Electric vehicle remote monitoring management method based on Internet of Things

Abstract

The invention discloses an electric vehicle remote monitoring management method based on the Internet of Things, and the method comprises the steps: drawing the full-load parking conditions of all electric vehicle parking areas of an office building, simulating the condition that an electric vehicle at each parking point is firstly taken based on the full-load parking conditions, and analyzing the time needed for taking the vehicle; according to the time value analyzed by each parking point, dividing all electric vehicle parking areas of the office building into three levels of areas, namely a convenient taking area, a conventional taking area and a difficult taking area; wherein the time required for vehicle taking in the three grade areas is ranked as follows: the convenient taking area is smaller than the conventional taking area and smaller than the difficult taking area; establishing an electric vehicle parking record database, and capturing and recording dynamic data of the electric vehicle entering and exiting the electric vehicle garage by arranging a high-definition camera unit at the entrance and exit of the electric vehicle garage; the system has the advantages of being high in practicability and high in intelligent degree.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a remote monitoring and management method for electric vehicles based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization, electric vehicles, as an environmentally friendly and convenient means of transportation, have been favored by the general public. However, the rapid growth in the number of electric vehicles has also brought 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 at will, which brings inconvenience to car owners and increases management costs.

[0003] Especially in densely populated areas such as office buildings, there is a large demand for electric vehicle parking and limited parking areas. How to achieve orderly parking and efficient retrieval of electric vehicles in limited parking spaces has become an urgent problem to be solved. Therefore, developing an electric vehicle remote monitoring and management method based on the Internet of Things 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 the present invention is to provide an electric vehicle remote monitoring and management method based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an electric vehicle remote monitoring and management method 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, and based on the full-load parking situation, simulate the situation where electric vehicles at each parking spot are the first to be picked up, and analyze the time required to pick up the vehicle;

[0007] Step S2: According to the time value of each parking spot analysis, all electric vehicle parking areas in the office building are divided into three levels, namely, convenient pickup area, regular pickup area, and difficult pickup area, wherein the time required for vehicle pickup in the three levels is ranked as follows: convenient pickup area is shorter than regular pickup area, which is shorter than difficult pickup area;

[0008] Step S3: Establishing 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 electric vehicle license plate number, and capture and record the dynamic data of the electric vehicle entering and leaving the electric vehicle garage;

[0009] Step S4: predicting the trend of the proportion of the number of electric vehicle parking spaces to the total number of parking spots based on the dynamic data in the database, and then predicting the pick-up time and pick-up time accuracy of each electric vehicle entering the electric vehicle garage;

[0010] Step S5: Supervise the electric vehicles entering the electric vehicle garage, match the best parking space for the target electric vehicle according to the prediction result of step S4, and park the electric vehicle at the target parking spot according to the guide indicator light. The target parking spot is captured by light and shadow to monitor whether the electric vehicle is parked in a standardized manner;

[0011] Step S6: When the moment when the target electric vehicle enters the electric vehicle garage is in the time period when the predicted proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots is on the rise, the proportion of the actual number of parking spaces in the "convenient pickup area", "conventional pickup area" and "difficult pickup area" to the total number of parking spots in the corresponding areas is obtained in real time, and the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots is obtained. The proportion values ​​of each level of area and the proportion value of the total parking area are compared to perform parking filling processing.

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

[0013] Step S11: determining and analyzing the parking location of the target vehicle;

[0014] Step S12: With the parking spot as the center, mark the nearest public parking passage in the drawn 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: taking the parking spot as the center, marking the nearest electric car garage entrance and exit in the office building electric car parking area map, connecting the parking spot to the nearest electric car garage entrance and exit route, and measuring the geometrically enlarged distance l according to the drawing scale;

[0016] Step S14: By formula The time value T required to pick up the vehicle is calculated when the electric vehicle at each parking spot is the first vehicle to be picked up. Where t and v are standard values ​​preset by the system, and a·t represents the time required to move other vehicles when picking up the target vehicle for analysis. Indicates the time required to enter the electric vehicle garage and arrive next to the vehicle.

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

[0018] Step S41: Obtain historical dynamic data from 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, and finally calculate the proportion P of the number of electric vehicle parking spaces in the parking area to the total number of parking spots based on the accumulated number of parking spaces;

[0019] Step S42: dividing the ratio P of the number of parking spaces in the historical electric vehicle parking area to the total number of parking spaces into time periods with "day" as the unit;

[0020] Step S43: Create a line analysis chart for the unit time period, further divide each unit time into several groups of statistical calculation data points, and calculate the average proportion of historical dynamic data in each data point. The vertical axis corresponds to the line analysis chart, while the horizontal axis is the time point of the statistical calculation data point in a "day" cycle, connecting all the coordinate points in sequence;

[0021] Step S44: predicting the trend of the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spaces in different time periods of the day according to the established broken line analysis chart.

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

[0023] Step S4a: Obtain the dynamic data of the database history, determine an electric vehicle license plate, and extract the time t of the historical outbound record of the electric vehicle license plate recorded in the database. c1 ,t c2 ,...,t cn ;

[0024] Step S4b: The calculation formula for predicting the pickup time of the electric vehicle is: where t cy To predict the pickup time value of the target vehicle;

[0025] Step S4c: The calculation formula for the prediction accuracy of the electric vehicle pick-up time is: 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 of the time period is "minutes". When the time period is divided into "days", t cy It means that from 0:00 on the day of the forecast, the tth cy Minutes is the predicted pick-up time for the electric vehicle.

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

[0028] Step S51: When the high-definition camera unit captures the target electric vehicle license plate number, the predicted pickup time t of the target electric vehicle is quickly retrieved. cy and the prediction accuracy index S j , and retrieve the trend of the proportion of the number of electric vehicle parking areas to the total number of parking spots in different time periods of the day;

[0029] Step S52: Input the predicted pick-up time t of the target electric vehicle from the line analysis chart of the predicted share trend cy , query the horizontal axis at t cy The vertical axis at the time is the average proportion value

[0030] Step S53: When When the target electric vehicle is matched to the convenient pickup area, the next vacant parking spot in the convenient pickup area is matched in sequence according to the actual parking situation in the current convenient pickup area;

[0031] when When the target electric vehicle is matched to the regular pick-up area, the next vacant parking spot in the regular pick-up area is matched in sequence according to the actual parking situation in the current regular pick-up area;

[0032] when When the target electric vehicle is matched to the difficult-to-pick-up area, the next vacant parking spot in the difficult-to-pick-up area is matched in sequence according to the actual parking situation in the current difficult-to-pick-up area;

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

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

[0035] Step S61: respectively obtaining in real time the proportions of the measured parking numbers of the "convenient pickup area", "conventional pickup area" and "difficult pickup area" to the total number of parking spots in the corresponding areas Z1, Z2 and Z3, and obtaining in real time the proportion of the parking numbers of the electric vehicle parking area to the total number of parking spots P;

[0036] Step S62: sort the percentages of the total number of parking spots in the corresponding area Z1, Z2, and Z3 by numerical priority to obtain the maximum value Z max and the minimum value Z min , and then determine the maximum value Z max and the minimum value Z minThe corresponding level areas are updated continuously with the real-time proportion value changes;

[0037] Step S63: Setting the control threshold d1, when Z max -When P>d1, the control program is started to perform parking space filling processing for the electric vehicle that enters the electric vehicle garage next;

[0038] Step S64: When the electric vehicle enters the electric vehicle garage, the parking level area matched by the electric vehicle analysis and the prediction accuracy S of the pick-up time of the electric vehicle are obtained. j , if S j <S jb , where S jb The lower limit of the prediction accuracy is set, and the parking level area matched by the electric vehicle analysis happens to be the current Z max The parking level area corresponding to the value is adjusted to the current Z min The level area corresponding to the value is used to perform parking space filling processing.

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

[0040] Plan all parking spots in the electric vehicle parking area, and place indicator lights and photosensitive components 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 best parking space for the target electric vehicle is matched according to the prediction result of step S4. By giving the color indicator light of the current passing vehicle, the owner is guided to the best parking space through the color indicator light. After parking, the photosensitive component senses that the light at the parking spot is blocked by the electric vehicle to capture the signal, thereby realizing the function of monitoring the real-time parking quantity for subsequent data calculation and processing. At the same time, if the color indicator light indicates for more than 1 minute and the photosensitive component still fails to capture the signal, a parking abnormality prompt is issued to the electric vehicle garage staff, so as to timely discover whether the owner has parked in a standardized manner and realize 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 electric vehicle garage and record the dynamic data of electric vehicles;

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

[0045] The parking area simulation and division module simulates the situation of electric vehicles being picked up first at each parking spot according to the full-load parking situation of the electric vehicle parking area, analyzes the time required to pick up the vehicle, and divides the parking area into convenient pickup area, regular pickup area, and difficult pickup area;

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

[0047] The best parking space matching module matches the best parking space for the target electric vehicle based on the predicted pick-up time and parking area share trend of the electric vehicle;

[0048] The guidance indicator light control module guides the owner to park the electric vehicle at the target parking spot through color indicator lights;

[0049] The parking space filling processing module is used to fill the parking spaces of electric vehicles according to the real-time percentage changes when the number of electric vehicle parking spaces increases sharply;

[0050] The abnormal prompt module is used to send a parking abnormality prompt to the electric vehicle garage staff when the color indicator light indicates that the parking signal has not been captured for more than a certain period of time.

[0051] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention intelligently divides the areas into three levels: convenient access, conventional access and difficult access, by mapping and analyzing the full-load parking situation in the electric vehicle parking area, and combines the high-definition camera unit to capture the dynamic data of electric vehicles entering and exiting, predict the trend of the parking quantity ratio and the time and accuracy of vehicle pickup, and achieve the best parking space matching and guided parking for electric vehicles. It effectively solves the problems of low efficiency, chaotic parking and random movement of vehicles in traditional electric vehicle parking management, especially in scenarios with large parking demand and limited area such as office buildings, significantly improves parking efficiency, optimizes parking resource allocation and reduces management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0053] In the attached picture:

[0054] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 The present invention provides a technical solution: an electric vehicle remote monitoring and management method 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, and based on the full-load parking situation, simulate the situation where electric vehicles at each parking spot are the first to be picked up, and analyze the time required to pick up the vehicle;

[0058] Step S2: According to the time value of each parking spot analysis, all electric vehicle parking areas in the office building are divided into three levels, namely, convenient pickup area, regular pickup area, and difficult pickup area, wherein the time required for vehicle pickup in the three levels is ranked as follows: convenient pickup area is shorter than regular pickup area, which is shorter than difficult pickup area;

[0059] Step S3: Establishing 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 electric vehicle license plate number, and capture and record the dynamic data of the electric vehicle entering and leaving the electric vehicle garage;

[0060] Step S4: predicting the trend of the proportion of the number of electric vehicle parking spaces to the total number of parking spots based on the dynamic data in the database, and then predicting the pick-up time and pick-up time accuracy of each electric vehicle entering the electric vehicle garage;

[0061] Step S5: Supervise the electric vehicles entering the electric vehicle garage, match the best parking space for the target electric vehicle according to the prediction result of step S4, and park the electric vehicle at the target parking spot according to the guide indicator light. The target parking spot is captured by light and shadow to monitor whether the electric vehicle is parked in a standardized manner;

[0062] Step S6: When the moment when the target electric vehicle enters the electric vehicle garage is in the time period when the predicted proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots is on the rise, the proportion of the actual number of parking spaces in the "convenient pickup area", "conventional pickup area" and "difficult pickup area" to the total number of parking spots in the corresponding areas is obtained in real time, and the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots is obtained. The proportion values ​​of each level of area and the proportion value of the total parking area are compared to perform parking filling processing.

[0063] In step S1, the specific method for analyzing the time required to pick up the vehicle is:

[0064] Step S11: determining and analyzing the parking location of the target vehicle;

[0065] Step S12: With the parking spot as the center, mark the nearest public parking passage in the drawn 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: taking the parking spot as the center, marking the nearest electric car garage entrance and exit in the office building electric car parking area map, connecting the parking spot to the nearest electric car garage entrance and exit route, and measuring the geometrically enlarged distance l according to the drawing scale;

[0067] Step S14: By formula The time value T required to pick up the vehicle is calculated when the electric vehicle at each parking spot is the first vehicle to be picked up. Where t and v are standard values ​​preset by the system, and a·t represents the time required to move other vehicles when picking up the target vehicle for analysis. It indicates the time required to enter the electric vehicle garage and arrive next to the vehicle; it represents the factors of whether the parking spot is blocked by other vehicles and whether the parking spot is remote in the garage; by analyzing and predicting the time required for all vehicles to pick up the vehicle under full-load parking conditions in turn, the difficulty of picking up the vehicle at each parking spot can be numerically processed, which helps to understand the convenience of picking up electric vehicles at each parking spot and provide a basis for the subsequent division and comprehensive management of electric vehicle parking areas.

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

[0069] Step S41: Obtain historical dynamic data from 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, and finally calculate the proportion P of the number of electric vehicle parking spaces in the parking area to the total number of parking spots based on the accumulated number of parking spaces;

[0070] Step S42: dividing the ratio P of the number of parking spaces in the historical electric vehicle parking area to the total number of parking spaces into time periods with "day" as the unit;

[0071] Step S43: Create a line analysis chart for the unit time period, further divide each unit time into several groups of statistical calculation data points, and calculate the average proportion of historical dynamic data in each data point. The vertical axis corresponds to the line analysis chart, while the horizontal axis is the time point of the statistical calculation data point in a "day" cycle, connecting all the coordinate points in sequence;

[0072] Step S44: predicting the trend of the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spaces in different time periods of the day according to the established broken line analysis chart.

[0073] In step S4, the specific method for predicting the pick-up time of each electric vehicle entering the electric vehicle garage and the accuracy of the pick-up time is:

[0074] Step S4a: Obtain the dynamic data of the database history, determine an electric vehicle license plate, and extract the time t of the historical outbound record of the electric vehicle license plate recorded in the database. c1 ,t c2 ,...,t cn ;

[0075] Step S4b: The calculation formula for predicting the pickup time of the electric vehicle is: where t cy To predict the pickup time value of the target vehicle;

[0076] Step S4c: The calculation formula for the prediction accuracy of the electric vehicle pick-up time is: 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 of the time period is "minutes". When the time period is divided into "days", t cy It means that from 0:00 on the day of the forecast, the tth cy Minutes is the predicted pick-up time for the electric vehicle;

[0078] The predicted pickup time value of the target vehicle is t cy It is the average time value of all the times recorded in the database when the electric vehicle license plate is captured by the high-definition camera unit each time the target vehicle leaves the electric vehicle garage. The prediction accuracy index is calculated by analyzing the fluctuation of the historical pick-up time compared with the predicted time. If the fluctuation is greater, the accuracy of the predicted pick-up time is lower, and vice versa. Therefore, by assigning an accuracy index to the predicted pick-up time, we can effectively respond to the historical pick-up situation of each vehicle and take corresponding response measures to achieve the role of comprehensive management.

[0079] The specific method of matching the best parking space for the target electric vehicle in step S5 is:

[0080] Step S51: When the high-definition camera unit captures the target electric vehicle license plate number, the predicted pickup time t of the target electric vehicle is quickly retrieved. cy and the prediction accuracy index S j , and retrieve the trend of the proportion of the number of electric vehicle parking areas to the total number of parking spots in different time periods of the day;

[0081] Step S52: Input the predicted pick-up time t of the target electric vehicle from the line analysis chart of the predicted share trend cy , query the horizontal axis at t cy The vertical axis at the time is the average proportion value

[0082] Step S53: When When the target electric vehicle is matched to the convenient pickup area, the next vacant parking spot in the convenient pickup area is matched in sequence according to the actual parking situation in the current convenient pickup area;

[0083] when When the target electric vehicle is matched to the regular pick-up area, the next vacant parking spot in the regular pick-up area is matched in sequence according to the actual parking situation in the current regular pick-up area;

[0084] when When the target electric vehicle is matched to the difficult-to-pick-up area, the next vacant parking spot in the difficult-to-pick-up area is matched in sequence according to the actual parking situation in the current difficult-to-pick-up area;

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

[0086] Through the above steps, the trend of the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots and the time for electric vehicles to pick up the vehicle are analyzed and predicted respectively, and then the area matching is divided according to the proportion of the predicted electric vehicle pick-up time in the line analysis chart of the proportion trend. If the proportion of vehicles in the garage when the target electric vehicle is predicted to pick up the vehicle is higher than P2, it means that when the user picks up the vehicle, the garage is full of vehicles. If the vehicle is parked randomly, it is very likely to be blocked by other electric vehicles when picking up the vehicle, making it difficult to move, time-consuming and labor-intensive. Therefore, the target vehicle is matched to the convenient pickup area, because in the initial step, it has been calculated that the electric vehicles parked in the convenient pickup area will have a relatively short pickup time even when the electric vehicle garage is full, so the purpose of efficient vehicle pickup is achieved; similarly, if the predicted proportion of vehicles in the garage when electric vehicles are picking up the vehicle is lower than P2 or even lower than P1, it means that there are relatively few electric vehicles in the garage when the target electric vehicle is picking up the vehicle, and there is redundant space to pick up the vehicle. Therefore, it can be preferentially allocated to the regular pickup area or even the difficult pickup area to avoid the parking disorder caused by randomly parking electric vehicles that intercept and block other electric vehicles; finally, a method for orderly parking and orderly pickup of electric vehicles is realized, which greatly improves the efficiency of electric vehicle parking and retrieval, reduces the problem of electric vehicles being arbitrarily moved due to chaotic parking and blocking other vehicles, and reduces the possibility of electric vehicles bumping into rearview mirrors due to artificial movement of crowded electric vehicles, thereby avoiding disputes.

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

[0088] Step S61: respectively obtaining in real time the proportions of the measured parking numbers of the "convenient pickup area", "conventional pickup area" and "difficult pickup area" to the total number of parking spots in the corresponding areas Z1, Z2 and Z3, and obtaining in real time the proportion of the parking numbers of the electric vehicle parking area to the total number of parking spots P;

[0089] Step S62: sort the percentages of the total number of parking spots in the corresponding area Z1, Z2, and Z3 by numerical priority to obtain the maximum value Z max and the minimum value Z min , and then determine the maximum value Z max and the minimum value Z min The corresponding level areas are updated continuously with the real-time proportion value changes;

[0090] Step S63: Setting the control threshold d1, when Z max -When P>d1, the control program is started to perform parking space filling processing for the electric vehicle that enters the electric vehicle garage next;

[0091] Step S64: When the electric vehicle enters the electric vehicle garage, the parking level area matched by the electric vehicle analysis and the prediction accuracy S of the pick-up time of the electric vehicle are obtained. j , if S j <S jb , where S jb The lower limit of the prediction accuracy is set, and the parking level area matched by the electric vehicle analysis happens to be the current Z max The parking level area corresponding to the value is adjusted to the current Z min When the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots is in an upward period, in order to avoid a surge in the number of parking spaces in one of the level areas, which may lead to interception and obstruction after the level area is fully loaded or affect the normal parking situation in other level areas, a parking supplement management method is needed. By real-time monitoring of the measured parking number in each level area, a threshold is set for the area where the measured parking ratio is obviously higher than the total parking spot ratio. Only electric vehicles with a prediction accuracy that meets the standard can park, while electric vehicles with low prediction accuracy are supplemented to the level area with the lowest measured parking ratio. In this way, the electric vehicle garage is effectively macro-controlled, so that the growth trend of the number of electric vehicle parking spaces divided into three level areas is roughly equal to the growth trend of the total electric vehicle parking area, and the corresponding level area relationship is continuously updated according to the changes in the real-time proportion value, which effectively improves the parking efficiency of electric vehicles in the garage while ensuring that the best parking space for matching the target electric vehicle in the aforementioned steps can be maximized. Because electric vehicles with low accuracy in predicting pick-up time may have irregular working hours and the pick-up time after get off work is very random, it is difficult to effectively predict when they will pick up the vehicle. Therefore, such vehicles are used as supplementary spaces 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 comprises:

[0093] Plan all parking spots in the electric vehicle parking area, and place indicator lights and photosensitive components 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 best parking space for the target electric vehicle is matched according to the prediction result of step S4. By giving the color indicator light of the current passing vehicle, the owner is guided to the best parking space through the color indicator light. After parking, the photosensitive component senses that the light at the parking spot is blocked by the electric vehicle to capture the signal, thereby realizing the function of monitoring the real-time parking quantity for subsequent data calculation and processing. At the same time, if the color indicator light indicates for more than 1 minute and the photosensitive component still fails to capture the signal, a parking abnormality prompt is issued to the electric vehicle garage staff, so as to timely discover whether the owner has parked in a standardized manner and realize the function of efficient management.

[0095] A system for implementing the method, comprising:

[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 electric vehicle garage and record the dynamic data of electric vehicles;

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

[0098] The parking area simulation and division module simulates the situation of electric vehicles being picked up first at each parking spot according to the full-load parking situation of the electric vehicle parking area, analyzes the time required to pick up the vehicle, and divides the parking area into convenient pickup area, regular pickup area, and difficult pickup area;

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

[0100] The best parking space matching module matches the best parking space for the target electric vehicle based on the predicted pick-up time and parking area share trend of the electric vehicle;

[0101] The guidance indicator light control module guides the owner to park the electric vehicle at the target parking spot through color indicator lights;

[0102] The parking space filling processing module is used to fill the parking spaces of electric vehicles according to the real-time percentage changes when the number of electric vehicle parking spaces increases sharply;

[0103] The abnormal prompt module is used to send a parking abnormality prompt to the electric vehicle garage staff when the color indicator light indicates that the parking signal has not been captured for more than a certain period of time.

[0104] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0105] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A remote monitoring and management method for electric vehicles based on the Internet of Things, characterized in that: include: Step S1: Draw the full-load parking situation of all electric vehicle parking areas in the office building, and based on the full-load parking situation, simulate the situation where electric vehicles at each parking spot are the first to be picked up, and analyze the time required to pick up the vehicle; Step S2: According to the time value of each parking spot analysis, all electric vehicle parking areas in the office building are divided into three levels, namely, convenient pickup area, regular pickup area, and difficult pickup area, wherein the time required for vehicle pickup in the three levels is ranked as follows: convenient pickup area is shorter than regular pickup area, which is shorter than difficult pickup area; Step S3: Establishing 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 electric vehicle license plate number, and capture and record the dynamic data of the electric vehicle entering and leaving the electric vehicle garage; Step S4: predicting the trend of the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spaces based on the dynamic data in the database, and then predicting the pick-up time and pick-up time accuracy of each electric vehicle entering the electric vehicle garage; Step S5: Supervise the electric vehicles entering the electric vehicle garage, match the best parking space for the target electric vehicle according to the prediction result of step S4, and park the electric vehicle at the target parking spot according to the guide indicator light. The target parking spot is captured by light and shadow to monitor whether the electric vehicle is parked in a standardized manner; Step S6: When the moment when the target electric vehicle enters the electric vehicle garage is in the time period when the predicted proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots is on the rise, the proportion of the actual number of parking spaces in the "convenient pickup area", "conventional pickup area" and "difficult pickup area" to the total number of parking spots in the corresponding areas is obtained in real time, and the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots is obtained. The proportion values ​​of each level of area and the proportion value of the total parking area are compared to perform parking filling processing.

2. The electric vehicle remote monitoring and management method based on the Internet of Things according to claim 1 is characterized in that: In step S1, the specific method for analyzing the time required to pick up the vehicle is: Step S11: determining and analyzing the parking location of the target vehicle; Step S12: With the parking spot as the center, mark the nearest public parking passage in the office building electric vehicle parking area map, and search for the number of other drawn parking spots a that the target vehicle must pass through between the parking spot and the nearest public parking passage; Step S13: Taking the parking spot as the center, mark the nearest electric car garage entrance and exit in the office building electric car parking area map, connect the parking spot to the nearest electric car garage entrance and exit route, and measure the geometrically enlarged distance l according to the drawing scale; Step S14: By formula The time value T required to pick up the vehicle is calculated when the electric vehicle at each parking spot is the first vehicle to be picked up. Where t and v are standard values ​​preset by the system, and a·t represents the time required to move other vehicles when picking up the target vehicle for analysis. Indicates the time required to enter the electric vehicle garage and arrive next to the vehicle.

3. The electric vehicle remote monitoring and management method based on the Internet of Things according to claim 1 is characterized in that: The method for predicting the trend of the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spaces in step S4 is specifically as follows: Step S41: Obtain historical dynamic data from 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, and finally calculate the proportion P of the number of electric vehicle parking spaces in the parking area to the total number of parking spots based on the accumulated number of parking spaces; Step S42: dividing the percentage P of the number of parking spaces in the historical electric vehicle parking area to the total number of parking spaces into time periods with "day" as the unit; Step S43: Create a line analysis chart for the unit time period, further divide each unit time into several groups of statistical calculation data points, and calculate the average proportion of historical dynamic data in each data point. The vertical coordinates correspond to the line analysis chart, while the horizontal coordinates are the time points of the statistical calculation data points in a "day" cycle, connecting all coordinate points in sequence; Step S44: predicting the trend of the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spaces in different time periods of the day according to the established broken line analysis chart.

4. The electric vehicle remote monitoring and management method based on the Internet of Things according to claim 3 is characterized in that: In step S4, the specific method for predicting the pick-up time of each electric vehicle entering the electric vehicle garage and the accuracy of the pick-up time is: Step S4a: Obtain the dynamic data of the database history, determine an electric vehicle license plate, and extract the time t of the historical outbound record of the electric vehicle license plate recorded in the database. c1 ,t c2 ,…,t cn ; Step S4b: The calculation formula for predicting the pickup time of the electric vehicle is: where t cy To predict the pickup time value of the target vehicle; Step S4c: The calculation formula for the prediction accuracy of the electric vehicle pick-up time is: Where S j The prediction accuracy index and ε are control parameters, which are constants greater than 0; In the above formula, t c1 ,t c2 ,…,t cn and t cy The unit of the time period is "minutes". When the time period is divided into "days", t cy It means that from 0:00 on the day of the forecast, the tth cy Minutes is the predicted pick-up time for the electric vehicle.

5. The electric vehicle remote monitoring and management method based on the Internet of Things according to claim 4 is characterized in that: The specific method for matching the best parking space for the target electric vehicle in step S5 is: Step S51: When the high-definition camera unit captures the target electric vehicle license plate number, the predicted pickup time t of the target electric vehicle is quickly retrieved. cy and the prediction accuracy index S j , and retrieve the trend of the proportion of the number of electric vehicle parking areas to the total number of parking spots in different time periods of the day; Step S52: Input the predicted pick-up time t of the target electric vehicle from the line analysis chart of the predicted share trend cy , query the horizontal axis at t cy The vertical axis at the time is the average proportion value Step S53: When When the target electric vehicle is matched to the convenient pickup area, the next vacant parking spot in the convenient pickup area is matched in sequence according to the actual parking situation in the current convenient pickup area; when When the target electric vehicle is matched to the regular pick-up area, the next vacant parking spot in the regular pick-up area is matched in sequence according to the actual parking situation in the current regular pick-up area; when When the target electric vehicle is matched to the difficult-to-pick-up area, the next vacant parking spot in the difficult-to-pick-up area is matched in sequence according to the actual parking situation in the current difficult-to-pick-up area; P1 and P2 are the system preset standard division thresholds, and P1 <P2。 6. The electric vehicle remote monitoring and management method based on the Internet of Things according to claim 4 is characterized in that: In step S6, the specific method of performing parking space filling processing is: Step S61: respectively obtaining in real time the proportions of the measured number of parking spaces in the "convenient pickup area", "conventional pickup area" and "difficult pickup area" to the total number of parking spots in the corresponding areas Z1, Z2 and Z3, and obtaining in real time the proportion of the number of parking spaces in the electric vehicle parking area to the total number of parking spots P; Step S62: sort the percentages of the total number of parking spots in the corresponding area Z1, Z2, and Z3 by numerical priority to obtain the maximum value Z max and the minimum value Z min , and then determine the maximum value Z max and the minimum value Z min The corresponding level areas are updated continuously with the real-time proportion value changes; Step S63: Setting the control threshold d1, when Z max -When P>d1, the control program is started to perform parking space filling processing for the electric vehicle that enters the electric vehicle garage next; Step S64: When the electric vehicle enters the electric vehicle garage, the parking level area matched by the electric vehicle analysis and the prediction accuracy S of the pick-up time of the electric vehicle are obtained. j , if S j jb , where S jb The lower limit of the prediction accuracy is set, and the parking level area matched by the electric vehicle analysis happens to be the current Z max The parking level area corresponding to the value is adjusted to the current Z min The level area corresponding to the value is used to perform parking space filling processing.​ 7. The electric vehicle remote monitoring and management method based on the Internet of Things according to claim 5 is characterized in that: The step S5 further comprises: Plan all parking spots in the electric vehicle parking area, and place indicator lights and photosensitive components under each parking spot; When an electric vehicle enters the electric vehicle parking area through the entrance and exit of the electric vehicle garage, the best parking space for the target electric vehicle is matched according to the prediction result of step S4. By giving the color indicator light of the current passing vehicle, the owner is guided to the best parking space through the color indicator light. After parking, the photosensitive component senses that the light at the parking spot is blocked by the electric vehicle to capture the signal, thereby realizing the function of monitoring the real-time parking quantity for subsequent data calculation and processing. At the same time, if the color indicator light indicates for more than 1 minute and the photosensitive component still fails to capture the signal, a parking abnormality prompt is issued to the electric vehicle garage staff, so as to timely discover whether the owner has parked in a standardized manner and realize the function of efficient management.

8. A system for implementing the method of claim 1, characterized in that: include: 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 electric vehicle garage and record the dynamic data of electric vehicles; Photosensitive components are placed under each parking spot to sense whether the light at the parking spot is blocked by the electric vehicle, thereby capturing the parking signal; The parking area simulation and division module simulates the situation of electric vehicles being picked up first at each parking spot according to the full-load parking situation of the electric vehicle parking area, analyzes the time required to pick up the vehicle, and divides the parking area into convenient pickup area, regular pickup area, and difficult pickup area; The parking quantity and pick-up time prediction module predicts the trend of the proportion of parking quantity in the electric vehicle parking area to the total number of parking spots, as well as the pick-up time and pick-up time accuracy of each electric vehicle entering the electric vehicle garage based on the historical dynamic data in the database; The best parking space matching module matches the best parking space for the target electric vehicle based on the predicted pick-up time and parking area share trend of the electric vehicle; The guidance indicator light control module guides the owner to park the electric vehicle at the target parking spot through color indicator lights; The parking space filling processing module is used to fill the parking spaces of electric vehicles according to the real-time percentage changes when the number of electric vehicle parking spaces increases sharply; The abnormal prompt module is used to send a parking abnormality prompt to the electric vehicle garage staff when the color indicator light indicates that the parking signal has not been captured for more than a certain period of time.

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