Charging parking space management system and method based on Internet of Things

Abstract

The present invention discloses a charging parking space management system and method based on the Internet of Things, which comprises: a background server, a control terminal, a charging pile installed in each parking space, a geomagnetic sensor and a parking space lock installed in each parking space; wherein the control terminal is used to receive and transmit a parking space lock control instruction to the background server, and the charging pile, the geomagnetic sensor and the parking space lock are communicatively connected to the background server. In this way, the parking space lock can be locked by default and the control terminal can unlock the parking space, so that the charging pile parking space cannot be occupied at will.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to a charging parking space management system and method based on the Internet of Things. Background Art

[0002] In recent years, new energy vehicles have become more and more popular. Charging pile parking spaces are used to charge electric vehicles. In the related art charging pile parking spaces, charging piles are generally installed in open parking spaces. Charging pile parking spaces are generally scattered, and each point is often small in number, making it difficult to arrange dedicated personnel to be on duty.

[0003] The existing charging pile parking spaces lack effective management measures, and are easily occupied by non-charging vehicles, resulting in the inability to use the charging piles normally. At the same time, charging vehicles are often not moved in time after charging is completed, and it is common for charging pile parking spaces to be occupied at will.

[0004] Therefore, a charging parking space management solution is expected. Summary of the invention

[0005] The embodiment of the present invention provides a charging parking space management system and method based on the Internet of Things, which includes: a background server, a control terminal, a charging pile installed in each parking space, a geomagnetic sensor and a parking lock installed in each parking space; wherein the control terminal is used to receive and transmit parking lock control instructions to the background server, and the charging pile, the geomagnetic sensor and the parking lock are communicatively connected to the background server. In this way, the parking space can be locked by default through the parking lock and the control terminal unlocks the parking space, so that the charging pile parking space cannot be occupied at will.

[0006] The embodiment of the present invention also provides a charging parking space management system based on the Internet of Things, which includes:

[0007] A backend server, a control terminal, a charging pile installed in each parking space, and a geomagnetic sensor and a parking lock installed in each parking space;

[0008] Wherein, the control terminal is used to receive and transmit the parking lock control instruction to the background server, and the charging pile, the geomagnetic sensor and the parking lock are communicatively connected to the background server.

[0009] The embodiment of the present invention also provides a charging parking space management method based on the Internet of Things, which includes:

[0010] Obtaining the working power values ​​of the monitored charging pile at multiple predetermined time points within a predetermined time period;

[0011] Acquiring a voltage signal of the monitored charging pile within the predetermined time period;

[0012] Performing feature extraction and feature interaction on the working power values ​​and the voltage signal at the plurality of predetermined time points to obtain a working power-voltage waveform interaction feature diagram; and

[0013] Based on the working power-voltage waveform interaction characteristic diagram, determine whether to generate a charging pile fault warning prompt. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0015] Figure 1 The present invention provides a schematic diagram of the structure of a charging parking space management system based on the Internet of Things.

[0016] Figure 2 The present invention provides a block diagram of a charging pile fault warning module in a charging parking space management system based on the Internet of Things in an embodiment of the present invention.

[0017] Figure 3 The present invention provides a flow chart of a charging parking space management method based on the Internet of Things in an embodiment of the present invention.

[0018] Figure 4 A schematic diagram of the system architecture of a charging parking space management method based on the Internet of Things provided in an embodiment of the present invention.

[0019] Figure 5 This 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

[0020] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0021] Unless otherwise specified, all technical and scientific terms used in the embodiments of the present application have the same meaning as those commonly understood by those skilled in the art of the present application. The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit the scope of this application.

[0022] In the embodiments of the present application, it should be noted that, unless otherwise specified and limited, the term "connection" should be understood in a broad sense. For example, it can be an electrical connection or a connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0023] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that the specific order or sequence of "first\second\third" can be interchanged where permitted. It should be understood that the objects distinguished by "first\second\third" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0024] Charging pile parking spaces are used to charge electric vehicles. In the related art, charging piles are generally installed in open parking spaces. Charging pile parking spaces are generally scattered, and there are often not many of them at each point, making it difficult to arrange for dedicated personnel to be on duty. Existing charging pile parking spaces lack effective management measures. Therefore, charging pile parking spaces are easily occupied by non-charging vehicles, resulting in the inability to use charging piles normally. At the same time, charging vehicles often fail to move their vehicles in time after charging is completed, and it is common for charging pile parking spaces to be occupied at will.

[0025] Charging pile parking spaces are parking spaces dedicated to providing charging services for electric vehicles. With the popularity of electric vehicles, the demand for charging pile parking spaces is also increasing. The design and layout of charging pile parking spaces need to consider the convenience of installation and use of charging equipment, as well as the convenience and safety of user charging.

[0026] Charging piles are generally installed on one side or in the center of the parking space to facilitate the connection of electric vehicles to charging equipment. The location of the charging piles should be reasonable, convenient for users to connect and disconnect the charger, and not hinder the parking of other vehicles. Charging pile parking spaces are generally scattered to meet the charging needs of different locations. The layout of charging piles needs to be planned according to the specific parking lot or parking area, taking into account factors such as the number of parking spaces, the number of charging piles and power requirements. In order to facilitate users to find charging pile parking spaces, charging piles should be clearly marked, such as setting charging pile signs, marking the number or logo of charging pile parking spaces, etc., so that users can quickly find charging pile parking spaces and perform charging operations. In order to prevent charging piles from being occupied or damaged by non-charging vehicles, some measures can be taken to protect them. For example, set up dedicated charging pile parking spaces, limit only electric vehicles to park in the parking spaces, and add physical barriers or protective facilities to prevent non-charging vehicles from parking or colliding. In order to improve the management efficiency and service quality of charging pile parking spaces, a charging pile management system can be used, which can remotely monitor and control the status, charging progress and charging data of charging piles, provide appointment and reservation functions, as well as fault warning and remote maintenance functions.

[0027] There are some problems in the existing management of charging pile parking spaces, such as the charging pile parking spaces being occupied by non-charging vehicles and the vehicles not being moved in time after charging is completed. These problems affect the normal use and charging efficiency of the charging piles and effective management measures need to be taken to solve them.

[0028] Use devices such as sensors or cameras to monitor the occupancy of charging pile parking spaces. These devices can detect in real time whether non-charging vehicles are parked in the parking spaces and transmit the data to the management system. When a non-charging vehicle is detected occupying a charging pile parking space, the system can send an alarm to notify relevant personnel to handle it. Through license plate recognition or other identity authentication technologies, vehicles parked in charging pile parking spaces are distinguished from charging vehicles. Only certified charging vehicles can use charging piles, and non-charging vehicles need to move out of the parking spaces in time. This can avoid non-charging vehicles being stranded in charging pile parking spaces and ensure the availability of charging piles. Provide users with the functions of booking and reserving charging pile parking spaces, so that users can arrange charging plans in advance and ensure the availability of parking spaces. The reservation system can be operated through mobile applications or websites, which is convenient for users to make reservations and management. The reservation function can reduce the waiting time of charging vehicles and improve the utilization efficiency of charging piles. During the charging process, the system can send a reminder to the user that charging is completed. After receiving the reminder, the user should move the vehicle out of the charging pile parking space in time so that other vehicles can use it. At the same time, the system can record the charging completion time and vehicle removal time for subsequent management and statistical analysis. Through publicity and education activities, users can be more aware of and understand charging pile parking spaces. Users should be informed of the importance of charging pile parking spaces and the need to move their cars in a timely manner. The charging pile parking space management plan can provide user guidance, inform users how to use charging pile parking spaces correctly, and provide relevant usage specifications and precautions.

[0029] Through the above management measures, the problem of non-charging vehicles occupying the charging pile parking spaces and the failure to move the vehicles in time after charging can be effectively solved, the availability and charging efficiency of the charging pile parking spaces can be improved, and better charging services can be provided for electric vehicle users. In one embodiment of the present invention, Figure 1 FIG. 1 is a schematic diagram of a charging parking space management system based on the Internet of Things provided in an embodiment of the present invention. Figure 1 As shown, the charging parking space management system 100 based on the Internet of Things according to an embodiment of the present invention includes: a background server 1, a control terminal 2, a charging pile 4 installed in each parking space 3, and a geomagnetic sensor 5 and a parking lock 6 installed in each parking space 3; wherein, the control terminal 2 is used to receive and transmit the parking lock control instruction to the background server 1, and the charging pile 4, the geomagnetic sensor 5 and the parking lock 6 can be communicatively connected to the background server 1.

[0030] During the operation of the charging parking space management system based on the Internet of Things, the parking lock 6 of the parking space 3 is locked by default, so that the parking space 3 cannot be driven into by vehicles, and it is prevented from being occupied by vehicles at will. The user can open the parking lock 6 only after unlocking it through the control terminal, so that the vehicle can drive into the parking space 3 and use the charging pile 4 for charging; when the vehicle leaves, the parking lock 6 is closed so that the vehicle that has not been unlocked by the control terminal cannot park in the parking space; thus, the parking lock 6 is locked 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 lock 6 are respectively connected to the background server through network signals to realize the background to control each device separately.

[0031] The technical route of this application is: formulate and design the technical plan for the management of IoT charging parking spaces - research and development of real-time monitoring technology for parking space status - research and development of technology for limiting occupancy behavior - research and development of charging status feedback interaction technology - preparation and debugging - simulated overcharging test - optimization of the IoT charging parking space management system.

[0032] This can produce beneficial effects including:

[0033] 1. Effectively improve the utilization rate of charging parking spaces;

[0034] 2. Make the charging status better presented to users;

[0035] 3. Compatible with most electric vehicle models on the market, which is conducive to the upgrading and transformation of charging stations.

[0036] In particular, considering that charging piles are important facilities for charging new energy vehicles, they may fail to work properly due to various reasons (such as equipment aging, power supply problems, communication failures, etc.). This will affect the user's charging experience and reduce the availability of charging piles. In response to this technical problem, the technical concept of this application is to design a charging pile fault warning module in the background server to monitor and detect the fault of the charging pile, so as to take maintenance measures in time to ensure the normal operation of the charging pile.

[0037] Among them, in the technical solution of the present application, the charging pile fault warning module realizes the charging pile fault warning by utilizing artificial intelligence technology based on deep learning and combining the power data and voltage signal data of the charging pile. It should be understood that the working power value and voltage signal of the charging pile can characterize and measure the operating status of the charging pile. Specifically, the real-time working conditions of the charging pile, including charging speed, power conversion efficiency, etc., can be understood through the working power value. If the charging pile fails, the working power value may be abnormal or decrease. In addition, the voltage signal of the charging pile can reflect the power supply and power supply stability of the charging pile. Abnormal voltage signals may indicate power supply problems, grid connection failures, or problems with the charging pile itself. This information is an important source of data for determining whether a charging pile has failed.

[0038] Figure 2 FIG. 1 is a block diagram of a charging pile fault warning module in a charging parking space management system based on the Internet of Things provided in an embodiment of the present invention. Figure 2 As shown, the background server also includes a charging pile fault warning module 7; wherein the charging pile fault warning module 7 includes: a power value acquisition unit 110, used to obtain the working power value of the monitored charging pile at multiple predetermined time points within a predetermined time period; a voltage signal acquisition unit 120, used to obtain the voltage signal of the monitored charging pile within the predetermined time period; a feature extraction and interaction unit 130, used to perform feature extraction and feature interaction on the working power values ​​at the multiple predetermined time points and the voltage signal to obtain a working power-voltage waveform interaction feature diagram; and a fault warning prompt unit 140, used to determine whether to generate a charging pile fault warning prompt based on the working power-voltage waveform interaction feature diagram.

[0039] In the power value acquisition unit 110, the accuracy and real-time performance of the power value acquisition are ensured. Select a suitable sensor or device to obtain the power value of the charging pile, and ensure its accuracy and precision, and ensure the real-time collection and update of the power value for subsequent feature extraction and fault warning analysis. Accurately obtaining the working power value of the charging pile can provide real-time charging status information, providing basic data for subsequent feature extraction and fault warning.

[0040] In the voltage signal acquisition unit 120, the voltage signal of the monitored charging pile within a predetermined time period is acquired. Use a suitable sensor to acquire the voltage signal of the charging pile to ensure the accuracy and stability of the sensor. Ensure accurate acquisition and processing of the voltage signal for subsequent feature extraction and interactive analysis. Acquiring the voltage signal of the charging pile can provide power quality information during the charging process, which is of great significance for fault warning and charging pile operation status monitoring.

[0041] In the feature extraction and interaction unit 130, feature extraction and feature interaction are performed on the working power values ​​and voltage signals at multiple predetermined time points to obtain a working power-voltage waveform interaction feature diagram. Select a suitable feature extraction algorithm to extract meaningful features from the working power value and voltage signal, such as spectrum features, time domain features, etc. The extracted power features and voltage features are interacted, and a mathematical model or statistical method can be used to obtain a working power-voltage waveform interaction feature diagram. Through feature extraction and interaction, the key features of the working state of the charging pile can be extracted, providing useful information for fault warning and anomaly detection.

[0042] In the fault warning prompt unit 140, it is determined whether to generate a charging pile fault warning prompt based on the working power-voltage waveform interactive characteristic diagram. Establish reasonable fault judgment criteria, and judge whether there is a charging pile fault based on the abnormal situation of the working power-voltage waveform interactive characteristic diagram. Determine the prompt method of the fault warning, such as sound, text prompt or alarm system, so as to promptly notify relevant personnel for processing. Through the fault warning prompt unit, the fault of the charging pile can be discovered and handled in time, and the reliability and safety of the charging equipment can be improved.

[0043] Based on this, in the technical solution of the present application, the encoding process of the charging pile fault warning module includes: first, obtaining the working power value of the monitored charging pile at multiple predetermined time points within a predetermined time period; at the same time, obtaining the voltage signal of the monitored charging pile within the predetermined time period.

[0044] In a specific embodiment of the present application, the feature extraction and interaction unit includes: a vector arrangement subunit, used to arrange the working power values ​​of the multiple predetermined time points according to the time dimension into a working power timing input vector; a voltage waveform feature extraction subunit, used to extract the voltage waveform features of the voltage signal to obtain a voltage waveform feature graph; and, an interactive feature extraction subunit, used to pass the working power timing input vector and the voltage waveform feature graph through a working power-voltage waveform interactive feature extractor based on a MetaNet module to obtain the working power-voltage waveform interactive feature graph.

[0045] The vector arrangement subunit arranges the working power values ​​at multiple predetermined time points according to the time dimension as a working power time series input vector. Arranging the power values ​​at multiple time points in chronological order can provide clear time series data, which is convenient for subsequent feature extraction and analysis. By analyzing the time series data of working power, the usage pattern and change trend of charging piles can be revealed, providing useful reference for fault warning and management decision-making.

[0046] The voltage waveform feature extraction subunit extracts the voltage waveform features of the voltage signal to obtain a voltage waveform feature diagram. By extracting the waveform features of the voltage signal, the power quality of the charging pile, such as voltage stability, waveform distortion, etc., can be evaluated to provide a reference for fault diagnosis and power system optimization. By analyzing the features of the voltage waveform, voltage anomalies or faults in the charging pile, such as voltage waveform distortion, voltage fluctuation, etc., can be detected and diagnosed.

[0047] The interactive feature extraction subunit uses the working power-voltage waveform interactive feature extractor based on the MetaNet module to interact the working power time series input vector and the voltage waveform feature map to obtain the working power-voltage waveform interactive feature map. By interacting the working power and voltage waveform features, richer and more accurate features can be extracted to better reflect the working status and characteristics of the charging pile. Interactive feature extraction can reveal the correlation between power and voltage, help improve the accuracy and sensitivity of fault warning, and promptly detect abnormal conditions in the charging pile.

[0048] Then, the working power values ​​at the multiple predetermined time points are arranged as working power time series input vectors according to the time dimension to convert the discrete time series distribution of the working power values ​​into a structured vector representation, so as to facilitate the subsequent model to read and identify; then, the waveform of the voltage signal is obtained by a voltage waveform feature extractor using a spatial attention mechanism to obtain a voltage waveform feature graph. Here, the waveform of the voltage signal reflects the voltage change of the charging pile and can show the curve of the voltage signal changing over time. This waveform can provide information about voltage stability, power supply fluctuations, grid connection quality, etc. The spatial attention mechanism is a method for focusing on and emphasizing specific areas or features. Using the spatial attention mechanism in the voltage waveform feature extractor enables the model to learn the key areas or features in the voltage signal waveform more accurately and specifically to obtain feature representations with higher representation capabilities.

[0049] In a specific embodiment of the present application, the voltage waveform feature extraction subunit is used to: obtain the voltage waveform feature graph by using a voltage waveform feature extractor using a spatial attention mechanism to obtain the voltage waveform feature graph.

[0050] The spatial attention mechanism is a commonly used attention mechanism in deep learning. It is used to weight different spatial positions of the input data in order to pay more attention to positions with important information. Applying the spatial attention mechanism in the voltage waveform feature extractor can improve the perception of important waveform areas in the voltage signal.

[0051] The basic idea of ​​the spatial attention mechanism is to obtain a set of weights through learning, which are used to weight different positions of the input data. These weights can be represented as a matrix of the same size as the input data, with each element corresponding to the weight value of a position. In the voltage waveform feature extractor, these weights can be represented as the degree of importance of each position in the voltage waveform graph. By applying the spatial attention mechanism, the voltage waveform feature extractor can pay more attention to the waveform area with important information in the voltage signal, and improve the expressiveness and discrimination ability of the features. This helps to improve the quality and accuracy of the voltage waveform features, and provide a more reliable foundation for subsequent fault warning and anomaly detection.

[0052] Furthermore, the working power timing input vector and the voltage waveform feature map are passed through a working power-voltage waveform interaction feature extractor based on a MetaNet module to obtain a working power-voltage waveform interaction feature map. That is, the charging pile working power timing change characteristics expressed by the working power timing input vector and the charging pile voltage timing change characteristics are interacted and fused, so that the working power-voltage waveform interaction feature map can express the correlation between the two. Among them, the MetaNet module uses one-dimensional timing data to assist in optimizing the visual feature information expressed in the feature map. In other words, the MetaNet module enables the one-dimensional timing data to interact directly with the visual features, directly controls the relevant characteristics of each feature channel, and helps the network focus on the specific parts of each feature channel.

[0053] In a specific embodiment of the present application, the interactive feature extraction subunit is used to: pass the working power timing input vector through a point convolution layer to obtain a first convolution feature vector; pass the first convolution feature vector through a corrected linear unit based on a ReLU function to obtain a first corrected convolution feature vector; pass the first corrected convolution feature vector through a point convolution layer to obtain a second convolution feature vector; pass the second convolution feature vector through a corrected linear unit based on a Sigmoid function to obtain a second corrected convolution feature vector; and, fuse the second corrected convolution feature vector with the voltage waveform feature graph to obtain the working power-voltage waveform interactive feature graph.

[0054] Then, the working power-voltage waveform interactive feature graph is passed through a classifier to obtain a classification result, and the classification result is used to indicate whether the charging pile fault warning prompt is generated. By using the working power-voltage waveform interactive feature graph as input and combining it with the classifier for classification, the abnormality and failure mode of the charging pile can be effectively captured, and the classifier can learn the characteristic differences between the normal and abnormal states of the charging pile, thereby improving the accuracy of the fault warning. By real-time monitoring of the interactive features of the working power and voltage waveforms, the classifier can quickly determine whether the charging pile has a fault. Timely generation of fault warning prompts can prompt operation and maintenance personnel to take corresponding measures to avoid further deterioration of the fault and ensure the reliable operation of the charging pile. Combining the working power-voltage waveform interactive feature graph with the classifier can realize the automatic processing of the charging pile fault warning, without manual intervention, the system can automatically identify and judge the fault state of the charging pile, and improve management efficiency and operation and maintenance effects. By timely generating charging pile fault warning prompts, the situation where users cannot use the charging pile normally due to faulty charging piles can be reduced. Users can understand the status of the charging pile in time, select other available charging piles, and improve the charging experience and convenience.

[0055] By combining the working power-voltage waveform interactive feature map with the classifier, the automatic processing of charging pile fault warning can be realized, the accuracy and real-time performance of fault warning can be improved, the user experience can be enhanced, and useful information and reference can be provided for the operation, maintenance and management of charging piles.

[0056] In a specific embodiment of the present application, the fault warning prompt unit is used to: pass the working power-voltage waveform interaction characteristic diagram through a classifier to obtain a classification result, and the classification result is used to indicate whether to generate a charging pile fault warning prompt.

[0057] In one embodiment of the present application, the IoT-based charging parking space management system also 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 the training module includes: a training data acquisition unit for acquiring training data, the training data including the training working power value of the monitored charging pile at multiple predetermined time points within a predetermined time period, the training voltage signal of the monitored charging pile within the predetermined time period, and whether to generate a true value of a charging pile fault warning prompt; a training vector arrangement unit for arranging the training working power values ​​of the multiple predetermined time points into a training working power timing input vector according to the time dimension; a training spatial attention unit for arranging the waveform diagram of the training voltage signal through the spatial attention mechanism A voltage waveform feature extractor is used to obtain a training voltage waveform feature graph; a training waveform interaction feature extraction unit is used to pass the training working power timing input vector and the training voltage waveform feature graph through the working power-voltage waveform interaction feature extractor based on the MetaNet module to obtain a training working power-voltage waveform interaction feature graph; a training feature distribution optimization unit is used to optimize the feature distribution of the training working power-voltage waveform interaction feature graph to obtain an optimized working power-voltage waveform interaction feature graph; a training classification unit is used to pass the optimized working power-voltage waveform interaction feature graph through a classifier to obtain a classification loss function value; and a training unit is used to train 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.

[0058] In the technical solution of the present application, each feature matrix of the training voltage waveform feature map is used to express the image semantic features enhanced by the spatial distribution of the waveform map of the training voltage signal, and each feature matrix of the training voltage waveform feature map follows the channel distribution of the voltage waveform feature extractor using the spatial attention mechanism. Therefore, when the training working power timing input vector and the training voltage waveform feature map pass through the working power-voltage waveform interaction feature extractor based on the MetaNet module, the channel distribution between each feature matrix of the training voltage waveform feature map is constrained based on the timing distribution of the working power value expressed by the training working power timing input vector, so that each feature matrix of the training working power-voltage waveform interaction feature map follows the timing distribution of the training working power value. In this way, the training working power-voltage waveform interaction feature map has a timing parameter-image semantic cross-correlation representation in multiple dimensions of time and space.

[0059] However, when the cross-correlation representation of the timing parameter-image semantics in the spatiotemporal multi-dimensionality of the training working power-voltage waveform interaction feature graph passes through the classifier, the difference in correlation accuracy of the correlation features across timing parameter-image semantics in different dimensions will affect the training effect of the training working power-voltage waveform interaction feature graph when it passes through the classifier. Therefore, during the training process, the applicant of the present application expands the training working power-voltage waveform interaction feature graph to obtain the training working power-voltage waveform interaction feature vector, for example, recorded as The feature accuracy alignment based on dimensional characterization and inverse recovery is specifically expressed as follows: the training working power-voltage waveform interactive feature vector obtained by expanding the training working power-voltage waveform interactive feature graph is aligned with the feature accuracy based on dimensional characterization and inverse recovery using the following optimization formula; wherein the optimization formula is: in, is the training working power-voltage waveform interaction feature vector obtained by expanding the training working power-voltage waveform interaction feature graph, is the training working power-voltage waveform interaction feature vector No. The eigenvalues ​​at the positions, Represents the training working power-voltage waveform interaction feature vector The zero norm of is the training working power-voltage waveform interaction feature vector The length of is the weight hyperparameter, It is the optimized working power-voltage waveform interaction feature vector obtained after the optimized working power-voltage waveform interaction feature diagram is expanded.

[0060] Here, in response to the accuracy contradiction between the dimensionality-based high-dimensional feature space encoding of timing parameters and image semantics and the multi-dimensional feature association editing, the feature accuracy alignment based on dimensional representation and inverse recovery is generated by treating the multi-dimensional feature association editing as the inverse embedding generation of the high-dimensional feature space encoding of timing parameters and image semantics, and by equipping the feature values ​​represented by the encoding with sparse distributed balancing of scale representation, and performing inverse recovery of the association details based on vector counting, so as to achieve adaptive alignment of accuracy differences in the training process, thereby improving the training effect of the training working power-voltage waveform interaction feature map when it is classified and regressed through a classifier.

[0061] In summary, the IoT-based charging parking space management system 100 based on the embodiment of the present invention is explained, and the charging pile fault warning is realized by using artificial intelligence technology based on deep learning and combining the power data and voltage signal data of the charging pile. It should be understood that the working power value and voltage signal of the charging pile can characterize and measure the operating status of the charging pile. Specifically, the real-time working conditions of the charging pile, including charging speed, power conversion efficiency, etc., can be understood through the working power value. If the charging pile fails, the working power value may be abnormal or decrease. In addition, the voltage signal of the charging pile can reflect the power supply and power supply stability of the charging pile. Abnormal voltage signals may indicate power supply problems, grid connection failures, or problems with the charging pile itself. This information is an important source of data for determining whether the charging pile has failed.

[0062] As described above, the charging parking space management system 100 based on the Internet of Things according to an embodiment of the present invention can be implemented in various terminal devices, such as a server for charging parking space management based on the Internet of Things. In one example, the charging parking space management system 100 based on the Internet of Things according to an embodiment of the present invention can be integrated into the terminal device as a software module and / or a hardware module. For example, the charging parking space management system 100 based on the Internet of Things can be a software module in the operating system of the terminal device, or it can be an application developed for the terminal device; of course, the charging parking space management system 100 based on the Internet of Things can also be one of the many hardware modules of the terminal device.

[0063] Alternatively, in another example, the IoT-based charging parking space management system 100 and the terminal device may also be separate devices, and the IoT-based charging parking space management system 100 may be connected to the terminal device via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0064] Figure 3 The present invention provides a flow chart of a charging parking space management method based on the Internet of Things in an embodiment of the present invention. Figure 4 Schematic diagram of the system architecture of a charging parking space management method based on the Internet of Things provided in an embodiment of the present invention. Figure 3 and Figure 4 As shown, a charging parking space management method based on the Internet of Things includes: 210, obtaining the working power value of the monitored charging pile at multiple predetermined time points within a predetermined time period; 220, obtaining the voltage signal of the monitored charging pile within the predetermined time period; 230, performing feature extraction and feature interaction on the working power values ​​of the multiple predetermined time points and the voltage signal to obtain a working power-voltage waveform interaction feature diagram; and, 240, determining whether to generate a charging pile fault warning prompt based on the working power-voltage waveform interaction feature diagram.

[0065] In the charging parking space management method based on the Internet of Things, feature extraction and feature interaction are performed on the working power values ​​at the multiple predetermined time points and the voltage signal to obtain a working power-voltage waveform interaction feature diagram, including: arranging the working power values ​​at the multiple predetermined time points into a working power timing input vector according to the time dimension; extracting the voltage waveform features of the voltage signal to obtain a voltage waveform feature diagram; and, passing the working power timing input vector and the voltage waveform feature diagram through a working power-voltage waveform interaction feature extractor based on a MetaNet module to obtain the working power-voltage waveform interaction feature diagram.

[0066] In the charging parking space management method based on the Internet of Things, the voltage waveform characteristics of the voltage signal are extracted to obtain a voltage waveform feature graph, including: the waveform graph of the voltage signal is obtained by a voltage waveform feature extractor using a spatial attention mechanism.

[0067] Those skilled in the art will appreciate that the specific operations of each step in the above-mentioned charging parking space management method based on the Internet of Things have been described in detail above. Figure 1 to Figure 2 The description of the IoT-based charging parking space management system has been introduced in detail, and therefore, its repeated description will be omitted.

[0068] Figure 5 FIG. 1 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. Figure 5 As shown, in this application scenario, first, the working power values ​​of the monitored charging pile at multiple predetermined time points within a predetermined time period are obtained (for example, Figure 5 C1 shown in FIG), and obtaining a voltage signal of the monitored charging pile within the predetermined time period (for example, Figure 5 Then, the obtained working power value and voltage signal are input to a server (for example, Figure 5 In the S) shown in FIG. 1 , 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 warning prompt.

[0069] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A charging parking space management system based on the Internet of Things, characterized in that: include: A backend server, a control terminal, a charging pile installed in each parking space, and a geomagnetic sensor and a parking lock installed in each parking space; Wherein, the control terminal is used to receive and transmit the parking lock control instruction to the background server, and the charging pile, the geomagnetic sensor and the parking lock are communicatively connected to the background server; Wherein, the background server also includes a charging pile fault warning module; Wherein, the charging pile fault warning module includes: A power value acquisition unit, used to acquire the working power value of the monitored charging pile at multiple predetermined time points within a predetermined time period; A voltage signal acquisition unit, used to acquire a voltage signal of the monitored charging pile within the predetermined time period; a feature extraction and interaction unit, configured to perform feature extraction and feature interaction on the working power values ​​and the voltage signal at the plurality of predetermined time points to obtain a working power-voltage waveform interaction feature diagram; and A fault warning prompt unit, used to determine whether to generate a charging pile fault warning prompt based on the working power-voltage waveform interaction characteristic diagram; Wherein, the feature extraction and interaction unit includes: A vector arrangement subunit, used for arranging the working power values ​​of the plurality of predetermined time points into a working power time series input vector according to a time dimension; a voltage waveform feature extraction subunit, configured to extract the voltage waveform feature of the voltage signal to obtain a voltage waveform feature graph; and An interactive feature extraction subunit, used for passing the working power time series input vector and the voltage waveform feature diagram through a working power-voltage waveform interactive feature extractor based on a MetaNet module to obtain the working power-voltage waveform interactive feature diagram; Wherein, the interactive feature extraction subunit is used to: Passing the working power time series input vector through a point convolution layer to obtain a first convolution feature vector; Passing the first convolution feature vector through a rectified linear unit based on a ReLU function to obtain a first rectified convolution feature vector; Passing the first modified convolutional feature vector through a point convolution layer to obtain a second convolutional feature vector; Passing the second convolution feature vector through a rectified linear unit based on a Sigmoid function to obtain a second rectified convolution feature vector; and Fusion of the second modified convolution feature vector and the voltage waveform feature graph to obtain the working power-voltage waveform interaction feature graph; Wherein, the fault warning prompt unit is used to: Passing the working power-voltage waveform interaction characteristic diagram through a classifier to obtain a classification result, wherein the classification result is used to indicate whether a charging pile fault warning prompt is generated; Among them, it also 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, the training module includes: A training data acquisition unit, used to acquire training data, wherein the training data includes training working power values ​​of the monitored charging pile at multiple predetermined time points within a predetermined time period, training voltage signals of the monitored charging pile within the predetermined time period, and a true value of whether to generate a charging pile fault warning prompt; A training vector arrangement unit, used for arranging the training working power values ​​of the plurality of predetermined time points into a training working power timing input vector according to a time dimension; A training spatial attention unit, used for passing the waveform diagram of the training voltage signal through the voltage waveform feature extractor using the spatial attention mechanism to obtain a training voltage waveform feature diagram; A training waveform interaction feature extraction unit, used for passing the training working power time series input vector and the training voltage waveform feature graph through the working power-voltage waveform interaction feature extractor based on the MetaNet module to obtain a training working power-voltage waveform interaction feature graph; A training feature distribution optimization unit, used for performing feature distribution optimization on the training working power-voltage waveform interaction feature diagram to obtain an optimized working power-voltage waveform interaction feature diagram; A training classification unit, used for passing the optimized working power-voltage waveform interaction feature map through a classifier to obtain a classification loss function value; and A training unit is used to train 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 with the classification loss function value.

2. The charging parking space management system based on the Internet of Things according to claim 1 is characterized in that: The voltage waveform feature extraction subunit is used to: The waveform graph of the voltage signal is extracted by a voltage waveform feature extractor using a spatial attention mechanism to obtain the voltage waveform feature graph.

3. A charging parking space management method based on the Internet of Things, characterized in that: include: Obtaining the working power values ​​of the monitored charging pile at multiple predetermined time points within a predetermined time period; Acquire a voltage signal of the monitored charging pile within the predetermined time period; Performing feature extraction and feature interaction on the working power values ​​and the voltage signal at the plurality of predetermined time points to obtain a working power-voltage waveform interaction feature diagram; as well as Based on the working power-voltage waveform interactive characteristic diagram, determining whether to generate a charging pile fault warning prompt; The method of performing feature extraction and feature interaction on the working power values ​​and the voltage signal at the plurality of predetermined time points to obtain a working power-voltage waveform interaction feature diagram includes: Arranging the working power values ​​of the plurality of predetermined time points into a working power time series input vector according to the time dimension; extracting a voltage waveform feature of the voltage signal to obtain a voltage waveform feature graph; and The working power time series input vector and the voltage waveform characteristic diagram are passed through a working power-voltage waveform interaction feature extractor based on a MetaNet module to obtain the working power-voltage waveform interaction characteristic diagram; The working power time series input vector and the voltage waveform feature diagram are passed through a working power-voltage waveform interaction feature extractor based on a MetaNet module to obtain the working power-voltage waveform interaction feature diagram, including: Passing the working power time series input vector through a point convolution layer to obtain a first convolution feature vector; Passing the first convolution feature vector through a rectified linear unit based on a ReLU function to obtain a first rectified convolution feature vector; Passing the first modified convolutional feature vector through a point convolution layer to obtain a second convolutional feature vector; Passing the second convolution feature vector through a rectified linear unit based on a Sigmoid function to obtain a second rectified convolution feature vector; and Fusion of the second modified convolution feature vector and the voltage waveform feature graph to obtain the working power-voltage waveform interaction feature graph; Wherein, based on the working power-voltage waveform interaction characteristic diagram, determining whether to generate a charging pile fault warning prompt includes: Passing the working power-voltage waveform interaction characteristic diagram through a classifier to obtain a classification result, wherein the classification result is used to indicate whether a charging pile fault warning prompt is generated; Among them, it also includes a training step of 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, the training step includes: Acquire training data, the training data including training working power values ​​of the monitored charging pile at multiple predetermined time points within a predetermined time period, training voltage signals of the monitored charging pile within the predetermined time period, and whether a real value of a charging pile fault warning prompt is generated; Arranging the training work power values ​​at the plurality of predetermined time points into a training work power time series input vector according to the time dimension; Passing the waveform graph of the training voltage signal through the voltage waveform feature extractor using the spatial attention mechanism to obtain a training voltage waveform feature graph; The training working power time series input vector and the training voltage waveform feature graph are passed through the working power-voltage waveform interaction feature extractor based on the MetaNet module to obtain a training working power-voltage waveform interaction feature graph; Performing feature distribution optimization on the training working power-voltage waveform interaction feature graph to obtain an optimized working power-voltage waveform interaction feature graph; Passing the optimized working power-voltage waveform interaction characteristic diagram through a classifier to obtain a classification loss function value; and 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 are trained using the classification loss function value.

4. The method for managing charging parking spaces based on the Internet of Things according to claim 3 is characterized in that: Extracting a voltage waveform feature of the voltage signal to obtain a voltage waveform feature graph includes: The waveform graph of the voltage signal is extracted by a voltage waveform feature extractor using a spatial attention mechanism to obtain the voltage waveform feature graph.

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