Supervision method, supervision system and supervision device for prohibiting electric vehicle from entering elevator

Abstract

The invention relates to the technical field of elevator safety control, in particular to a supervision method, system and device for forbidding an electric vehicle to enter an elevator, and the supervision method for forbidding the electric vehicle to enter the elevator comprises the following steps: obtaining lift car pressure data, monitoring pressure change, analyzing a pressure difference value before and after door opening, recording door opening and closing states, and comparing load change. Calculating load center coordinates and an offset rate, analyzing a unit area pressure concentration ratio, screening a pressure sudden increase region, and extracting load distribution characteristics. According to the invention, through accurately capturing sudden load increase in a short time and distinguishing the in-out characteristics of the electric vehicle and passengers, the recognition precision is improved, pressure concentration area detection is combined with operation state analysis, illegal loads can be timely found and records can be generated, subsequent trend analysis and management are facilitated, and the efficiency is improved. And the elevator operation authority is dynamically adjusted according to the violation times and grades, warning is triggered, violation behaviors are effectively restrained, the misjudgment rate is reduced, the response speed and the control accuracy are improved, and the elevator use safety is enhanced.

Description

Technical Field

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

[0002] The field of elevator safety control technology includes elevator operation status monitoring, abnormal state detection and early warning, personnel and object access control, elevator overload and safety protection, remote supervision, etc. The core content of elevator safety control includes real-time monitoring of elevator operation through sensor equipment, control system, data processing device and communication network, 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 authority management based on elevator controllers, etc., to ensure that the elevator operation meets safety standards.

[0003] Among them, the supervision method for prohibiting electric vehicles from entering elevators refers to the access management method based on electric vehicle feature recognition, detection and elevator control mechanism. This method targets the safety risks of electric vehicles entering elevators, including methods such as collecting images based on cameras and identifying electric vehicles, detecting additional loads based on weight sensors and comparing electric vehicle weight features, detecting electric vehicle electronic tags based on RFID or Bluetooth signals and comparing them, to determine whether electric vehicles have entered the elevator, and combining the elevator control system to execute measures such as door opening rejection, voice prompts or remote alarms to achieve control over the behavior of electric vehicles entering the elevator.

[0004] The existing technology has problems such as insufficient recognition accuracy, untimely response and single data utilization when identifying electric vehicles entering the elevator. Camera-based image recognition is easily affected by environmental factors such as lighting changes and occlusion, resulting in frequent misjudgments or missed judgments. Although the weight sensor can detect load changes, it is difficult to distinguish between different types of loads, and it is easy to misjudge passengers carrying large items as electric vehicles, affecting the accuracy of recognition. The detection method based on RFID or Bluetooth signals relies on the electric vehicle itself to have an electronic tag or signal transmitting device. It is powerless in the face of electric vehicles without electronic identification and cannot fully cover violations in actual scenarios. These deficiencies lead to delays or failures in the supervision system, which can easily cause electric vehicles to illegally take the elevator without being discovered in time, increase the safety risks of elevator operation, affect the passenger experience, and even cause 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 shortcomings 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] In order to achieve the above object, the present invention adopts the following technical solutions: A method for supervising electric vehicles to be prohibited from entering an elevator comprises the following steps: 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 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.

[0007] As a further solution of the present invention, the load distribution characteristics include load center point coordinates, load offset rate, pressure concentration index, and pressure surge area coordinates; the abnormal load pattern includes 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 violation count statistics, violation behavior level, electric vehicle entry identification information, elevator authority adjustment results, and voice warning prompt content.

[0008] As a further solution of the present invention, the specific steps of obtaining car pressure data, monitoring pressure changes, analyzing the pressure difference before and after door opening, recording door opening and closing status, comparing load changes, calculating load center coordinates and offset rate, analyzing pressure concentration per unit area, screening pressure sudden increase areas, and extracting 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.

[0009] As a further solution of the present invention, the calculation formula of the horizontal coordinate value of the load center point is specifically: ; in, Represents the horizontal coordinate value of the load center point, Representative The pressure difference measured by the sensor nodes is Represents the arithmetic mean of the pressure differences of all sensor nodes, Representative The horizontal coordinate value of each sensor node in the car coordinate system, Representative The distance weight coefficient between the sensor node and the load center is Represents the total number of sensor nodes involved in the calculation, Represents all sensor nodes from the first to the To sum, Indicates The absolute difference between the pressure difference of each node and the average pressure difference, Indicates The square root of the sum of the square of the pressure difference of each node and the square of the weight coefficient.

[0010] As a further solution of the present invention, 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 reference 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 of identifying the abnormal load mode are: 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.

[0011] As a further solution of the present invention, the calculation formula of the load center point sequence offset distance and time ratio is specifically: ; in, Represents the ratio of the load center point sequence offset distance to time, Representative The horizontal coordinate of the load center point at each time point, Representative The horizontal coordinate of the load center point at each time point, Representative The vertical coordinate of the load center point at each time point, Representative The vertical coordinate of the load center point at each time point, Representative The instantaneous speed of the load center at a time point, Represents the average speed of the load center point during this continuous time period, Represents the total duration of a continuous time period. Representative Time value of a time point, Represents the average time value of all time points in this time period. represents the total number of load center point measurements during the time period, Represents the sum of data at all time points.

[0012] 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 are recorded, the pressure concentration change in a short period of time is analyzed, the elevator operation status is extracted, the matching degree between the load change and the operation status is calculated, and the specific steps of screening illegal events and generating illegal load records 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.

[0013] As a further solution of the present invention, the specific steps of recording illegal loads, counting the number of violations, extracting electric vehicle entry information, analyzing the trend of violation level changes, calling property regulations to compare punishment rules, adjusting elevator permissions, triggering voice warnings and generating warnings and disposal results are as follows: 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.

[0014] Based on the same inventive concept, a monitoring system for prohibiting electric vehicles from entering elevators is also proposed, which is used to implement the above-mentioned monitoring method for prohibiting electric vehicles from entering elevators, including: 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.

[0015] Based on the same inventive concept, a monitoring device for prohibiting electric vehicles from entering elevators is also proposed, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the above-mentioned monitoring method for prohibiting electric vehicles from entering elevators is implemented.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by accurately capturing the sudden increase in load in a short period of time and distinguishing the entry and exit characteristics of electric vehicles and passengers, the recognition accuracy is improved. The pressure concentration area detection combined with the operation status analysis is helpful to timely discover illegal loads and generate records, which is convenient for subsequent trend analysis and management. The elevator operation authority is dynamically adjusted according to the number and level of violations and alarms are triggered, which effectively curbs illegal behaviors, reduces the misjudgment rate, improves the response speed and management accuracy, and enhances the safety of elevator use. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a schematic diagram of the workflow of the present invention.

[0019] Figure 2 The flowchart of step S1 of the present invention is refined.

[0020] Figure 3 The flowchart of step S2 of the present invention is refined.

[0021] Figure 4 The flowchart of step S3 of the present invention is refined.

[0022] Figure 5 The flowchart of step S4 of the present invention is refined.

[0023] Figure 6 It is a flow chart of the device of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0025] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0026] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0027] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0028] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0029] See also Figure 1 The present invention provides a technical solution: a method for supervising electric vehicles to be prohibited from entering an elevator, comprising the following steps: 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 is opened, and record the elevator door opening and closing status information, compare the pressure change before and after the door is opened with the total load change value of the car, calculate the load center point coordinates and unit time displacement rate, analyze the unit area pressure concentration, screen the unit area pressure sudden increase area in a short period of time, and obtain the load distribution characteristics; S2: Based on the load distribution characteristics, extract the load center point unit time offset rate, compare the rate with the pedestrian entry and exit benchmark rate, analyze the pressure sensor data change trend, determine the load surge in a short period of time, calculate the total load unit time increase, compare the increase with the pedestrian entry and exit benchmark increase, screen abnormal data that meets the electric vehicle entry characteristics, analyze the load surge degree per unit area in the target area of ​​the car, and obtain the abnormal load pattern; S3: Based on the abnormal load pattern, extract the elevator number, timestamp, and car load distribution snapshot, record the illegal load distribution data, analyze the change value of load concentration per unit area in a short period of time, extract the elevator operation status record, calculate the matching degree between load change and elevator operation status, filter the illegal event data, compare the current elevator operation environment with the sudden increase in load, and generate illegal load records; S4: Based on the record of illegal loads, call the statistical information of the number of violations, extract the electric vehicle entry information, analyze the number of violations and the trend of level changes, call the property management regulations, compare the violation level and punishment rules, adjust the elevator operation authority, trigger the elevator voice warning, and generate early warning and disposal results.

[0030] 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 records include 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.

[0031] See also Figure 2 , the specific steps of S1 are: 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; First, pressure sensors are installed in various corners and the central area of ​​the elevator car to capture the pressure changes in the car in real time. The data collection frequency is set to ten times per second to ensure the continuity and accuracy of the data. 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. Then, the central processor calculates the mean pressure data at each time point before and after the door is opened. The specific calculation method is to perform arithmetic averaging on the data of each sensor within one minute before and after the door is opened. Considering the frequency of use and load changes of the elevator, such a time window can better reflect the actual situation of pressure changes. By comparing the pressure means before and after the door is opened, the pressure mean difference is obtained. This difference reflects the impact of the door opening event on the pressure inside the car. Based on the pressure mean difference, the pressure difference distribution is further generated. This distribution is achieved by treating the interior of the car as a plane grid. The value on each grid point is the pressure difference recorded by the corresponding position sensor. The data between the grid points are filled in by a two-dimensional interpolation method to generate a continuous pressure difference distribution map. The distribution map visualizes the specific location and degree of pressure change in the car. As the basis for subsequent analysis, the process of generating the pressure difference distribution is combined with the actual elevator operation data. For example, in a door opening event, assuming that the initial mean pressure inside the car is 1013 hPa and 1010 hPa after the door is opened, the corresponding pressure difference distribution can reveal the air flow and pressure adjustment caused by the door opening.

[0032] S102: calling the pressure difference distribution and the sensor node coordinates, analyzing the pressure difference weighted coordinates, obtaining the load center coordinates, calculating the horizontal coordinate value of the load center, and generating the load offset rate value; The calculation formula of the horizontal coordinate value of the load center point is as follows: ; in, Represents the horizontal coordinate value of the load center point, Representative The pressure difference measured by the sensor nodes is Represents the arithmetic mean of the pressure differences of all sensor nodes, Representative The horizontal coordinate value of each sensor node in the car coordinate system, Representative The distance weight coefficient between the sensor node and the load center is Represents the total number of sensor nodes involved in the calculation, Represents all sensor nodes from the first to the To sum, Indicates The absolute difference between the pressure difference of each node and the average pressure difference, Indicates The square root of the sum of the square of the pressure difference of each node and the square of the weight coefficient.

[0033] Load center point horizontal coordinate value The calculation of is based on the pressure difference measured by the sensor node, the node coordinates and the weight coefficient, and is obtained by weighted summing the data of all sensor nodes and dividing by the weighted sum. The parameter explanation and acquisition method are as follows: Sensor node pressure difference The pressure data is collected in real time by the pressure sensor installed on the car floor. According to the GB / T9969-2021 pressure sensor standard, the sensor measurement range 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.

[0034] Average value of node pressure difference The pressure difference collected by all sensor nodes at the same time point is calculated by arithmetic average, and the calculation formula is: .

[0035] Sensor node coordinates The coordinate system measurement is established according to 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 meter.

[0036] Weight coefficient The calculation is based on the distance between the sensor node and the center of the car. The setting basis is that the farther the distance, the smaller the impact weight. The calculation method is: ,in 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.

[0037] The actual example data collection and calculation process are as follows: Set four sensor nodes in the car (N=4), and collect the pressure difference as follows: ; Calculate the average pressure difference: ; The coordinates of the sensor nodes are: ; Calculation of the distance between the node and the center of the car: ; Weight coefficient calculation: ; Compute the numerator term for each node: Node 1: ; ; ; Node 2: ; ; ; Node 3: ; ; ; Node 4: ; ; ; Numerator sum: ; Calculate the denominator: ; Calculate the horizontal coordinate value of the load center point: ; The result shows that the horizontal coordinate position of the load center point is biased to the right side of the car. The numerical result of 36.58m is used for subsequent center point sequence offset distance calculation to help identify load distribution offset.

[0038] S103: Based on the load offset rate value and the pressure difference distribution, the pressure concentration degree per unit area is calculated, the sudden increase area is screened, and the load distribution characteristics are obtained; First, according to the pressure difference distribution map generated above, the pressure value in each grid unit is calculated, and then these pressure values ​​are statistically analyzed to find out the areas where the pressure value per unit area exceeds a certain proportion (for example, 10%) of the average pressure. These areas are marked as pressure concentration areas. Subsequently, a mathematical model is used 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. Next, the sudden increase areas are screened out, that is, those areas that show a rapid increase in pressure in continuous measurements. This screening process involves calculating the pressure change rate of each grid unit, and selecting areas with a change rate higher than a set threshold (for example, a change rate of more than 5% per second) as sudden increase areas. In this way, the load distribution characteristics are obtained, which comprehensively consider the speed and concentration of pressure changes, and provide valuable data for elevator maintenance and safety monitoring. For example, during the peak use period of the elevator, a sudden increase in pressure may be observed in a corner of a car, indicating possible overload or uneven distribution. At this time, the elevator control system can automatically adjust or issue an alarm to ensure passenger safety.

[0039] See also Figure 3 , the specific steps of S2 are: S201: Obtain load distribution characteristics, calculate the ratio of load center point coordinate offset distance to time interval in a continuous time period, and generate a load offset rate value; The specific calculation formula for the load center point sequence offset distance and time ratio is: ; in, Represents the ratio of the load center point sequence offset distance to time, Representative The horizontal coordinate of the load center point at each time point, Representative The horizontal coordinate of the load center point at each time point, Representative The vertical coordinate of the load center point at each time point, Representative The vertical coordinate of the load center point at each time point, Representative The instantaneous speed of the load center at a time point, Represents the average speed of the load center point during this continuous time period, Represents the total duration of a continuous time period. Representative Time value of a time point, Represents the average time value of all time points in this time period. represents the total number of load center point measurements during the time period, Represents the sum of data at all time points.

[0040] The ratio of load center point sequence offset distance to time The calculation is based on the load center coordinate sequence, load center instantaneous speed and time data. The parameter description and acquisition method are as follows: Load center point horizontal coordinate and the vertical coordinate The coordinates are measured in real time by the pressure sensor array. The coordinates are based on the center of the car to establish a two-dimensional coordinate system. The sensor measurement frequency is 10Hz, and the coordinate data acquisition accuracy is ±0.01 meters. The calculation formula is: . Average load center speed Calculated by arithmetic average of all instantaneous speeds. Total duration of continuous time segment Obtained from the time difference between the beginning and the end of the sequence. Automatically recorded by the sensor system, average time value The calculation formula is . Sensor data acquisition is based on the requirements of the building equipment standard GB50034-2021.

[0041] The actual monitoring data was collected through four sensors installed in the elevator car at 14:32 on February 22, 2024. The data is as follows: Time point series ; Load center coordinates: ; ; ; ; Instantaneous speed calculation: Speed ​​at the second time point: ; Speed ​​at time 3: ; Speed ​​at time point 4: ; Average speed calculation: ; Calculation of the offset distance numerator: ; Speed ​​deviation and: ; Molecular results: ; Denominator calculation: Total time period: ; Average time value: ; The sum of squares of time deviations: ; Square root of time deviation: ; Denominator calculation: ; Calculation of load center offset distance and time ratio: ; The result shows that the average displacement speed of the load center point within 3 seconds is 0.0180m / s. The value is related to the load stability and movement speed, and is used for subsequent load dynamic analysis and displacement trend determination.

[0042] S202: calling the load offset rate value and the pedestrian entry and exit reference rate, calculating the offset rate difference between the two, setting the 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 the load sudden increase trend value; First, the load offset rate is compared with the pedestrian entry and exit rate specified in the elevator design or actually measured. The pedestrian baseline rate is the average value obtained based on the frequency of elevator use and passenger flow statistics. The difference between the two offset rates is calculated, and the difference between the two rates is calculated by simple arithmetic difference. The offset rate threshold is set. This threshold is set based on historical data analysis and safety standards, reflecting the maximum allowable rate deviation. Data segments with offset rate differences exceeding the threshold are screened out. These data segments may indicate abnormalities or potential safety issues in elevator operation. The abnormal offset rate interval sequence is obtained, and the pressure sensor value sequence in these intervals is further analyzed to view its change trend. This analysis involves calculating the change slope of the continuous data sequence, and finding the trend line of pressure change by performing linear regression analysis on continuous data points. Based on the slope, it is determined whether there is a sudden increase in load in a short period of time, and a load sudden increase trend value is generated. This value reflects the speed and direction of change of the elevator load in a short period of time. For example, if the slope of pressure change increases significantly over a period of time, it indicates that more people may suddenly enter or leave the elevator. This analysis helps identify and respond to potential overload events.

[0043] 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; The load surge trend value is used to calculate the total load change within a certain time window. The absolute value of the total load change is obtained by integral calculation. The difference is compared with the pedestrian entry and exit benchmark increase. The pedestrian benchmark increase is preset based on the elevator's designed maximum load and typical usage conditions, reflecting the expected load change amplitude under normal usage conditions. The abnormal data segments whose difference exceeds the load increase threshold per unit time are screened out. This threshold is set based on safety standards and historical load data, and is intended to identify load changes that do not conform to normal operating modes. The obtained load abnormal increase value indicates that the rate of load increase or decrease within the observation window is abnormal. This indicator is crucial for elevator safety monitoring and maintenance management. For example, during busy working hours, elevators may experience rapid load changes. By real-time monitoring and analysis of these data, the elevator operation strategy can be adjusted in time or an alarm can be issued to avoid accidents caused by overloading.

[0044] 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; The abnormal load increase value and the load sudden increase trend value are called to calculate the load difference per unit area and the area ratio of the target area in the car. First, according to the abnormal load increase value and the load sudden increase trend value, the specific areas inside the elevator car are located. These areas may show abnormalities due to uneven load distribution, and the load difference per unit area of ​​each area is calculated. This calculation takes into account the specific size of the area and the corresponding load change. The load difference per unit area is obtained by dividing it by the area of ​​the area, and further screens out data with a sudden increase exceeding the load sudden increase threshold 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 on possible load concentration or uneven distribution in specific areas, which is crucial to maintaining the normal operation of the elevator and the safety of passengers. For example, during elevator operation, if it is detected that the load difference per unit area in a certain area is significantly higher than that in other areas, this may indicate that there is accumulation or aggregation in the area, thereby triggering a safety inspection or emergency measures.

[0045] See also Figure 4 , the specific steps of S3 are: 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; First, all qualified abnormal load data are retrieved from the abnormal load pattern recognition module, and the corresponding elevator number is extracted for each abnormal data to uniquely identify the specific device. The timestamp is used to record the exact time when the abnormality occurred. The car load distribution snapshot is generated by the pressure value matrix obtained by the pressure sensor in real time during the abnormal time period. This information is integrated to form a preliminary data set containing the elevator number, abnormal time and load distribution. It is then screened according to the pre-set load abnormality threshold. This threshold is determined by the elevator design standard and historical operation data. The setting range is, for example, when the load deviation rate exceeds 0.15 meters per second and the unit area pressure exceeds 100 kilograms per square meter. It is identified as abnormal when the load snapshot exceeds the threshold value. By judging whether the pressure of any cell in the load snapshot exceeds the threshold value and matching the data of the corresponding time period, the qualified data is recorded in the abnormal load snapshot set. Taking an actual example, it is assumed 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 of ​​the left front corner reaches 120 kilograms per square meter and the load center point offset rate is 0.18 meters per second, which meets the abnormal judgment conditions. Therefore, it is selected into the abnormal snapshot set to generate records. This snapshot set can eventually be used for subsequent load evolution trend analysis and elevator status evaluation to form an abnormal load snapshot set for monitoring abnormal status.

[0046] S302: Call the abnormal load snapshot set, calculate the load concentration change value per unit area in a short period of time, extract the elevator operation status record, compare the load concentration change value with the elevator operation status change sequence, calculate the time series matching degree between the two, and filter the data with a matching degree lower than the load operation matching degree threshold to generate a load operation matching degree value; First, for each record in the abnormal load snapshot set, the unit area load data between consecutive snapshots is extracted, and the load concentration change value is calculated by comparing the pressure difference at the same position of the 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 the total area of ​​the car to obtain the overall concentration change value. Then, the elevator operation status record is extracted, which contains the elevator ascent, descent, door opening and closing, start and stop status information, and the time series is arranged to compare with the load concentration change value, and the time series matching degree of the two is calculated. By counting the synchronization rate of the two time points, that is, calculating the load change per unit time. The overlap percentage of the peak value and the operation status change point is calculated, and the load operation matching threshold is set, such as 80%. The data segments with matching degrees lower than this value are screened out, and the load operation matching value is finally generated. For example, when the load concentration per unit area 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. In the corresponding time period, the elevator only performs stop and door closing actions. The calculated matching degree is 65%, which is lower than the set threshold. Therefore, this data segment is marked as an abnormal operation load matching record, forming a load operation matching value for further illegal load analysis.

[0047] S303: Based on the load operation matching value and the abnormal load snapshot set, the current elevator operation environment and the load sudden increase are compared, and the data sequence that meets the illegal load characteristics is selected to obtain the illegal load record; First, call the load operation matching value and abnormal load snapshot set generated in step S302, and compare the operating environment information of the elevator in the corresponding time period. The operating environment parameters include the floor where the elevator is located, peak hours, full-load operation warning status, etc. By directly comparing whether the load surge trend value overlaps with the time period in which the operating status occurs, filter out the data sequence that does not match the operating status but has an obvious load surge, and then identify whether there is an illegal load feature by judging the sudden change in load offset rate and load concentration. The judgment criteria for illegal load features are, for example, a short-term load increase of more than 50 kg / m2 and an operating status of There is no corresponding change. This type of sequence is extracted to generate a record of illegal loads. The example shows that at 14:35 on February 22, when the elevator No. "EL1001" did not change its operating status, the load center position offset rate suddenly increased from 0.05 m / s to 0.22 m / s and the pressure in the left area increased by 60 kg / m2 within 4 seconds, which meets the characteristics of illegal loads. The illegal load record number "EL1001-20250222-1435" is generated. This record is used in the illegal load file of the elevator load behavior analysis and monitoring system to form a record of illegal loads for subsequent processing and review.

[0048] See also Figure 5 , the specific steps of S4 are: S401: Based on the illegal load record, call the illegal number statistical information, extract the electric vehicle entry related data, calculate the illegal number and generate the illegal level, and obtain the illegal level change value; First, retrieve the generated illegal load records, which contain information such as elevator number, violation time, illegal load mode and load offset rate. For each record, retrieve the entry and exit device data related to the elevator operation in the database, especially extract the associated data when electric vehicles enter the elevator. This data is collected in real time by the camera and RFID scanning system at the elevator entrance. The data fields include device type, entry and exit time, vehicle weight estimation and owner information. Then, count the illegal load records of the same elevator within a set period (for example, 30 days), calculate the total number of violations for each elevator, and implement the violation count through a simple counting function. For example, the elevator numbered "EL2002" has a total of 1,000 violations. During the statistical period, a total of 7 illegal load events were detected, including 4 illegal entry events of electric vehicles. The number of violations was compared with the preset violation level classification standard, and the violation level was set according to the number of statistics. 0-1 times corresponds to "level 1", 2-3 times to "level 2", 4-5 times to "level 3", and 6 times and above to "level 4". According to the above example, the elevator "EL2002" has 4 violations, and its violation level is determined to be "level 3". Then the change value between the historical and current violation levels is calculated. The change value is calculated by subtracting the previous level from the current level. For example, the elevator was "level 2" last month and was upgraded to "level 3" this month. The change value is +1, and the violation level change value is finally generated.

[0049] 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; First, retrieve the punishment rule file in the property management system, which lists the management measures corresponding to different violation levels in detail, extract the punishment level corresponding to the current violation level change value, and calculate the level difference, which is the current violation level minus the starting level specified in the punishment rule. For example, when the violation level is "level three" and the property stipulates that the starting penalty is "level two", the level difference is calculated as 3-2=1. Then set the punishment 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 punishment measures need to be triggered, and all data records that meet the conditions are screened out. The elevators that meet the conditions are marked, and their elevator operation permissions are adjusted according to the property management system regulations. The adjustment of operation permissions includes operating speed restrictions, prohibition of peak hours, and prohibition of stopping at some floors. The range of operation permission adjustment values ​​is set: no adjustment is 0, partial restriction is 1, and severe restriction is 2. For example, the elevator "EL2002" meets the triggering conditions because the violation level change value is +1 and the level difference is equal to 1. The property stipulates that some restrictions are performed on level three illegal elevators, so the generated elevator operation permission adjustment value is 1.

[0050] S403: Based on the elevator operation authority adjustment value, the elevator voice device is triggered to generate a voice warning instruction sequence and execute the prompt content output to obtain the warning and disposal results; First, the elevator operation authority adjustment value generated in step S402 is called, and the preset voice prompt content is matched according to different adjustment values. When the adjustment value is 1, the prompt content is "The elevator has been partially restricted, please use it in a standardized manner"; if the adjustment value is 2, it will prompt "The elevator has been severely restricted and will be suspended during the restriction period". The system retrieves and locates the voice device address of the target elevator according to the elevator number, and generates a corresponding voice command sequence, which includes the elevator number, trigger time, voice content and playback duration. For example, for elevator "EL2002", the adjustment value is 1, and the generated voice command sequence is "EL2002-20250222-153000-Partial Restriction Prompt-10 seconds". The system sends instructions to the elevator's internal speaker through the central controller. The device plays the prompt content immediately after receiving it. After the playback is completed, the system records the prompt completion status and feeds back the execution time to the data log, and finally generates an early warning and disposal result.

[0051] See also Figure 6 Based on the same inventive concept, a monitoring system for prohibiting electric vehicles from entering elevators is also proposed, which is used to implement the above-mentioned monitoring method for prohibiting electric vehicles from entering elevators, including: 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 benchmark rate, screens abnormal data that meets the characteristics of electric vehicle entry, 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 based on 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 status, screens the violation events and generates the illegal load record; The early warning management module is based on the illegal load records, counts the number of violations, extracts the electric vehicle entry information, analyzes the trend of changes in violation levels, calls the property regulations to compare the punishment rules, triggers voice warnings and generates early warnings and disposal results.

[0052] Based on the same inventive concept, a monitoring device for prohibiting electric vehicles from entering elevators is also proposed, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the above-mentioned monitoring method for prohibiting electric vehicles from entering elevators is implemented.

[0053] The above is only a specific embodiment 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, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on 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 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.

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: ； in, Represents the horizontal coordinate value of the load center point, Representative The pressure difference measured by the sensor nodes is Represents the arithmetic mean of the pressure differences of all sensor nodes, Representative The horizontal coordinate value of each sensor node in the car coordinate system, Representative The distance weight coefficient between the sensor node and the load center is Represents the total number of sensor nodes involved in the calculation, Represents all sensor nodes from the first to the To sum, Indicates The absolute difference between the pressure difference of each node and the average pressure difference, Indicates The square root of the sum of the square of the pressure difference of each node and the square of the weight coefficient.

5. The method for monitoring electric vehicles against entering elevators according to claim 1, characterized in that: 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.

6. The method for monitoring electric vehicles against entering elevators according to claim 5, characterized in that: The specific calculation formula of the load center point sequence offset distance and time ratio is: ； in, Represents the ratio of the load center point sequence offset distance to time, Representative The horizontal coordinate of the load center point at each time point, Representative The horizontal coordinate of the load center point at each time point, Representative The vertical coordinate of the load center point at each time point, Representative The vertical coordinate of the load center point at each time point, Representative The instantaneous speed of the load center at a time point, Represents the average speed of the load center point during this continuous time period, Represents the total duration of a continuous time period. Representative Time value of a time point, Represents the average time value of all time points in this time period. represents the total number of load center point measurements during the time period, Represents the sum of data at all time points.

7. 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.

8. The method for monitoring electric vehicles against entering elevators according to claim 7, 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.

9. A monitoring system for prohibiting electric vehicles from entering elevators, characterized in that: A method for monitoring the prohibition of electric vehicles from entering an elevator according to any one of claims 1 to 8, comprising: 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.

10. 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 8 is implemented.

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