CN116215295B - Charging pile monitoring and early warning method, device, equipment and storage medium - Google Patents

Charging pile monitoring and early warning method, device, equipment and storage medium Download PDF

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Publication number
CN116215295B
CN116215295B CN202310343643.5A CN202310343643A CN116215295B CN 116215295 B CN116215295 B CN 116215295B CN 202310343643 A CN202310343643 A CN 202310343643A CN 116215295 B CN116215295 B CN 116215295B
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charging
information
target
state
anomaly
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CN116215295A (en
Inventor
阮耀灿
廖锦星
钟泽鹏
夏嘉豪
麦丽凡
吕雯禧
张�浩
梁忠友
王增华
陈浩杰
林耀鸿
陈健华
周源昊
陈述锋
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Guangdong Jianyi Investment Co ltd
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Guangdong Jianyi Investment Co ltd
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Priority to CN202310343643.5A priority Critical patent/CN116215295B/en
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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
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • 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
    • B60L53/30—Constructional details of charging stations
    • B60L53/31—Charging columns specially adapted for electric vehicles
    • 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
    • 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
    • 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
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The embodiment of the application provides a charging pile monitoring and early warning method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring sample charging information, and determining an abnormal model according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile; acquiring a plurality of target charging information in a target time period, wherein the target charging information is determined by a charging pile and a charging type vehicle charged by the charging pile, and the charging type vehicle is an electric automobile or an electric bicycle; based on the anomaly model, carrying out anomaly analysis on each target charging information in the target time period, and judging whether the target charging information is abnormal charging information or not; and when the target charging information is abnormal charging information, generating early warning information for indicating that the charging type vehicle is an electric bicycle. The embodiment of the application can timely find the charging pile for charging the electric bicycle, avoid fire, reduce the workload and lower the labor cost.

Description

Charging pile monitoring and early warning method, device, equipment and storage medium
Technical Field
The application relates to the technical field of charging piles, in particular to a charging pile monitoring and early warning method, a device, equipment and a storage medium.
Background
The new energy charging station is a station for an electric vehicle, and a plurality of charging piles for charging the electric vehicle are generally arranged in the new energy charging station.
At present, the condition that the electric bicycle is charged to the electric bicycle through the automobile charging pile is realized through the connection converter exists, however, the parameter configuration of the automobile charging pile is not suitable for the electric bicycle generally, if the electric bicycle is charged by using the automobile charging pile, great potential safety hazards exist, fire disaster can be caused when serious, the manual inspection is generally adopted to determine the charging condition of each charging pile in the charging station, the efficiency is very low, the automobile charging pile charged to the electric bicycle cannot be timely found, the risk of fire disaster exists, the manual inspection workload is large, and the cost is high.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the application provides a charging pile monitoring and early warning method, a device, equipment and a storage medium, which can timely find a charging pile for charging an electric bicycle, avoid fire, reduce workload and lower labor cost.
In order to achieve the above object, a first aspect of an embodiment of the present application provides a monitoring and early warning method for a charging pile, including: acquiring sample charging information, and determining an abnormal model according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile; acquiring a plurality of target charging information in a target time period, wherein the target charging information is determined by the charging pile and a charging type vehicle charged by using the charging pile, and the charging type vehicle is an electric automobile or an electric bicycle; based on the anomaly model, performing anomaly analysis on each piece of target charging information in the target time period, and judging whether the target charging information is charging anomaly information or not; and when the target charging information is abnormal charging information, generating early warning information for indicating that the rechargeable vehicle is the electric bicycle.
In some embodiments, the sample charging information includes sample power supply information determined by the charging post and sample state information determined by an electric bicycle charged using the charging post, the target charging information includes target power supply information determined by the charging post and target state information determined by a charging vehicle charged using the charging post, the anomaly model includes a charging post state anomaly model determined by the sample power supply information and a vehicle state anomaly model determined by the sample state information; the step of performing anomaly analysis on each piece of target charging information in the target time period based on the anomaly model, and judging whether the target charging information is charging anomaly information, includes: based on the state anomaly model of the charging pile, carrying out anomaly analysis on each piece of target power supply information in the target time period, and judging whether the target power supply information is power supply anomaly information or not; based on the vehicle state abnormality model, carrying out abnormality analysis on each piece of target state information in the target time period, and judging whether the target state information is state abnormality information or not; and when the target power supply information is power supply abnormality information or the target state information is state abnormality information, determining that the target charging information is charging abnormality information.
In some embodiments, the charging pile state anomaly model includes a power anomaly characteristic threshold value determined by the sample power supply information, the target power supply information including a target charging power; based on the charging pile state anomaly model, anomaly analysis is performed on each target power supply information in the target time period, and whether the target power supply information is power supply anomaly information or not is judged, including: comparing the target charging power in the target time period, and determining a target power characteristic value according to the maximum target charging power; judging whether the target power characteristic value is smaller than or equal to the power abnormality characteristic threshold value; and when the target power characteristic value is smaller than or equal to the power abnormality characteristic threshold value, determining that the target power supply information is power supply abnormality information.
In some embodiments, the vehicle state anomaly model includes a demand anomaly characteristic, the demand anomaly characteristic determined from the sample state information, the target state information including a target charge demand; the step of performing anomaly analysis on each piece of target state information in the target time period based on the vehicle state anomaly model to determine whether the target state information is state anomaly information, includes: calculating the variance and the average value of each target charging demand in the target time period, and determining target demand characteristics; judging whether the target demand characteristic accords with the demand abnormal characteristic or not; and when the target demand characteristic accords with the demand abnormal characteristic, determining the target state information as state abnormal information.
In some embodiments, the vehicle state anomaly model includes a vehicle anomaly identification code, a first state of charge anomaly characteristic threshold value, a second state of charge anomaly characteristic threshold value, and an efficiency anomaly characteristic, the vehicle anomaly identification code, the first state of charge anomaly characteristic threshold value, the second state of charge anomaly characteristic threshold value, and the efficiency anomaly characteristic each being determined by the sample state information, the target state information including a target vehicle identification code and a target state of charge; the step of performing anomaly analysis on each piece of target state information in the target time period based on the vehicle state anomaly model to determine whether the target state information is state anomaly information, includes: judging whether the target vehicle identification code is identical to the vehicle abnormal identification code; when the target vehicle identification code is the same as the vehicle abnormality identification code, determining that the target state information is state abnormality information; comparing the target states of charge in the target time period, determining a first state of charge characteristic value according to the minimum target state of charge, and determining a second state of charge characteristic value according to the maximum target state of charge; judging whether the first state of charge characteristic value is equal to the first state of charge abnormal characteristic threshold value or not, and judging whether the second state of charge characteristic value is equal to the second state of charge abnormal characteristic threshold value or not; determining the target state information as state anomaly information when the first state of charge characteristic value is equal to the first state of charge anomaly characteristic threshold value or when the second state of charge characteristic value is equal to the second state of charge anomaly characteristic threshold value; determining target charging efficiency according to each target state of charge in the target time period, and judging whether the target charging efficiency accords with the efficiency abnormal characteristics; and when the target charging efficiency accords with the efficiency abnormal characteristic, determining the target state information as state abnormal information.
In some embodiments, the sample power supply information includes a sample charge power, and the sample status information includes a sample charge demand; the obtaining sample charging information and determining an abnormal model according to the sample charging information comprises the following steps: acquiring a plurality of sample charging information in a sample time period; comparing the sample charging power in the sample time period, and determining a power abnormality characteristic threshold according to the maximum sample charging power; determining the state anomaly model of the charging pile according to the power anomaly characteristic threshold; determining a demand abnormal feature according to each sample charging demand in the sample time period; and determining the vehicle state abnormality model according to the abnormal demand characteristics.
In some embodiments, the sample state information further includes a sample vehicle identification code and a sample state of charge; after the step of determining the vehicle state abnormality model according to the demand abnormality characteristic, the method further comprises the following steps: determining a vehicle abnormal identification code according to the sample vehicle identification code; comparing the sample states of charge in the sample time period, determining a first state of charge abnormal characteristic threshold according to the minimum sample state of charge, and determining a second state of charge abnormal characteristic threshold according to the maximum sample state of charge; determining sample charging efficiency according to each sample charge state in the sample time period, and determining an efficiency abnormality characteristic according to the sample charging efficiency; and updating the vehicle state anomaly model according to the vehicle anomaly identification code, the first state of charge anomaly characteristic threshold value, the second state of charge anomaly characteristic threshold value and the efficiency anomaly characteristic.
A second aspect of an embodiment of the present application provides a monitoring and early warning device for a charging pile, including: the model determining unit is used for obtaining sample charging information and determining an abnormal model according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile; an acquisition unit configured to acquire a plurality of target charging information within a target period, wherein the target charging information is determined by the charging pile and a charging vehicle that is an electric vehicle or the electric bicycle and is charged using the charging pile; the analysis unit is used for carrying out anomaly analysis on each piece of target charging information in the target time period based on the anomaly model and judging whether the target charging information is abnormal charging information or not; and the early warning unit is used for generating early warning information for indicating that the rechargeable vehicle is the electric bicycle when the target charging information is abnormal charging information.
To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, where the electronic device includes a memory and a processor, where the memory stores a computer program, and the processor implements the method for monitoring and early warning a charging pile according to the first aspect when executing the computer program.
In order to achieve the above object, a fourth aspect of the embodiments of the present application provides a storage medium, where the storage medium is a computer readable storage medium, and the storage medium stores a computer program, where the computer program is executed by a processor to implement the method for monitoring and early warning a charging pile according to the first aspect.
The application provides a charging pile monitoring and early warning method, a device, equipment and a storage medium, and the embodiment of the application comprises the following steps: acquiring sample charging information, and determining an abnormal model according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile; acquiring a plurality of target charging information in a target time period, wherein the target charging information is determined by the charging pile and a charging type vehicle charged by using the charging pile, and the charging type vehicle is an electric automobile or an electric bicycle; based on the anomaly model, performing anomaly analysis on each piece of target charging information in the target time period, and judging whether the target charging information is charging anomaly information or not; and when the target charging information is abnormal charging information, generating early warning information for indicating that the rechargeable vehicle is the electric bicycle. According to the scheme provided by the embodiment of the application, in the model generation stage, the sample charging information determined when the charging pile charges the electric bicycle is obtained, then the sample charging information used for representing the abnormal charging behavior is taken as sample data of the model, the abnormal model is determined, in the monitoring stage, the target charging information determined when the charging pile charges the charging type vehicle is obtained, the target charging information is analyzed in real time by using the abnormal model, the early warning information is not generated when the charging type vehicle is an electric automobile, and the target charging information is determined to be the charging abnormal information used for indicating the abnormal charging behavior when the charging type vehicle is the electric bicycle, so that the early warning information is generated, the real-time early warning of the abnormal charging behavior is realized, the charging pile charged to the electric bicycle can be timely found, the fire disaster is avoided, the workload is reduced, and the labor cost is reduced.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application.
Fig. 1 is a flowchart of a charging pile monitoring and early warning method according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for determining charging anomaly information according to another embodiment of the present application;
FIG. 3 is a flow chart of a method for determining power supply abnormality information according to another embodiment of the present application;
FIG. 4 is a flow chart of a method for determining status anomaly information provided by another embodiment of the present application;
FIG. 5 is a flow chart of another method for determining status anomaly information provided by another embodiment of the present application;
FIG. 6 is a flow chart of a method of determining an anomaly model provided by another embodiment of the present application;
FIG. 7 is a flow chart of a method of updating a vehicle state anomaly model provided by another embodiment of the present application;
FIG. 8 is a flow chart of a method for stopping charging according to another embodiment of the present application;
FIG. 9 is a first system block diagram of an early warning system according to another embodiment of the present application;
FIG. 10 is a second system block diagram of an early warning system according to another embodiment of the present application;
fig. 11 is a schematic diagram of charging information of an electric vehicle according to another embodiment of the present application;
fig. 12 is a schematic diagram of charging information of an electric bicycle according to another embodiment of the present application;
fig. 13 is a schematic structural diagram of a monitoring and early warning device for a charging pile according to another embodiment of the present application;
fig. 14 is a schematic hardware structure of an electronic device according to another embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In the description of the present application, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the above, below, within, etc. are understood to include the present number.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description, in the claims and in the above-described figures, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
At present, the condition that the electric bicycle is charged to the electric bicycle through the automobile charging pile is realized through the connection converter exists, however, the parameter configuration of the automobile charging pile is not suitable for the electric bicycle generally, if the electric bicycle is charged by using the automobile charging pile, great potential safety hazards exist, fire disaster can be caused when serious, the manual inspection is generally adopted to determine the charging condition of each charging pile in the charging station, the efficiency is very low, the automobile charging pile charged to the electric bicycle cannot be timely found, the risk of fire disaster exists, the manual inspection workload is large, and the cost is high.
Aiming at the problems that an automobile charging pile for charging an electric bicycle cannot be found in time, and fire risks exist, manual inspection workload is large, and cost is high, the application provides a charging pile monitoring and early warning method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring sample charging information, and determining an abnormal model according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile; acquiring a plurality of target charging information in a target time period, wherein the target charging information is determined by a charging pile and a charging type vehicle charged by the charging pile, and the charging type vehicle is an electric automobile or an electric bicycle; based on the anomaly model, carrying out anomaly analysis on each target charging information in the target time period, and judging whether the target charging information is abnormal charging information or not; and when the target charging information is abnormal charging information, generating early warning information for indicating that the charging type vehicle is an electric bicycle. According to the scheme provided by the embodiment of the application, in the model generation stage, the sample charging information determined when the charging pile charges the electric bicycle is obtained, then the sample charging information used for representing the abnormal charging behavior is taken as sample data of the model, the abnormal model is determined, in the monitoring stage, the target charging information determined when the charging pile charges the charging type vehicle is obtained, the target charging information is analyzed in real time by using the abnormal model, the early warning information is not generated when the charging type vehicle is an electric automobile, and the target charging information is determined to be the charging abnormal information used for indicating the abnormal charging behavior when the charging type vehicle is the electric bicycle, so that the early warning information is generated, the real-time early warning of the abnormal charging behavior is realized, the charging pile charged to the electric bicycle can be timely found, the fire disaster is avoided, the workload is reduced, and the labor cost is reduced.
The method, the device, the equipment and the storage medium for monitoring and early warning of the charging pile provided by the embodiment of the application are specifically described through the following embodiment, and the method for monitoring and early warning of the charging pile in the embodiment of the application is firstly described.
The embodiment of the application provides a charging pile monitoring and early warning method, and relates to the technical field of data processing. The charging pile monitoring and early warning method provided by the embodiment of the application can be applied to a terminal, a server and software running in the terminal or the server. In some embodiments, the terminal may be a smart phone, tablet, notebook, desktop, etc.; the server side can be configured as an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like; the software may be an application for implementing the monitoring and early warning method of the charging pile, but is not limited to the above form.
The application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
It should be noted that, in each specific embodiment of the present application, when related processing is required according to user information, user behavior data, user history data, user location information, and other data related to user identity or characteristics, permission or consent of the user is obtained first, and the collection, use, processing, and the like of the data comply with related laws and regulations and standards. In addition, when the embodiment of the application needs to acquire the sensitive personal information of the user, the independent permission or independent consent of the user is acquired through popup or jump to a confirmation page and the like, and after the independent permission or independent consent of the user is definitely acquired, the necessary relevant data of the user for enabling the embodiment of the application to normally operate is acquired.
Embodiments of the present application will be further described below with reference to the accompanying drawings.
As shown in fig. 1, fig. 1 is a flowchart of a charging pile monitoring and early warning method according to an embodiment of the present application, where the charging pile monitoring and early warning method may be executed by a server, or may also be executed by a terminal, or may also be executed by a server in cooperation with the terminal, and the charging pile monitoring and early warning method includes, but is not limited to, the following steps S110 to S140:
step S110, sample charging information is obtained, and an abnormal model is determined according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile;
step S120, acquiring a plurality of target charging information in a target time period, wherein the target charging information is determined by a charging pile and a charging type vehicle charged by the charging pile, and the charging type vehicle is an electric automobile or an electric bicycle;
step S130, based on the anomaly model, carrying out anomaly analysis on each target charging information in the target time period, and judging whether the target charging information is abnormal charging information or not;
in step S140, when the target charging information is the charging abnormality information, early warning information for indicating that the charging type vehicle is an electric bicycle is generated.
It can be understood that when the charging pile charges the charging type vehicle, a difference exists between charging information determined by the charging pile for charging the electric vehicle and charging information determined by the charging pile for charging the electric bicycle, so that the charging information determined by the charging pile for charging the electric bicycle in a historical charging time period is used as sample charging information, an accurate abnormal model can be determined, further, abnormal analysis is carried out through the abnormal model, analysis efficiency is high, accuracy is high, and early warning information is timely generated when the target charging information is determined to be charging abnormal information; based on the method, sample charging information determined when the charging pile charges the electric bicycle is obtained in a model generation stage, then the sample charging information used for representing abnormal charging behaviors is taken as sample data of the model, an abnormal model is determined, target charging information determined when the charging pile charges the charging type vehicle is obtained in a monitoring stage, the target charging information is analyzed in real time by using the abnormal model, early warning information cannot be generated when the charging type vehicle is an electric automobile, the target charging information is determined to be charging abnormal information used for indicating the abnormal charging behaviors, and early warning information is generated.
It should be noted that, in order to effectively detect electric bicycles of various vehicle types and converters of various models, the data size of sample charging information needs to be increased, and the analysis capability of an abnormal model is increased, wherein the converter can convert 315V direct current voltage output by the charging pile into 220V alternating current voltage, so as to meet the charging requirement of the electric bicycle.
It is noted that the abnormal model will analyze each target charging information in the target time period and then determine the determination result of the charging abnormal information, the abnormal model will not only determine the determination result of the charging abnormal information by the analysis result of part of the target charging information in the target time period, but also the target time period has reasonable analysis duration, so that the reliability of the determination result can be ensured, for example, the duration of the target time period is set to be 5 minutes.
In addition, referring to fig. 2, in an embodiment, the sample charging information includes sample power supply information determined by a charging post and sample state information determined by an electric bicycle charged using the charging post, the target charging information includes target power supply information determined by the charging post and target state information determined by a charging vehicle charged using the charging post, the anomaly model includes a charging post state anomaly model and a vehicle state anomaly model, the charging post state anomaly model is determined by the sample power supply information, and the vehicle state anomaly model is determined by the sample state information; step S130 in the embodiment shown in fig. 1 includes, but is not limited to, the following steps:
Step S210, based on the state anomaly model of the charging pile, anomaly analysis is carried out on each piece of target power supply information in a target time period, and whether the target power supply information is power supply anomaly information is judged;
step S220, based on the vehicle state abnormality model, performing abnormality analysis on each piece of target state information in the target time period, and judging whether the target state information is state abnormality information or not;
in step S230, when the target power supply information is power supply abnormality information, or when the target state information is state abnormality information, it is determined that the target charging information is charging abnormality information.
It can be understood that the charging information can be divided into power supply information for representing output information of the charging pile and state information for representing a vehicle state, so that a charging pile state anomaly model is determined through sample power supply information, a vehicle state anomaly model is determined through sample state information, target power supply information is analyzed through the charging pile state anomaly model, and target state information is analyzed through the vehicle state anomaly model, thereby realizing targeted analysis of target charging information.
Additionally, referring to fig. 3, in an embodiment, the charging pile state anomaly model includes a power anomaly characteristic threshold value, the power anomaly characteristic threshold value being determined by sample power supply information, the target power supply information including a target charging power; step S210 in the embodiment shown in fig. 2 includes, but is not limited to, the following steps:
Step S310, comparing each target charging power in the target time period, and determining a target power characteristic value according to the maximum target charging power;
step S320, judging whether the target power characteristic value is smaller than or equal to a power abnormality characteristic threshold value;
in step S330, when the target power characteristic value is less than or equal to the power abnormality characteristic threshold, the target power supply information is determined to be power supply abnormality information.
It can be understood that, during the charging process of the charging type vehicle, when the charging pile charges the electric vehicle, the charging power of the charging pile is generally distributed in a step shape, for example, in a step with the largest charging power, the charging power is about 60KW, and in a step with the smallest charging power, the charging power is about 10 KW; when the charging pile charges the electric bicycle, the charging power of the charging pile is usually between 0.5 KW and 1.5KW, so that whether the charging object of the charging pile is an electric automobile or an electric bicycle can be effectively distinguished through the value of the charging power, the maximum charging power of the charging pile when the charging pile charges the electric bicycle can be used as a power abnormality characteristic threshold value, then in the early warning process, the maximum target charging power in a target time period is used as a target power characteristic value, the target power characteristic value is smaller than or equal to the power abnormality characteristic threshold value when the charging object of the charging pile is the electric bicycle, and the target power characteristic value is obviously larger than the power abnormality characteristic threshold value when the charging object of the charging pile is the electric bicycle.
Additionally, referring to FIG. 4, in one embodiment, the vehicle state anomaly model includes demand anomaly characteristics, the demand anomaly characteristics being determined from sample state information, the target state information including a target charge demand; step S220 in the embodiment shown in fig. 2 includes, but is not limited to, the following steps:
step S410, calculating the variance and average value of each target charging demand in the target time period, and determining the target demand characteristics;
step S420, judging whether the target demand characteristic accords with the demand abnormal characteristic;
in step S430, when the target demand characteristic meets the demand anomaly characteristic, the target state information is determined to be state anomaly information.
It can be understood that, during the charging process of the charging type vehicle, when the charging pile charges the electric vehicle, the charging requirements of the electric vehicle are generally distributed in a step shape, the charging requirements include a required voltage and a required current, for example, when the state of charge is smaller, the required current is larger, when the state of charge is larger, the required current is smaller, and the charging power can correspondingly change according to the required current; when the charging pile charges the electric bicycle, as the converter converts the direct current voltage into the alternating current voltage, the direct current voltage input into the converter has a working range, jump or exceeding the working range is avoided, and the generated demand current cannot change, for example, the demand current is kept at 40A, so that the charging object of the charging pile can be effectively distinguished to be an electric automobile or an electric bicycle through the value and the change trend of the charging demand, the variance and the average value of each target charging demand in a target time period can be used as target demand characteristics, and the target demand characteristics are consistent with the demand abnormal characteristics when the charging object of the charging pile is the electric bicycle, the variance is 0, and the average value is the same or similar; when the charging object of the charging pile is an electric vehicle, the variance of the target demand characteristic is not 0, and the average value of the target demand characteristic and the average value of the demand abnormal characteristic are obviously different.
Additionally, referring to fig. 5, in one embodiment, the vehicle state anomaly model includes a vehicle anomaly identification code, a first state of charge anomaly characteristic threshold value, a second state of charge anomaly characteristic threshold value, and an efficiency anomaly characteristic, each of which is determined by sample state information, the target state information including a target vehicle identification code and a target state of charge; step S220 in the embodiment shown in fig. 2 includes, but is not limited to, the following steps:
step S510, judging whether the target vehicle identification code is the same as the vehicle abnormal identification code;
step S520, when the target vehicle identification code is the same as the vehicle abnormality identification code, determining the target state information as state abnormality information;
step S530, comparing the target states of charge in the target time period, determining a first state of charge characteristic value according to the minimum target state of charge, and determining a second state of charge characteristic value according to the maximum target state of charge;
step S540, judging whether the first state of charge characteristic value is equal to the first state of charge abnormal characteristic threshold value, and judging whether the second state of charge characteristic value is equal to the second state of charge abnormal characteristic threshold value;
Step S550, when the first state of charge characteristic value is equal to the first state of charge abnormal characteristic threshold value or when the second state of charge characteristic value is equal to the second state of charge abnormal characteristic threshold value, determining the target state information as state abnormal information;
step S560, determining target charging efficiency according to each target state of charge in the target time period, and judging whether the target charging efficiency accords with the efficiency abnormality feature;
in step S570, when the target charging efficiency meets the efficiency anomaly characteristic, the target state information is determined to be state anomaly information.
It can be understood that, in the charging process of the charging type vehicle, when the charging pile charges the electric vehicle, the charging pile can acquire the vehicle identification code, namely the VIN code, of the electric vehicle, and the charging pile can also acquire the real-time state of charge of the electric vehicle, the real-time state of charge and the charging efficiency of the electric vehicle can be analyzed through the state of charge, the real-time state of charge of the electric vehicle can be any one of 0-100% of state values when the charging is started, and the state of charge of the electric vehicle is at most 100% when the charging is ended, and in the charging process, the state of charge is not linearly changed, namely the charging efficiency cannot be kept unchanged; in order to facilitate the establishment of a simple connection between the converter and the charging post, the charging post may be configured to obtain virtual information, specifically, the charging post obtains a virtual target vehicle identification code, for example, 17 bits of the virtual target vehicle identification code are all 0, the virtual target vehicle identification code has obvious specificity, the charging post also obtains a virtual target state of charge, in which the minimum target state of charge is fixed, for example, the minimum target state of charge is fixed to 10%, and the maximum target state of charge is fixed to 95%, so that a first state of charge characteristic value is determined by the minimum target state of charge, a second state of charge characteristic value is determined by the maximum target state of charge, and in addition, the virtual target state of charge is linearly changed in a target period of time, so that the target charging efficiency is kept unchanged; when the charging object of the charging pile is an electric bicycle, the target vehicle identification code is the same as the vehicle abnormal identification code, the first charge state characteristic value is equal to the first charge state abnormal characteristic threshold value, the second charge state characteristic value is equal to the second charge state abnormal characteristic threshold value, and the target charging efficiency accords with the efficiency abnormal characteristic; when the charging object of the charging pile is an electric car, the target vehicle identification code and the vehicle abnormal identification code are different, the first charge state characteristic value is not necessarily equal to the first charge state abnormal characteristic threshold value, the second charge state characteristic value is not necessarily equal to the second charge state abnormal characteristic threshold value, and the target charging efficiency does not accord with the efficiency abnormal characteristic.
It should be noted that, due to the duration limitation of the target period, when the charging duration of the rechargeable vehicle is short, the anomaly model may not analyze the second state of charge characteristic value.
Additionally, referring to fig. 6, in one embodiment, the sample power supply information includes sample charging power, and the sample status information includes sample charging demand; step S110 in the embodiment shown in fig. 1 includes, but is not limited to, the following steps:
step S610, acquiring a plurality of sample charging information in a sample time period;
step S620, comparing the charging power of each sample in the sample time period, and determining a power abnormality characteristic threshold according to the maximum sample charging power;
step S630, determining a charging pile state anomaly model according to the power anomaly characteristic threshold;
step S640, determining abnormal demand characteristics according to the charge demand of each sample in the sample time period;
step S650, determining a vehicle state abnormality model according to the demand abnormality feature.
It can be understood that, based on the descriptions from step S210 to step S230, the anomaly models can be classified into a charging pile state anomaly model and a vehicle state anomaly model, specifically, the power anomaly characteristic threshold can be determined by the sample charging power, then the charging pile state anomaly model is determined by the power anomaly characteristic threshold, then the demand anomaly characteristic is determined by the sample charging demand, and then the vehicle state anomaly model is determined by the demand anomaly characteristic.
Additionally, referring to FIG. 7, in one embodiment, the sample state information further includes a sample vehicle identification code and a sample state of charge; following step S650 in the embodiment shown in fig. 6, the following steps are included, but not limited to:
step S710, determining a vehicle abnormal identification code according to the sample vehicle identification code;
step S720, comparing the states of charge of all samples in the sample time period, determining a first state of charge abnormal characteristic threshold according to the minimum state of charge of the samples, and determining a second state of charge abnormal characteristic threshold according to the maximum state of charge of the samples;
step S730, determining sample charging efficiency according to each sample charge state in the sample time period, and determining efficiency abnormal characteristics according to the sample charging efficiency;
step S740, updating the vehicle state anomaly model according to the vehicle anomaly identification code, the first state of charge anomaly characteristic threshold value, the second state of charge anomaly characteristic threshold value and the efficiency anomaly characteristic.
It can be understood that the vehicle state anomaly model can be updated and perfected through the vehicle anomaly identification code, the first state of charge anomaly characteristic threshold value, the second state of charge anomaly characteristic threshold value and the efficiency anomaly characteristic, so that the analysis capability of the vehicle state anomaly model is improved.
In addition, referring to fig. 8, in an embodiment, after step S130 in the embodiment shown in fig. 1, the following steps are included, but not limited to:
step S810, generating a suspension instruction when the target charging information is the abnormal charging information;
step S810, sending a suspension instruction to the charging pile to stop the charging pile from charging the charging vehicle.
It can be understood that under the condition that the target charging information is determined to be the abnormal charging information, a suspension instruction is generated and sent to the charging pile, so that the charging pile responds to the suspension instruction and timely stops charging the charging type vehicle, the charging pile is not required to be manually controlled, the control efficiency can be improved, abnormal charging behaviors can be timely eliminated, and fire disasters are avoided.
In addition, referring to fig. 9, fig. 9 is a first system block diagram of an early warning system according to another embodiment of the present application.
It can be understood that the charging pile monitoring and early warning method can be applied to the cloud server, when the charging pile monitoring and early warning method is applied to the cloud server, the charging pile sends target charging information to the cloud server through remote communication, and the cloud server is high in processing performance and can be used for information processing efficiency, so that the real-time performance of early warning is guaranteed.
It should be noted that, the charging pile is in normal charging behavior when charging the electric automobile, and fig. 9 shows the situation that abnormal charging behavior occurs, at this time, the charging pile charges the electric bicycle, and the electric bicycle is connected with the charging pile through the converter, so as to obtain the electric energy of the charging pile.
In addition, referring to fig. 10, fig. 10 is a second system block diagram of an early warning system according to another embodiment of the present application.
It can be understood that the charging pile monitoring and early warning method can be applied to a local monitoring device, and when the charging pile monitoring and early warning method is applied to the local monitoring device, the charging pile sends target charging information to the local monitoring device through local communication, the time delay of the local communication is low, and the information transmission efficiency can be improved, so that the real-time performance of early warning is ensured.
It should be noted that, the charging pile is in normal charging behavior when charging the electric automobile, and fig. 10 shows the situation that abnormal charging behavior occurs, at this time, the charging pile charges the electric bicycle, and the electric bicycle is connected with the charging pile through the converter, so as to obtain the electric energy of the charging pile.
In addition, referring to fig. 11 and 12, fig. 11 is a schematic diagram of charging information of an electric vehicle according to another embodiment of the present application, and fig. 12 is a schematic diagram of charging information of an electric bicycle according to another embodiment of the present application.
As can be appreciated, by comparing the two schematic diagrams, it can be seen that when the charging pile charges the electric vehicle and charges the electric bicycle, there is an obvious difference in charging information between the charging pile and the electric vehicle, the charging power of the charging pile is generally in a stepwise distribution, in a step with the maximum charging power, the charging power is about 60KW, in a step with the minimum charging power, the charging demand of the electric vehicle is about 10KW, the charging demand includes a required voltage and a required current, for example, when the charging state is smaller, the required current is larger, when the charging state is larger, the required current is smaller, the charging power can make a corresponding change according to the required current, when the charging starts, the real-time charging state of the electric vehicle can be any one state value of 0-100%, and when the charging ends, the charging state of the electric vehicle is at most 100%, in the charging process, the charging efficiency is not kept unchanged, for example, in a 3 month 20:27:55, the required current is 104A, the required voltage is 41.18V, the required voltage is 5725:15.06:57, and the real-time charging efficiency is 364.06:15.38; however, when the charging pile charges the electric bicycle, the charging power of the charging pile is usually between 0.5 and 1.5KW, the charging demand is not changed, the required current is kept at 40A, the minimum state of charge is fixed at 10%, the maximum state of charge is fixed at 95%, the charging efficiency is kept unchanged, for example, in the 2 month No. 17:25:00, the required current is 40A, the required voltage is 315V, the required power is 12.6KW, in the 2 month No. 17:04:52, the real-time current is 1.5A, the real-time voltage is 314.89V, the real-time power is 0.47KW, and the state of charge SOC is 60%; therefore, the abnormal model is determined by the difference of the charging information in the two charging processes, and the accuracy of the abnormal model can be ensured.
In addition, referring to fig. 13, the present application further provides a monitoring and early warning device 1300 for a charging pile, including:
a model determining unit 1310 for acquiring sample charging information and determining an abnormal model according to the sample charging information, wherein the sample charging information is determined by a charging post and an electric bicycle charged by the charging post;
an acquiring unit 1320 for acquiring a plurality of target charging information within a target period, wherein the target charging information is determined by a charging pile and a charging type vehicle charged using the charging pile, the charging type vehicle being an electric car or an electric bicycle;
an analysis unit 1330 configured to perform an anomaly analysis on each target charging information in the target period based on the anomaly model, and determine whether the target charging information is charging anomaly information;
and an early warning unit 1340 for generating early warning information for indicating that the rechargeable vehicle is an electric bicycle when the target charging information is the charging abnormality information.
It can be appreciated that the specific implementation of the charging pile monitoring and early warning device 1300 is substantially the same as the specific embodiment of the charging pile monitoring and early warning method described above, and will not be described herein.
In addition, referring to fig. 14, fig. 14 illustrates a hardware structure of an electronic device of another embodiment, the electronic device including:
The processor 1401 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc., for executing relevant programs to implement the technical scheme provided by the embodiments of the present application;
the Memory 1402 may be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a random access Memory (Random Access Memory, RAM). The memory 1402 may store an operating system and other application programs, and when the technical solution provided in the embodiments of the present specification is implemented by software or firmware, relevant program codes are stored in the memory 1402, and the processor 1401 invokes the execution of the charging pile monitoring and early warning method according to the embodiments of the present application, for example, the method steps S110 to S140 in fig. 1, the method steps S210 to S230 in fig. 2, the method steps S310 to S330 in fig. 3, the method steps S410 to S420 in fig. 4, the method steps S510 to S540 in fig. 5, the method steps S610 to S650 in fig. 6, and the method steps S710 to S740 in fig. 7 described above;
An input/output interface 1403 for implementing information input and output;
the communication interface 1404 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);
bus 1405) for transferring information between components of the device (e.g., processor 1401, memory 1402, input/output interface 1403, and communication interface 1404);
wherein processor 1401, memory 1402, input/output interface 1403 and communication interface 1404 enable communication connections between each other within the device via bus 1405.
The embodiment of the application also provides a storage medium, which is a computer readable storage medium and is used for computer readable storage, the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the charging pile monitoring and early warning method, for example, the method steps S110 to S140 in fig. 1, the method steps S210 to S230 in fig. 2, the method steps S310 to S330 in fig. 3, the method steps S410 to S420 in fig. 4, the method steps S510 to S540 in fig. 5, the method steps S610 to S650 in fig. 6 and the method steps S710 to S740 in fig. 7 are executed.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
According to the charging pile monitoring and early warning method, the device, the equipment and the storage medium, sample charging information is obtained, and an abnormal model is determined according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile; acquiring a plurality of target charging information in a target time period, wherein the target charging information is determined by a charging pile and a charging type vehicle charged by the charging pile, and the charging type vehicle is an electric automobile or an electric bicycle; based on the anomaly model, carrying out anomaly analysis on each target charging information in the target time period, and judging whether the target charging information is abnormal charging information or not; and when the target charging information is abnormal charging information, generating early warning information for indicating that the charging type vehicle is an electric bicycle. Based on the method, sample charging information determined when the charging pile charges the electric bicycle is obtained in a model generation stage, then the sample charging information used for representing abnormal charging behaviors is taken as sample data of the model, an abnormal model is determined, target charging information determined when the charging pile charges the charging type vehicle is obtained in a monitoring stage, the target charging information is analyzed in real time by using the abnormal model, early warning information cannot be generated when the charging type vehicle is an electric automobile, the target charging information is determined to be charging abnormal information used for indicating the abnormal charging behaviors, and early warning information is generated.
The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
It will be appreciated by those skilled in the art that the solutions shown in fig. 1-8 are not limiting on the embodiments of the application and may include more or fewer steps than shown, or certain steps may be combined, or different steps.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over 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 application 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 application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause 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 of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.
The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.

Claims (7)

1. The utility model provides a charging pile monitoring and early warning method which is characterized in that the method comprises the following steps:
acquiring sample charging information, and determining an abnormal model according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile;
acquiring a plurality of target charging information in a target time period, wherein the target charging information is determined by the charging pile and a charging type vehicle charged by using the charging pile, and the charging type vehicle is an electric automobile or an electric bicycle;
based on the anomaly model, performing anomaly analysis on each piece of target charging information in the target time period, and judging whether the target charging information is charging anomaly information or not;
when the target charging information is abnormal charging information, generating early warning information for indicating that the rechargeable vehicle is the electric bicycle;
wherein the sample charging information includes sample power supply information determined by the charging post and sample state information determined by an electric bicycle charged using the charging post, the target charging information includes target power supply information determined by the charging post and target state information determined by a charging vehicle charged using the charging post, the anomaly model includes a charging post state anomaly model determined by the sample power supply information and a vehicle state anomaly model determined by the sample state information; the step of performing anomaly analysis on each piece of target charging information in the target time period based on the anomaly model, and judging whether the target charging information is charging anomaly information, includes:
Based on the state anomaly model of the charging pile, carrying out anomaly analysis on each piece of target power supply information in the target time period, and judging whether the target power supply information is power supply anomaly information or not;
based on the vehicle state abnormality model, carrying out abnormality analysis on each piece of target state information in the target time period, and judging whether the target state information is state abnormality information or not;
when the target power supply information is power supply abnormality information or the target state information is state abnormality information, determining that the target charging information is charging abnormality information;
wherein the sample power supply information includes a sample charging power, and the sample status information includes a sample charging demand; the obtaining sample charging information and determining an abnormal model according to the sample charging information comprises the following steps:
acquiring a plurality of sample charging information in a sample time period;
comparing the sample charging power in the sample time period, and determining a power abnormality characteristic threshold according to the maximum sample charging power;
determining the state anomaly model of the charging pile according to the power anomaly characteristic threshold;
Determining a demand abnormal feature according to each sample charging demand in the sample time period;
determining the vehicle state abnormality model according to the abnormal demand characteristics;
the sample state information also comprises a sample vehicle identification code and a sample state of charge; after the step of determining the vehicle state abnormality model according to the demand abnormality characteristic, the method further comprises the following steps:
determining a vehicle abnormal identification code according to the sample vehicle identification code;
comparing the sample states of charge in the sample time period, determining a first state of charge abnormal characteristic threshold according to the minimum sample state of charge, and determining a second state of charge abnormal characteristic threshold according to the maximum sample state of charge;
determining sample charging efficiency according to each sample charge state in the sample time period, and determining an efficiency abnormality characteristic according to the sample charging efficiency;
and updating the vehicle state anomaly model according to the vehicle anomaly identification code, the first state of charge anomaly characteristic threshold value, the second state of charge anomaly characteristic threshold value and the efficiency anomaly characteristic.
2. The method of claim 1, wherein the charging pile state anomaly model includes a power anomaly characteristic threshold value, the power anomaly characteristic threshold value being determined by the sample power supply information, the target power supply information including a target charging power; based on the charging pile state anomaly model, anomaly analysis is performed on each target power supply information in the target time period, and whether the target power supply information is power supply anomaly information or not is judged, including:
comparing the target charging power in the target time period, and determining a target power characteristic value according to the maximum target charging power;
judging whether the target power characteristic value is smaller than or equal to the power abnormality characteristic threshold value;
and when the target power characteristic value is smaller than or equal to the power abnormality characteristic threshold value, determining that the target power supply information is power supply abnormality information.
3. The method of claim 1, wherein the vehicle state anomaly model includes a demand anomaly signature, the demand anomaly signature determined from the sample state information, the target state information including a target charge demand; the step of performing anomaly analysis on each piece of target state information in the target time period based on the vehicle state anomaly model to determine whether the target state information is state anomaly information, includes:
Calculating the variance and the average value of each target charging demand in the target time period, and determining target demand characteristics;
judging whether the target demand characteristic accords with the demand abnormal characteristic or not;
and when the target demand characteristic accords with the demand abnormal characteristic, determining the target state information as state abnormal information.
4. The method of claim 1, wherein the vehicle state anomaly model includes a vehicle anomaly identification code, a first state of charge anomaly characteristic threshold value, a second state of charge anomaly characteristic threshold value, and an efficiency anomaly characteristic, the vehicle anomaly identification code, the first state of charge anomaly characteristic threshold value, the second state of charge anomaly characteristic threshold value, and the efficiency anomaly characteristic each being determined by the sample state information, the target state information including a target vehicle identification code and a target state of charge; the step of performing anomaly analysis on each piece of target state information in the target time period based on the vehicle state anomaly model to determine whether the target state information is state anomaly information, includes:
judging whether the target vehicle identification code is identical to the vehicle abnormal identification code;
When the target vehicle identification code is the same as the vehicle abnormality identification code, determining that the target state information is state abnormality information;
comparing the target states of charge in the target time period, determining a first state of charge characteristic value according to the minimum target state of charge, and determining a second state of charge characteristic value according to the maximum target state of charge;
judging whether the first state of charge characteristic value is equal to the first state of charge abnormal characteristic threshold value or not, and judging whether the second state of charge characteristic value is equal to the second state of charge abnormal characteristic threshold value or not;
determining the target state information as state anomaly information when the first state of charge characteristic value is equal to the first state of charge anomaly characteristic threshold value or when the second state of charge characteristic value is equal to the second state of charge anomaly characteristic threshold value;
determining target charging efficiency according to each target state of charge in the target time period, and judging whether the target charging efficiency accords with the efficiency abnormal characteristics;
and when the target charging efficiency accords with the efficiency abnormal characteristic, determining the target state information as state abnormal information.
5. A charging pile monitoring and early warning device, characterized in that the charging pile monitoring and early warning device is used for executing the charging pile monitoring and early warning method according to any one of claims 1 to 4, and the charging pile monitoring and early warning device comprises:
the model determining unit is used for obtaining sample charging information and determining an abnormal model according to the sample charging information, wherein the sample charging information is determined by a charging pile and an electric bicycle charged by the charging pile;
an acquisition unit configured to acquire a plurality of target charging information within a target period, wherein the target charging information is determined by the charging pile and a charging vehicle that is an electric vehicle or the electric bicycle and is charged using the charging pile;
the analysis unit is used for carrying out anomaly analysis on each piece of target charging information in the target time period based on the anomaly model and judging whether the target charging information is abnormal charging information or not;
and the early warning unit is used for generating early warning information for indicating that the rechargeable vehicle is the electric bicycle when the target charging information is abnormal charging information.
6. An electronic device, characterized in that the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the charging pile monitoring and early warning method according to any one of claims 1 to 4 when executing the computer program.
7. A storage medium storing a computer program, wherein the computer program when executed by a processor implements the charging pile monitoring and early warning method according to any one of claims 1 to 4.
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