Electric vehicle remote monitoring management method based on internet of things

Abstract

The application discloses a remote monitoring management method for electric vehicles based on the Internet of Things, which comprises the following steps: drawing a full-load parking situation of all electric vehicle parking areas in an office building; based on the full-load parking situation, simulating a situation that an electric vehicle at each parking point is first taken by a vehicle, and analyzing a time required for taking the vehicle; dividing all electric vehicle parking areas in the office building into three level areas, i.e., a convenient taking area, a regular taking area and a difficult taking area, according to the time values analyzed by each parking point, wherein the time required for taking a vehicle in the three level areas is sorted as the convenient taking area < the regular taking area < the difficult taking area; and establishing an electric vehicle parking record database, capturing an electric vehicle license plate through a high-definition camera unit arranged at an entrance and exit of an electric vehicle garage, and capturing dynamic data of the electric vehicle entering and exiting the electric vehicle garage; the application has the characteristics of high practicability and high intelligence.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, specifically to a method for remote monitoring and management of electric vehicles based on IoT. Background Technology

[0002] With the acceleration of urbanization, electric vehicles, as an environmentally friendly and convenient means of transportation, have become increasingly popular among citizens. However, the rapid increase in the number of electric vehicles has also brought about many problems in parking management. Traditional electric vehicle parking management methods mostly rely on manual guidance or simple parking space division. This management method is not only inefficient, but also prone to parking chaos and vehicles being moved arbitrarily, causing inconvenience to car owners and increasing management costs.

[0003] Especially in densely populated areas such as office buildings, the demand for electric vehicle parking is high, while parking space is limited. How to achieve orderly parking and efficient retrieval of electric vehicles within limited parking space has become an urgent problem to be solved. Therefore, developing an IoT-based remote monitoring and management method for electric vehicles to achieve intelligent management of electric vehicle parking areas is of great significance for improving parking efficiency, optimizing parking resource allocation, and reducing management costs. Summary of the Invention

[0004] The purpose of this invention is to provide a remote monitoring and management method for electric vehicles based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a remote monitoring and management method for electric vehicles based on the Internet of Things, comprising:

[0006] Step S1: Draw the full-load parking situation of all electric vehicle parking areas in the office building. Based on the full-load parking situation, simulate the situation where the electric vehicle at each parking point is the first vehicle to be picked up, and analyze the time required to pick up the vehicle.

[0007] Step S2: Based on the time values ​​analyzed at each parking location, all electric vehicle parking areas in the office building are divided into three levels: convenient retrieval area, regular retrieval area, and difficult retrieval area. The time required to retrieve vehicles in the three levels is ranked as follows: convenient retrieval area < regular retrieval area < difficult retrieval area.

[0008] Step S3: Establish an electric vehicle parking record database by setting up high-definition camera units at the entrance and exit of the electric vehicle garage to capture the license plate numbers of electric vehicles and capture and record dynamic data of electric vehicles entering and leaving the electric vehicle garage.

[0009] Step S4: Based on the dynamic data in the database, predict the trend of the proportion of the number of electric vehicles parked in the parking area to the total number of parking spots, and then predict the retrieval time of each electric vehicle entering the electric vehicle garage and the accuracy of the retrieval time.

[0010] Step S5: Monitor electric vehicles entering the electric vehicle garage. Based on the prediction results of step S4, match the best parking space for the target electric vehicle and park the electric vehicle at the target parking point according to the guide indicator. The target parking point is monitored to ensure that the electric vehicle is parked in a standardized manner through light and shadow capture.

[0011] Step S6: When the target electric vehicle enters the electric vehicle garage during a time period in which the predicted proportion of the number of electric vehicles parked in the parking area to the total number of parking spots is on the rise, the measured proportions of the number of vehicles parked in the "easy-to-access area", "normal access area", and "difficult-to-access area" to the total number of parking spots in the corresponding areas are obtained in real time, as well as the proportion of the number of vehicles parked in the parking area to the total number of parking spots. The proportions of each level of area are compared with the proportions of the total parking area to perform parking space replenishment processing.

[0012] According to the above technical solution, the specific method for analyzing the time required to retrieve the vehicle in step S1 is as follows:

[0013] Step S11: Determine the parking location of the target vehicle for analysis;

[0014] Step S12: Using the parking spot as the center, mark the nearest public parking passage in the drawing of the office building electric vehicle parking area map, and search for the number a of other drawn parking spots that the target vehicle must pass through between the parking spot and the nearest public parking passage.

[0015] Step S13: Using the parking point as the center, mark the nearest electric vehicle garage entrance / exit on the drawing map of the office building electric vehicle parking area, connect the parking point to the nearest electric vehicle garage entrance / exit, and measure the proportionally enlarged distance l according to the drawing scale.

[0016] Step S14: Using the formula The calculation yields the time T required to retrieve a vehicle at each simulated parking location when the electric vehicle is the first to be retrieved; where t and v are system-preset standard values, and a·t represents the time required to move other vehicles when retrieving the target vehicle. This indicates the time required from entering the electric vehicle garage to arriving next to the vehicle.

[0017] According to the above technical solution, the method for predicting the trend of the proportion of electric vehicle parking area parking numbers to the total number of parking locations in step S4 is as follows:

[0018] Step S41: Obtain historical dynamic data from the database, extract the number of electric vehicles entering and leaving the database, and sum them up. Finally, calculate the percentage P of the number of electric vehicles parked in the parking area relative to the total number of parking spots based on the summed numbers.

[0019] Step S42: Divide the percentage P of the number of historical electric vehicles parked in the parking area to the total number of parking spots into time periods in "days".

[0020] Step S43: Create a line chart for each unit time period, further divide each unit time into several groups of statistical data points, and calculate the average proportion of historical dynamic data in each data point. The vertical axis of the line graph is marked accordingly, while the horizontal axis represents the time point of the statistically calculated data points within a "day" cycle. All coordinate points are connected sequentially.

[0021] Step S44: Based on the established line graph, predict the trend of the proportion of electric vehicle parking area parking area to the total number of parking spots at different time periods of the day.

[0022] According to the above technical solution, the specific method for predicting the retrieval time of each electric vehicle entering the electric vehicle garage and the accuracy of the retrieval time in step S4 is as follows:

[0023] Step S4a: Obtain historical dynamic data from the database, identify an electric vehicle license plate, and extract the time t from the database records of the historical outbound records for that electric vehicle license plate. c1 t c2 ... t cn ;

[0024] Step S4b: The formula for predicting the pickup time of the electric vehicle is as follows: Where t cy To predict the pickup time of the target vehicle;

[0025] Step S4c: The formula for calculating the accuracy of electric vehicle pickup time prediction is as follows: Where S j The prediction accuracy index and ε are control parameters, which are constants greater than 0;

[0026] In the above formula, t c1 t c2 ... t cn and t cy The unit for all values ​​is "minutes". When the time period is divided into units of "days", t cy This indicates that starting from 0:00 on the day for which the forecast is needed, the t-th... cy The estimated pickup time for the electric vehicle is in minutes.

[0027] According to the above technical solution, the specific method for matching the optimal parking space for the target electric vehicle in step S5 is as follows:

[0028] Step S51: When the high-definition camera unit captures the license plate number of the target electric vehicle, quickly retrieve the predicted pick-up time t of the target electric vehicle. cy And prediction accuracy index S j At the same time, retrieve the trend of the proportion of electric vehicle parking area parking area to the total number of parking spots at different time periods of the day.

[0029] Step S52: Input the predicted pick-up time t of the target electric vehicle from the line graph of the predicted proportion trend. cy Query the horizontal axis at t cy The vertical axis at time point is the average percentage.

[0030] Step S53: When When the target electric vehicle is located in the convenient pick-up area, the system will match the next available parking spot in the convenient pick-up area according to the actual parking situation in the current convenient pick-up area.

[0031] when When the target electric vehicle is matched to the regular pick-up area, the next available parking spot in the regular pick-up area is matched sequentially based on the actual parking situation in the current regular pick-up area.

[0032] when When the target electric vehicle is located in the difficult-to-access area, the system will match the next available parking spot in the difficult-to-access area according to the actual parking situation in the current difficult-to-access area.

[0033] Where P1 and P2 are the system's preset standard division thresholds, and P1 <P2。

[0034] According to the above technical solution, the specific method for performing parking space replenishment in step S6 is as follows:

[0035] Step S61: Obtain in real time the percentage of the actual number of parking spaces in the "Easy Access Area", "Regular Access Area" and "Difficult Access Area" relative to the total number of parking spaces in the corresponding areas, Z1, Z2 and Z3 respectively, and obtain in real time the percentage of the number of parking spaces in the electric vehicle parking area relative to the total number of parking spaces, P.

[0036] Step S62: Sort the percentage values ​​Z1, Z2, and Z3 of the total number of parking spots in the corresponding area according to their numerical priority, and obtain the maximum value Z. max and the minimum value Z min Then determine the maximum value Z respectively. max and the minimum value Z minThe corresponding level regions are defined, and the relationship between the corresponding level regions is continuously updated as the real-time percentage value changes.

[0037] Step S63: Set the control threshold d1, when Z max When -P>d1, the control program is activated to fill in parking spaces for electric vehicles entering the electric vehicle garage.

[0038] Step S64: When an electric vehicle enters the electric vehicle garage, obtain the parking level area matched to the electric vehicle and the accuracy S of the predicted retrieval time for the electric vehicle. j If S j <S jb S jb To set a lower limit for prediction accuracy, and the parking level area matched by the analysis of this electric vehicle is exactly the current Z... max If the value corresponds to a certain level zone, then the parking level zone matched for that electric vehicle will be adjusted to the current Z level. min The corresponding level area is used to perform parking space replenishment.

[0039] According to the above technical solution, step S5 further includes:

[0040] All parking spots are planned in the electric vehicle parking area, and indicator lights and photosensitive components are installed under each parking spot.

[0041] When an electric vehicle enters the electric vehicle parking area through the entrance and exit of the electric vehicle garage, the optimal parking space for the target electric vehicle is matched according to the prediction result of step S4. The vehicle owner is guided to the optimal parking space by the color indicator light of the currently passing vehicle. After parking, the photosensitive element detects that the light at the parking point is blocked by the electric vehicle to capture the signal, thereby realizing the function of monitoring the number of parked vehicles in real time for subsequent data processing. At the same time, if the color indicator light is on for more than 1 minute and the photosensitive element still has not captured the signal, a parking abnormality prompt is issued to the electric vehicle garage staff, so as to promptly detect whether the vehicle owner is parking in accordance with regulations and achieve the function of efficient management.

[0042] According to the above technical solution, the system for implementing the method includes:

[0043] High-definition camera units are installed at the entrance and exit of the electric vehicle garage to capture the license plate numbers of electric vehicles entering and leaving the garage and record the dynamic data of the electric vehicles.

[0044] Photosensitive components are deployed below each parking spot to sense whether the light at the parking spot is blocked by an electric vehicle, thereby capturing the parking signal;

[0045] The parking area simulation and division module simulates the full-load parking situation of electric vehicle parking areas, analyzes the time required to retrieve the vehicle when the electric vehicle is the first vehicle to be retrieved at each parking point, and divides the parking area into convenient retrieval area, regular retrieval area, and difficult retrieval area.

[0046] The parking quantity and retrieval time prediction module predicts the trend of the proportion of electric vehicle parking area parking quantity to the total number of parking points, as well as the retrieval time and accuracy of each electric vehicle entering the electric vehicle garage, based on historical dynamic data in the database.

[0047] The optimal parking space matching module matches the best parking space for the target electric vehicle based on the predicted electric vehicle retrieval time and parking area ratio trend.

[0048] The guide indicator control module uses color indicator lights to guide vehicle owners to park their electric vehicles at the target parking location;

[0049] The parking space replenishment module is used to replenish parking spaces for electric vehicles based on real-time changes in the proportion of vehicles parked in the electric vehicle parking area when the number of vehicles parked surges.

[0050] The abnormality alert module is used to send a parking abnormality alert to the electric vehicle garage staff when the color indicator light fails to detect a parking signal for a certain period of time.

[0051] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention intelligently divides electric vehicle parking areas into three levels—easy-to-access, regular-access, and difficult-to-access—by mapping and analyzing the full-load parking situation. Combined with high-definition camera units capturing dynamic data of electric vehicles entering and exiting, it predicts the trend of parking quantity ratios and retrieval times with accuracy, achieving optimal parking space matching and guidance for electric vehicles. This effectively solves the problems of low efficiency, chaotic parking, and arbitrary vehicle movement in traditional electric vehicle parking management. Especially in scenarios with high parking demand and limited space, such as office buildings, it significantly improves parking efficiency, optimizes parking resource allocation, and reduces management costs. Attached Figure Description

[0052] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0053] In the attached diagram:

[0054] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please see Figure 1 This invention provides a technical solution: a remote monitoring and management method for electric vehicles based on the Internet of Things, comprising:

[0057] Step S1: Draw the full-load parking situation of all electric vehicle parking areas in the office building. Based on the full-load parking situation, simulate the situation where the electric vehicle at each parking point is the first vehicle to be picked up, and analyze the time required to pick up the vehicle.

[0058] Step S2: Based on the time values ​​analyzed at each parking location, all electric vehicle parking areas in the office building are divided into three levels: convenient retrieval area, regular retrieval area, and difficult retrieval area. The time required to retrieve vehicles in the three levels is ranked as follows: convenient retrieval area < regular retrieval area < difficult retrieval area.

[0059] Step S3: Establish an electric vehicle parking record database by setting up high-definition camera units at the entrance and exit of the electric vehicle garage to capture the license plate numbers of electric vehicles and capture and record dynamic data of electric vehicles entering and leaving the electric vehicle garage.

[0060] Step S4: Based on the dynamic data in the database, predict the trend of the proportion of the number of electric vehicles parked in the parking area to the total number of parking spots, and then predict the retrieval time of each electric vehicle entering the electric vehicle garage and the accuracy of the retrieval time.

[0061] Step S5: Monitor electric vehicles entering the electric vehicle garage. Based on the prediction results of step S4, match the best parking space for the target electric vehicle and park the electric vehicle at the target parking point according to the guide indicator. The target parking point is monitored to ensure that the electric vehicle is parked in a standardized manner through light and shadow capture.

[0062] Step S6: When the target electric vehicle enters the electric vehicle garage during a time period in which the predicted proportion of the number of electric vehicles parked in the parking area to the total number of parking spots is on the rise, the measured proportions of the number of vehicles parked in the "easy-to-access area", "normal access area", and "difficult-to-access area" to the total number of parking spots in the corresponding areas are obtained in real time, as well as the proportion of the number of vehicles parked in the parking area to the total number of parking spots. The proportions of each level of area are compared with the proportions of the total parking area to perform parking space replenishment processing.

[0063] In step S1, the specific method for analyzing the time required to retrieve the vehicle is as follows:

[0064] Step S11: Determine the parking location of the target vehicle for analysis;

[0065] Step S12: Using the parking spot as the center, mark the nearest public parking passage in the drawing of the office building electric vehicle parking area map, and search for the number a of other drawn parking spots that the target vehicle must pass through between the parking spot and the nearest public parking passage.

[0066] Step S13: Using the parking point as the center, mark the nearest electric vehicle garage entrance / exit on the drawing map of the office building electric vehicle parking area, connect the parking point to the nearest electric vehicle garage entrance / exit, and measure the proportionally enlarged distance l according to the drawing scale.

[0067] Step S14: Using the formula The calculation yields the time T required to retrieve a vehicle at each simulated parking location when the electric vehicle is the first to be retrieved; where t and v are system-preset standard values, and a·t represents the time required to move other vehicles when retrieving the target vehicle. This represents the time required from entering the electric vehicle garage to arriving at the vehicle; it also represents factors such as obstruction by other vehicles and the remoteness of the parking spot within the garage. By sequentially analyzing and predicting the time required to retrieve all vehicles under full parking conditions, the difficulty of retrieving vehicles from each parking spot can be numerically processed. This helps to clarify the convenience of retrieving electric vehicles from each parking spot and provides a basis for subsequent electric vehicle parking area division and comprehensive management.

[0068] The method for predicting the trend of the proportion of electric vehicle parking areas to the total number of parking spots in step S4 is as follows:

[0069] Step S41: Obtain historical dynamic data from the database, extract the number of electric vehicles entering and leaving the database, and sum them up. Finally, calculate the percentage P of the number of electric vehicles parked in the parking area relative to the total number of parking spots based on the summed numbers.

[0070] Step S42: Divide the percentage P of the number of historical electric vehicles parked in the parking area to the total number of parking spots into time periods in "days".

[0071] Step S43: Create a line chart for each unit time period, further divide each unit time into several groups of statistical data points, and calculate the average proportion of historical dynamic data in each data point. The vertical axis of the line graph is marked accordingly, while the horizontal axis represents the time point of the statistically calculated data points within a "day" cycle. All coordinate points are connected sequentially.

[0072] Step S44: Based on the established line graph, predict the trend of the proportion of electric vehicle parking area parking area to the total number of parking spots at different time periods of the day.

[0073] In step S4, the specific method for predicting the retrieval time of each electric vehicle entering the electric vehicle garage and the accuracy of the retrieval time is as follows:

[0074] Step S4a: Obtain historical dynamic data from the database, identify an electric vehicle license plate, and extract the time t from the database records of the historical outbound records for that electric vehicle license plate. c1 t c2 ... t cn ;

[0075] Step S4b: The formula for predicting the pickup time of the electric vehicle is as follows: Where t cy To predict the pickup time of the target vehicle;

[0076] Step S4c: The formula for calculating the accuracy of electric vehicle pickup time prediction is as follows: Where S j The prediction accuracy index and ε are control parameters, which are constants greater than 0;

[0077] In the above formula, t c1 t c2 ... t cn and t cy The unit for all values ​​is "minutes". When the time period is divided into units of "days", t cy This indicates that starting from 0:00 on the day for which the forecast is needed, the t-th... cy The estimated pickup time for the electric vehicle is in minutes.

[0078] The predicted vehicle retrieval time value t cy The accuracy index is calculated by averaging the number of times a target vehicle's license plate is captured by a high-definition camera unit each time it leaves the electric vehicle garage, as recorded in the database. The prediction accuracy index is calculated by analyzing the fluctuations between historical vehicle retrieval times and predicted times. The greater the fluctuation, the lower the accuracy of the predicted retrieval time, and vice versa. By assigning an accuracy index to the predicted retrieval time, the system can effectively address the historical retrieval situations of each vehicle and take corresponding countermeasures to achieve comprehensive management.

[0079] The specific method for matching the optimal parking space for the target electric vehicle in step S5 is as follows:

[0080] Step S51: When the high-definition camera unit captures the license plate number of the target electric vehicle, quickly retrieve the predicted pick-up time t of the target electric vehicle. cy And prediction accuracy index S j At the same time, retrieve the trend of the proportion of electric vehicle parking area parking area to the total number of parking spots at different time periods of the day.

[0081] Step S52: Input the predicted pick-up time t of the target electric vehicle from the line graph of the predicted proportion trend. cy Query the horizontal axis at t cy The vertical axis at time point is the average percentage.

[0082] Step S53: When When the target electric vehicle is located in the convenient pick-up area, the system will match the next available parking spot in the convenient pick-up area according to the actual parking situation in the current convenient pick-up area.

[0083] when When the target electric vehicle is matched to the regular pick-up area, the next available parking spot in the regular pick-up area is matched sequentially based on the actual parking situation in the current regular pick-up area.

[0084] when When the target electric vehicle is located in the difficult-to-access area, the system will match the next available parking spot in the difficult-to-access area according to the actual parking situation in the current difficult-to-access area.

[0085] Where P1 and P2 are the system's preset standard division thresholds, and P1 <P2;

[0086] The above steps analyze and predict the trend of the proportion of electric vehicle parking areas to the total number of parking spots and the retrieval time of electric vehicles. Then, based on the proportion of the predicted electric vehicle retrieval time in the line analysis chart, the area is divided for matching. If the predicted proportion of vehicles in the garage when the target electric vehicle is retrieved is higher than P2, it means that the garage is full of vehicles when the user retrieves the vehicle. If the vehicles are parked randomly, they are likely to be blocked by other electric vehicles when the user retrieves the vehicle, causing difficulties in moving the vehicle and wasting time and effort. Therefore, the target vehicle is matched to a convenient retrieval area. Since the initial steps already calculated that even when the electric vehicle garage is full, the retrieval time for electric vehicles parked in the convenient retrieval area is relatively short, thus achieving efficient vehicle retrieval. Similarly, if the predicted percentage of vehicles in the garage when retrieving an electric vehicle is lower than P2 or even lower than P1, it indicates that the number of electric vehicles in the garage is relatively low, with ample space for retrieval. Therefore, the target electric vehicle can be prioritized for allocation to a regular retrieval area or even a difficult retrieval area, avoiding chaotic parking caused by randomly parked electric vehicles blocking other vehicles. Ultimately, this achieves a method for orderly parking and retrieval of electric vehicles, greatly improving the efficiency of parking and retrieval, reducing the problem of electric vehicles blocking other vehicles due to chaotic parking, and minimizing the possibility of electric vehicles bumping into each other's rearview mirrors due to human intervention in crowded areas, thus avoiding disputes.

[0087] In step S6, the specific method for performing parking space replacement processing is as follows:

[0088] Step S61: Obtain in real time the percentage of the actual number of parking spaces in the "Easy Access Area", "Regular Access Area" and "Difficult Access Area" relative to the total number of parking spaces in the corresponding areas, Z1, Z2 and Z3 respectively, and obtain in real time the percentage of the number of parking spaces in the electric vehicle parking area relative to the total number of parking spaces, P.

[0089] Step S62: Sort the percentage values ​​Z1, Z2, and Z3 of the total number of parking spots in the corresponding area according to their numerical priority, and obtain the maximum value Z. max and the minimum value Z min Then determine the maximum value Z respectively. max and the minimum value Z min The corresponding level regions are defined, and the relationship between the corresponding level regions is continuously updated as the real-time percentage value changes.

[0090] Step S63: Set the control threshold d1, when Z max When -P>d1, the control program is activated to fill in parking spaces for electric vehicles entering the electric vehicle garage.

[0091] Step S64: When an electric vehicle enters the electric vehicle garage, obtain the parking level area matched to the electric vehicle and the accuracy S of the predicted retrieval time for the electric vehicle. j If S j <S jb S jb To set a lower limit for prediction accuracy, and the parking level area matched by the analysis of this electric vehicle is exactly the current Z... max If the value corresponds to a certain level zone, then the parking level zone matched for that electric vehicle will be adjusted to the current Z level. min Parking is allocated to the corresponding grade area. When the proportion of electric vehicle parking areas to the total number of parking spots is increasing, a parking allocation management method is needed to prevent a surge in parking in one grade area from causing it to become overloaded and blocking or affecting the normal parking situation of other grade areas. This is achieved by monitoring the actual number of parking spaces in each grade area in real time and setting a threshold for areas where the actual parking proportion is significantly higher than the total number of parking spots. Only electric vehicles with the predicted accuracy can park, while those with low prediction accuracy are allocated to the grade area with the lowest actual parking proportion. This method effectively controls the electric vehicle garage, ensuring that the growth trend of the number of electric vehicles in the three grade areas is roughly equal to the growth trend of the total number of electric vehicle parking areas. The relationship between the corresponding grade areas is continuously updated based on the real-time proportion changes, effectively improving the parking efficiency of electric vehicles in the garage while maximizing the effect of matching the target electric vehicle with the optimal parking space in the aforementioned steps. Because electric vehicles with low accuracy in predicting retrieval time may be due to irregular commuting hours and random retrieval times after get off work, making it difficult to effectively predict when they will pick up their vehicles, this type of vehicle is used as a supplement in the parking management process, which greatly improves the fault tolerance rate of electric vehicle parking management and makes the management model sustainable and effective.

[0092] Step S5 further includes:

[0093] All parking spots are planned in the electric vehicle parking area, and indicator lights and photosensitive components are installed under each parking spot.

[0094] When an electric vehicle enters the electric vehicle parking area through the entrance and exit of the electric vehicle garage, the optimal parking space for the target electric vehicle is matched according to the prediction result of step S4. The vehicle owner is guided to the optimal parking space by the color indicator light of the currently passing vehicle. After parking, the photosensitive element detects that the light at the parking point is blocked by the electric vehicle to capture the signal, thereby realizing the function of monitoring the number of parked vehicles in real time for subsequent data processing. At the same time, if the color indicator light is on for more than 1 minute and the photosensitive element still has not captured the signal, a parking abnormality prompt is issued to the electric vehicle garage staff, so as to promptly detect whether the vehicle owner is parking in accordance with regulations and achieve the function of efficient management.

[0095] A system for implementing the method includes:

[0096] High-definition camera units are installed at the entrance and exit of the electric vehicle garage to capture the license plate numbers of electric vehicles entering and leaving the garage and record the dynamic data of the electric vehicles.

[0097] Photosensitive components are deployed below each parking spot to sense whether the light at the parking spot is blocked by an electric vehicle, thereby capturing the parking signal;

[0098] The parking area simulation and division module simulates the full-load parking situation of electric vehicle parking areas, analyzes the time required to retrieve the vehicle when the electric vehicle is the first vehicle to be retrieved at each parking point, and divides the parking area into convenient retrieval area, regular retrieval area, and difficult retrieval area.

[0099] The parking quantity and retrieval time prediction module predicts the trend of the proportion of electric vehicle parking area parking quantity to the total number of parking points, as well as the retrieval time and accuracy of each electric vehicle entering the electric vehicle garage, based on historical dynamic data in the database.

[0100] The optimal parking space matching module matches the best parking space for the target electric vehicle based on the predicted electric vehicle retrieval time and parking area ratio trend.

[0101] The guide indicator control module uses color indicator lights to guide vehicle owners to park their electric vehicles at the target parking location;

[0102] The parking space replenishment module is used to replenish parking spaces for electric vehicles based on real-time changes in the proportion of vehicles parked in the electric vehicle parking area when the number of vehicles parked surges.

[0103] The abnormality alert module is used to send a parking abnormality alert to the electric vehicle garage staff when the color indicator light fails to detect a parking signal for a certain period of time.

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0105] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for remote monitoring and management of electric vehicles based on the Internet of Things, characterized by: The method comprises the following steps: Step S1: Draw the full-load parking situation of all electric vehicle parking areas in the office building, and analyze the time required to take the vehicle based on the condition that the electric vehicle at each parking point is first taken by the vehicle; Step S2: According to the time value analyzed by each parking point, all electric vehicle parking areas in the office building are divided into three level areas, namely convenient taking area, regular taking area and difficult taking area, and the time required to take the vehicle in the three level areas is sorted as convenient taking area < regular taking area < difficult taking area; Step S3: Establish an electric vehicle parking record database, capture the electric vehicle license plate through the high-definition camera unit arranged at the entrance and exit of the electric vehicle garage, and capture and record the dynamic data of the electric vehicle entering and exiting the electric vehicle garage; Step S4: According to the dynamic data of the database, predict the trend of the proportion of the number of electric vehicle parking areas to the total number of parking points, and then predict the taking time and accuracy of each electric vehicle entering the electric vehicle garage; Step S5: Supervise the electric vehicle entering the electric vehicle garage, match the best parking space of the target electric vehicle according to the prediction result of step S4, and park the electric vehicle to the target parking point according to the guide indicator light. The target parking point captures the light and shadow to monitor whether the electric vehicle is parked normally; Step S6: When the time when the target electric vehicle enters the electric vehicle garage is in the rising time zone of the trend of the proportion of the number of electric vehicle parking areas to the total number of parking points, the proportion of the actual number of parking areas in the "convenient taking area", "regular taking area" and "difficult taking area" to the total number of parking points is obtained respectively, and the proportion of the number of electric vehicle parking areas to the total number of parking points is obtained. Compare the proportion values of each level area and the proportion value of the total parking area, and perform parking supplement processing; The method for predicting the trend of the proportion of the number of electric vehicle parking areas to the total number of parking points in step S4 is as follows: Step S41: Obtain the historical dynamic data of the database, extract the number of electric vehicles entering the garage and the number of electric vehicles leaving the garage recorded in the database, and accumulate them. Finally, calculate the proportion P of the number of electric vehicle parking areas to the total number of parking points according to the accumulated number; Step S42: Divide the historical proportion P of the number of electric vehicle parking areas to the total number of parking points into time periods with "day" as the unit; Step S43: Establishing a broken line analysis chart for each unit time period, further dividing each unit time into several groups of statistical calculation data points, and calculating the average proportion of historical dynamic data at each data point The vertical coordinate corresponds to the marking on the broken line analysis chart, and the horizontal coordinate is the time point of the statistical calculation data point in a "day" period. All coordinate points are connected in sequence; Step S44: According to the established broken line analysis chart, predict the trend of the proportion of the number of electric vehicle parking areas to the total number of parking points in different time periods of the day; In step S4, the specific method for predicting the taking time and accuracy of each electric vehicle entering the electric vehicle garage is as follows: Step S4a: Acquire dynamic data of database history, determine an electric vehicle license plate, extract the time of the electric vehicle license plate history out-of-warehouse record of the database record ; Step S4b: The calculation formula for predicting the pickup time of the electric vehicle is wherein is the predicted target vehicle pickup time value; Step S4c: The calculation formula of the prediction accuracy of the pickup time of the electric vehicle is wherein the prediction accuracy index, is a control parameter, and is a constant greater than 0. The units of the above formula are "minutes", and in the case of dividing the time period by "days", and The units of the above formula are "minutes", and in the case of dividing the time period by "days", represents the predicted pick-up time of the electric vehicle from 0 o'clock of the day on which the prediction is required; and represents the predicted pick-up time of the electric vehicle from 0 o'clock of the day on which the prediction is required; and In step S6, the specific method for performing parking supplement processing is as follows: Step S61: Real-time acquisition of the proportion of the number of measured parking in the "convenient area", "regular area", "difficult area" to the total number of parking points in the corresponding area, respectively and the proportion of the number of electric vehicle parking area to the total number of parking points P Step S62: the proportion value of the total parking point number in the corresponding area The numerical size priority sorting respectively obtains the maximum value And the minimum value Then respectively determine the maximum value And the minimum value The corresponding grade area respectively, and constantly update the corresponding grade area relationship with the change of real-time proportion value; Step S63: setting a control threshold When , the control program is started, and parking supplement processing is performed on the electric vehicle entering the electric vehicle garage next. Step S64: When the electric vehicle enters the electric vehicle garage, the electric vehicle analyzes the matched parking level area and the prediction accuracy of the pickup time of the electric vehicle , if , wherein is the lower limit value of the prediction accuracy setting, and the parking level area matched by the electric vehicle analysis is exactly the level area corresponding to the current value, the matched parking level area of the electric vehicle is adjusted to the level area corresponding to the current value, so as to perform parking supplement processing.

2. The IoT-based remote monitoring management method for electric vehicles according to claim 1, characterized in that: In step S1, the specific method for analyzing the time required to take the vehicle is as follows: Step S11: Determine the parking point of the analysis target vehicle; Step S12: Mark the nearest parking public passage in the office building electric vehicle parking area map with the parking point as the center, search the number a of other mapped parking points that the target vehicle must pass between the parking point and the nearest parking public passage; Step S13: Again taking the parking point as the center, mark the nearest electric vehicle garage entrance in the office building electric vehicle parking area map, connect the parking point to the nearest electric vehicle garage entrance route, and measure the distance after isometric enlargement according to the drawing scale ; Step S14: Calculate the time value T needed to take the vehicle in the case that the electric vehicle at each parking point is the first to be taken, by the formula ; wherein and are standard values preset by the system, represents the time needed to move other vehicles necessary when taking the analysis target vehicle, represents the time needed from entering the electric vehicle garage to reaching the side of the vehicle. 3.The IoT-based remote monitoring management method of electric vehicles according to claim 1, characterized in that: The specific method for matching the best parking space of the target electric vehicle in step S5 is: Step S51: When the high-definition camera unit captures the license plate number of the target electric vehicle, quickly retrieve the predicted pick-up time of the target electric vehicle. and prediction accuracy index At the same time, retrieve the trend of the proportion of electric vehicle parking area parking area to the total number of parking spots at different time periods of the day. Step S52: input the predicted pickup time of the target electric vehicle from the broken line analysis chart of the predicted proportion trend , query the ordinate, i.e. the average proportion value at the abscissa at moment ; Step S53: When the target electric vehicle is matched to the convenient pickup area, the next empty parking point of the convenient pickup area is sequentially matched according to the actual parking condition of the current convenient pickup area. When the target electric vehicle is matched to a regular pickup area, the next empty parking point of the regular pickup area is sequentially matched according to the actual parking condition of the current regular pickup area. When the target electric vehicle is matched to the difficult-to-reach area, the next empty parking point of the difficult-to-reach area is sequentially matched according to the actual parking condition of the current difficult-to-reach area. wherein are system preset standard division threshold values, respectively, and .

4. The IoT-based remote monitoring management method for electric vehicles according to claim 3, characterized in that: The step S5 further comprises: All parking points are planned in the electric vehicle parking area, and an indicator light and a photosensitive component are arranged below each parking point; When the electric vehicle enters the electric vehicle parking area through the electric vehicle garage entrance and exit, according to the prediction result of step S4, the best parking space of the target electric vehicle is matched, the color indicator light of the current passing vehicle is given, the color indicator light guides the vehicle owner to park at the best parking space, and after the vehicle owner parks and completes, the photosensitive component senses that the parking point light is blocked by the electric vehicle to capture the signal, so as to realize the function of monitoring the real-time parking quantity, so as to facilitate subsequent data operation processing, and if the color indicator light indicates more than 1 minute and the photosensitive component still does not capture the signal, an abnormal parking prompt is sent to the electric vehicle garage staff, so as to timely find out whether the vehicle owner parks normally, and realize the function of efficient management.

5. A system for implementing the method of claim 1, characterized by It comprises: A high-definition camera unit is installed at the entrance and exit of the electric vehicle garage for capturing the license plate number of the electric vehicle entering and leaving the electric vehicle garage and recording the dynamic data of the electric vehicle; A photosensitive component is arranged below each parking point for sensing whether the parking point light is blocked by the electric vehicle to capture the parking signal; A parking area simulation and division module simulates the situation that the electric vehicle at each parking point is the first vehicle to be taken, analyzes the time required to take the vehicle, and divides the parking area into convenient taking area, regular taking area and difficult taking area according to the full load parking situation of the electric vehicle parking area; A parking quantity and taking time prediction module predicts the trend of the parking quantity of the electric vehicle parking area accounting for the total number of parking points, and the taking time and taking time accuracy of each electric vehicle entering the electric vehicle garage according to the historical dynamic data in the database; A best parking space matching module matches the best parking space of the target electric vehicle according to the predicted electric vehicle taking time and parking area proportion trend; A guide indicator light control module guides the vehicle owner to park the electric vehicle at the target parking point through the color indicator light; A parking supplement processing module is used to supplement the parking of the electric vehicle according to the real-time proportion value change when the parking quantity of the electric vehicle parking area increases rapidly; An abnormal prompt module is used to send an abnormal parking prompt to the electric vehicle garage staff when the color indicator light indicates more than a certain time and still does not capture the parking signal.

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