected flame is present comprises:
and carrying out region growth on the detected pixel points belonging to the moving target part in the gray image to obtain flame region pixel points, and obtaining the minimum external rectangle of all the flame region pixel points to be used as a region with suspected flame in the enhanced image.
In one embodiment, identifying flames in an area where suspected flames exist and determining whether flames exist comprises:
storing pixel points belonging to a suspected flame area in the gray-scale image into a set ysU, and storing the rest pixel points into a set resU;
performing wavelet decomposition on the gray level image to obtain a high-frequency coefficient image GPPkAnd low frequency coefficient image LPP, k ∈ {1,2,3}, GPP1、GPP2、GPP3High-frequency coefficient images respectively representing a vertical direction, a horizontal direction, and a diagonal direction;
calculating the GPP of the Pixel points in the set ysUkIdentification index of (1):
recording the number of the corresponding frame image of the grayed image in the monitoring video as t, and calculating the t frame image
Wherein sidex represents an identification index, r, ζ, e represent a first parameter, a second parameter, and a third parameter, respectively, qz represents an absolute value of the parameter in parentheses,
where Efrot represents the impact index of a pixel in ysU, Eback represents the impact index of a pixel in resU,
in the formula, w1、w2、w3Indicating a set proportionality coefficient, w1+w2+w3＝1，GPP1(qj)、GPP2(qj)、GPP3(qj) indicating ysU pixel points qj in GPP respectively1、GPP2、GPP3Pixel value of (3), GPP1(bj)、GPP2(bj)、GPP3(bj) indicating ysU pixel points bj in GPP respectively1、GPP2、GPP3The value of the pixel of (1) is,
Efrotn+1an influence index Eback representing pixel points of a region of suspected flame of a frame image of an n +1 th frame in a surveillance videon+1Expressing the influence index Efrot of all the pixel points except the pixel point of the suspected flame area in the frame image of the (n + 1) th frame in the monitoring videon+1And Ebackn+1The calculation method of (2) is consistent with the calculation mode of Efrot and Eback, only different from the frame image,
in the formula, EfrotiThe influence indexes of pixel points in a suspected flame area of the frame image of the ith frame are represented, aveEfrot represents the average value of the influence indexes of the pixel points in the suspected flame area from the frame image of the nth frame to the frame image of the (n + q) th frame, n represents the frame number of the frame image to be detected in the monitoring video, q represents a preset calculation interval parameter,
if the size is smaller than the set flame identification threshold, the flame is present in the area of the suspected flame, otherwise, the flame is not present in the area of the suspected flame.
The method has the advantages that the gray level image is subjected to wavelet transformation and then is judged whether flames exist or not, the characteristic that the high-frequency coefficient image identification index is reduced when the flames exist in the gray level image is ingeniously utilized, and the proper flame identification threshold value is obtained through multiple experiments to identify whether the flames exist or not. In the setting of the identification index, not only the influence index of the frame image currently being calculated, namely the frame image of the nth frame, but also the influence index of the frame image in front of the frame image of the nth frame in the monitoring video on the judgment are considered, so that the problem that the flame is judged for a single frame image easily to generate false identification is avoided, and the identification accuracy is improved.
While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (6)
1. The electric vehicle charging safety monitoring and early warning system is characterized by comprising a data acquisition module, a data transmission module, a monitoring module and an alarm module;
the data acquisition module is used for acquiring monitoring data of the electric automobile during charging and sending the monitoring data to the data transmission module;
the data transmission module is used for transmitting the monitoring data to the monitoring module;
the monitoring module is used for judging whether a fire accident occurs according to the monitoring data;
the alarm module is used for giving an alarm to the person on duty when a fire accident happens.
2. The electric vehicle charging safety monitoring and early warning system according to claim 1, wherein the data acquisition module comprises a temperature acquisition unit and a video acquisition unit;
the temperature acquisition unit is used for acquiring the temperature of the electric automobile during charging;
the video acquisition unit is used for acquiring a monitoring video when the electric automobile is charged.
3. The electric vehicle charging safety monitoring and early warning system of claim 1, wherein the data transmission module comprises a near wireless communication unit and a wired communication unit;
the wireless communication unit comprises a ZigBee communication device, a wireless cellular network communication device and a WiFi communication device;
the wired communication unit comprises an optical fiber communication device which is used for connecting a communication optical fiber provided by a network operator with the monitoring module.
4. The electric vehicle charging safety monitoring and early warning system according to claim 2, wherein the monitoring module comprises a receiving unit, a storage unit, a judging unit and a transmitting unit;
the receiving unit is used for receiving the monitoring data sent by the data transmission module and transmitting the monitoring data to the storage unit;
the storage unit is used for storing the monitoring data;
the judging unit is used for extracting the monitoring data from the storage unit, judging whether a fire accident occurs according to the monitoring data and sending a judgment result to the sending unit;
and the sending unit is used for transmitting the judgment result to an alarm module.
5. The electric vehicle charging safety monitoring and early warning system as claimed in claim 4, wherein the alarm module is configured to identify the determination result, and determine whether an alarm prompt needs to be given to an operator on duty according to the determination result;
if the judgment result is that a fire accident occurs, the alarm module gives an alarm prompt to an attendant;
and if the judgment result shows that no fire accident occurs, the alarm module does not give an alarm prompt to the person on duty.
6. The system of claim 4, wherein the step of determining whether a fire accident occurs comprises:
judging whether the temperature of the electric automobile during charging exceeds a set temperature threshold value or not and judging whether flame is generated in a monitoring video or not according to the monitoring video;
recording the duration that the temperature of the electric automobile exceeds a set temperature threshold value when the electric automobile is charged as T1, if T1 is larger than Tthre1, judging that a fire accident occurs, and the Tthre1 is a set first time threshold value;
and recording the continuous existence time of the flame in the monitoring video as T2, judging that a fire accident occurs if T2 is greater than Tthre2, wherein Tthre2 is a set second time threshold.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103514610A (en) * | 2013-09-17 | 2014-01-15 | 四川虹微技术有限公司 | Method for parting moving target with static background |
| CN103544498A (en) * | 2013-09-25 | 2014-01-29 | 华中科技大学 | Video content detection method and video content detection system based on self-adaption sampling |
| CN105589456A (en) * | 2016-02-04 | 2016-05-18 | 金龙联合汽车工业（苏州）有限公司 | Electric vehicle safety monitoring system |
| US20160214535A1 (en) * | 2011-04-22 | 2016-07-28 | Angel A. Penilla | Vehicle contact detect notification system and cloud services system for interfacing with vehicle |
| CN106707296A (en) * | 2017-01-09 | 2017-05-24 | 华中科技大学 | Dual-aperture photoelectric imaging system-based unmanned aerial vehicle detection and recognition method |
| CN106934789A (en) * | 2015-12-30 | 2017-07-07 | 航天信息股份有限公司 | Packaging dynamic testing method and device based on Three image difference |
| CN106951821A (en) * | 2016-12-27 | 2017-07-14 | 湘潭大学 | A kind of black smoke car intelligent monitoring recognition methods based on image processing techniques |
| CN108447219A (en) * | 2018-05-21 | 2018-08-24 | 中国计量大学 | System and method for detecting fire hazard based on video image |
| CN109466362A (en) * | 2018-12-07 | 2019-03-15 | 中能易电新能源技术有限公司 | Charging pile safety monitoring system |
| CN110415268A (en) * | 2019-06-24 | 2019-11-05 | 台州宏达电力建设有限公司 | A kind of moving region foreground image algorithm combined based on background differential technique and frame difference method |
| CN111002864A (en) * | 2019-12-25 | 2020-04-14 | 深圳市联合通电新能源科技有限公司 | Battery charging management system for new energy electric vehicle |
| CN111598010A (en) * | 2020-05-15 | 2020-08-28 | 安徽江淮汽车集团股份有限公司 | Dynamic obstacle detection method, device, electronic device and storage medium |
-
2020
- 2020-10-28 CN CN202011176331.2A patent/CN112288986A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160214535A1 (en) * | 2011-04-22 | 2016-07-28 | Angel A. Penilla | Vehicle contact detect notification system and cloud services system for interfacing with vehicle |
| CN103514610A (en) * | 2013-09-17 | 2014-01-15 | 四川虹微技术有限公司 | Method for parting moving target with static background |
| CN103544498A (en) * | 2013-09-25 | 2014-01-29 | 华中科技大学 | Video content detection method and video content detection system based on self-adaption sampling |
| CN106934789A (en) * | 2015-12-30 | 2017-07-07 | 航天信息股份有限公司 | Packaging dynamic testing method and device based on Three image difference |
| CN105589456A (en) * | 2016-02-04 | 2016-05-18 | 金龙联合汽车工业（苏州）有限公司 | Electric vehicle safety monitoring system |
| CN106951821A (en) * | 2016-12-27 | 2017-07-14 | 湘潭大学 | A kind of black smoke car intelligent monitoring recognition methods based on image processing techniques |
| CN106707296A (en) * | 2017-01-09 | 2017-05-24 | 华中科技大学 | Dual-aperture photoelectric imaging system-based unmanned aerial vehicle detection and recognition method |
| CN108447219A (en) * | 2018-05-21 | 2018-08-24 | 中国计量大学 | System and method for detecting fire hazard based on video image |
| CN109466362A (en) * | 2018-12-07 | 2019-03-15 | 中能易电新能源技术有限公司 | Charging pile safety monitoring system |
| CN110415268A (en) * | 2019-06-24 | 2019-11-05 | 台州宏达电力建设有限公司 | A kind of moving region foreground image algorithm combined based on background differential technique and frame difference method |
| CN111002864A (en) * | 2019-12-25 | 2020-04-14 | 深圳市联合通电新能源科技有限公司 | Battery charging management system for new energy electric vehicle |
| CN111598010A (en) * | 2020-05-15 | 2020-08-28 | 安徽江淮汽车集团股份有限公司 | Dynamic obstacle detection method, device, electronic device and storage medium |
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