CN116552305A - Electric vehicle charging station safety monitoring system and method - Google Patents
Electric vehicle charging station safety monitoring system and method Download PDFInfo
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- CN116552305A CN116552305A CN202310628685.3A CN202310628685A CN116552305A CN 116552305 A CN116552305 A CN 116552305A CN 202310628685 A CN202310628685 A CN 202310628685A CN 116552305 A CN116552305 A CN 116552305A
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/68—Off-site monitoring or control, e.g. remote control
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The application discloses electric automobile charging station safety monitoring system and method, the electric automobile charging station safety monitoring system that this application provided, including charging station safety control cloud platform, charging pile state early warning unit and charging station video monitoring unit embedding degree of deep learning algorithm, be used for realizing the real-time early warning prediction of charging pile state and the unusual state real-time supervision discernment of charging station, replace traditional manual real-time supervision video's method, with the help of the real-time discernment of degree of deep learning theory be close to the danger source of charging station, improve personnel's utilization efficiency, when detecting that there is the danger source to be close to charging station or charging pile operation to have unusual, the electric automobile charger in the charging pile is cut off in real time through the charging pile controller, in order to prevent that the danger source from causing more serious influence to the charging station, current electric automobile charging station monitoring reliability low technical problem has been solved.
Description
Technical Field
The application relates to the technical field of electric vehicle charging safety monitoring, in particular to an electric vehicle charging station safety monitoring system and method.
Background
The cleaner electric automobile is significant for China to replace the traditional petroleum gas automobile. In recent years, along with the gradual increase of the number of electric vehicles, the electric vehicles become important vehicles for daily traveling of residents, and an electric vehicle charging station is used as one of important media for charging the electric vehicles, however, the electric vehicles are provided with a plurality of devices in the charging station, and the traffic flow in the charging station is large, if the daily management of the electric vehicle charging station is not enhanced, the electric vehicle charging station is damaged, so that the normal development of electric vehicle charging service is negatively affected.
At present, a video monitoring means is generally adopted for the management of the electric automobile charging station, and the real-time operation condition of the charging station can be recorded through video monitoring, but if the operation condition in the charging station is known at any time, a person needs to be specially sent to check the monitoring, so that the technical problem of low monitoring reliability exists.
Disclosure of Invention
The application provides an electric vehicle charging station safety monitoring system and method, which are used for solving the technical problem of low monitoring reliability of the existing electric vehicle charging station.
To solve the above technical problem, a first aspect of the present application provides an electric vehicle charging station safety monitoring system, including: the charging pile control system comprises a charging pile state early warning unit, a charging station video monitoring unit, a charging pile control unit and a charging station safety management and control cloud platform;
the charging pile state early warning unit is configured to: acquiring equipment operation data of the charging pile through a built-in sensing device of the charging pile, and combining a charging pile state evaluation model carried by the charging pile state early-warning unit according to the equipment operation data to acquire a state evaluation result of the charging pile;
the charging station video monitoring unit is configured to: acquiring monitoring video data of a charging station through video monitoring equipment in the charging station, and combining a dangerous source identification model built in a charging station video monitoring unit according to the monitoring video data to acquire a dangerous source identification result of the charging station;
the charging pile control unit is in communication connection with the charging pile state early warning unit and the charging station video monitoring unit, and is used for performing real-time on-off control on the charging pile according to the state evaluation result or the dangerous source identification result when the state evaluation result or the dangerous source identification result is abnormal;
the charging station safety management and control cloud platform is in communication connection with the charging pile state early warning unit and the charging station video monitoring unit through communication links and is used for receiving data uploaded by the charging pile state early warning unit and the charging station video monitoring unit.
Preferably, the device operation data includes: temperature data, gas concentration data, and/or power parameter data.
Preferably, the method further comprises: the dangerous source identification model construction unit is specifically used for:
acquiring monitoring video sample data containing a dangerous source, and constructing a dangerous source database according to the monitoring video sample data;
extracting characteristics of the monitoring video sample data to obtain sample local characteristics;
performing secondary fusion on the sample local features and original monitoring video sample data to obtain sample global features;
and training an initial deep learning model according to the sample local features and the sample global features to obtain a dangerous source identification model.
Preferably, the deep learning model is specifically a deep learning model based on an R-FCN network.
Preferably, according to the monitoring video data, combining a dangerous source identification model built in the charging station video monitoring unit to obtain a dangerous source identification result of the charging station specifically includes:
according to the monitoring video data, extracting the characteristics of the monitoring video data to obtain local characteristics of the monitoring video data;
performing secondary fusion on the local features and original monitoring video data to obtain global features of the monitoring video data;
inputting the sample local features and the sample global features into the dangerous source identification model to obtain a dangerous source identification result of the charging station through operation of the dangerous source identification model;
and carrying out secondary identification by combining an OpenCV algorithm with the dangerous source database according to the dangerous source identification result so as to update the dangerous source identification result according to the secondary identification result.
Preferably, the hazard source specifically includes: smoke and fire early warning, dangerous vehicles, invasion of dangerous personnel and artificial damage of equipment.
Preferably, the method further comprises: the charging pile state evaluation model construction unit is specifically configured to:
acquiring historical equipment operation data of a charging pile, and extracting equipment operation data and fault records in the historical equipment operation data;
and training a deep learning model according to the equipment operation data and the fault record to obtain a charging pile state evaluation model.
Preferably, the method further comprises: a charging station abnormality alarm unit;
the charging station abnormality warning unit is configured to: and outputting an abnormal warning signal of the charging station and triggering corresponding protection actions when the state evaluation result or the dangerous source identification result is abnormal.
Preferably, the charging station abnormality warning signal includes: the message informs the alert signal, the audible alert signal and/or the optical alert signal.
Meanwhile, the second aspect of the present application also provides an electric vehicle charging station safety monitoring method, which is applied to the electric vehicle charging station safety monitoring system provided in the first aspect of the present application, and includes:
acquiring equipment operation data of the charging pile through a built-in sensing device of the charging pile, and combining a charging pile state evaluation model carried by the charging pile state early-warning unit according to the equipment operation data to acquire a state evaluation result of the charging pile;
acquiring monitoring video data of a charging station through video monitoring equipment in the charging station, and combining a dangerous source identification model built in a charging station video monitoring unit according to the monitoring video data to acquire a dangerous source identification result of the charging station;
when the state evaluation result or the dangerous source identification result is abnormal, carrying out real-time on-off control on the charging pile according to the state evaluation result or the dangerous source identification result;
and uploading the state evaluation result and the dangerous source identification result to a charging station safety management and control cloud platform through a communication link.
From the above technical scheme, the application has the following advantages:
the utility model provides an electric automobile charging station safety monitoring system, including charging station safety control cloud platform, fill electric pile state early warning unit and charging station video monitoring unit embedding degree of deep learning algorithm, be used for realizing to fill electric pile state real-time early warning prediction and the unusual state real-time supervision discernment of charging station, replace traditional manual real-time monitoring video's method, discern the danger source that is close to the charging station in real time with the help of degree of deep learning theory, improve personnel utilization efficiency, when detecting that there is the danger source to be close to the charging station or fill electric pile operation and have unusual, fill electric automobile charger in the electric pile through filling electric pile controller real-time break, with prevent that the danger source from causing more serious influence to the charging station, solved the technical problem that current electric automobile charging station control reliability is low.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic structural diagram of an embodiment of an electric vehicle charging station safety monitoring system provided in the present application.
Fig. 2 is a schematic flow chart of an embodiment of a method for monitoring safety of an electric vehicle charging station.
Detailed Description
At present, a video monitoring means is generally adopted for the management of the electric vehicle charging station, the real-time operation condition of the charging station can be recorded through video monitoring, but the operation condition in the charging station is known at any time, and people are required to be specially sent to check the monitoring, so that on one hand, the manpower waste is caused, the monitoring effect is directly influenced by the level of monitoring personnel, the monitoring effect is unstable, and the technical problem of low safety monitoring reliability of the electric vehicle charging station is caused.
In view of this, the embodiment of the application provides an electric vehicle charging station safety monitoring system and method, which are used for solving the technical problem of low monitoring reliability of the existing electric vehicle charging station.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Firstly, the embodiment of the electric vehicle charging station safety monitoring system provided by the application is described in detail as follows:
referring to fig. 1, an electric vehicle charging station safety monitoring system provided in this embodiment includes: the charging pile state early warning unit A, the charging station video monitoring unit B, the charging pile control unit C and the charging station safety management and control cloud platform S;
the charging pile state early warning unit a is configured to: acquiring equipment operation data of the charging pile through a sensing device arranged in the charging pile, and combining a charging pile state evaluation model carried by a charging pile state early-warning unit A according to the equipment operation data to acquire a state evaluation result of the charging pile;
the charging station video monitoring unit B is configured to: acquiring monitoring video data of the charging station through video monitoring equipment in the charging station, and combining a dangerous source identification model built in a video monitoring unit B of the charging station according to the monitoring video data to acquire a dangerous source identification result of the charging station;
the charging pile control unit C is in communication connection with the charging pile state early warning unit A and the charging station video monitoring unit B, and is used for carrying out real-time switching control on and off on the charging pile according to the state evaluation result or the dangerous source identification result when the state evaluation result or the dangerous source identification result is abnormal;
and the charging station safety management and control cloud platform S is in communication connection with the charging pile state early warning unit A and the charging station video monitoring unit B through communication links and is used for receiving data uploaded by the charging pile state early warning unit A and the charging station video monitoring unit B.
It should be noted that, the charging pile state early warning unit a and the charging station video monitoring unit are embedded with a deep learning algorithm, and are used for realizing real-time early warning prediction of the charging pile state and real-time monitoring and identification of the abnormal state of the charging station.
The charging pile state early warning unit A in the system acquires equipment operation data of the charging pile through a sensing device built in the charging pile, and then obtains state evaluation results of the charging piles through a charging pile state evaluation model carried in the unit. The content of the device operation data of the charging pile according to the embodiment includes, but is not limited to: temperature data, gas concentration data, power parameter data, including voltage data, current data, and/or power data, etc. of the charging stake.
Regarding the charging pile state evaluation model mentioned in the charging pile state early warning unit a, the construction thereof may be implemented with reference to the following examples: extracting equipment operation data and fault records in the historical equipment operation data by acquiring the historical equipment operation data of the charging pile; and training a deep learning model according to the equipment operation data and the fault record, and obtaining a charging pile state evaluation model after training is completed.
In the charging station video monitoring unit B mentioned in this embodiment, the monitoring video data around the charging station is collected by using a plurality of video cameras in the charging station, and the dangerous source recognition model built in the charging station video monitoring unit B is combined according to the monitoring video data, so as to obtain the dangerous source recognition result of the charging station.
As for the hazard source identification model in the charging station video monitoring unit B, the construction method thereof can be implemented with reference to the following examples:
acquiring monitoring video sample data containing a dangerous source, and constructing a dangerous source database according to the monitoring video sample data; extracting characteristics of labeling information contained in the monitoring video sample data to obtain sample local characteristics; carrying out secondary fusion on the local characteristics of the sample and the original monitoring video sample data to obtain global characteristics of the sample; training an initial deep learning model according to the local characteristics of the sample and the global characteristics of the sample, obtaining a dangerous source identification model after training, and merging the original R-FCN network into a global characteristic prediction module in parallel to supplement the defect that the original network only identifies a dangerous source through the local characteristics.
Based on the above manner of constructing the hazard source recognition model, it may be understood that the specific process of obtaining the hazard source recognition result by the hazard source recognition model in this embodiment may be: according to the monitoring video data, extracting the characteristics of the monitoring video data to obtain local characteristics of the monitoring video data; performing secondary fusion on the local features and the original monitoring video data to obtain global features of the monitoring video data; inputting the sample local features and the sample global features into a dangerous source identification model to obtain a dangerous source identification result of the charging station through operation of the dangerous source identification model; according to the dangerous source identification result, and then in order to further optimize the dangerous source identification effect, in the embodiment, after the dangerous source identification result is obtained, secondary identification is further performed by combining the OpenCV algorithm with the dangerous source database, so that the dangerous source identification result is updated according to the secondary identification result, and a final dangerous source identification result is obtained.
The dangerous source data mentioned in this embodiment mainly includes: four kinds of fire and smoke early warning, dangerous vehicles, dangerous personnel invasion and equipment artificial damage, it can be understood that a dangerous source identification model constructed based on monitoring video sample data containing the four kinds of dangerous sources comprises the following functions:
1) Smoke and fire early warning and identification: the method comprises the steps that video information around a charging station is collected by utilizing a plurality of video cameras in the charging station, the video information is transmitted to a charging station safety management and control cloud platform S in real time, a dangerous source identification model based on an improved area full convolution network is utilized to analyze dangerous source characteristic images of the charging station, and smoke and fire early warning conditions existing around a transformer substation are identified;
2) Dangerous vehicle identification: based on a dangerous source typical database established in the charging station safety management and control cloud platform S, based on dangerous vehicle data in the dangerous source typical database, analyzing a charging station dangerous source characteristic image by utilizing a dangerous source identification model based on an improved area full convolution network, and identifying dangerous vehicle conditions around the charging station in real time;
3) Dangerous personnel intrusion identification: the charging station safety management and control cloud platform S is characterized in that charging station two-dimensional plane information is divided into dangerous areas and non-dangerous areas, a dangerous source identification model based on an improved area full convolution network is utilized for analyzing charging station dangerous source characteristic images, and when personnel entering is detected in the dangerous areas, the situation that dangerous personnel enter can be judged;
4) And (5) identifying the artificial damage of equipment: the condition of equipment in the charging station is monitored in real time by utilizing a plurality of video cameras in the charging station, the dangerous source characteristic images of the charging station are analyzed by utilizing a dangerous source identification model based on an improved area full convolution network, and when the condition that the equipment is damaged manually is identified to exist in the charging station, the condition that the equipment is damaged manually can be judged.
The charging pile control unit C mentioned in this embodiment may be in communication connection with the charging pile state early warning unit a and the charging station video monitoring unit B, and is configured to perform real-time on-off control on the charging pile according to the state evaluation result or the dangerous source identification result when the state evaluation result or the dangerous source identification result is displayed as abnormal, so as to prevent secondary accidents.
The charging station safety management and control cloud platform S can be used for receiving and storing information such as charging pile running states and charging station video monitoring information acquired by a charging pile state early warning system and a charging station video monitoring system, charging pile original running data, dangerous early warning states, dangerous sources possibly existing in surrounding environments of the charging station and the like, and is used for workers to read and review at any time by means of a mobile phone APP or from a rear-end master station so as to master the in-station safety condition of the charging station.
Further, the electric vehicle charging station safety monitoring system provided by the application can further include: a charging station abnormality alarm unit D;
the charging station abnormality alarm unit D is configured to: when the state evaluation result or the dangerous source identification result is abnormal, outputting an abnormal warning signal of the charging station and triggering corresponding protection actions.
The charging station abnormality warning signal includes: the message informs the alert signal, the audible alert signal and/or the optical alert signal.
For example, when the state evaluation result or the dangerous source identification result is abnormal, the charging station 5G information communication private network can be utilized to feed back the detected dangerous information to the staff or the rear-end master station through the mobile phone APP, so that the abnormal situation of the charging station can be inquired in real time, and meanwhile, an audible and visual alarm signal can be sent out to remind the field staff of timely avoiding danger.
The foregoing is a detailed description of an embodiment of an electric vehicle charging station safety monitoring system provided in the present application, and the following is a detailed description of an embodiment of an electric vehicle charging station safety monitoring method provided in the present application, which is specifically as follows:
referring to fig. 2, the method for monitoring the safety of an electric vehicle charging station provided in this embodiment may be applied to the electric vehicle charging station safety monitoring system provided in the previous embodiment, and includes:
step 101, acquiring equipment operation data of a charging pile through a built-in sensing device of the charging pile, and combining a charging pile state evaluation model carried by a charging pile state early-warning unit according to the equipment operation data to acquire a state evaluation result of the charging pile;
102, acquiring monitoring video data of a charging station through video monitoring equipment in the charging station, and combining a dangerous source identification model built in a video monitoring unit of the charging station according to the monitoring video data to acquire a dangerous source identification result of the charging station;
step 103, when the state evaluation result or the dangerous source identification result is abnormal, performing real-time on-off control on the charging pile according to the state evaluation result or the dangerous source identification result;
and 104, uploading the state evaluation result and the dangerous source identification result to a charging station safety management and control cloud platform through a communication link.
More specifically, the dangerous source identification model in this embodiment is constructed in the following manner:
acquiring monitoring video sample data containing a dangerous source, and constructing a dangerous source database according to the monitoring video sample data; extracting characteristics of labeling information contained in the monitoring video sample data to obtain sample local characteristics; carrying out secondary fusion on the local characteristics of the sample and the original monitoring video sample data to obtain global characteristics of the sample; training an initial deep learning model according to the local characteristics of the sample and the global characteristics of the sample, obtaining a dangerous source identification model after training, and merging the original R-FCN network into a global characteristic prediction module in parallel to supplement the defect that the original network only identifies a dangerous source through the local characteristics.
More specifically, according to the monitoring video data, combining the hazard source recognition model built in the charging station video monitoring unit in step 102 to obtain the hazard source recognition result of the charging station specifically includes:
according to the monitoring video data, extracting the characteristics of the monitoring video data to obtain local characteristics of the monitoring video data;
performing secondary fusion on the local features and the original monitoring video data to obtain global features of the monitoring video data;
inputting the sample local features and the sample global features into a dangerous source identification model to obtain a dangerous source identification result of the charging station through operation of the dangerous source identification model;
and carrying out secondary identification by combining an OpenCV algorithm with a dangerous source database according to the dangerous source identification result, so as to update the dangerous source identification result according to the secondary identification result.
Further, the method provided by the embodiment may further include:
when the state evaluation result or the dangerous source identification result is abnormal, outputting an abnormal warning signal of the charging station and triggering corresponding protection actions.
The charging station abnormality warning signal includes: the message informs the alert signal, the audible alert signal and/or the optical alert signal.
In the description of the present application, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of description of the present application and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Unless specifically stated or limited otherwise, the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. An electric vehicle charging station safety monitoring system, comprising: the charging pile control system comprises a charging pile state early warning unit, a charging station video monitoring unit, a charging pile control unit and a charging station safety management and control cloud platform;
the charging pile state early warning unit is configured to: acquiring equipment operation data of the charging pile through a built-in sensing device of the charging pile, and combining a charging pile state evaluation model carried by the charging pile state early-warning unit according to the equipment operation data to acquire a state evaluation result of the charging pile;
the charging station video monitoring unit is configured to: acquiring monitoring video data of a charging station through video monitoring equipment in the charging station, and combining a dangerous source identification model built in a charging station video monitoring unit according to the monitoring video data to acquire a dangerous source identification result of the charging station;
the charging pile control unit is in communication connection with the charging pile state early warning unit and the charging station video monitoring unit, and is used for performing real-time on-off control on the charging pile according to the state evaluation result or the dangerous source identification result when the state evaluation result or the dangerous source identification result is abnormal;
the charging station safety management and control cloud platform is in communication connection with the charging pile state early warning unit and the charging station video monitoring unit through communication links and is used for receiving data uploaded by the charging pile state early warning unit and the charging station video monitoring unit.
2. The electric vehicle charging station safety monitoring system of claim 1, wherein the equipment operation data comprises: temperature data, gas concentration data, and/or power parameter data.
3. The electric vehicle charging station safety monitoring system of claim 1, further comprising: the dangerous source identification model construction unit is specifically used for:
acquiring monitoring video sample data containing a dangerous source, and constructing a dangerous source database according to the monitoring video sample data;
extracting characteristics of the monitoring video sample data to obtain sample local characteristics;
performing secondary fusion on the sample local features and original monitoring video sample data to obtain sample global features;
and training an initial deep learning model according to the sample local features and the sample global features to obtain a dangerous source identification model.
4. An electric vehicle charging station safety monitoring system according to claim 3, characterized in that the deep learning model is in particular a deep learning model based on an R-FCN network.
5. The electric vehicle charging station safety monitoring system according to claim 4, wherein the step of combining the monitoring video data with a hazard source recognition model built in a charging station video monitoring unit to obtain a hazard source recognition result of the charging station specifically comprises:
according to the monitoring video data, extracting the characteristics of the monitoring video data to obtain local characteristics of the monitoring video data;
performing secondary fusion on the local features and original monitoring video data to obtain global features of the monitoring video data;
inputting the sample local features and the sample global features into the dangerous source identification model to obtain a dangerous source identification result of the charging station through operation of the dangerous source identification model;
and carrying out secondary identification by combining an OpenCV algorithm with the dangerous source database according to the dangerous source identification result so as to update the dangerous source identification result according to the secondary identification result.
6. An electric vehicle charging station safety monitoring system according to claim 3, wherein the hazard source comprises: smoke and fire early warning, dangerous vehicles, invasion of dangerous personnel and artificial damage of equipment.
7. The electric vehicle charging station safety monitoring system of claim 1, further comprising: the charging pile state evaluation model construction unit is specifically configured to:
acquiring historical equipment operation data of a charging pile, and extracting equipment operation data and fault records in the historical equipment operation data;
and training a deep learning model according to the equipment operation data and the fault record to obtain a charging pile state evaluation model.
8. The electric vehicle charging station safety monitoring system of claim 1, further comprising: a charging station abnormality alarm unit;
the charging station abnormality warning unit is configured to: and outputting an abnormal warning signal of the charging station and triggering corresponding protection actions when the state evaluation result or the dangerous source identification result is abnormal.
9. The electric vehicle charging station safety monitoring system of claim 7, wherein the charging station anomaly alert signal comprises: the message informs the alert signal, the audible alert signal and/or the optical alert signal.
10. An electric vehicle charging station safety monitoring method applied to an electric vehicle charging station safety monitoring system as claimed in any one of claims 1 to 9, comprising:
acquiring equipment operation data of the charging pile through a built-in sensing device of the charging pile, and combining a charging pile state evaluation model carried by the charging pile state early-warning unit according to the equipment operation data to acquire a state evaluation result of the charging pile;
acquiring monitoring video data of a charging station through video monitoring equipment in the charging station, and combining a dangerous source identification model built in a charging station video monitoring unit according to the monitoring video data to acquire a dangerous source identification result of the charging station;
when the state evaluation result or the dangerous source identification result is abnormal, carrying out real-time on-off control on the charging pile according to the state evaluation result or the dangerous source identification result;
and uploading the state evaluation result and the dangerous source identification result to a charging station safety management and control cloud platform through a communication link.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310628685.3A CN116552305A (en) | 2023-05-30 | 2023-05-30 | Electric vehicle charging station safety monitoring system and method |
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| CN117690189A (en) * | 2024-01-26 | 2024-03-12 | 深圳市喂车科技有限公司 | Charging station dangerous behavior identification method and monitoring system based on artificial intelligence |
| CN117690189B (en) * | 2024-01-26 | 2024-05-17 | 深圳市喂车科技有限公司 | Charging station dangerous behavior identification method and monitoring system based on artificial intelligence |
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