A supervision method, supervision system and supervision device for prohibiting electric vehicles from entering elevators

Abstract

The present invention relates to the technical field of elevator safety control, and specifically provides a supervision method, a supervision system and a supervision device for preventing electric vehicles from entering elevators. The supervision method for preventing electric vehicles from entering elevators includes the following steps: obtaining car pressure data, monitoring pressure changes, analyzing the pressure difference before and after the door opens, recording the door opening and closing states, comparing load changes, calculating the load center coordinates and offset rates, analyzing the pressure concentration per unit area, screening areas with sudden pressure increases, and extracting load distribution characteristics. In the present invention, by accurately capturing sudden load increases within a short period of time and distinguishing the characteristics of the entry and exit of electric vehicles and passengers, the recognition accuracy is improved. The detection of pressure concentration areas combined with the analysis of the operating state helps to timely detect illegal loads and generate records, facilitating subsequent trend analysis and management. Dynamically adjusting the elevator operation permissions and triggering warnings based on the number and level of violations can effectively curb illegal behaviors, reduce the misjudgment rate, improve the response speed and control accuracy, and enhance the safety of elevator use.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator safety control, and particularly to a supervision method, a supervision system and a supervision device for prohibiting electric vehicles from entering elevators. Background Art

[0002] The technical field of elevator safety control includes aspects such as elevator operation status monitoring, abnormal status detection and warning, personnel and item access control, elevator overload and safety protection, and remote supervision. The core content of elevator safety control includes real-time monitoring of the operation of the elevator through sensing devices, control systems, data processing devices and communication networks, and taking control measures according to safety standards and management specifications. The overall technical field covers load monitoring based on weight sensors, abnormal behavior detection based on cameras and image recognition, remote management and alarm based on wireless communication, and permission management based on elevator controllers, etc., to ensure that the elevator operation complies with safety standards.

[0003] Among them, the supervision method for prohibiting electric vehicles from entering elevators refers to an access management method based on electric vehicle feature recognition, detection and elevator control mechanisms. This method aims at the safety risks of electric vehicles entering elevators, and includes methods such as collecting images through cameras and performing electric vehicle target recognition, detecting additional loads based on weight sensors and comparing the weight characteristics of electric vehicles, detecting electric vehicle electronic tags based on RFID or Bluetooth signals and making comparisons, etc., to determine whether an electric vehicle enters the elevator, and combining with the elevator control system to execute measures such as door opening refusal, voice prompt or remote alarm, etc., to realize the control of the behavior of electric vehicles entering the elevator.

[0004] The existing technologies have problems such as insufficient recognition accuracy, untimely response and single data utilization when identifying electric vehicles entering elevators. Image recognition based on cameras is easily affected by environmental factors such as light changes and occlusion, resulting in frequent misjudgments or missed judgments. Although weight sensors can detect load changes, it is difficult to distinguish different types of loads, and it is easy to misjudge passengers carrying large items as electric vehicles entering, affecting the recognition accuracy. The detection method based on RFID or Bluetooth signals depends on the electric vehicle itself having an electronic tag or a signal transmitting device, and is powerless in the face of electric vehicles without electronic identification, and cannot fully cover the illegal situations in the actual scenario. These deficiencies lead to delays or failures in the supervision system, making it easy for illegal elevator rides by electric vehicles to go undetected in a timely manner, increasing the safety hazards of elevator operation, affecting the user experience of passengers, and even triggering safety accidents, affecting the overall management effect and user trust. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a supervision method, a supervision system and a supervision device for prohibiting electric vehicles from entering elevators.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A supervision method for prohibiting electric vehicles from entering elevators, comprising the following steps:

[0008] S1: Obtain the car pressure data, monitor the pressure change, analyze the pressure difference before and after the door opening, record the door opening and closing states, compare the load change, calculate the load center coordinates and the offset rate, analyze the pressure concentration per unit area, screen the pressure sudden increase area, and extract the load distribution characteristics;

[0009] S2: Based on the load distribution characteristics, extract the center point offset rate and compare it with the pedestrian entry and exit rate, analyze the change trend of the pressure data, judge the sudden increase in load, calculate the total load increase and compare it with the reference rate, screen the abnormal data conforming to the electric vehicle entry characteristics, analyze the degree of pressure sudden increase in the target area of the car, and identify the abnormal load mode;

[0010] S3: Based on the abnormal load mode, extract the elevator number, timestamp and load distribution snapshot, record the illegal load data, analyze the change of pressure concentration in a short time, extract the elevator operation state, calculate the matching degree between the load change and the operation state, screen the illegal events and generate the illegal load record;

[0011] S4: Based on the illegal load record, count the number of violations, extract the electric vehicle entry information, analyze the change trend of the violation level, call the property regulations to compare the disciplinary rules, adjust the elevator permissions, trigger a voice warning and generate a warning and disposal result.

[0012] As a further solution of the present invention, the load distribution characteristics include the load center point coordinates, the load offset rate, the pressure concentration index, and the pressure sudden increase area coordinates. The abnormal load mode includes the center point offset rate index, the total load increase value, the abnormal load data set, and the target area pressure sudden increase value. The illegal load record includes the elevator number information, the timestamp data, the load distribution image, the illegal load data set, the pressure concentration change value, and the operation state matching degree. The warning and disposal result includes the violation count statistic value, the violation behavior level, the entered electric vehicle identification information, the elevator permission adjustment result, and the voice warning prompt content.

[0013] As a further solution of the present invention, the specific steps of obtaining the car pressure data, monitoring the pressure change, analyzing the pressure difference before and after the door opening, recording the door opening and closing states, comparing the load change, calculating the load center coordinates and the offset rate, analyzing the pressure concentration per unit area, and screening the pressure sudden increase area to extract the load distribution characteristics are as follows:

[0014] S101: Obtain the pressure sensor data, monitor the pressure change in the car area, calculate the average pressure difference of the nodes before and after the door opening, and generate the pressure difference distribution quantity;

[0015] S102: Invoke the pressure difference distribution quantity and the sensor node coordinates, analyze the weighted coordinates of the pressure difference, obtain the coordinates of the load center point, calculate the abscissa value of the load center point, and generate the load offset rate value;

[0016] S103: Based on the load offset rate value and the pressure difference distribution quantity, calculate the pressure concentration degree per unit area, screen out the sudden increase areas, and obtain the load distribution characteristics.

[0017] As a further solution of the present invention, the calculation formula for the abscissa value of the load center point is specifically:

[0018] ;

[0019] Wherein, represents the abscissa value of the load center point, represents the pressure difference measured by the th sensor node, represents the arithmetic mean of the pressure differences of all sensor nodes, represents the abscissa value of the th sensor node in the car coordinate system, represents the distance weight coefficient between the th sensor node and the load center, represents the total number of sensor nodes participating in the calculation, represents the summation of all sensor nodes from the 1st to the th, represents the absolute difference between the pressure difference of the th node and the average pressure difference, represents the square root of the sum of the squares of the pressure difference of the th node and the square of the weight coefficient.

[0020] As a further solution of the present invention, based on the load distribution characteristics, the specific steps for extracting the center point offset rate, comparing it with the pedestrian entry and exit rate, analyzing the change trend of the pressure data, judging the sudden increase of the load, calculating the total load increase and comparing it with the reference rate, screening out the abnormal data conforming to the characteristics of the electric vehicle entry, and analyzing the degree of sudden increase of the pressure in the target area of the car and identifying the abnormal load mode are as follows:

[0021] S201: Obtain the load distribution characteristics, calculate the ratio of the offset distance of the load center point coordinates to the time interval within a continuous time period, and generate the load offset rate value;

[0022] S202: Call the load offset rate value and the pedestrian in-and-out reference rate, calculate the offset rate difference between the two, set an offset rate threshold, screen the data segments where the offset rate difference exceeds the threshold, obtain the abnormal offset rate interval sequence, analyze the change trend of the pressure sensor numerical sequence within the abnormal interval, analyze the change slope of the continuous data sequence, and judge the sudden increase in load within a short period of time to generate a sudden load increase trend value;

[0023] S203: Based on the sudden load increase trend value, calculate the difference of the total load per unit time, compare the difference with the reference increase of the pedestrian in-and-out, screen the abnormal data segments where the difference exceeds the threshold of the load increase per unit time to obtain the abnormal load increase value;

[0024] S204: Call the abnormal load increase value and the sudden load increase trend value, calculate the load difference per unit area and the area ratio in the target area inside the car, screen the data where the sudden increase degree exceeds the threshold of the load sudden increase per unit area to obtain the abnormal load mode.

[0025] As a further solution of the present invention, the specific calculation formula of the ratio of the offset distance of the load center point sequence to time is:

[0026] ;

[0027] Among them, represents the ratio of the offset distance of the load center point sequence to time, represents the abscissa of the load center point at the th time point, represents the abscissa of the load center point at the th time point, represents the ordinate of the load center point at the th time point, represents the ordinate of the load center point at the th time point, represents the instantaneous speed of the load center point at the th time point, represents the average speed of the load center point within this continuous time period, represents the total duration of the continuous time period, represents the time value at the th time point, represents the average time value of all time points within this time period, represents the total number of measurements of the load center point within the time period, represents the summation of the data of all time points.

[0028] As a further solution of the present invention, according to the abnormal load mode, the elevator number, timestamp and load distribution snapshot are extracted, the illegal load data is recorded, the change of pressure concentration degree within a short time is analyzed, the elevator operation state is extracted, the matching degree between the load change and the operation state is calculated, and the specific steps for screening illegal events and generating illegal load records are as follows:

[0029] S301: Based on the abnormal load mode, the elevator number, timestamp and car load distribution snapshot are extracted, the data meeting the load abnormal threshold conditions are screened, and an abnormal load snapshot set is generated;

[0030] S302: Call the abnormal load snapshot set, calculate the change value of load concentration degree per unit area within a short time, extract the elevator operation state record, compare the change value of load concentration degree with the elevator operation state change sequence, calculate the time series matching degree of the two, and screen the data with the matching degree lower than the load operation matching degree threshold to generate a load operation matching degree value;

[0031] S303: Based on the load operation matching degree value and the abnormal load snapshot set, compare the current elevator operation environment with the sudden increase in load, screen the data sequence meeting the illegal load characteristics, and obtain the illegal load record.

[0032] As a further solution of the present invention, for the illegal load record, the specific steps for counting the number of violations, extracting the information of electric vehicle entry, analyzing the change trend of violation levels, calling the property management regulations to compare with the disciplinary rules, adjusting the elevator permissions, triggering voice warnings and generating early warnings and disposal results are as follows:

[0033] S401: Based on the illegal load record, call the statistical information of the number of violations, extract the associated data of electric vehicle entry, calculate the number of violations and generate violation levels to obtain the change value of violation levels;

[0034] S402: Call the change value of violation levels and the property management regulations, calculate the level difference between the violation level and the disciplinary rules, compare whether the difference exceeds the disciplinary trigger threshold, screen the data records meeting the conditions and adjust the corresponding elevator operation permissions to generate an elevator operation permission adjustment value;

[0035] S403: Based on the elevator operation permission adjustment value, trigger the elevator voice device, generate a voice warning instruction sequence and execute the output of the prompt content to obtain the early warning and disposal results.

[0036] Based on the same inventive concept, a supervision system for prohibiting electric vehicles from entering the elevator is also proposed, which is used to execute the above-mentioned supervision method for prohibiting electric vehicles from entering the elevator, including;

[0037] The pressure monitoring module acquires the car pressure data, monitors the pressure change, analyzes the pressure difference before and after the door opening, compares the load change, calculates the load center coordinates and the offset rate, analyzes the pressure concentration per unit area, screens the areas with sudden pressure increase, and extracts the load distribution characteristics;

[0038] Based on the load distribution characteristics, the load identification module extracts the center point offset rate and compares it with the pedestrian in-and-out rate, analyzes the change trend of the pressure data, calculates the total load increase and compares it with the reference rate, screens the abnormal data that conforms to the characteristics of an electric vehicle entering, analyzes the degree of sudden pressure increase in the target area of the car, and identifies the abnormal load mode;

[0039] Based on the abnormal load mode, the violation detection module extracts the elevator number, timestamp and load distribution snapshot, records the violation load data, analyzes the change of pressure concentration in a short time, calculates the matching degree between the load change and the operating state, screens the violation events and generates a violation load record;

[0040] Based on the violation load record, the early warning management module counts the number of violations, extracts the information of electric vehicle entry, analyzes the change trend of the violation level, calls the property regulations to compare with the disciplinary rules, triggers a voice warning and generates an early warning and disposal result.

[0041] Based on the same inventive concept, a supervision device for preventing electric vehicles from entering the elevator is also proposed, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned supervision method for preventing electric vehicles from entering the elevator is realized.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In the present invention, by accurately capturing the sudden increase in load within a short time and distinguishing the characteristics of electric vehicle entry and passenger entry and exit, the recognition accuracy is improved. The detection of the pressure concentration area combined with the analysis of the operating state helps to timely discover the violation load and generate a record, which is convenient for subsequent trend analysis and management. According to the number of violations and levels, the elevator operation authority is dynamically adjusted and a warning is triggered, effectively curbing the violation behavior, reducing the misjudgment rate, improving the response speed and control accuracy, and enhancing the safety of elevator use. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a schematic diagram of the working process of the present invention.

[0046] Figure 2 It is a detailed flowchart of step S1 of the present invention.

[0047] Figure 3 It is a detailed flowchart of step S2 of the present invention.

[0048] Figure 4 It is a detailed flowchart of step S3 of the present invention.

[0049] Figure 5 It is a detailed flowchart of step S4 of the present invention.

[0050] Figure 6 It is a device flowchart of the present invention. Detailed implementation manners

[0051] Next, in combination with the accompanying drawings, the technical solutions in the present invention will be described.

[0052] In the embodiments of the present invention, words such as "exemplarily", "for example", etc. are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0053] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0054] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0055] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail in combination with the accompanying drawings and specific embodiments.

[0056] Please refer to Figure 1 , the present invention provides a technical solution: a supervision method for preventing electric vehicles from entering elevators, including the following steps:

[0057] S1: Obtain the data of the elevator car floor pressure sensor, monitor the pressure change in the car area, analyze the pressure difference in the car floor area before and after the door opens, record the elevator door opening and closing status information, compare the pressure change amount before and after the door opens with the change value of the total car load, calculate the coordinates of the load center point and the offset rate per unit time, analyze the pressure concentration degree per unit area, screen the area where the pressure per unit area suddenly increases in a short time, and obtain the load distribution characteristics;

[0058] S2: Based on the load distribution characteristics, extract the offset rate per unit time of the load center point, compare the rate with the reference rate of pedestrians entering and leaving, analyze the change trend of the pressure sensor data, judge the sudden increase in load in a short time, calculate the increase rate of the total load per unit time, compare the increase rate with the reference increase rate of pedestrians entering and leaving, screen the abnormal data that conforms to the characteristics of an electric vehicle entering, analyze the degree of sudden increase in load per unit area in the target area of the car, and obtain the abnormal load mode;

[0059] S3: Based on the abnormal load mode, extract the elevator number, timestamp, and snapshot of the car load distribution, record the illegal load distribution data, analyze the change value of the pressure concentration degree per unit area in a short time, extract the elevator operation status record, calculate the matching degree between the load change and the elevator operation status, screen the illegal event data, compare the current operating environment of the elevator with the sudden increase in load, and generate an illegal load record;

[0060] S4: Based on the illegal load record, call the statistical information of the number of violations, extract the information of the electric vehicle entering, analyze the change trend of the number and level of illegal behaviors, call the property management regulations, compare the violation level with the disciplinary rules, adjust the elevator operation permissions, trigger the elevator voice warning, and generate the warning and disposal results.

[0061] The load distribution characteristics include the coordinates of the load center point, the load offset rate, the pressure concentration index, and the coordinates of the pressure sudden increase area. The abnormal load mode includes the center point offset rate index, the total load increase value, the abnormal load data set, and the pressure sudden increase value in the target area. The illegal load record includes the elevator number information, the timestamp data, the load distribution image, the illegal load data set, the change value of the pressure concentration degree, and the operation status matching degree. The warning and disposal results include the statistical value of the number of violations, the level of illegal behavior, the identification information of the entering electric vehicle, the elevator permission adjustment result, and the content of the voice warning prompt.

[0062] Please refer to Figure 2 for the specific steps of S1:

[0063] S101: Obtain the pressure sensor data, monitor the pressure change in the car area, calculate the mean pressure difference between nodes before and after the door opens, and generate the pressure difference distribution quantity;

[0064] First, pressure sensors are installed in various corners and the central area of the elevator car to capture the pressure changes inside the car in real time. The data acquisition frequency is set to ten times per second to ensure data continuity and accuracy. The pressure data generated by each sensor is transmitted to the central processor via wired or wireless means. The central processor performs preliminary cleaning and formatting on the received pressure data for subsequent processing. Subsequently, the central processor calculates the average pressure data at each time point before and after the door opens. The specific calculation method is to perform arithmetic averaging on the data of each sensor within one minute before and after the door opens. Considering the usage frequency and load changes of the elevator, such a time window can better reflect the actual situation of pressure changes. By comparing the average pressure before and after the door opens, the average pressure difference is obtained. This difference reflects the impact of the door opening event on the internal pressure of the car. Based on the average pressure difference, a pressure difference distribution quantity is further generated. This distribution quantity is obtained by considering the interior of the car as a planar grid, where the value at each grid point is the pressure difference recorded by the corresponding position sensor. The data between grid points is filled by two-dimensional interpolation to generate a continuous pressure difference distribution map. This map visually shows the specific locations and degrees of pressure changes inside the car, serving as the basis for subsequent analysis. The process of generating the pressure difference distribution quantity incorporates actual elevator operation data. For example, in a door opening event, assuming the initial average pressure inside the car is 1013 hPa and it becomes 1010 hPa after the door opens, the corresponding pressure difference distribution quantity can reveal the air flow and pressure adjustment caused by the door opening.

[0065] S102: Call the pressure difference distribution quantity and the sensor node coordinates, analyze the weighted coordinates of the pressure difference, obtain the coordinates of the load center point, calculate the abscissa value of the load center point, and generate the load offset rate value;

[0066] The specific calculation formula for the abscissa value of the load center point is:

[0067] ;

[0068] Where, represents the abscissa value of the load center point, represents the pressure difference measured by the th sensor node, represents the arithmetic average of the pressure differences of all sensor nodes, represents the abscissa value of the th sensor node in the car coordinate system, represents the distance weight coefficient between the th sensor node and the load center, represents the total number of sensor nodes participating in the calculation, indicates the summation of all sensor nodes from the 1st to the th, represents the absolute difference between the pressure difference of the th node and the average pressure difference, represents the square root of the sum of the squares of the pressure difference of the th node and the square of the weight coefficient.

[0069] The abscissa value of the load center point is calculated based on the pressure difference measured by the sensor nodes, the node coordinates, and the weight coefficient. It is obtained by weighted summing the data of all sensor nodes and then dividing by the sum of the weights. The parameter explanations and acquisition methods are as follows:

[0070] Pressure difference of sensor nodes is collected in real time by the pressure sensors installed on the car floor. According to the GB / T9969-2021 pressure sensor standard, the measurement range of the sensor is set to 0-2000N. The pressure data of each node is collected by the sensor and updated by the data acquisition module at a frequency of 0.1 seconds.

[0071] Average value of node pressure difference is calculated by arithmetic mean of the pressure differences collected by all sensor nodes at the same time point. The calculation formula is .

[0072] Sensor node coordinates are measured according to the coordinate system established based on the geometric center of the car. The position of each sensor is measured in advance and entered into the database, with an accuracy of ±0.01 meters.

[0073] Weight coefficient is calculated according to the distance of the sensor node from the center of the car. The setting basis is that the influence weight is smaller as the distance is farther. The calculation method is , where is the Euclidean distance from the node to the center of the car, and 0.1 is a constant set to avoid division by zero.

[0074] The actual example data collection and calculation process are as follows:

[0075] Set four sensor nodes in the car (N = 4), and the pressure difference collection is as follows:

[0076] ;

[0077] Calculate the average value of the pressure difference:

[0078] ;

[0079] The sensor node coordinates are respectively:

[0080] ;

[0081] Calculation of the distance between the node and the center of the car:

[0082] ;

[0083] Calculation of the weight coefficient:

[0084] ;

[0085] Calculate the molecular term of each node:

[0086] The 1st node:

[0087] ;

[0088] ;

[0089] ;

[0090] The 2nd node:

[0091] ;

[0092] ;

[0093] ;

[0094] The 3rd node:

[0095] ;

[0096] ;

[0097] ;

[0098] The 4th node:

[0099] ;

[0100] ;

[0101] ;

[0102] Sum of the molecules:

[0103] ;

[0104] Calculate the denominator term:

[0105] ;

[0106] Calculate the abscissa value of the load center point:

[0107] ;

[0108] The result shows that the abscissa position of the load center point is biased towards the right side of the car, and the numerical result of 36.58 m is used for the subsequent calculation of the offset distance of the center point sequence to help identify the offset of the load distribution.

[0109] S103: Based on the load offset rate value and the pressure difference distribution amount, calculate the pressure concentration degree per unit area, screen out the sudden increase areas, and obtain the load distribution characteristics;

[0110] First, according to the previously generated pressure difference distribution map, calculate the pressure values in each grid cell, and then conduct statistical analysis on these pressure values to find the areas where the pressure values per unit area exceed a certain proportion (e.g., 10%) of the average pressure. These areas are marked as pressure concentration areas. Subsequently, use a mathematical model to calculate the ratio of the area of these pressure concentration areas to the total area to determine whether the pressure is abnormally concentrated in certain areas. Then, screen out the sudden increase areas, that is, the areas where the pressure shows a rapid increase in continuous measurements. This screening process involves calculating the pressure change rate of each grid cell, and selecting the areas with a change rate higher than the set threshold (e.g., the change rate per second exceeds 5%) as the sudden increase areas. Through this method, the load distribution characteristics are obtained, which comprehensively consider the speed and concentration degree of pressure change, providing valuable data for elevator maintenance and safety monitoring. For example, during the peak usage period of the elevator, it may be observed that the pressure suddenly increases in a certain corner of the car, indicating possible overloading or uneven distribution. At this time, the elevator control system can automatically make adjustments or issue an alarm to ensure the safety of passengers.

[0111] Please refer to Figure 3 , and the specific steps of S2 are as follows:

[0112] S201: Obtain the load distribution characteristics, calculate the ratio of the offset distance of the load center point coordinates to the time interval within a continuous time period, and generate the load offset rate value;

[0113] The specific calculation formula for the ratio of the offset distance of the load center point sequence to the time is:

[0114] ;

[0115] Among them, represents the ratio of the offset distance of the load center point sequence to the time, represents the abscissa of the load center point at the th time point, represents the abscissa of the load center point at the th time point, represents the ordinate of the load center point at the th time point, represents the ordinate of the load center point at the th time point, represents the The instantaneous speed of the load center point at a certain time point, represents the average speed of the load center point within this continuous time period, represents the total duration of the continuous time period, represents the time value at the represents the average time value of all time points within this time period, represents the total number of measurements of the load center point within the time period, represents the summation of data for all time points.

[0116] The ratio of the offset distance of the load center point sequence to time The calculation is based on the load center coordinate sequence, the instantaneous speed of the load center, and time data. The parameter descriptions and acquisition methods are as follows:

[0117] The abscissa of the load center point and the ordinate are measured in real time through a pressure sensor array. The coordinates are based on a two-dimensional coordinate system established with the car center. The sensor measurement frequency is 10 Hz, and the coordinate data acquisition accuracy is ±0.01 meters. The instantaneous speed is calculated by the displacement of the load center between two consecutive time points and the time difference. The calculation formula is . The average speed of the load center is obtained by the arithmetic mean of all instantaneous speeds. The total duration of the continuous time period is obtained from the time difference between the beginning and the end of the sequence. The time point is automatically recorded by the sensor system. The average time value The calculation formula is . The acquisition of sensor data is based on the requirements of the building equipment standard GB50034-2021.

[0118] The actual monitoring data was collected through 4 sensors installed in the elevator car at 14:32 on February 22, 2024. The data is as follows:

[0119] Time point sequence ;

[0120] Load center coordinates:

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] Instantaneous speed calculation:

[0126] Speed at the second time point:

[0127] ;

[0128] Speed at the third time point:

[0129] ;

[0130] Speed at the fourth time point:

[0131] ;

[0132] Average speed calculation:

[0133] ;

[0134] Calculation of the numerator term of the offset distance:

[0135] ;

[0136] Sum of speed deviations:

[0137] ;

[0138] Result of the numerator:

[0139] ;

[0140] Calculation of the denominator term:

[0141] Total time period:

[0142] ;

[0143] Average time value:

[0144] ;

[0145] Sum of squared time deviations:

[0146] ;

[0147] Square root of time deviation:

[0148] ;

[0149] Denominator calculation:

[0150] ;

[0151] Calculation of the ratio of the offset distance of the load center to time:

[0152] ;

[0153] The result shows that the average offset speed of the load center point is 0.0180 m / s within 3 seconds. The numerical value is related to the load stability and moving speed, and is used for subsequent load dynamic analysis and offset trend determination.

[0154] S202: Call the load offset rate value and the pedestrian in-and-out reference rate, calculate the difference between the two offset rates, set the offset rate threshold, and filter out the data segments where the offset rate difference exceeds the threshold to obtain the abnormal offset rate interval sequence. Analyze the change trend of the pressure sensor numerical sequence within the abnormal interval, analyze the change slope of the continuous data sequence, and judge the sudden increase in load within a short time to generate the sudden load increase trend value.

[0155] First, compare the load offset rate with the pedestrian in-and-out rate specified in the elevator design or actually measured. The pedestrian reference rate is the average value obtained based on the elevator usage frequency and passenger flow statistics. Calculate the difference between the two offset rates, which is obtained by a simple arithmetic difference calculation. Set the offset rate threshold, which is set based on historical data analysis and safety standards and reflects the maximum allowable rate deviation. Filter out the data segments where the offset rate difference exceeds this threshold. These data segments may indicate abnormalities or potential safety problems during elevator operation. Obtain the abnormal offset rate interval sequence, and further analyze the pressure sensor numerical sequence within these intervals to view its change trend. This analysis involves calculating the change slope of the continuous data sequence. By performing linear regression analysis on the continuous data points, find the trend line of the pressure change, and judge whether there is a sudden increase in load within a short time based on the slope to generate the sudden load increase trend value. This value reflects the change speed and direction of the elevator load within a short time. For example, if the pressure change slope increases significantly within a period of time, it indicates that there may be more people suddenly entering or leaving the elevator. This analysis helps to identify and respond to potential overloading events.

[0156] S203: Based on the sudden load increase trend value, calculate the difference of the total load per unit time, compare the difference with the reference increase of the pedestrian in-and-out, and filter out the abnormal data segments where the difference exceeds the load increase threshold per unit time to obtain the abnormal load increase value.

[0157] Using the sudden load increase trend value, calculate the total load change within a certain time window. Obtain the absolute value of the total load change through integration. Compare this difference with the baseline increase in pedestrian traffic. The baseline increase in pedestrian traffic is preset based on the maximum designed load of the elevator and typical usage scenarios, reflecting the expected load change amplitude under normal usage conditions. Screen out abnormal data segments where the difference exceeds the threshold of load increase per unit time. This threshold is set based on safety standards and historical load data, aiming to identify load changes that do not conform to the normal operating mode. The obtained abnormal load increase value indicates an abnormal rate of load increase or decrease within the observation window. This indicator is crucial for elevator safety monitoring and maintenance management. For example, during busy working hours, the elevator may experience rapid load changes. By real-time monitoring and analyzing these data, the elevator operation strategy can be adjusted in a timely manner or an alarm can be issued to avoid accidents caused by overloading.

[0158] S204: Call the abnormal load increase value and the sudden load increase trend value, calculate the ratio of the load difference per unit area to the area in the target area inside the car, and screen out data with a sudden increase degree exceeding the threshold of sudden load increase per unit area to obtain an abnormal load pattern;

[0159] Call the abnormal load increase value and the sudden load increase trend value, calculate the ratio of the load difference per unit area to the area in the target area inside the car. First, based on the abnormal load increase value and the sudden load increase trend value, locate specific areas inside the elevator car. These areas may exhibit abnormalities due to uneven load distribution. Calculate the load difference per unit area for each area. This calculation takes into account the specific size of the area and the corresponding load change amount. Divide by the area of the region to obtain the load difference per unit area. Further screen out data with a sudden increase degree exceeding the threshold of sudden load increase per unit area. This threshold is set based on the design parameters and safety requirements of the elevator to identify possible dangerous load patterns. The obtained abnormal load pattern provides detailed information about possible load concentration or uneven distribution in specific areas, which is crucial for maintaining the normal operation of the elevator and passenger safety. For example, during elevator operation, if it is detected that the load difference per unit area in a certain area is significantly higher than other areas, this may indicate the presence of accumulation or aggregation in that area, thus triggering a safety inspection or emergency measures.

[0160] Please refer to Figure 4 , the specific steps of S3 are as follows:

[0161] S301: Based on the abnormal load pattern, extract the elevator number, timestamp, and a snapshot of the car load distribution, screen out data that meet the conditions of the load abnormality threshold, and generate a set of abnormal load snapshots;

[0162] First, retrieve all eligible abnormal load data from the abnormal load pattern recognition module. For each piece of abnormal data, extract its corresponding elevator number to uniquely identify the specific device. The timestamp is used to record the exact time when the abnormality occurs. The car load distribution snapshot is generated from the matrix of pressure values collected in real-time by the pressure sensors during the abnormal time period. Integrate this information to form a preliminary data set containing the elevator number, abnormal time, and load distribution. Subsequently, screen it according to the pre-set load abnormality threshold, which is determined by integrating the elevator design standard and historical operation data. For example, the set range is that when the load offset rate exceeds 0.15 meters per second and the pressure per unit area exceeds 100 kilograms per square meter, it is considered abnormal. By judging whether the pressure of any cell in the load snapshot exceeds the threshold and matching the data in the corresponding time period, record the eligible data into the abnormal load snapshot set. Taking an actual example, assume that the elevator numbered "EL1001" detects concentrated pressure in the left area of the car at 14:32 on February 22, 2025. The distribution snapshot shows that the pressure per unit area at the front left corner reaches 120 kilograms per square meter and the offset rate of the load center point is 0.18 meters per second, meeting the abnormal determination conditions, so it is selected into the abnormal snapshot set to generate a record. This snapshot set can ultimately be used for subsequent load evolution trend analysis and elevator status assessment, forming an abnormal load snapshot set for monitoring abnormal states.

[0163] S302: Invoke the abnormal load snapshot set, calculate the change value of the load concentration per unit area within a short period of time, extract the elevator operation status record, compare the change value of the load concentration with the elevator operation status change sequence, calculate the time series matching degree of the two, and screen the data with a matching degree lower than the load operation matching degree threshold to generate the load operation matching degree value;

[0164] First, for each record in the abnormal load snapshot set, extract the unit area load data between consecutive snapshots. Calculate the change value of load concentration by comparing the pressure differences at the same positions in consecutive snapshots. The calculation process uses the average of the sum of the absolute value of the pressure difference and the area ratio to obtain the single-point concentration, and then combines it with the total car area to obtain the overall change value of concentration. Next, extract the elevator operation status records, which contain status information such as elevator ascending, descending, door opening and closing, starting, and stopping, and arrange them in a time series for comparison with the change value of load concentration. Calculate the time series matching degree between the two. By statistically calculating the time point synchronization rate of the two, that is, calculating the overlapping percentage of the peak load change and the operation status change point per unit time, set a load operation matching degree threshold such as 80%, and filter out the data segments with a matching degree lower than this value. Finally, generate the load operation matching degree value. For example, when the unit area load concentration of the elevator numbered "EL1001" increases from 90 kg per square meter to 130 kg per square meter within 10 seconds during the abnormal time period, the change value is 40 kg / square meter, and the elevator only performs stop and door closing actions during the corresponding time period. The calculated matching degree is 65% lower than the set threshold, so this data segment is marked as an abnormal operation load matching degree record, forming a load operation matching degree value for further analysis of illegal loads.

[0165] S303: Based on the load operation matching degree value and the abnormal load snapshot set, compare the current operation environment of the elevator with the sudden increase in load, filter out the data sequences that meet the characteristics of illegal loads, and obtain the illegal load records;

[0166] First, call the load operation matching degree value generated in step S302 and the abnormal load snapshot set, and compare the operation environment information of the elevator during the corresponding time period. The operation environment parameters include the floor where the elevator is located, peak hours, full-load operation warning status, etc. By directly comparing whether the sudden increase trend value of the load overlaps with the time period when the operation status occurs, filter out the data sequences that do not match the operation status but have an obvious sudden increase in load. Then, by judging the mutation situation of the load offset rate and the load concentration, identify whether there are characteristics of illegal loads. The judgment criteria for illegal load characteristics are, for example, the load increase amplitude exceeds 50 kg / square meter in a short time and there is no corresponding change in the operation status. Extract such sequences to generate illegal load records. For example, at 14:35 on February 22, for the elevator numbered "EL1001", when there is no change in the operation status, the load center position offset rate suddenly rises from 0.05 m / s to 0.22 m / s, and the pressure in the left area increases by 60 kg / square meter within 4 seconds, which meets the characteristics of illegal loads. Generate an illegal load record numbered "EL1001-20250222-1435", and this record is used in the illegal load file of the elevator load behavior analysis and monitoring system, forming an illegal load record for subsequent processing and review.

[0167] Please refer to Figure 5, the specific steps of S4 are as follows:

[0168] S401: Based on the violation load records, call the violation count information, extract the relevant data of the electric vehicle entering, calculate the number of violations and generate a violation level, and obtain the violation level change value;

[0169] First, retrieve the generated violation load records. This record contains information such as elevator number, violation time, violation load mode, and load offset rate. For each record, retrieve the access device data related to the elevator operation in the database, and specifically extract the relevant data when the electric vehicle enters the elevator. This data is collected in real time by the cameras and RFID scanning systems at the elevator entrance. The data fields include device type, access time, estimated vehicle weight, and owner information. Then, count the violation load records of the same elevator within a set period (e.g., 30 days), calculate the total number of violations for each elevator, and use a simple counting function to count the number of violations. For example, elevator "EL2002" detected a total of 7 violation load events during the statistical period, including 4 events of electric vehicles entering illegally. Compare this number of violations with the preset violation level classification standard. The violation level is set according to the statistical number. 0 - 1 time corresponds to "Level 1", 2 - 3 times is "Level 2", 4 - 5 times is "Level 3", and 6 times and above is "Level 4". According to the above example, since the number of violations of elevator "EL2002" is 4 times, its violation level is determined to be "Level 3". Subsequently, calculate the change value between the historical and current violation levels. The change value is calculated by subtracting the previous level from the current level. For example, if this elevator was "Level 2" last month and rose to "Level 3" this month, the change value is +1. Finally, generate the violation level change value.

[0170] S402: Call the violation level change value and the property management regulations, calculate the level difference between the violation level and the disciplinary rules, compare whether the difference exceeds the disciplinary trigger threshold, filter the data records that meet the conditions and adjust the corresponding elevator operation permissions, and generate the elevator operation permission adjustment value;

[0171] First, retrieve the disciplinary rule file in the property management system, which details the management measures corresponding to different violation levels. Extract the disciplinary level corresponding to the current change value of the violation level, and calculate the level difference, that is, the current violation level minus the starting level stipulated by the disciplinary rules. For example, when the violation level is "Level Three" and the property management stipulates that the penalty starts from "Level Two", the level difference is calculated as 3 - 2 = 1. Then, set the disciplinary trigger threshold. For example, if the threshold is set to 1, when the level difference is greater than or equal to the threshold, it is considered that the corresponding disciplinary measures need to be triggered. Screen out all data records that meet the conditions, mark the elevators that meet the conditions, and adjust their elevator operation permissions according to the regulations of the property management system. The content of the operation permission adjustment includes speed limit, prohibited peak operation hours, and prohibited stops at some floors. Set the range of operation permission adjustment values: no adjustment is 0, partial restriction is 1, and severe restriction is 2. Taking an example, for elevator "EL2002", since the change value of the violation level is +1 and the level difference is equal to 1, which meets the trigger condition, and the property management stipulates that partial restriction operations are to be performed on elevators with Level Three violations, so an elevator operation permission adjustment value of 1 is generated.

[0172] S403: Based on the elevator operation permission adjustment value, trigger the elevator voice device, generate a voice warning instruction sequence and execute the output of the prompt content to obtain the early warning and disposal result;

[0173] First, call the elevator operation permission adjustment value generated in step S402, and match the preset voice prompt content according to different adjustment values. When the adjustment value is 1, the prompt content is "This elevator has been partially restricted. Please use it properly." If the adjustment value is 2, the prompt is "This elevator has been severely restricted and will be suspended during the restricted period." The system retrieves and locates the voice device address of the target elevator according to the elevator number, and generates a corresponding voice instruction sequence, which includes the elevator number, trigger time, voice content, and playback duration. Taking an example, for elevator "EL2002", the adjustment value is 1, and the generated voice instruction sequence is "EL2002 - 20250222 - 153000 - Partial restriction prompt - 10 seconds". The system sends an instruction to the elevator internal speaker through the central controller. After the device receives it, it immediately plays the prompt content. After the playback is completed, the system records the status that the prompt has been completed and feeds back the execution time to the data log, and finally generates the early warning and disposal result.

[0174] Please refer to Figure 6 , based on the same inventive concept, a supervision system for prohibiting electric vehicles from entering elevators is also proposed, which is used to execute the above-mentioned supervision method for prohibiting electric vehicles from entering elevators, including:

[0175] The pressure monitoring module obtains the car pressure data, monitors the pressure change, analyzes the pressure difference before and after the door opens, compares the load change, calculates the load center coordinates and offset rate, analyzes the pressure concentration per unit area, screens out the areas with sudden pressure increase, and extracts the load distribution characteristics;

[0176] Based on the load distribution characteristics, the load recognition module extracts the central point offset rate and compares it with the pedestrian entry and exit rate, analyzes the changing trend of the pressure data, calculates the total load increase and compares it with the reference rate, screens out the abnormal data that conforms to the characteristics of the electric vehicle entering, analyzes the degree of sudden increase in pressure in the target area of the car, and identifies the abnormal load pattern;

[0177] Based on the abnormal load pattern, the violation detection module extracts the elevator number, timestamp and load distribution snapshot, records the violation load data, analyzes the change in pressure concentration within a short period of time, calculates the matching degree between the load change and the operating state, screens out the violation events and generates a violation load record;

[0178] Based on the violation load record, the early warning management module counts the number of violations, extracts the information of the electric vehicle entering, analyzes the changing trend of the violation level, calls the property regulations to compare with the disciplinary rules, triggers a voice warning and generates an early warning and disposal result.

[0179] Based on the same inventive concept, a supervision device for prohibiting electric vehicles from entering the elevator is also proposed, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned supervision method for prohibiting electric vehicles from entering the elevator is implemented.

[0180] As mentioned above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all of them should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for monitoring electric vehicles against entering elevators, characterized in that: The following steps are involved: S1: Obtain car pressure data, monitor pressure changes, analyze pressure differences before and after door opening, record door opening and closing status, compare load changes, calculate load center coordinates and offset rate, analyze pressure concentration per unit area, screen pressure sudden increase areas, and extract load distribution characteristics; S2: Based on the load distribution characteristics, extract the center point deviation rate and compare it with the pedestrian entry and exit rate, analyze the pressure data change trend, determine the load surge, calculate the total load increase and compare it with the benchmark rate, screen the abnormal data that meets the electric vehicle entry characteristics, analyze the pressure surge degree of the target area of ​​the car, and identify the abnormal load mode; S3: Based on the abnormal load pattern, extract the elevator number, timestamp and load distribution snapshot, record the illegal load data, analyze the pressure concentration change in a short period of time, extract the elevator operation status, calculate the matching degree between the load change and the operation status, filter the illegal events and generate illegal load records; S4: Based on the illegal load record, count the number of violations, extract the electric vehicle entry information, analyze the trend of the violation level, call the property regulations to compare the punishment rules, adjust the elevator permissions, trigger the voice warning and generate the warning and disposal results; Based on the load distribution characteristics, the center point deviation rate is extracted and compared with the pedestrian entry and exit rate, the pressure data change trend is analyzed, the load surge is determined, the total load increase is calculated and compared with the benchmark rate, the abnormal data that meets the electric vehicle entry characteristics is screened, and the pressure surge degree of the target area of ​​the car is analyzed. The specific steps for identifying abnormal load patterns are as follows: S201: Acquire the load distribution characteristics, calculate the ratio of the load center point coordinate offset distance to the time interval in a continuous time period, and generate a load offset rate value; S202: calling the load offset rate value and the pedestrian entry and exit reference rate, calculating the offset rate difference between the two, setting an offset rate threshold, and filtering the data segment whose offset rate difference exceeds the threshold, obtaining the abnormal offset rate interval sequence, analyzing the change trend of the pressure sensor value sequence in the abnormal interval, analyzing the change slope of the continuous data sequence, and judging the load sudden increase in a short period of time, and generating a load sudden increase trend value; S203: Based on the load sudden increase trend value, the difference of the total load per unit time is calculated, and the difference is compared with the pedestrian entry and exit reference increase, and the abnormal data segment whose difference exceeds the load increase threshold per unit time is selected to obtain the load abnormal increase value; S204: Calling the load abnormal increase value and the load sudden increase trend value, calculating the load difference per unit area and the area ratio of the target area in the car, screening the data whose sudden increase degree exceeds the load sudden increase threshold per unit area, and obtaining the abnormal load pattern.

2. The method for monitoring electric vehicles against entering elevators according to claim 1, characterized in that: The load distribution characteristics include the load center point coordinates, load offset rate, pressure concentration index, and pressure surge area coordinates; the abnormal load mode includes the center point offset rate index, total load increase value, abnormal load data set, and target area pressure surge value; the illegal load record includes elevator number information, timestamp data, load distribution image, illegal load data set, pressure concentration change value, and operating status matching degree; the early warning and disposal results include the number of violations, violation level, electric vehicle entry identification information, elevator authority adjustment results, and voice warning prompt content.

3. The method for monitoring electric vehicles prohibited from entering elevators according to claim 1, characterized in that: The specific steps to obtain cabin pressure data, monitor pressure changes, analyze the pressure difference before and after door opening, record door opening and closing status, compare load changes, calculate load center coordinates and offset rate, analyze pressure concentration per unit area, screen pressure sudden increase areas, and extract load distribution characteristics are as follows: S101: Obtain pressure sensor data, monitor changes in car area pressure, calculate the difference in node pressure mean before and after door opening, and generate pressure difference distribution; S102: calling the pressure difference distribution and the sensor node coordinates, analyzing the pressure difference weighted coordinates, obtaining the load center point coordinates, calculating the horizontal coordinate value of the load center point, and generating a load offset rate value; S103: Based on the load offset rate value and the pressure difference distribution, the pressure concentration degree per unit area is calculated, and the sudden increase area is screened to obtain the load distribution characteristics.

4. The method for monitoring electric vehicles against entering elevators according to claim 3, characterized in that: The calculation formula of the horizontal coordinate value of the load center point is specifically: Among them, X c Represents the horizontal coordinate value of the load center point, P d，i represents the pressure difference measured by the i-th sensor node, Represents the arithmetic mean of the pressure differences of all sensor nodes, x i represents the horizontal coordinate value of the i-th sensor node in the car coordinate system, w i represents the distance weight coefficient between the i-th sensor node and the load center, N represents the total number of sensor nodes involved in the calculation, It means to sum all sensor nodes from the 1st to the Nth. represents the absolute difference between the pressure difference of the ith node and the average pressure difference, Represents the square root of the sum of the square of the pressure difference at the i-th node and the square of the weight coefficient.

5. The method for monitoring electric vehicles prohibited from entering elevators according to claim 1, characterized in that: The specific calculation formula of the ratio of the load center point coordinate offset distance to the time interval is: Among them, D Δt Represents the ratio of the load center point coordinate offset distance to the time interval, X c，j represents the horizontal coordinate of the load center point at the jth time point, X c，j-1 represents the horizontal coordinate of the load center point at the j-1th time point, Y c，j Represents the ordinate of the load center point at the jth time point, Y c，j-1 Represents the vertical coordinate of the load center point at the j-1th time point, V c，j represents the instantaneous speed of the load center at the jth time point, represents the average speed of the load center point in this continuous time period, Δt represents the total duration of the continuous time period, T j represents the time value at the jth time point, represents the average time value of all time points in the time period, M represents the total number of load center point measurements in the time period, Represents the sum of data at all time points.

6. The method for monitoring electric vehicles against entering elevators according to claim 1, characterized in that: According to the abnormal load pattern, the specific steps of extracting the elevator number, timestamp and load distribution snapshot, recording the illegal load data, analyzing the pressure concentration change in a short period of time, extracting the elevator operation status, calculating the matching degree between the load change and the operation status, screening the illegal events and generating the illegal load record are as follows: S301: Based on the abnormal load pattern, extract the elevator number, timestamp and car load distribution snapshot, filter the data that meets the load abnormality threshold condition, and generate an abnormal load snapshot set; S302: calling the abnormal load snapshot set, calculating the load concentration change value per unit area in a short period of time, extracting the elevator operation status record, comparing the load concentration change value with the elevator operation status change sequence, calculating the time series matching degree between the two, and filtering the data with a matching degree lower than the load operation matching degree threshold, and generating a load operation matching degree value; S303: Based on the load operation matching value and the abnormal load snapshot set, compare the current elevator operation environment with the load sudden increase situation, filter the data sequence that meets the illegal load characteristics, and obtain the illegal load record.

7. The method for monitoring electric vehicles against entering elevators according to claim 6, characterized in that: Based on the illegal load records, the number of violations is counted, the electric vehicle entry information is extracted, the trend of the violation level is analyzed, the property regulations are called to compare the punishment rules, the elevator permissions are adjusted, the voice warning is triggered, and the warning and disposal results are generated. The specific steps are: S401: Based on the illegal load record, call the violation number statistical information, extract the electric vehicle entry related data, calculate the violation number and generate the violation level, and obtain the violation level change value; S402: calling the violation level change value and the property management regulations, calculating the level difference between the violation level and the punishment rule, comparing whether the difference exceeds the punishment trigger threshold, screening the data records that meet the conditions and adjusting the corresponding elevator operation authority, and generating the elevator operation authority adjustment value; S403: Based on the elevator operation authority adjustment value, the elevator voice device is triggered to generate a voice warning instruction sequence and execute prompt content output to obtain an early warning and disposal result.

8. A monitoring system for prohibiting electric vehicles from entering elevators, characterized in that: The method for supervising the prohibition of electric vehicles from entering an elevator according to any one of claims 1 to 7 comprises: The pressure monitoring module obtains the car pressure data, monitors the pressure changes, analyzes the pressure difference before and after the door is opened, compares the load changes, calculates the load center coordinates and the offset rate, analyzes the pressure concentration per unit area, screens the pressure sudden increase area, and extracts the load distribution characteristics; Based on the load distribution characteristics, the load identification module extracts the center point deviation rate and compares it with the pedestrian entry and exit rate, analyzes the pressure data change trend, calculates the total load increase and compares it with the reference rate, screens abnormal data that meets the electric vehicle entry characteristics, analyzes the sudden increase in pressure in the target area of ​​the car, and identifies abnormal load patterns; The violation detection module extracts the elevator number, timestamp and load distribution snapshot according to the abnormal load mode, records the illegal load data, analyzes the pressure concentration change in a short period of time, calculates the matching degree between the load change and the operating state, screens the violation events and generates the illegal load record; Based on the illegal load records, the early warning management module counts the number of violations, extracts the electric vehicle entry information, analyzes the trend of changes in the violation level, calls the property regulations to compare the punishment rules, triggers voice warnings and generates early warnings and disposal results.

9. A monitoring device for prohibiting electric vehicles from entering an elevator, comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the processor executes the computer program, the method for supervising the prohibition of electric vehicles from entering an elevator as described in any one of claims 1 to 7 is implemented.

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