Dual passenger monitoring system for electric vehicle based on multi-sensor dynamic threshold decoupling

Abstract

The utility model relates to a kind of electric vehicle double-person carrying monitoring system based on multi-sensor dynamic threshold decoupling, comprising: sensor unit, data collector, communication unit, intelligent processing unit, decision control unit and alarm unit.The beneficial effects of the utility model are: in multiple position distributed arrangement pressure sensor array, combined with the setting motion state sensor, cover the typical position of electric single car multiple people ride;Dynamic threshold adjustment module and anti-interference processing module significantly improve the accuracy and reliability of double-person ride detection;Pressure sensor can be used for directly monitoring saddle stress distribution, and vehicle shock absorption structure is completely decoupled, avoid the interference of traditional factors such as frame assembly accuracy, screw tightness on detection result, enhance the stability and adaptability of system;Solve the precision low, structure coupling strong, high cost and other problems existing in the monitoring of shared electric single car, two-wheeled electric vehicle, three-wheeled electric vehicle and other vehicle carrying irregularities.

Description

Technical Field

[0001] This utility model belongs to the field of electric vehicle safety monitoring technology, and in particular relates to an electric vehicle dual-passenger monitoring system based on multi-sensor dynamic threshold decoupling. Background Technology

[0002] With the widespread use of electric vehicles in urban transportation, passenger safety has become a key focus of traffic management. Traffic regulations clearly stipulate that adults may carry one child under the age of 12 while driving an electric vehicle on urban roads, and the total number of passengers on an electric vehicle on other roads must not exceed one. Shared electric vehicles typically only have a single seat and are strictly prohibited from carrying persons over 12 years of age. However, in reality, there are still instances of two or more users illegally riding electric vehicles, which increases the risk of accidents during riding. Therefore, effective technical monitoring methods are urgently needed to detect whether electric vehicles are overloaded.

[0003] Currently, most existing electric vehicles are not equipped with load monitoring modules, making it impossible to effectively monitor whether a person is carrying passengers. Although a small number of existing electric vehicles have load monitoring functions, they can only detect whether a load is being carried and cannot accurately determine whether the electric vehicle is carrying one person or multiple people.

[0004] In existing technologies, electric vehicles with load monitoring functions mostly calculate the load capacity by monitoring the deformation of the vehicle's shock absorber structure (e.g., by monitoring the shock absorber deformation using a linear Hall sensor and combining it with the load-bearing elastic deformation coefficient of the shock absorber to calculate the load capacity, or by combining strain gauges with damping springs and approximating the overall vehicle load capacity by the change in the resistance of the strain gauges). However, existing load monitoring methods still have the following drawbacks:

[0005] 1) Low monitoring accuracy and inability to distinguish the number of people: Existing load monitoring technology can only output a binary judgment of "loaded / unloaded", and cannot accurately identify the scenario of "one person carrying load" or "multiple people carrying load" based on the difference in load weight (such as when the weight difference between children and adults is small, or when a single person is carrying heavy objects, it is easy to make a misjudgment).

[0006] 2) Strongly coupled with the vehicle structure and highly susceptible to environmental interference: Based on monitoring methods such as shock absorber deformation and strain gauges, the monitoring results are significantly affected by factors such as the assembly precision of the frame (e.g., screw tightness), batch differences of shock absorbers, and the degree of road bumps, resulting in huge monitoring errors between different vehicles, which cannot meet the accuracy requirements of traffic management for passenger monitoring.

[0007] 3) Privacy and cost issues are prominent: The technical solutions using weighing sensors or computer vision (such as cameras to identify the number of passengers) are problematic. The former requires contact measurement, which may cause user resistance, while the latter involves image data collection, which poses a risk of privacy leakage and has high hardware costs (the cost of a single vision system exceeds 1,000 yuan), making it difficult to achieve large-scale application in scenarios such as shared electric vehicles.

[0008] In summary, existing electric vehicle passenger monitoring technologies cannot simultaneously meet core requirements such as accurately distinguishing the number of passengers, decoupling from the vehicle structure, low cost, and low privacy risks. Therefore, there is an urgent need to develop an electric vehicle dual-passenger monitoring system to address the shortcomings of existing technologies and improve the safety and compliance of electric vehicle riding. Utility Model Content

[0009] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a dual-passenger monitoring system for electric vehicles based on multi-sensor dynamic threshold decoupling.

[0010] This electric vehicle dual-occupancy monitoring system based on multi-sensor dynamic threshold decoupling includes: a sensor unit, a data acquisition unit, a communication unit, an intelligent processing unit, a decision control unit, and an alarm unit; the sensor unit is electrically connected to the data acquisition unit and the decision control unit, the data acquisition unit is electrically connected to the communication unit, the communication unit is electrically connected to the intelligent processing unit, the intelligent processing unit is electrically connected to the decision control unit, and the decision control unit is electrically connected to the alarm unit.

[0011] The sensor unit is used to acquire multi-source data and to send multi-source data to the data acquisition unit.

[0012] A data acquisition unit is used to send multi-source data to an intelligent processing unit via a communication unit.

[0013] A communication unit is used to form a communication link between the data acquisition unit and the intelligent processing unit;

[0014] The intelligent processing unit is used to preprocess multi-source data, dynamically adjust the judgment threshold and continuously optimize the built-in threshold model, filter out unstable data in the preprocessed multi-source data, identify and eliminate sudden interference, and send the multi-source data processed as described above to the decision control unit.

[0015] The decision control unit is used to determine the passenger status of the electric vehicle based on the data processed by the intelligent processing unit, and to send graded response instructions to the alarm module based on the determination of the passenger status.

[0016] The alarm unit is used to trigger a level one or level two alarm based on the tiered response command.

[0017] Preferably, the sensor unit includes a pressure sensor array composed of multiple pressure sensors, which are distributed in the areas of the electric vehicle seat that are frequently subjected to force, such as the front and rear sides and the interior of the electric vehicle seat; the multi-source data acquired by the sensor unit is the pressure value obtained by the pressure sensor array.

[0018] As a preferred option:

[0019] The intelligent processing unit includes a data processing module, a dynamic threshold adjustment module, and an anti-interference processing module;

[0020] The data processing module is used for preprocessing multi-source data;

[0021] The dynamic threshold adjustment module is used to dynamically adjust the judgment threshold and continuously optimize the built-in threshold model.

[0022] The anti-interference processing module is used to filter out unstable data in the preprocessed multi-source data and to identify and eliminate sudden interference.

[0023] Preferably, the sensor unit includes a motion state sensor and a pressure sensor array consisting of multiple pressure sensors; the pressure sensor array is distributed in the areas of frequent stress on the front and rear sides of the electric vehicle seat and inside the electric vehicle seat; the motion state sensor is installed at the vehicle saddle position.

[0024] As a preferred option:

[0025] The intelligent processing unit includes a data processing module and a dynamic threshold adjustment module;

[0026] The data processing module is used for preprocessing multi-source data;

[0027] The dynamic threshold adjustment module is used to dynamically adjust the judgment threshold and continuously optimize the built-in threshold model.

[0028] The beneficial effects of this utility model are:

[0029] A multi-location distributed pressure sensor array, combined with motion state sensors, covers typical locations where multiple people ride on electric bicycles. Dynamic threshold adjustment and anti-interference processing modules significantly improve the accuracy and reliability of double-person passenger detection. The pressure sensors can directly monitor the force distribution on the saddle, completely decoupled from the vehicle's shock absorption structure, avoiding interference from traditional factors such as frame assembly precision and screw tightness, thus enhancing the system's stability and adaptability. This solves the problems of low accuracy, strong structural coupling, and high cost in monitoring violations involving passenger transport on shared electric bicycles, two-wheeled electric vehicles, and three-wheeled electric vehicles. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the sensor arrangement;

[0031] Figure 2 This is a schematic diagram of a pressure value acquisition and processing device;

[0032] Figure 3 This is a schematic diagram of the stress areas on the car seat;

[0033] Figure 4 This is an architecture diagram of the electric vehicle dual-passenger monitoring system based on multi-sensor dynamic threshold decoupling according to this utility model.

[0034] Explanation of reference numerals in the attached diagram: 1. Pressure plate; 2. Soft sleeve; 3. Spring; 4. Pressure plate base; 5. Sensor unit; 6. Base. Detailed Implementation

[0035] The present invention will be further described below with reference to embodiments. The description of the embodiments below is only for the purpose of helping to understand the present invention. It should be noted that, for those skilled in the art, several modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0036] Example 1

[0037] A dual-occupancy monitoring system for electric vehicles based on multi-sensor dynamic threshold decoupling includes: a sensor unit, a data acquisition unit, a communication unit, an intelligent processing unit, a decision control unit, and an alarm unit; the sensor unit is electrically connected to the data acquisition unit and the decision control unit, the data acquisition unit is electrically connected to the communication unit, the communication unit is electrically connected to the intelligent processing unit, the intelligent processing unit is electrically connected to the decision control unit, and the decision control unit is electrically connected to the alarm unit;

[0038] The sensor unit includes a pressure sensor array composed of multiple pressure sensors, which are distributed in areas frequently subjected to force, such as the front and rear sides of the electric vehicle seat and inside the electric vehicle seat; the multi-source data acquired by the sensor unit is the pressure value obtained by the pressure sensor array.

[0039] The intelligent processing unit includes a data processing module, a dynamic threshold adjustment module, and an anti-interference processing module;

[0040] The data processing module is used for preprocessing multi-source data;

[0041] The dynamic threshold adjustment module is used to dynamically adjust the judgment threshold and continuously optimize the built-in threshold model.

[0042] The anti-interference processing module is used to filter out unstable data in the preprocessed multi-source data and to identify and eliminate sudden interference.

[0043] The sensor unit is used to acquire multi-source data and to send multi-source data to the data acquisition unit.

[0044] A data acquisition unit is used to send multi-source data to an intelligent processing unit via a communication unit.

[0045] A communication unit is used to form a communication link between the data acquisition unit and the intelligent processing unit;

[0046] The intelligent processing unit is used to preprocess multi-source data, dynamically adjust the judgment threshold and continuously optimize the built-in threshold model, filter out unstable data in the preprocessed multi-source data, identify and eliminate sudden interference, and send the multi-source data processed as described above to the decision control unit.

[0047] The decision control unit is used to determine the passenger status of the electric vehicle based on the data processed by the intelligent processing unit, and to send graded response instructions to the alarm module based on the determination of the passenger status.

[0048] The alarm unit is used to trigger a level one or level two alarm based on the tiered response command.

[0049] Example 2

[0050] A dual-occupancy monitoring system for electric vehicles based on multi-sensor dynamic threshold decoupling, such as Figure 4 As shown, it includes: a sensor unit, a data acquisition unit, a communication unit, an intelligent processing unit, a decision control unit, and an alarm unit; the sensor unit is electrically connected to the data acquisition unit and the decision control unit, the data acquisition unit is electrically connected to the communication unit, the communication unit is electrically connected to the intelligent processing unit, the intelligent processing unit is electrically connected to the decision control unit, and the decision control unit is electrically connected to the alarm unit;

[0051] like Figure 3 As shown, the sensor unit includes a motion state sensor and a pressure sensor array composed of multiple pressure sensors; the pressure sensor array is distributed in a distributed manner on the front and rear sides of the electric vehicle seat (with a spacing of 20-30cm between them) and in areas inside the electric vehicle seat that are frequently subjected to force: as shown Figure 2 As shown, the first pressure sensor array (three high-precision pressure sensors) is fixedly installed at the front of the electric vehicle seat (10cm from the front edge of the seat) with screws, and the second pressure sensor array (three high-precision pressure sensors) is fixedly installed at the rear of the electric vehicle seat (8cm from the rear edge of the seat) with screws; the motion state sensor (including a three-axis accelerometer and a three-axis gyroscope) is installed at the center of gravity of the frame.

[0052] The sensor unit is used to acquire multi-source data and send it to the data acquisition unit. The multi-source data acquired by the sensor unit includes: pressure values ​​(pressure distribution characteristics) acquired by the pressure sensor array, vehicle acceleration, tilt angle, and vibration characteristics (vibration characteristics serve as a reference for subsequent anti-interference) acquired by the motion state sensor.

[0053] A data acquisition unit is used to send multi-source data to an intelligent processing unit via a communication unit.

[0054] The communication unit is used to form a communication link (485 signal line) between the data acquisition unit and the intelligent processing unit.

[0055] The intelligent processing unit includes a data processing module, a dynamic threshold adjustment module, and an anti-interference processing module. The data processing module is used to preprocess multi-source data. The dynamic threshold adjustment module is used to dynamically adjust the judgment threshold and continuously optimize the built-in threshold model. The anti-interference processing module is used to filter out unstable data in the preprocessed multi-source data and to identify and eliminate sudden interference.

[0056] The intelligent processing unit is used to preprocess multi-source data, dynamically adjust the judgment threshold and continuously optimize the built-in threshold model, filter out unstable data in the preprocessed multi-source data, identify and eliminate sudden interference, and send the multi-source data processed as described above to the decision control unit.

[0057] The decision control unit is used to determine the passenger status of the electric vehicle (whether the electric vehicle is in a state of multiple passengers) based on the data processed by the intelligent processing unit, and to send graded response instructions to the alarm module based on the passenger status determination result; a microcontroller MCU is used as the intelligent processing unit and the decision control unit, enabling the intelligent processing unit to have data processing capabilities and the decision control unit to have logical judgment capabilities.

[0058] The alarm unit is used to trigger a level one or level two alarm based on the tiered response command.

[0059] like Figure 1As shown, sensor unit 5 integrates motion state sensors and multiple pressure sensors, effectively distinguishing between human riding status and cargo carrying status, improving the recognition accuracy to over 95%. The sensors are fixed as follows: spring 3 is nested within the outer wall of soft sleeve 2 (silicone material), with both ends of spring 3 fixedly connected to pressure plate 1 and pressure plate seat 4 respectively. The bottom of pressure plate seat 4 is fixedly connected to base 6, and sensor unit 5, including multiple sensors, is located between pressure plate seat 4 and base 6. The anti-interference processing module significantly reduces the impact of road bumps, temporary placement of items, etc., controlling the false alarm rate to within 3%. The edge computing capability of the data processing module within the intelligent processing unit improves the system response speed by more than 5 times, while reducing power consumption by 40% and extending battery life to over 12 months. The multi-level warning of the decision control unit and the data recording function of the data collector improve the system's practicality and traceability, facilitating management and optimization.

Claims

1. A dual-occupancy monitoring system for electric vehicles based on multi-sensor dynamic threshold decoupling, characterized in that, include: Sensor unit, data acquisition unit, communication unit, intelligent processing unit, decision control unit, and alarm unit; The sensor unit is electrically connected to the data acquisition unit and the decision control unit; the data acquisition unit is electrically connected to the communication unit; the communication unit is electrically connected to the intelligent processing unit; the intelligent processing unit is electrically connected to the decision control unit; and the decision control unit is electrically connected to the alarm unit. The sensor unit is used to acquire multi-source data and to send the multi-source data to the data acquisition unit. The data acquisition device is used to send the multi-source data to the intelligent processing unit through the communication unit; The communication unit is used to form a communication link between the data collector and the intelligent processing unit; The intelligent processing unit is used to preprocess the multi-source data, dynamically adjust the judgment threshold and continuously optimize the built-in threshold model, filter out unstable data in the preprocessed multi-source data, identify and eliminate sudden interference, and send the multi-source data processed as described above to the decision control unit. The decision control unit is used to determine the passenger status of the electric vehicle based on the data processed by the intelligent processing unit, and to send a graded response command to the alarm unit based on the determination result of the passenger status. The alarm unit is used to trigger a level one or level two alarm according to the level response command.

2. The electric vehicle dual-occupancy monitoring system based on multi-sensor dynamic threshold decoupling according to claim 1, characterized in that: The sensor unit includes a pressure sensor array composed of multiple pressure sensors, which are distributed in areas frequently subjected to force, such as the front and rear sides of the electric vehicle seat and inside the electric vehicle seat; the multi-source data acquired by the sensor unit is the pressure value obtained by the pressure sensor array.

3. The electric vehicle dual-occupancy monitoring system based on multi-sensor dynamic threshold decoupling according to claim 2, characterized in that: The intelligent processing unit includes a data processing module, a dynamic threshold adjustment module, and an anti-interference processing module. The data processing module is used to preprocess the multi-source data; The dynamic threshold adjustment module is used to dynamically adjust the judgment threshold and continuously optimize the built-in threshold model. The anti-interference processing module is used to filter out unstable data in the preprocessed multi-source data and to identify and eliminate sudden interference.

4. The electric vehicle dual-occupancy monitoring system based on multi-sensor dynamic threshold decoupling according to claim 1, characterized in that: The sensor unit includes a motion state sensor and a pressure sensor array consisting of multiple pressure sensors; the pressure sensor array is distributed in the areas of frequent stress on the front and rear sides of the electric vehicle seat and inside the electric vehicle seat; the motion state sensor is installed at the vehicle saddle position.

5. The electric vehicle dual-occupancy monitoring system based on multi-sensor dynamic threshold decoupling according to claim 4, characterized in that: The intelligent processing unit includes a data processing module and a dynamic threshold adjustment module; The data processing module is used to preprocess the multi-source data; The dynamic threshold adjustment module is used to dynamically adjust the judgment threshold and continuously optimize the built-in threshold model.

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