Charging pile demand statistical system and method

Abstract

The invention discloses a charging pile demand statistical system and method, a new energy vehicle is identified through a video acquisition module integrated in a charging pile body, and real-time statistics and dynamic analysis of charging demands are realized in combination with state sensor data. The system adopts a framework combining edge computing and cloud analysis, supports the functions of deployment optimization, fault early warning, commercial insight and the like, and has the characteristics of low cost and high reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and specifically refers to a charging pile demand statistics system and method. Background Art

[0002] Currently, most operators only estimate the demand for charging piles based on the density of the flow of people and the degree of commercial prosperity, and do not collect the demand for charging piles to deploy charging piles. However, the charging demand is affected by various factors, and the actual charging demand is difficult to evaluate in advance. Moreover, the charging demand may change over time and is not fixed, so it cannot reflect the real-time actual charging demand.

[0003] Currently, the deployment of charging piles mainly relies on static prediction models (such as CN202210814307.X), but the actual demand is significantly affected by dynamic factors such as time periods and vehicle type distributions. The existing technologies have the following defects: relying on manual experience judgment and lacking real-time data support; not considering the correlation between the status of the charging piles and the demand; unable to detect false demands caused by charging pile failures. Summary of the Invention

[0004] The present invention aims to at least solve one of the technical problems existing in the prior art. For this reason, an object of the present invention is to provide a charging pile demand statistics system and method with low cost and high real-time performance, so as to solve the problem that the traditional deployment method is out of touch with the actual demand.

[0005] To solve the above technical problems, the technical solution provided by the present invention is: a charging pile demand statistics method, including the following steps:

[0006] S1. Video acquisition: Collect vehicle video information within the coverage area through a camera on the top of the charging pile. The coverage area is a rectangular area with a horizontal length of 15 meters and a width of 8 meters;

[0007] S2. Target recognition: Use a video recognition module to process the video information, and determine the target vehicle by recognizing new energy vehicle license plates with a green background and an aspect ratio of 24:7;

[0008] S3. Data statistics: Store the target vehicle data in a local database, and the database only retains the most recent 500M data;

[0009] S4. Data reporting: Regularly report the summary data to the operator's background at 0:00 every day, and trigger an abnormal report when the number of target vehicles exceeds the threshold;

[0010] S5. Demand analysis: The operator's background generates an optimized charging pile deployment plan based on the target vehicle density, charging pile utilization rate, and status parameters.

[0011] Preferably, the target recognition step further includes:

[0012] a. Extract the license plate target area and confirm the new energy license plate through character rule verification;

[0013] b. Combine the vehicle's low-speed driving characteristics to assist in judging the charging demand.

[0014] Preferably, the state parameters include: the current charging state of the charging pile, the remaining power, the parking space occupancy situation, the fault state, and the energy replenishment state.

[0015] A charging pile demand statistics system, including:

[0016] Video acquisition module: A camera installed on top of the charging pile, covering a horizontal area of 15 meters × 8 meters;

[0017] Video recognition module: A license plate recognition unit based on deep learning algorithms, used for new energy license plate detection;

[0018] Data processing module: Includes a local database and a data statistics unit, supporting 500M data rolling storage;

[0019] Communication module: Supports scheduled reporting, exception reporting, and original data query response;

[0020] Background analysis module: Generates optimization solutions based on the target vehicle density model, charging pile utilization model, and fault detection model.

[0021] Preferably, the background analysis module further includes:

[0022] Deployment decision-making unit: When the target vehicle density > threshold, it is recommended to increase the charging piles; when the utilization rate < threshold, it is recommended to remove them;

[0023] Fault diagnosis unit: Triggers a fault warning when the charging piles in the high-density area have zero usage for 2 consecutive hours.

[0024] After adopting the above structure, the present invention has the following advantages:

[0025] Cost optimization: Reuse the monitoring equipment of the charging pile body, reducing additional hardware investment;

[0026] Real-time response: Minute-level data update, supporting dynamic adjustment of the deployment plan;

[0027] Multi-dimensional analysis: In addition to demand statistics, extended functions such as fault warning and user behavior analysis can be synchronously realized.

[0028] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the above-described illustrative aspects, embodiments, and features, through reference to the accompanying drawings and the following detailed description, further aspects, embodiments, and features of the present invention will be readily apparent. Brief Description of the Drawings

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0030] Figure 1 is the system block diagram of the present invention.

[0031] Figure 2 is the horizontal schematic diagram of the installation position of the present invention.

[0032] Figure 3 is the top view schematic diagram of the installation position of the present invention. Detailed Description of the Embodiments

[0033] The following details the embodiments of the present application. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present application and should not be construed as a limitation of the present application.

[0034] In the description of the present application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0035] The following further details the present invention in combination with the full text.

[0036] Combined with Figures 1 - 3 , when a vehicle user needs to charge, the vehicle will approach the charging pile and look for an available charging pile. At this time, the vehicle will be in a low-speed driving state. The camera recognizes the license plate information of the vehicle. If it is determined to be a new energy vehicle, the vehicle passing by near the charging pile is considered the target charging vehicle.

[0037] Install a camera on the charging pile. The camera identifies the vehicles passing by the charging pile and feeds the video information back to the video recognition module of the charging pile. The video recognition module determines the target vehicles based on the collected video information of the vehicles. When the density of target vehicles reaches a certain threshold, it is considered that more charging piles are needed here, and a suggestion is sent to the operator's background. The operator needs to re-evaluate the charging demand around this charging pile.

[0038] This solution optimizes the deployment of charging piles by automatically counting, analyzing, and reporting the charging demand through the charging piles. Therefore, this solution is divided into a video acquisition module, a video recognition module, a data statistics module, a data reporting module, and a data analysis module. Among them, the video acquisition module, the video recognition module, the data statistics module, and the data reporting module are implemented on the charging pile, and the data analysis module is implemented in the operator's background and comprehensively evaluated by professionals.

[0039] (1) Video acquisition module:

[0040] The charging pile uses a monitoring camera to identify the target vehicles, which are the users with charging needs. As shown in the figure above, we install a camera on the top of the charging pile. The identification angle range of the camera is shown in the figure above: on the horizontal plane, it is 12 meters long and 8 meters wide, so the horizontal length is 15 meters; the height of the charging pile is about 2 meters.

[0041] When an object passes by, the monitoring camera transmits the sampled video signal obtained to the video processing module of the charging pile.

[0042] (2) Video recognition module:

[0043] The video recognition module extracts the target area of the license plate based on the collected vehicle video information and identifies the target area. If the processor determines that the target area is a license plate with a green background and the first letter is a Chinese character containing 7 letters or numbers (or the collected target area is a license plate with a green background and an aspect ratio of 24:7), it is a new energy license plate, and the vehicle with the new energy license plate belongs to the target vehicle. The video recognition module sends the number of target vehicles obtained after processing the collected video to the data statistics module.

[0044] (3) Data statistics module:

[0045] The data statistics module stores all the received target vehicle data in the database; the stored data fields include: time (accurate to seconds), the number of target vehicles, charging pile ID, current charging status of the charging pile, remaining power of the charging pile, whether the charging pile is available, charging pile parking space occupancy, whether the charging pile is faulty, charging pile energy replenishment status, charging pile location, charging pile station number.

[0046] Due to the storage space limitation of the charging pile, the database only stores the latest 500M data and deletes the old data;

[0047] At 0:00 every night, the data statistics module will execute the statistical program and summarize and send the data of the day to the data reporting module.

[0048] (4) Data reporting module:

[0049] The data reporting module is divided into two types: regular reporting and special reporting;

[0050] After the data statistics module completes the daily data statistics, the data reporting module will send the statistical results to the background of the charging pile operator;

[0051] If the data reporting module finds that the number of target vehicles is abnormally large during a certain period, it will separately send an alarm message to the operator's background;

[0052] If the operator's background needs to obtain the original data, it will send a message to the data reporting module, and the data reporting module will send the original data within the requested time period to the operator's background; it can also query and report the corresponding data according to the specific requests of the operator's background;

[0053] (5) Data analysis module:

[0054] The data analysis module includes the operator's background, the analysis system, and the analyst; each operator can establish different data analysis modules according to its own situation.

[0055] The data analysis module analyzes whether additional charging piles are needed near a charging pile based on the number of target vehicles near each charging pile;

[0056] The data analysis module can also judge whether a charging pile needs to be reduced near a charging pile based on the charging utilization of the charging pile;

[0057] The data analysis module can also judge whether a charging pile is faulty based on the charging status of the charging pile and the density of target vehicles; if the density of target vehicles near a charging pile is very high but the charging pile is not in use, it can be suspected that the charging pile may be damaged;

[0058] The data analysis module can also comprehensively consider the situation of multiple charging piles and multiple charging stations, and use big data analysis methods to analyze the charging pile demand and user situation, and explore business needs.

[0059] The above description of the present invention and its implementation manners is not restrictive. What is shown throughout the text is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. In summary, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, design similar structural manners and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. A charging pile demand statistics method, characterized in that: The following steps are involved: S1. Video acquisition: The camera on the top of the charging pile is used to collect the vehicle video information in the coverage area, which is a rectangular area with a horizontal length of 15 meters and a width of 8 meters; S2, target recognition: using the video recognition module to process the video information, and determine the target vehicle by identifying the new energy vehicle license plate with a green background and an aspect ratio of 24:7; S3, data statistics: the target vehicle data is stored in a local database, and the database only retains the latest 500M data; S4. Data reporting: Report the aggregated data to the operator backend at 0:00 every day. When the number of target vehicles exceeds the threshold, an abnormal report is triggered; S5. Demand analysis: The operator backend generates a charging pile deployment optimization plan based on the target vehicle density, charging pile utilization rate and status parameters.

2. A charging pile demand statistics method according to claim 1, characterized in that: The target identification step further comprises: a. Extract the target area of ​​the license plate and verify the new energy license plate through character rules; b. Combine the vehicle's low-speed driving characteristics to assist in determining charging needs.

3. A charging pile demand statistics method according to claim 1, characterized in that: The status parameters include: the current charging status of the charging pile, the remaining power, the parking space occupancy, the fault status and the charging status.

4. A charging pile demand statistics system, characterized in that: include: Video acquisition module: A camera installed on the top of the charging pile, covering a horizontal area of ​​15 meters by 8 meters; Video recognition module: a license plate recognition unit based on deep learning algorithm, used for new energy vehicle license plate detection; Data processing module: includes local database and data statistics unit, supports 500M data rolling storage; Communication module: supports scheduled reporting, abnormal reporting and raw data query response; Backend analysis module: Generates optimization solutions based on the target vehicle density model, charging pile utilization model and fault detection model.

5. A charging pile demand statistics system according to claim 4, characterized in that: The background analysis module further includes: Deployment decision unit: when the target vehicle density is greater than the threshold, it is recommended to add charging piles; when the utilization rate is less than the threshold, it is recommended to remove them; Fault diagnosis unit: A fault warning is triggered when a charging pile in a high-density area has not been used for 2 consecutive hours.

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