CN117292576A - Charging parking space management system and method based on Internet of Things - Google Patents
Charging parking space management system and method based on Internet of Things Download PDFInfo
- Publication number
- CN117292576A CN117292576A CN202311306239.7A CN202311306239A CN117292576A CN 117292576 A CN117292576 A CN 117292576A CN 202311306239 A CN202311306239 A CN 202311306239A CN 117292576 A CN117292576 A CN 117292576A
- Authority
- CN
- China
- Prior art keywords
- voltage waveform
- working power
- parking space
- training
- interaction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000004891 communication Methods 0.000 claims abstract description 6
- 230000003993 interaction Effects 0.000 claims description 94
- 238000012549 training Methods 0.000 claims description 76
- 238000010586 diagram Methods 0.000 claims description 60
- 238000007726 management method Methods 0.000 claims description 56
- 239000013598 vector Substances 0.000 claims description 52
- 238000000605 extraction Methods 0.000 claims description 32
- 230000007246 mechanism Effects 0.000 claims description 19
- 238000012544 monitoring process Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 4
- 230000006855 networking Effects 0.000 claims 1
- 230000002159 abnormal effect Effects 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 230000008859 change Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 238000012423 maintenance Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 238000002075 inversion recovery Methods 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012482 interaction analysis Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000009783 overcharge test Methods 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 238000002407 reforming Methods 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
- G08G1/148—Management of a network of parking areas
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F15/00—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity
- G07F15/003—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity for electricity
- G07F15/005—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity for electricity dispensed for the electrical charging of vehicles
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F15/00—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity
- G07F15/10—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity with alarm or warning devices, e.g. indicating the interrupting of the supply
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/149—Traffic control systems for road vehicles indicating individual free spaces in parking areas coupled to means for restricting the access to the parking space, e.g. authorization, access barriers, indicative lights
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a charging parking space management system and method based on the Internet of things, comprising the following steps: the system comprises a background server, a control terminal, charging piles respectively installed on each parking space, geomagnetic sensors respectively installed on each parking space and parking space locks; the control terminal is used for receiving and transmitting a parking space lock control instruction to the background server, and the charging pile, the geomagnetic sensor and the parking space lock are connected to the background server in a communication mode. Therefore, the parking space can be locked by default through the parking space lock and the control terminal can unlock the parking space, so that the parking space of the charging pile cannot be occupied at will.
Description
Technical Field
The invention relates to the technical field of intelligent management, in particular to a charging parking space management system and method based on the Internet of things.
Background
In recent years, new energy automobiles are increasingly favored. The charging pile parking space is used for charging the electric automobile. Among the charging pile parking stall of related art, charging pile is generally installed on open parking stall, and the general scattered arrangement of charging pile parking stall, every point often quantity is little moreover, is difficult to arrange the special personnel and is on duty.
The existing charging pile parking space lacks effective management measures, and the charging pile parking space is easily occupied by non-charging vehicles, so that the charging pile cannot be normally used; meanwhile, the phenomenon that the vehicle is not moved timely often occurs after the charging of the charging vehicle is completed, and the situation that the charging pile parking space is occupied at will is common.
Therefore, a charging parking space management scheme is desired.
Disclosure of Invention
The embodiment of the invention provides a charging parking space management system and method based on the Internet of things, wherein the system comprises the following steps: the system comprises a background server, a control terminal, charging piles respectively installed on each parking space, geomagnetic sensors respectively installed on each parking space and parking space locks; the control terminal is used for receiving and transmitting a parking space lock control instruction to the background server, and the charging pile, the geomagnetic sensor and the parking space lock are connected to the background server in a communication mode. Therefore, the parking space can be locked by default through the parking space lock and the control terminal can unlock the parking space, so that the parking space of the charging pile cannot be occupied at will.
The embodiment of the invention also provides a charging parking space management system based on the Internet of things, which comprises the following steps:
the system comprises a background server, a control terminal, charging piles respectively installed on each parking space, geomagnetic sensors respectively installed on each parking space and parking space locks;
the control terminal is used for receiving and transmitting a parking space lock control instruction to the background server, and the charging pile, the geomagnetic sensor and the parking space lock are connected to the background server in a communication mode.
The embodiment of the invention also provides a charging parking space management method based on the Internet of things, which comprises the following steps:
acquiring working power values of the monitored charging pile at a plurality of preset time points in a preset time period;
acquiring a voltage signal of the monitored charging pile in the preset time period;
performing feature extraction and feature interaction on the working power values and the voltage signals at the plurality of preset time points to obtain a working power-voltage waveform interaction feature map; and
and determining whether to generate a charging pile fault early warning prompt or not based on the working power-voltage waveform interaction characteristic diagram.
Drawings
In order to more clearly illustrate the embodiments of the invention 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, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a schematic structural diagram of a charging parking space management system based on the internet of things provided in an embodiment of the invention.
Fig. 2 is a block diagram of the charging pile fault early warning module in the charging parking space management system based on the internet of things provided by the embodiment of the invention.
Fig. 3 is a flowchart of a charging parking space management method based on the internet of things, which is provided in an embodiment of the invention.
Fig. 4 is a schematic diagram of a system architecture of a charging parking space management method based on the internet of things according to an embodiment of the present invention.
Fig. 5 is an application scenario diagram of a charging parking space management system based on the internet of things provided in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
Unless defined otherwise, all technical and scientific terms used in the examples of this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In the description of the embodiments of the present application, unless otherwise indicated and defined, the term "connected" should be construed broadly, and for example, may be an electrical connection, may be a communication between two elements, may be a direct connection, or may be an indirect connection via an intermediary, and it will be understood by those skilled in the art that the specific meaning of the term may be understood according to the specific circumstances.
It should be noted that, the term "first\second\third" in the embodiments of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that the embodiments of the present application described herein may be implemented in sequences other than those illustrated or described herein.
The charging pile parking space is used for charging the electric automobile. Among the charging pile parking stall of related art, charging pile is generally installed on open parking stall, and the general scattered arrangement of charging pile parking stall, every point often quantity is little moreover, is difficult to arrange the special personnel and is on duty. The existing charging pile parking space lacks effective management measures, so that the charging pile parking space is easily occupied by non-charging vehicles, and the charging pile cannot be normally used; meanwhile, the phenomenon that the vehicle is not moved timely often occurs after the charging of the charging vehicle is completed, and the situation that the charging pile parking space is occupied at will is common.
The charging pile parking space is a parking space specially used for providing charging service for the electric automobile, and along with popularization of the electric automobile, the requirement of the charging pile parking space is also increasing. The design and the arrangement of charging pile parking stall need consider charging equipment's installation and use convenience to and convenience and the security that the user charged.
The charging pile is generally installed at one side or the central position of a parking space, so that the electric automobile is convenient to connect with the charging equipment, the position of the charging pile is reasonable, a user is convenient to connect and disconnect the charger, and parking of other vehicles is not hindered. The charging piles are generally distributed in a scattered manner to meet the charging requirements of different places, the layout of the charging piles is planned according to the specific parking lots or parking areas, and factors such as the number of the parking spaces, the number of the charging piles and the power requirements are considered. In order to facilitate the user to find the charging pile parking space, the charging pile should be marked obviously, for example, a charging pile mark, a number or a mark for marking the charging pile parking space is set, and the like, so that the user can quickly find the charging pile parking space and perform charging operation. In order to prevent the charging pile from being occupied or damaged by non-charging vehicles, some measures can be taken for protection. For example, a dedicated charging pile parking space is provided, on which only electric vehicles are restricted to park, and physical obstacles or protective facilities are added to prevent non-charging vehicles from parking or colliding. In order to improve the management efficiency and the service quality of the charging pile parking space, a charging pile management system can be used, the state of a charging pile, the charging progress and charging data can be remotely monitored and controlled, reservation and reservation functions are provided, and functions such as fault early warning and remote maintenance are realized.
There are some problems in the management of the existing charging pile parking space, such as the situation that the charging pile parking space is occupied by a non-charging vehicle and the vehicle is not moved in time after the charging is completed, and these problems affect the normal use and charging efficiency of the charging pile, and an effective management measure needs to be adopted to solve the problems.
And monitoring the occupation condition of the charging pile parking space by using equipment such as a sensor or a camera. The equipment can detect whether the non-charging vehicle is parked on the parking space in real time and transmit data to the management system. When it is detected that the non-charging vehicle occupies the charging stake space, the system can send an alarm to notify relevant personnel to process. And distinguishing the vehicle parked on the parking space of the charging pile from the charging vehicle through license plate recognition or other identity authentication technologies. Only authenticated charging vehicles can use the charging piles, and non-charging vehicles need to move away from the parking spaces in time. Therefore, the non-charging vehicle can be prevented from being detained on the parking space of the charging pile, and the usability of the charging pile is ensured. The reservation and reservation functions of the charging pile parking space are provided for the user, so that the user can arrange a charging plan in advance, and the availability of the parking space is ensured. The reservation system can operate through a mobile phone application program or a website, and is convenient for a user to reserve and manage. The reservation function can reduce waiting time of the charging vehicle and improve utilization efficiency of the charging pile. During the charging process, the system may send a reminder to the user that the charging is complete. After the user receives the prompt, the user should move the vehicle away from the parking space of the charging pile in time so as to be convenient for other vehicles to use. Meanwhile, the charging completion time and the vehicle moving time can be recorded through the system so as to carry out subsequent management and statistical analysis. Through propaganda and educational activity, improve the user and to charge the understanding and understanding of stake parking stall. The user should be informed of the importance of the charging pile parking space and the necessity of timely moving the vehicle. The charging pile parking space management scheme can provide user guidance, inform the user how to correctly use the charging pile parking space, and provide relevant use specifications and notes.
Through the management measures, the problem that the charging pile parking space is occupied by a non-charging vehicle and the vehicle is not moved timely after charging is completed can be effectively solved, the availability and the charging efficiency of the charging pile parking space are improved, and better charging service is provided for electric automobile users. In an embodiment of the present invention, fig. 1 is a schematic structural diagram of a charging parking space management system based on the internet of things provided in the embodiment of the present invention. As shown in fig. 1, a charging parking space management system 100 based on the internet of things according to an embodiment of the present invention includes: the system comprises a background server 1, a control terminal 2, charging piles 4 respectively installed on each parking space 3, a geomagnetic sensor 5 and a parking space lock 6 respectively installed on each parking space 3; the control terminal 2 is configured to receive and transmit a parking space lock control instruction to the background server 1, and the charging pile 4, the geomagnetic sensor 5, and the parking space lock 6 are communicatively connected to the background server 1.
In the running process of the charging parking space management system based on the Internet of things, the parking space lock 6 of the parking space 3 defaults to be in a locking state, so that the parking space 3 cannot drive into a vehicle, the vehicle is prevented from being occupied randomly, a user can open the parking space lock 6 only after unlocking through a control terminal, and therefore the vehicle can drive into the parking space 3 and charge by using the charging pile 4; when the vehicle drives away, the parking space lock 6 is closed so that the vehicle which is not unlocked by the control terminal cannot be parked into a parking space; therefore, the parking space lock 6 is in a locking state by default and the control terminal unlocks the parking space, so that the charging pile parking space 3 cannot be occupied at will. The charging pile 4, the geomagnetic sensor 5 and the parking space lock 6 are respectively connected with a background server through network signals so as to realize independent control of each device by the background.
The technical route of the application is as follows: the method comprises the steps of establishing and designing a charging parking space management technical scheme of the Internet of things, researching and developing a parking space state real-time monitoring technology, researching and developing an occupation behavior limiting technology, researching and developing a charging state feedback interaction technology, preparing and debugging, simulating an overcharge test, and optimizing the charging parking space management system of the Internet of things.
Thus, beneficial effects that may be produced include:
1. the utilization rate of the charging parking space is effectively improved;
2. enabling a better presentation of the state of charge to the user;
3. and the electric vehicle is compatible with most electric vehicle types in the market, and is favorable for upgrading and reforming the charging station.
In particular, considering that the charging pile is an important facility for charging a new energy vehicle, the charging pile may malfunction to fail to operate properly due to various reasons (e.g., equipment aging, power supply problems, communication failures, etc.). This can affect the charging experience of the user and reduce the usability of the charging stake. Aiming at the technical problem, the technical concept of the application is to design a fault early warning module of the charging pile in a background server to monitor and detect the fault condition of the charging pile so as to take maintenance measures in time and ensure the normal operation of the charging pile.
In the technical scheme of the application, the charging pile fault early warning module realizes the charging pile fault early warning by utilizing an artificial intelligence technology based on deep learning and combining power data and voltage signal data of the charging pile. It should be appreciated that the operating power value and voltage signal of the charging stake can characterize and measure the operational state of the charging stake. Specifically, the real-time working condition of the charging pile, including the charging speed, the electric energy conversion efficiency and the like, can be known through the working power value. If the charging pile fails, the operating power value may be abnormal or lowered. In addition, the voltage signal of the charging pile can reflect the charging pile power supply and power stability, and an abnormal voltage signal may indicate that there is a power supply problem, a grid connection failure, or a problem of the charging pile itself. These information are important data sources for determining whether the charging pile is malfunctioning.
Fig. 2 is a block diagram of the charging pile fault early warning module in the charging parking space management system based on the internet of things provided by the embodiment of the invention. As shown in fig. 2, the background server further includes a charging pile fault early warning module 7; wherein, fill electric pile trouble early warning module 7 includes: a power value obtaining unit 110, configured to obtain working power values of the monitored charging pile at a plurality of predetermined time points within a predetermined time period; a voltage signal acquisition unit 120, configured to acquire a voltage signal of the monitored charging pile in the predetermined period of time; a feature extraction and interaction unit 130, configured to perform feature extraction and feature interaction on the working power values and the voltage signals at the plurality of predetermined time points to obtain a working power-voltage waveform interaction feature map; and a fault early warning prompting unit 140, configured to determine whether to generate a fault early warning prompt of the charging pile based on the working power-voltage waveform interaction feature map.
In the power value acquisition unit 110, accuracy and instantaneity of power value acquisition are ensured. And selecting a proper sensor or equipment to acquire the power value of the charging pile, ensuring the accuracy and precision of the power value, and ensuring the real-time acquisition and updating of the power value so as to facilitate the subsequent feature extraction and fault early warning analysis. The method can accurately acquire the working power value of the charging pile, provide real-time charging state information and provide basic data for subsequent feature extraction and fault early warning.
In the voltage signal acquisition unit 120, a voltage signal of the monitored charging pile for a predetermined period of time is acquired. And a proper sensor is used for acquiring a voltage signal of the charging pile, so that the accuracy and stability of the sensor are ensured. Accurate acquisition and processing of the voltage signals is ensured for subsequent feature extraction and interaction analysis. The method has the advantages that the electric power quality information in the charging process can be provided by acquiring the voltage signal of the charging pile, and the method has important significance for fault early warning and operation state monitoring of the charging pile.
In the feature extraction and interaction unit 130, feature extraction and feature interaction are performed on the operating power values and the voltage signals at a plurality of predetermined time points to obtain an operating power-voltage waveform interaction feature map. A suitable feature extraction algorithm is selected to extract meaningful features, such as spectral features, temporal features, etc., from the operating power values and the voltage signals. And the extracted power characteristics and the voltage characteristics are interacted, and a mathematical model or a statistical method can be adopted to obtain a working power-voltage waveform interaction characteristic diagram. Through feature extraction and interaction, key features of the working state of the charging pile can be extracted, and useful information is provided for fault early warning and abnormality detection.
In the fault early warning presentation unit 140, it is determined whether to generate a charging pile fault early warning presentation based on the working power-voltage waveform interaction characteristic diagram. And establishing a reasonable fault judgment criterion, and judging whether the charging pile has a fault or not according to the abnormal condition of the working power-voltage waveform interaction characteristic diagram. And determining a prompting mode of fault early warning, such as sound, text prompting or an alarm system, and the like, so as to timely inform related personnel to process. Through the fault early warning prompt unit, the fault condition of the charging pile can be timely found and processed, and the reliability and safety of the charging equipment are improved.
Based on this, in the technical scheme of this application, the coding process of electric pile fault early warning module includes: firstly, acquiring working power values of a monitored charging pile at a plurality of preset time points in a preset time period; and meanwhile, acquiring a voltage signal of the monitored charging pile in the preset time period.
In a specific embodiment of the present application, the feature extraction and interaction unit includes: a vector arrangement subunit, configured to arrange the working power values at the multiple predetermined time points into a working power time sequence input vector according to a time dimension; a voltage waveform feature extraction subunit, configured to extract a voltage waveform feature of the voltage signal to obtain a voltage waveform feature map; and the interaction characteristic extraction subunit is used for enabling the working power time sequence input vector and the voltage waveform characteristic diagram to pass through a working power-voltage waveform interaction characteristic extractor based on a MetaNet module to obtain the working power-voltage waveform interaction characteristic diagram.
The vector arrangement subunit arranges the working power values of a plurality of preset time points into working power time sequence input vectors according to the time dimension. The power values of the time points are arranged according to the time sequence, so that clear time sequence data can be provided, and the subsequent feature extraction and analysis are convenient. By analyzing the time sequence data of the working power, the use mode and the change trend of the charging pile can be revealed, and a useful reference is provided for fault early warning and management decision.
The voltage waveform characteristic extraction subunit extracts the voltage waveform characteristic of the voltage signal to obtain a voltage waveform characteristic diagram. By extracting waveform characteristics of the voltage signals, the power quality of the charging pile, such as voltage stability, waveform distortion and the like, can be evaluated, and references are provided for fault diagnosis and power system optimization. By analyzing the characteristics of the voltage waveform, voltage anomalies or faults, such as voltage waveform distortions, voltage fluctuations, etc., present in the charging stake can be detected and diagnosed.
The interaction characteristic extraction subunit uses a working power-voltage waveform interaction characteristic extractor based on the MetaNet module to interact the working power time sequence input vector and the voltage waveform characteristic graph so as to obtain the working power-voltage waveform interaction characteristic graph. Through the interaction of the working power and the voltage waveform characteristics, richer and accurate characteristics can be extracted, and the working state and the characteristics of the charging pile can be reflected better. The interaction feature extraction can reveal the relevance between power and voltage, is beneficial to improving the accuracy and sensitivity of fault early warning, and timely discovers the abnormal situation in the charging pile.
Then, the working power values at the plurality of preset time points are arranged into working power time sequence input vectors according to the time dimension so as to convert discrete time sequence distribution of the working power values into structured vector representation, and the subsequent models are convenient to read and identify; the waveform of the voltage signal is then passed through a voltage waveform profile extractor using a spatial attention mechanism to obtain a voltage waveform profile. Here, the waveform diagram of the voltage signal reflects the voltage change condition of the charging pile, and a change curve of the voltage signal with time can be displayed. This waveform diagram may provide information about voltage stability, power supply fluctuations, grid connection quality, etc. The spatial attention mechanism is a method for focusing on and emphasizing a specific region or feature. The use of a spatial attention mechanism in the voltage waveform feature extractor may enable the model to learn more accurately and specifically key regions or features in the voltage signal waveform to obtain a feature representation with higher characterization capabilities.
In a specific embodiment of the present application, the voltage waveform feature extraction subunit is configured to: the voltage waveform profile of the voltage signal is obtained by a voltage waveform profile extractor using a spatial attention mechanism.
The spatial attention mechanism is a common attention mechanism in deep learning and is used for weighting different spatial positions of input data so as to pay more attention to the position with important information, and the perceptibility of important waveform areas in a voltage signal can be improved by applying the spatial attention mechanism in a voltage waveform feature extractor.
The basic idea of the spatial attention mechanism is to learn a set of weights for weighting different positions of the input data. These weights may be represented as a matrix of the same size as the input data, with each element corresponding to a weight value for a location. In the voltage waveform feature extractor, these weights may be expressed as the degree of importance of each location in the voltage waveform graph. By applying a spatial attention mechanism, the voltage waveform feature extractor can pay more attention to waveform regions with important information in the voltage signals, and the expression capability and discrimination capability of the features are improved. The method is favorable for improving the quality and accuracy of the voltage waveform characteristics, and provides a more reliable basis for subsequent fault early warning and abnormality detection.
Further, the working power time sequence input vector and the voltage waveform characteristic diagram are passed through a working power-voltage waveform interaction characteristic extractor based on a MetaNet module to obtain a working power-voltage waveform interaction characteristic diagram. That is, the working power time sequence change feature of the charging pile expressed by the working power time sequence input vector and the charging pile voltage time sequence change feature are interacted and fused, so that the working power-voltage waveform interaction feature map can express the association relation between the working power time sequence change feature and the charging pile voltage time sequence change feature. Wherein the MetaNet module uses one-dimensional time series data to assist in optimizing visual feature information expressed in a feature map. That is, the MetaNet module enables one-dimensional time series data to directly interact with visual features, directly control the relevant characteristics of each feature channel, and help the network concentrate on specific parts of each feature channel.
In a specific embodiment of the present application, the interaction feature extraction subunit is configured to: the working power time sequence input vector passes through a point convolution layer to obtain a first convolution characteristic vector; passing the first convolution feature vector through a modified linear unit based on a ReLU function to obtain a first modified convolution feature vector; passing the first modified convolution feature vector through a point convolution layer to obtain a second convolution feature vector; passing the second convolution feature vector through a correction linear unit based on a Sigmoid function to obtain a second correction convolution feature vector; and fusing the second modified convolution feature vector with the voltage waveform feature map to obtain the working power-voltage waveform interaction feature map.
And then, the working power-voltage waveform interaction characteristic diagram is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether to generate a charging pile fault early warning prompt. By using the working power-voltage waveform interaction characteristic diagram as input and combining the classifier for classification, the abnormal and fault modes of the charging pile can be effectively captured, and the classifier can learn the characteristic difference between the normal and abnormal states of the charging pile, so that the accuracy of fault early warning is improved. Through the interactive characteristics of real-time monitoring working power and voltage waveform, the classifier can judge fast whether the electric pile has the fault condition. The fault early warning prompt is generated in time, so that operation and maintenance personnel can be prompted to take corresponding measures, further deterioration of the fault is avoided, and reliable operation of the charging pile is ensured. The working power-voltage waveform interaction characteristic diagram is combined with the classifier, so that automatic processing of early warning of the fault of the charging pile can be realized, manual intervention is not needed, the system can automatically identify and judge the fault state of the charging pile, and the management efficiency and the operation and maintenance effect are improved. Through in time generating the warning of filling electric pile trouble, can reduce the user and fill electric pile and unable normal use's condition because of the trouble, the user can in time know the state of filling electric pile, selects other available fills electric pile, improves and fills experience and convenience.
By combining the working power-voltage waveform interaction characteristic diagram with the classifier, the automatic processing of the fault early warning of the charging pile can be realized, the accuracy and the instantaneity of the fault early warning are improved, the user experience is improved, and beneficial information and reference are provided for the operation and the management of the charging pile.
In a specific embodiment of the present application, the fault early warning prompting unit is configured to: and the working power-voltage waveform interaction feature diagram is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether to generate a charging pile fault early warning prompt.
In an embodiment of the present application, the charging parking space management system based on the internet of things further includes a training module for training the voltage waveform feature extractor using the spatial attention mechanism, the working power-voltage waveform interaction feature extractor based on the MetaNet module, and the classifier; wherein, training module includes: the system comprises a training data acquisition unit, a monitoring unit and a storage unit, wherein the training data acquisition unit is used for acquiring training data, the training data comprises training working power values of a monitored charging pile at a plurality of preset time points in a preset time period, training voltage signals of the monitored charging pile in the preset time period and whether a true value of a charging pile fault early warning prompt is generated or not; the training vector arrangement unit is used for arranging the training working power values of the plurality of preset time points into training working power time sequence input vectors according to the time dimension; the training spatial attention unit is used for passing the waveform diagram of the training voltage signal through the voltage waveform characteristic extractor using the spatial attention mechanism to obtain a training voltage waveform characteristic diagram; the training waveform interaction feature extraction unit is used for enabling the training working power time sequence input vector and the training voltage waveform feature graph to pass through the working power-voltage waveform interaction feature extractor based on the MetaNet module so as to obtain a training working power-voltage waveform interaction feature graph; the training characteristic distribution optimizing unit is used for carrying out characteristic distribution optimization on the training working power-voltage waveform interaction characteristic diagram so as to obtain an optimized working power-voltage waveform interaction characteristic diagram; the training classification unit is used for enabling the optimized working power-voltage waveform interaction characteristic diagram to pass through a classifier to obtain a classification loss function value; and a training unit for training the voltage waveform feature extractor using a spatial attention mechanism, the working power-voltage waveform interaction feature extractor based on the MetaNet module, and the classifier with the classification loss function value.
In the technical solution of the present application, each feature matrix of the training voltage waveform feature diagram is used to express the image semantic feature of spatial distribution reinforcement of the waveform diagram of the training voltage signal, and each feature matrix of the training voltage waveform feature diagram follows the channel distribution of the voltage waveform feature extractor using the spatial attention mechanism, so when the training working power time sequence input vector and the training voltage waveform feature diagram pass through the working power-voltage waveform interaction feature extractor based on the MetaNet module, the channel distribution among each feature matrix of the training voltage waveform feature diagram is constrained based on the time sequence distribution of the working power value expressed by the training working power time sequence input vector, so that each feature matrix of the training working power-voltage waveform interaction feature diagram follows the time sequence distribution of the training working power value, and thus, the training working power-voltage waveform interaction feature diagram has time sequence parameter-image semantic cross correlation representation under the space-time multiple dimensions.
However, the time sequence parameter-image semantic cross correlation representation of the training working power-voltage waveform interaction characteristic diagram under the time-space multi-dimension can be due to poor correlation accuracy of the correlation characteristics of the cross time sequence parameter-image semantic under different dimensions when the cross time sequence parameter-image semantic cross correlation representation passes through the classifierIn addition, the training effect of the training power-voltage waveform interaction feature diagram when the classifier is trained is affected, so that the applicant of the application can obtain training power-voltage waveform interaction feature vectors, such as the following, for example, by marking the training power-voltage waveform interaction feature vectors after the training power-voltage waveform interaction feature diagram is unfolded in the training processFeature precision alignment based on dimension characterization and inversion recovery is performed, specifically expressed as: performing feature precision alignment based on dimension representation and inversion recovery on the training working power-voltage waveform interaction feature vector obtained after the training working power-voltage waveform interaction feature map is unfolded by using the following optimization formula; wherein, the optimization formula is:wherein (1)>Is the training working power-voltage waveform interaction feature vector obtained after the training working power-voltage waveform interaction feature map is unfolded, and is->Is the training working power-voltage waveform interaction characteristic vector +.>Is>Characteristic value of individual position->Representing the training power-voltage waveform interaction feature vector +.>Zero norm, ++>Is the training working power-voltage waveform interaction characteristic vector +.>Length of (2), and->Is a weight superparameter,/->The optimized working power-voltage waveform interaction feature vector is obtained after the optimized working power-voltage waveform interaction feature map is unfolded.
The feature precision alignment based on dimension representation and inversion type recovery is generated by inversion type embedding of the high-dimensional feature space coding of which the dimension representation is regarded as the time sequence parameter and the image semantic, sparse distribution balance of scale representation is arranged on the feature value which is used as the coding representation, and inversion type recovery of association details is carried out based on vector counting, so that self-adaptive alignment of precision difference in the training process is realized, and the training effect of the training working power-voltage waveform interaction feature diagram in the classifying regression training through a classifier is improved.
In summary, the charging parking space management system 100 based on the internet of things according to the embodiment of the invention is illustrated, and the fault early warning of the charging pile is realized by combining the power data and the voltage signal data of the charging pile by using the artificial intelligence technology based on deep learning. It should be appreciated that the operating power value and voltage signal of the charging stake can characterize and measure the operational state of the charging stake. Specifically, the real-time working condition of the charging pile, including the charging speed, the electric energy conversion efficiency and the like, can be known through the working power value. If the charging pile fails, the operating power value may be abnormal or lowered. In addition, the voltage signal of the charging pile can reflect the charging pile power supply and power stability, and an abnormal voltage signal may indicate that there is a power supply problem, a grid connection failure, or a problem of the charging pile itself. These information are important data sources for determining whether the charging pile is malfunctioning.
As described above, the charging parking space management system 100 based on the internet of things according to the embodiment of the invention may be implemented in various terminal devices, for example, a server for charging parking space management based on the internet of things, and the like. In one example, the charging parking space management system 100 based on the internet of things according to the embodiment of the present invention may be integrated into the terminal device as one software module and/or hardware module. For example, the charging parking space management system 100 based on the internet of things may be a software module in the operating system of the terminal device, or may be an application program developed for the terminal device; of course, the charging parking space management system 100 based on the internet of things can be one of numerous hardware modules of the terminal device.
Alternatively, in another example, the charging parking space management system 100 based on the internet of things and the terminal device may be separate devices, and the charging parking space management system 100 based on the internet of things may be connected to the terminal device through a wired and/or wireless network and transmit the interaction information according to the agreed data format.
Fig. 3 is a flowchart of a charging parking space management method based on the internet of things, which is provided in an embodiment of the invention. Fig. 4 is a schematic diagram of a system architecture of a charging parking space management method based on the internet of things according to an embodiment of the present invention. As shown in fig. 3 and 4, a charging parking space management method based on the internet of things includes: 210, acquiring working power values of the monitored charging pile at a plurality of preset time points in a preset time period; 220, acquiring a voltage signal of the monitored charging pile in the preset time period; 230, performing feature extraction and feature interaction on the working power values and the voltage signals at the plurality of preset time points to obtain a working power-voltage waveform interaction feature map; and 240, determining whether to generate a charging pile fault early warning prompt based on the working power-voltage waveform interaction characteristic diagram.
In the charging parking space management method based on the internet of things, performing feature extraction and feature interaction on the working power values and the voltage signals at the plurality of preset time points to obtain a working power-voltage waveform interaction feature map, wherein the method comprises the following steps: arranging the working power values of the plurality of preset time points into working power time sequence input vectors according to the time dimension; extracting voltage waveform characteristics of the voltage signals to obtain a voltage waveform characteristic diagram; and the working power time sequence input vector and the voltage waveform characteristic diagram are passed through a working power-voltage waveform interaction characteristic extractor based on a MetaNet module to obtain the working power-voltage waveform interaction characteristic diagram.
In the charging parking space management method based on the internet of things, extracting the voltage waveform characteristics of the voltage signals to obtain a voltage waveform characteristic diagram, including: the voltage waveform profile of the voltage signal is obtained by a voltage waveform profile extractor using a spatial attention mechanism.
It will be appreciated by those skilled in the art that the specific operations of the respective steps in the above-described internet of things-based charge parking space management method have been described in detail in the above description of the internet of things-based charge parking space management system with reference to fig. 1 to 2, and thus, repetitive descriptions thereof will be omitted.
Fig. 5 is an application scenario diagram of a charging parking space management system based on the internet of things provided in an embodiment of the present invention. As shown in fig. 5, in this application scenario, first, the operation power values of the monitored charging pile at a plurality of predetermined time points within a predetermined period of time (e.g., C1 as illustrated in fig. 5) are acquired, and the voltage signal of the monitored charging pile within the predetermined period of time (e.g., C2 as illustrated in fig. 5) is acquired; then, the obtained working power value and the voltage signal are input into a server (for example, S as illustrated in fig. 5) deployed with a charging parking space management algorithm based on the internet of things, wherein the server can process the working power value and the voltage signal based on the charging parking space management algorithm of the internet of things to determine whether to generate a charging pile fault early warning prompt.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. Charging parking stall management system based on thing networking, its characterized in that includes:
the system comprises a background server, a control terminal, charging piles respectively installed on each parking space, geomagnetic sensors respectively installed on each parking space and parking space locks;
the control terminal is used for receiving and transmitting a parking space lock control instruction to the background server, and the charging pile, the geomagnetic sensor and the parking space lock are connected to the background server in a communication mode.
2. The charging parking space management system based on the internet of things according to claim 1, wherein the background server further comprises a charging pile fault early warning module;
wherein, fill electric pile trouble early warning module includes:
the power value acquisition unit is used for acquiring the working power values of the monitored charging pile at a plurality of preset time points in a preset time period;
the voltage signal acquisition unit is used for acquiring a voltage signal of the monitored charging pile in the preset time period;
the characteristic extraction and interaction unit is used for carrying out characteristic extraction and characteristic interaction on the working power values and the voltage signals at a plurality of preset time points so as to obtain a working power-voltage waveform interaction characteristic diagram; and
and the fault early warning prompt unit is used for determining whether to generate a charging pile fault early warning prompt or not based on the working power-voltage waveform interaction characteristic diagram.
3. The charging parking space management system based on the internet of things according to claim 2, wherein the feature extraction and interaction unit comprises:
a vector arrangement subunit, configured to arrange the working power values at the multiple predetermined time points into a working power time sequence input vector according to a time dimension;
a voltage waveform feature extraction subunit, configured to extract a voltage waveform feature of the voltage signal to obtain a voltage waveform feature map; and
and the interaction characteristic extraction subunit is used for enabling the working power time sequence input vector and the voltage waveform characteristic diagram to pass through a working power-voltage waveform interaction characteristic extractor based on the MetaNet module to obtain the working power-voltage waveform interaction characteristic diagram.
4. The internet of things-based charging parking space management system according to claim 3, wherein the voltage waveform feature extraction subunit is configured to:
the voltage waveform profile of the voltage signal is obtained by a voltage waveform profile extractor using a spatial attention mechanism.
5. The internet of things-based charging parking space management system of claim 4, wherein the interaction feature extraction subunit is configured to:
the working power time sequence input vector passes through a point convolution layer to obtain a first convolution characteristic vector;
passing the first convolution feature vector through a modified linear unit based on a ReLU function to obtain a first modified convolution feature vector;
passing the first modified convolution feature vector through a point convolution layer to obtain a second convolution feature vector;
passing the second convolution feature vector through a correction linear unit based on a Sigmoid function to obtain a second correction convolution feature vector; and
and fusing the second modified convolution feature vector and the voltage waveform feature map to obtain the working power-voltage waveform interaction feature map.
6. The charging parking space management system based on the internet of things according to claim 5, wherein the fault early warning prompting unit is configured to:
and the working power-voltage waveform interaction feature diagram is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether to generate a charging pile fault early warning prompt.
7. The internet of things-based charging parking space management system of claim 6, further comprising a training module for training the voltage waveform feature extractor using a spatial attention mechanism, the working power-voltage waveform interaction feature extractor based on MetaNet module, and the classifier;
wherein, training module includes:
the system comprises a training data acquisition unit, a monitoring unit and a storage unit, wherein the training data acquisition unit is used for acquiring training data, the training data comprises training working power values of a monitored charging pile at a plurality of preset time points in a preset time period, training voltage signals of the monitored charging pile in the preset time period and whether a true value of a charging pile fault early warning prompt is generated or not;
the training vector arrangement unit is used for arranging the training working power values of the plurality of preset time points into training working power time sequence input vectors according to the time dimension;
the training spatial attention unit is used for passing the waveform diagram of the training voltage signal through the voltage waveform characteristic extractor using the spatial attention mechanism to obtain a training voltage waveform characteristic diagram;
the training waveform interaction feature extraction unit is used for enabling the training working power time sequence input vector and the training voltage waveform feature graph to pass through the working power-voltage waveform interaction feature extractor based on the MetaNet module so as to obtain a training working power-voltage waveform interaction feature graph;
the training characteristic distribution optimizing unit is used for carrying out characteristic distribution optimization on the training working power-voltage waveform interaction characteristic diagram so as to obtain an optimized working power-voltage waveform interaction characteristic diagram;
the training classification unit is used for enabling the optimized working power-voltage waveform interaction characteristic diagram to pass through a classifier to obtain a classification loss function value; and
and the training unit is used for training the voltage waveform characteristic extractor using the spatial attention mechanism, the working power-voltage waveform interaction characteristic extractor based on the MetaNet module and the classifier by using the classification loss function value.
8. The charging parking space management method based on the Internet of things is characterized by comprising the following steps of:
acquiring working power values of the monitored charging pile at a plurality of preset time points in a preset time period;
acquiring a voltage signal of the monitored charging pile in the preset time period;
performing feature extraction and feature interaction on the working power values and the voltage signals at the plurality of preset time points to obtain a working power-voltage waveform interaction feature map; and
and determining whether to generate a charging pile fault early warning prompt or not based on the working power-voltage waveform interaction characteristic diagram.
9. The method for managing a charging parking space based on the internet of things according to claim 8, wherein performing feature extraction and feature interaction on the working power values and the voltage signals at the plurality of predetermined time points to obtain a working power-voltage waveform interaction feature map comprises:
arranging the working power values of the plurality of preset time points into working power time sequence input vectors according to the time dimension;
extracting voltage waveform characteristics of the voltage signals to obtain a voltage waveform characteristic diagram; and
and the working power time sequence input vector and the voltage waveform characteristic diagram are passed through a working power-voltage waveform interaction characteristic extractor based on a MetaNet module to obtain the working power-voltage waveform interaction characteristic diagram.
10. The internet of things-based charging parking space management method of claim 9, wherein extracting the voltage waveform characteristics of the voltage signal to obtain a voltage waveform characteristic diagram comprises:
the voltage waveform profile of the voltage signal is obtained by a voltage waveform profile extractor using a spatial attention mechanism.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311306239.7A CN117292576A (en) | 2023-10-10 | 2023-10-10 | Charging parking space management system and method based on Internet of Things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311306239.7A CN117292576A (en) | 2023-10-10 | 2023-10-10 | Charging parking space management system and method based on Internet of Things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117292576A true CN117292576A (en) | 2023-12-26 |
Family
ID=89240664
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311306239.7A Pending CN117292576A (en) | 2023-10-10 | 2023-10-10 | Charging parking space management system and method based on Internet of Things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117292576A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20080074441A (en) * | 2007-02-09 | 2008-08-13 | 김영일 | Method for diagnosis and analysis of electric power quality using artificial intelligence |
| CN106997683A (en) * | 2017-03-30 | 2017-08-01 | 南京理工大学 | Electric automobile charging pile parking stall is preengage and management system |
| CN108621849A (en) * | 2018-05-28 | 2018-10-09 | 南京交通职业技术学院 | Energy-saving new-energy automobile charging pile parking space state monitoring system and monitoring method |
| CN109215143A (en) * | 2017-07-07 | 2019-01-15 | 南京理工大学 | Electric car intelligent parking system and management method based on Internet of Things |
| CN111161564A (en) * | 2020-01-18 | 2020-05-15 | 湖南湘华优路交通科技有限公司 | Intelligent management method and system for parking spaces of charging piles |
| US20200380337A1 (en) * | 2019-05-30 | 2020-12-03 | Hongfujin Precision Electronics (Zhengzhou) Co., Ltd. | User terminal hardware detection system and method |
| CN116639010A (en) * | 2023-07-24 | 2023-08-25 | 江西驴宝宝通卡科技有限公司 | Intelligent control system and method for charging pile |
| CN116683648A (en) * | 2023-06-13 | 2023-09-01 | 浙江华耀电气科技有限公司 | Intelligent power distribution cabinet and control system thereof |
-
2023
- 2023-10-10 CN CN202311306239.7A patent/CN117292576A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20080074441A (en) * | 2007-02-09 | 2008-08-13 | 김영일 | Method for diagnosis and analysis of electric power quality using artificial intelligence |
| CN106997683A (en) * | 2017-03-30 | 2017-08-01 | 南京理工大学 | Electric automobile charging pile parking stall is preengage and management system |
| CN109215143A (en) * | 2017-07-07 | 2019-01-15 | 南京理工大学 | Electric car intelligent parking system and management method based on Internet of Things |
| CN108621849A (en) * | 2018-05-28 | 2018-10-09 | 南京交通职业技术学院 | Energy-saving new-energy automobile charging pile parking space state monitoring system and monitoring method |
| US20200380337A1 (en) * | 2019-05-30 | 2020-12-03 | Hongfujin Precision Electronics (Zhengzhou) Co., Ltd. | User terminal hardware detection system and method |
| CN111161564A (en) * | 2020-01-18 | 2020-05-15 | 湖南湘华优路交通科技有限公司 | Intelligent management method and system for parking spaces of charging piles |
| CN116683648A (en) * | 2023-06-13 | 2023-09-01 | 浙江华耀电气科技有限公司 | Intelligent power distribution cabinet and control system thereof |
| CN116639010A (en) * | 2023-07-24 | 2023-08-25 | 江西驴宝宝通卡科技有限公司 | Intelligent control system and method for charging pile |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20180120384A1 (en) | Tester for equipment, apparatus or component with distributed processing function | |
| CN110239391A (en) | A kind of charging method and charging pile system suitable for electric car | |
| CN111060838B (en) | Medical electronic equipment switching power supply fault diagnosis method based on multi-dimensional feature fusion | |
| CN104166099A (en) | Battery testing system and method | |
| CN113682184B (en) | Charging detection method, device and system for electric automobile | |
| CN107680193A (en) | A kind of mobile patrolling and checking management system and its application method | |
| KR102126584B1 (en) | Charging apparatus, operation method of charging apparatus, charging station control server | |
| CN106093505A (en) | Intelligent electric meter | |
| CN205881158U (en) | Terminal test equipment of power consumption field usage | |
| CN113472079A (en) | Power distribution station operation and maintenance monitoring cloud robot system, background processing and operation task method | |
| CN115315698A (en) | System and method for detecting abnormal charging event | |
| CN117292576A (en) | Charging parking space management system and method based on Internet of Things | |
| CN113702837A (en) | Self-discharge abnormity detection method and device for power battery | |
| CN117454231A (en) | Subway direct current protection test system applying intelligent simulation technology | |
| CN109752664A (en) | A kind of charging detects the method and application of battery core internal resistance in battery pack | |
| CN116317171B (en) | Electric quantity and non-electric quantity internet of things monitoring device | |
| CN206187050U (en) | Detection apparatus for rail vehicle system failure | |
| CN112307647A (en) | Charging pile communication interruption testing method and device, storage medium and processor | |
| CN111308259A (en) | Portable evaluation device based on electric automobile fills electric pile reliability data | |
| CN1632783A (en) | Actual measurement based load modeling system | |
| CN115063921A (en) | Construction site intelligent gate system and construction method | |
| CN114067472A (en) | Substation arrival authorization management system and method | |
| CN114660405A (en) | Method for rapidly studying and judging fault points of power distribution network based on 5G communication | |
| CN110091505B (en) | Intelligent control system based on 3D printer | |
| KR20120013004A (en) | System for managing electric power plant and method thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |