Online car-hailing driver cheating identification method and system based on Bluetooth key

Abstract

The invention relates to the technical field of online car-hailing driver monitoring, and discloses an online car-hailing driver cheating identification method based on a Bluetooth key, and the method comprises the steps: S10, binding a unique identifier of a driver device with the Bluetooth key, the unique identifier comprising a driver device id and a mobile phone number, and the driver device comprising a driver mobile phone; and S20, collecting a data set of the driver vehicle to be identified through a data sensor, and transmitting the data set to the Bluetooth key. S30, obtaining order information of a driver, wherein the order information comprises starting point position information, passing point position information and end point position information in an order; and S40, the data set of the to-be-identified vehicle is sent to a monitoring module through the Bluetooth key, and the monitoring module judges whether the driver has a cheating behavior or not.

Description

Technical Field

[0001] The present invention relates to the technical field of online car-hailing driver monitoring, and particularly to a method and system for identifying cheating of online car-hailing drivers based on a Bluetooth key. Background Art

[0002] With the rapid development of the online car-hailing industry, more and more drivers have joined this industry, and the competition has become increasingly fierce. However, in order to seek improper benefits, some drivers have started to use cheating means such as location tampering. They falsify the GPS data of the mobile phone through false positioning or improper means, creating false order-taking trip records, so as to defraud the order fees paid by the platform. This kind of behavior not only seriously infringes on the interests of the platform, but also undermines the fairness and healthy development of the industry.

[0003] The existing methods for identifying cheating of online car-hailing drivers mainly judge whether there is cheating behavior of falsifying the GPS data of the mobile phone by reading the trajectory data in the driver's order and analyzing the driving trajectory of the driver. According to the preset rules, the trajectory data of the driver's order information is analyzed to judge whether there is an abnormality. For example, judging whether the moving speed of the driver exceeds the threshold, whether the distance between trajectory points is reasonable, etc. The existing identification methods based on trajectory data, although they can identify some obvious cheating behaviors, still have the problem of missed judgment. Since some drivers use more concealed cheating means, it is difficult for the identification method to detect their cheating behavior. For example, when some drivers use cheating software to falsify the GPS data of the mobile phone, they will simulate a normal driving trajectory, making the trajectory data look no different from normal driving. In this case, it is very difficult for the identification method based on the trajectory data of the order information to detect their cheating behavior, and it is also very difficult to identify some other cheating means, such as substituting for driving. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to solve the problem that the existing online car-hailing cheating identification method is difficult to identify drivers who falsify GPS data for cheating.

[0005] In order to solve the above technical problem, the present invention provides a method for identifying cheating of online car-hailing drivers based on a Bluetooth key, and the method includes:

[0006] S10, binding the unique identifier of the driver device with the Bluetooth key, where the unique identifier includes the driver device id and the mobile phone number, and the driver device includes the driver's mobile phone;

[0007] S20. Collect the data set of the driver's vehicle to be identified through data sensors and transmit the data set to the Bluetooth key. The data sensors include vehicle-mounted sensors and Bluetooth key sensors. The vehicle-mounted sensors include a vehicle-mounted GPS module and a door sensor module. The data set includes the GPS positioning data of the vehicle to be identified, the signal strength data of the Bluetooth key, the connection status data between the Bluetooth key and the vehicle to be identified, and the door sensor data.

[0008] S30. Obtain the driver's order information, where the order information includes the starting point location, waypoint locations, and end point location information in the order.

[0009] S40. Send the data set of the vehicle to be identified to the monitoring module through the Bluetooth key, and the monitoring module determines whether the driver has cheated.

[0010] Among them, the determination method for judging whether the driver has cheated includes:

[0011] S41. Compare the collected GPS positioning data of the vehicle to be identified with the order information. When the GPS positioning data is the same as the order information, it is determined that the driver has not cheated. When the GPS positioning data is different from the order information, it is determined that the driver has cheated.

[0012] Furthermore, the determination method for judging whether the driver has cheated also includes:

[0013] S42. Determine whether the driver has cheated by monitoring the signal strength data of the Bluetooth key and the connection status data between the Bluetooth key and the vehicle to be identified.

[0014] S43. Determine whether the driver has cheated by monitoring the door sensor data.

[0015] Furthermore, after it is determined that the driver has cheated, the method further includes:

[0016] S50. By identifying the driver's cheating behavior, judge the driver's cheating means, and the cheating means include malicious order grabbing, proxy car calling, and location tampering.

[0017] Furthermore, after it is determined that the driver has cheated, the method further includes:

[0018] S60. Mark the cheating means and driver information and trigger an alarm.

[0019] S70. Obtain the marked cheating means and driver information through the monitoring module.

[0020] S80. The monitoring module automatically takes measures to handle cheating behaviors based on preset rules. The handling of cheating behaviors includes disabling the accounts of cheating drivers, freezing relevant orders, suspending the operation permissions of drivers, and triggering a manual review mechanism to further verify cheating behaviors.

[0021] Furthermore, the method further includes:

[0022] S80. Based on the monitoring module, detailed logs of the driver's operation behaviors are recorded, including the connection / disconnection of the Bluetooth key each time, door switch data, GPS data, and abnormal monitoring results. The logs provide a basis for subsequent audits, investigations, and problem tracking.

[0023] Furthermore, the method further includes:

[0024] S90. Based on the monitoring module, the dataset is regularly backed up and archived. The backed-up data will be stored in the server.

[0025] According to another aspect of the present invention, a cheating identification system for online car-hailing drivers based on a Bluetooth key is provided. When using the cheating identification system for identification, it includes the cheating identification method for online car-hailing drivers described in any one of the above.

[0026] Furthermore, the system includes:

[0027] An identity authentication module, which is used to bind the unique identifier of the driver device to the Bluetooth key. The unique identifier includes the device id and mobile phone number of the driver;

[0028] A data collection module, which is used to collect a dataset of the vehicle of the driver to be identified through data sensors. The data sensors include in-vehicle sensors and Bluetooth key sensors. The in-vehicle sensors include an in-vehicle GPS module and a door sensor module. The dataset includes GPS positioning data of the vehicle to be identified, signal strength data of the Bluetooth key, connection status data between the Bluetooth key and the vehicle to be identified, and door sensor data;

[0029] An order information collection module, which is used to obtain the order information of the driver. The order information includes the starting point location, waypoint location, and end point location information in the order;

[0030] A monitoring module, which is used to collect the dataset of the vehicle of the driver to be identified sent through the Bluetooth key and determine whether the driver has cheating behaviors;

[0031] Among them, the monitoring module includes a cheating identification unit,

[0032] The cheating identification unit is used to compare the collected GPS positioning data of the driver's vehicle to be identified with the order information. When the GPS positioning data is the same as the order information, it is determined that the driver does not have a cheating behavior. When the GPS positioning data is different from the order information, it is determined that the driver has a cheating behavior.

[0033] Further, the system includes:

[0034] A marking and alarm module, which is used to mark the cheating means and driver information and trigger an alarm;

[0035] The monitoring module includes an abnormal trajectory determination unit, which is used to compare the vehicle GPS trajectory with the GPS data provided by the Bluetooth key. When the vehicle GPS trajectory shows an abnormal situation, an abnormal alarm is triggered.

[0036] Further, the system includes:

[0037] An anti-cheating module. After the monitoring module determines that the driver has a cheating behavior, the anti-cheating module automatically takes measures to handle the cheating behavior based on preset rules. The handling of the cheating behavior includes disabling the account of the cheating driver, freezing related orders, suspending the operation permission of the driver, and triggering a manual review mechanism to further verify the cheating behavior.

[0038] Compared with the prior art, the beneficial effect of the method for identifying cheating of online car-hailing drivers based on a Bluetooth key in an embodiment of the present invention is as follows:

[0039] In the embodiment of the present invention, by binding the unique identifier of the driver device to the Bluetooth key, it can be ensured that there is a one-to-one correspondence between each driver and their Bluetooth key, effectively preventing unauthorized personnel from using the Bluetooth key and effectively preventing online car-hailing drivers from providing substitute driving services;

[0040] In the embodiment of the present invention, through in-vehicle sensors (such as GPS modules, door sensor modules) and Bluetooth key sensors, data sets during the vehicle operation process can be comprehensively collected. These data sets are sent to the monitoring module through the Bluetooth key and compared with the order information, so as to be able to identify the cheating behavior that the driver changes the GPS data in the order information to create a false trip. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flowchart of the method for identifying cheating of online car-hailing drivers provided by an embodiment of the present invention;

[0042] Figure 2 is another flowchart of the method for identifying cheating of online car-hailing drivers provided by an embodiment of the present invention;

[0043] Figure 3 It is a schematic diagram of the online car-hailing driver cheating identification system provided by an embodiment of the present invention;

[0044] In the figure, 10 is the identity authentication module; 20 is the data acquisition module; 30 is the order information acquisition module; 40 is the monitoring module; 41 is the cheating identification unit. Specific implementation manners

[0045] The following describes exemplary embodiments of the present invention with reference to the accompanying drawings. Various details of the embodiments of the present invention are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted below.

[0046] As Figure 1 shown, in an alternative embodiment of the present invention, the method for identifying cheating by an online car-hailing driver includes:

[0047] S10. Bind the unique identifier of the driver device to the Bluetooth key. The unique identifier includes the driver device id and the mobile phone number, and the driver device includes the driver's mobile phone;

[0048] S20. Collect a data set of the vehicle to be identified by the driver through a data sensor and transmit the data set to the Bluetooth key. The data sensor includes an in-vehicle sensor and a Bluetooth key sensor. The in-vehicle sensor includes an in-vehicle GPS module and a door sensor module. The data set includes GPS positioning data of the vehicle to be identified, signal strength data of the Bluetooth key, connection status data between the Bluetooth key and the vehicle to be identified, and door sensor data;

[0049] S30. Obtain the driver's order information, where the order information includes the starting point location, waypoint location, and end point location information in the order;

[0050] S40. Send the data set of the vehicle to be identified to the monitoring module through the Bluetooth key and determine whether the driver has cheated through the monitoring module;

[0051] Among them, the determination method for determining whether the driver has cheated includes:

[0052] S41. Compare the GPS positioning data of the vehicle to be identified with the order information. When the GPS positioning data is the same as the order information, it is determined that the driver has not cheated. When the GPS positioning data is different from the order information, it is determined that the driver has cheated.

[0053] Specifically, the driver device refers to the personal device used by the driver, such as a mobile phone, a tablet, etc. This device has a unique identifier and is used to bind with the Bluetooth key. The Bluetooth key is an intelligent device that communicates with the vehicle through Bluetooth technology. It is used to identify the driver's identity, control the vehicle access permission, and transmit the vehicle data to be identified. A 4G transmission module and a data storage module are set in the Bluetooth key. The data sensor is a device for collecting vehicle operation data, including in-vehicle sensors (such as GPS module, door sensor module) and Bluetooth key sensors. The in-vehicle GPS module is a device for obtaining the real-time position information of the vehicle. The door sensor module is a device for monitoring the opening and closing state of the vehicle door. The monitoring module is used to receive and process the vehicle data set from the Bluetooth key and is a system component for judging whether the driver has cheating behavior.

[0054] Specifically, the function in S10 is to ensure a one-to-one correspondence between the driver and the Bluetooth key he uses, improve the accuracy of identity recognition, prevent unauthorized drivers from using the Bluetooth key, and increase security. For example, when a driver registers on the online car-hailing platform, he needs to bind the ID and mobile phone number of his personal mobile phone (as the driver device) with the Bluetooth key. During the process of starting the vehicle, the platform will send a verification code to the driver's mobile phone, and the driver can use the Bluetooth key to start the vehicle only after the identity is verified.

[0055] Specifically, the function in S20 is to collect the data set of the vehicle of the driver to be identified through the data sensor and transmit it to the Bluetooth key.

[0056] Comprehensively collect the key data during the vehicle operation process, provide a basis for subsequent judgment, provide rich data support, and improve the accuracy of cheating recognition. For example, when the driver starts to execute an order, the in-vehicle GPS module will collect the vehicle position information in real time; the door sensor module will record the opening and closing state of the vehicle door; the Bluetooth key sensor will collect the signal strength data of the Bluetooth key and the connection state data with the vehicle. These data will be integrated into a data set for subsequent analysis. These data are transmitted to the Bluetooth key and wait to be sent to the monitoring module through the Bluetooth key.

[0057] Specifically, the function in S30 is to obtain the order details of the driver, including the starting point, passing points, and end point position information, as a comparison benchmark. For example, when the driver accepts an order, the online car-hailing platform will automatically obtain the detailed information of this order and store it in the system for subsequent comparison with the vehicle data set.

[0058] Specifically, the function in S40 is to transmit vehicle data sets to the monitoring module in real time via Bluetooth for cheating behavior judgment. For example, the Bluetooth key will send the collected vehicle data sets to the monitoring module. The monitoring module will automatically compare the vehicle GPS positioning data with the order information. When a mismatch is found, the monitoring module will determine that the driver has committed a cheating behavior, and then trigger an alarm mechanism to notify the platform management staff for further processing.

[0059] Combined with Figure 2 A specific monitoring module is further described as follows:

[0060] In this monitoring module, we conduct monitoring through five sub-modules. The output of each sub-module is 0 or 1, where: 0 indicates no abnormality, and 1 indicates an abnormality is found.

[0061] If the output of any one sub-module is 1, the entire integrated monitoring module outputs 1 (indicating an abnormality). If the outputs of all sub-modules are 0, then 0 is output (indicating no abnormality).

[0062] For the sub-module design, we assume there are four sub-modules, namely the door opening and closing monitoring sub-module, the GPS trajectory abnormality monitoring sub-module, the Bluetooth key connection status monitoring sub-module, and the user device data binding monitoring sub-module. Each sub-module determines its output according to different abnormality monitoring logics. The following are the output rules for each module:

[0063] 1. Door opening and closing monitoring sub-module (M1): The input parameters include T order_start : Dispatch time (timestamp), T order_end Order end time (timestamp), T door_open : Door opening operation time (timestamp) and T door_close : Door closing operation time (timestamp). Calculation formula: If there is no door opening and closing operation between the order start time and the end time, it is judged as abnormal.

[0064]

[0065] Output result: M1 ∈ {0, 1}, 1 indicates an abnormality is detected (no door opening and closing operation), and 0 indicates no abnormality is detected (there is a door opening and closing operation).

[0066] 2. GPS positioning abnormality monitoring sub-module (M2), input parameters: L lat , L long1 is the latitude and longitude of the first point (e.g., vehicle position). L lat2 , L long2 is the latitude and longitude of the second point (e.g., GPS position provided by the driver side).

[0067] Calculate the difference: ΔLlat = L lat2 -L lat1 , ΔL long = L long2 -L long1

[0068] Calculate the distance

[0069] Once the distance d between two points is obtained, the distance difference can be further calculated and compared with the set threshold d threshold GPS difference = d, output the result. If the distance difference GPS difference exceeds the set threshold d threshold , an anomaly monitoring is triggered.

[0070] Comprehensive algorithm formula:

[0071] Assume L gps1 = (L lat1 , L long1 ): GPS coordinates of the actual vehicle position,

[0072] L gps2 = (L lat2 , L long2 ): GPS coordinates provided by the driver side.

[0073] Calculate the spherical distance Judge whether the difference exceeds the threshold Output: M2 = ∈{0, 1}, M2 = 1 indicates false positioning (GPS exceeds the threshold), M2 = 0 indicates correct positioning (GPS difference is within the allowable range).

[0074] 3. Bluetooth key usage status monitoring (M3). To effectively prevent drivers from creating false orders by tampering with the positioning, the anti-cheating module adds monitoring of the status of the Bluetooth key being used. The following are the key functions and technical implementations of the anti-cheating module:

[0075] 1) Bluetooth key authentication. By comparing the Bluetooth key device (UUID) with the driver information, the uniqueness of the driver's identity is ensured. At the start of each order, the system needs to confirm whether the binding information of the Bluetooth key device and the driver account is consistent.

[0076] 2) Bluetooth signal strength and usage status monitoring. Monitor the Bluetooth signal strength (RSSI) and judge the usage status of the Bluetooth key by calculating the signal fluctuation. If the signal strength fluctuates abnormally or is lost, the system determines that the device has been tampered with or is being used without authorization.

[0077] Formula:

[0078] When the signal change rate exceeds the set threshold, an alarm is triggered. Output 1 if the connection status of the Bluetooth key is abnormal, indicating that an unauthorized driver may be operating; otherwise, output 0.

[0079] 4. User device data binding monitoring sub-module (M4):

[0080] Output 1 if the device ID is abnormally bound to the mobile phone number (for example, the bound data does not match), otherwise output 0.

[0081] Finally, the results of the four modules are combined as input parameters, and the outputs of the four sub-modules M1, M2, M3, and M4 respectively represent the abnormal monitoring results of each sub-module.

[0082] If the output of any one module is 1, the entire abnormal monitoring module outputs 1, indicating that an abnormality is detected. If the outputs of all four sub-modules are 0, the abnormal monitoring module outputs 0, indicating that no abnormality is detected. When an abnormality is detected, it is possible to further identify and determine whether the driver has cheated.

[0083] In the embodiment of the present invention, by binding the unique identifier of the driver's device to the Bluetooth key, it is possible to ensure a one-to-one correspondence between each driver and their Bluetooth key, effectively preventing unauthorized personnel from using the Bluetooth key and effectively preventing online car-hailing drivers from providing substitute driving services.

[0084] In the embodiment of the present invention, through in-vehicle sensors (such as GPS modules and door sensor modules) and Bluetooth key sensors, datasets during the vehicle operation process can be comprehensively collected. These datasets are sent to the monitoring module through the Bluetooth key and compared with the order information, so as to be able to identify the cheating behavior of whether the driver changes the GPS data in the order information to create a false itinerary.

[0085] In an alternative embodiment of the present invention, the determination method for determining whether the driver has cheated further includes:

[0086] S42, determining whether the driver has cheated by monitoring the signal strength data of the Bluetooth key and the connection status data between the Bluetooth key and the vehicle to be identified;

[0087] S43, determining whether the driver has cheated by monitoring the door sensor data.

[0088] Specifically, the monitoring module receives the signal strength data transmitted by the Bluetooth key and compares it with a preset threshold. If the signal strength is too low or fluctuates abnormally, it may indicate that the distance between the Bluetooth key and the vehicle is too far or there is interference, thus suspecting that the driver may not be near the vehicle or there is cheating behavior. The monitoring module monitors the connection status between the Bluetooth key and the vehicle. If the connection is suddenly interrupted or frequently disconnected and reconnected, it may also indicate cheating behavior, such as the driver trying to evade monitoring by disconnecting the Bluetooth connection.

[0089] Specifically, the monitoring module receives the data transmitted by the door sensor in real time and monitors the opening and closing status of the door. If the door is frequently opened and closed during the order execution, or is opened at an inappropriate time (such as when the order is in progress but the vehicle has not reached the destination), it may indicate that the driver has cheating behavior, such as allowing the passenger to get off at a non-designated location or picking up other passengers privately.

[0090] In the embodiment of the present invention, by adding two determination methods S42 and S43, the method for identifying cheating behavior of online car-hailing drivers of the present invention is further improved and optimized. This not only improves the accuracy and reliability of cheating identification, but also increases the dimension and coverage of identification, enabling the platform to more comprehensively and accurately judge whether the driver's behavior complies with the regulations.

[0091] In an optional embodiment of the present invention, after it is determined that the driver has cheating behavior, the method further includes:

[0092] S50, by identifying the driver's cheating behavior, judge the driver's cheating means, and the cheating means include malicious order snatching, proxy car-hailing, and location tampering.

[0093] Among them, malicious order snatching refers to the driver's act of snatching orders that do not belong to himself through improper means (such as using cheating software, false information, etc.) to obtain more benefits. By monitoring the driver's order-snatching behavior and analyzing key indicators such as the order-snatching frequency, success rate, and order-snatching time. Combining the driver's historical order data and passenger feedback, judge whether there are behaviors such as frequent order snatching, snatching but not accepting, or deliberately canceling orders.

[0094] Among them, proxy car-hailing refers to the driver's act of using his own account to call a car for others to obtain additional benefits. This behavior usually occurs when there is a certain interest exchange or cooperation relationship between the driver and the passenger. By analyzing the driver's order data, especially the order initiation time, location, and passenger information, compare the GPS positioning data of the driver collected by the Bluetooth key to determine whether there is an abnormality. Combining the feedback and complaints of passengers, investigate whether there is a situation of the driver calling a car on behalf of others.

[0095] Among them, tampering with the location refers to the behavior of a driver modifying the location information of the vehicle or mobile phone to forge a trip or defraud fees. This behavior seriously damages the fairness of the platform and the interests of passengers. By comparing the driver's GPS location data collected by the Bluetooth key with the order information, it is determined whether the actual driving trajectory of the vehicle conforms to the order requirements. Advanced cheating behavior recognition technologies are used, such as monitoring the operation location information of the driver's device, repetitive operation evaluation parameters, etc., to identify the behavior of tampering with the location.

[0096] In an alternative embodiment of the present invention, after it is determined that the driver has cheating behavior, the method further includes:

[0097] S60, marking the cheating means and driver information and triggering an alarm;

[0098] S70, obtaining the marked cheating means and driver information through the monitoring module;

[0099] S80, the monitoring module automatically takes measures to handle the cheating behavior based on preset rules, and the handling of the cheating behavior includes disabling the account of the cheating driver, freezing relevant orders, suspending the operation permission of the driver, and triggering an artificial review mechanism to further verify the cheating behavior.

[0100] In an alternative embodiment of the present invention, the method further includes:

[0101] S80, based on the monitoring module, recording detailed logs of the driver's operation behavior, including the connection / disconnection of each Bluetooth key, door opening / closing data, GPS data, and abnormal monitoring results, and the logs provide a basis for subsequent auditing, investigation, and problem tracking.

[0102] In an alternative embodiment of the present invention, the method further includes:

[0103] S90, based on the monitoring module, regularly backing up and archiving the data set. The backed-up data will be stored in the server.

[0104] According to another aspect of the present invention, there is provided a cheating recognition system for online car-hailing drivers based on a Bluetooth key. When using the cheating recognition system for recognition, it includes the cheating recognition method for online car-hailing drivers described in any one of the above.

[0105] As Figure 3 shown in an alternative embodiment of the present invention, the system includes:

[0106] An identity authentication module 10, which is used to bind the unique identifier of the driver's device to the Bluetooth key, and the unique identifier includes the driver's device id and mobile phone number;

[0107] Data acquisition module 20, which is used to collect a data set of the driver's vehicle to be identified through data sensors. The data sensors include in-vehicle sensors and Bluetooth key sensors. The in-vehicle sensors include an in-vehicle GPS module and a door sensor module. The data set includes GPS positioning data of the vehicle to be identified, signal strength data of the Bluetooth key, connection status data between the Bluetooth key and the vehicle to be identified, and door sensor data;

[0108] Order information acquisition module 30, which is used to obtain the driver's order information. The order information includes the starting point location, waypoint location, and end point location information in the order;

[0109] Monitoring module 40, which is used to collect the data set of the driver's vehicle to be identified sent through the Bluetooth key and determine whether the driver has cheated;

[0110] Among them, the monitoring module 40 includes a cheating identification unit 41;

[0111] The cheating identification unit 41 is used to compare the collected GPS positioning data of the driver's vehicle to be identified with the order information. When the GPS positioning data is the same as the order information, it is determined that the driver has not cheated. When the GPS positioning data is different from the order information, it is determined that the driver has cheated.

[0112] In the embodiment of the present invention, by binding the unique identifier of the driver device to the Bluetooth key, it can ensure a one-to-one correspondence between each driver and their Bluetooth key, effectively preventing unauthorized personnel from using the Bluetooth key, and effectively preventing online car-hailing drivers from providing substitute driving services;

[0113] In the embodiment of the present invention, through in-vehicle sensors (such as GPS modules and door sensor modules) and Bluetooth key sensors, a data set during the vehicle operation process can be comprehensively collected. These data sets are sent to the monitoring module through the Bluetooth key and compared with the order information, so as to be able to identify whether the driver has cheated by changing the GPS data in the order information to create a false itinerary.

[0114] In an optional embodiment of the present invention, the system includes:

[0115] Marking and alarm module, which is used to mark the cheating means and driver information and trigger an alarm;

[0116] The monitoring module includes an abnormal trajectory determination unit, which is used to compare the vehicle GPS trajectory with the GPS data provided by the Bluetooth key. When the vehicle GPS trajectory shows an abnormal situation, an abnormal alarm is triggered.

[0117] In an alternative embodiment of the present invention, the system includes:

[0118] An anti-cheating module. After the monitoring module determines that a driver has cheated, the anti-cheating module automatically takes measures to handle the cheating behavior based on preset rules. The handling of the cheating behavior includes disabling the account of the cheating driver, freezing relevant orders, suspending the operation authority of the driver, and triggering a manual review mechanism to further verify the cheating behavior.

[0119] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved, and no limitations are imposed herein.

[0120] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying cheating of online car-hailing drivers based on Bluetooth keys, characterized in that: The method comprises: S10, binding a unique identifier of the driver's device to the Bluetooth key, wherein the unique identifier includes the driver's device ID and the mobile phone number, and the driver's device includes the driver's mobile phone; S20, collecting a data set of the vehicle of the driver to be identified through a data sensor and transmitting the data set to the Bluetooth key, the data sensor includes a vehicle sensor and a Bluetooth key sensor, the vehicle sensor includes a vehicle GPS module and a door sensor module, the data set includes GPS positioning data of the vehicle to be identified, signal strength data of the Bluetooth key, connection status data between the Bluetooth key and the vehicle to be identified, and door sensor data; S30, obtaining the driver's order information, wherein the order information includes the starting point location, the passing point location and the end point location information in the order; S40, sending the data set of the vehicle to be identified to the monitoring module through the Bluetooth key, and the monitoring module determines whether the driver has cheated; Among them, the methods for judging whether the driver has cheated include: S41, comparing the collected GPS positioning data of the vehicle to be identified with the order information. When the GPS positioning data is the same as the order information, it is determined that the driver has not cheated. When the GPS positioning data is different from the order information, it is determined that the driver has cheated.

2. The cheating identification method according to claim 1, characterized in that: The method for determining whether the driver has cheated also includes: S42, determining whether the driver has cheated by monitoring the signal strength data of the Bluetooth key and the connection status data between the Bluetooth key and the vehicle to be identified; S43, determining whether the driver has cheated by monitoring the door sensor data.

3. The cheating identification method according to claim 2, characterized in that: After determining that the driver has cheated, the method further includes: S50, by identifying the cheating behavior of the driver, determining the cheating means of the driver, wherein the cheating means include maliciously stealing orders, calling a car for other passengers, and tampering with the location.

4. The cheating identification method according to claim 3, characterized in that: After determining that the driver has cheated, the method further includes: S60, marking the cheating method and driver information and triggering an alarm; S70, obtaining the marked cheating means and driver information through a monitoring module; S80, the monitoring module automatically takes measures to handle the cheating behavior based on preset rules, and the handling of the cheating behavior includes disabling the cheating driver's account, freezing related orders, suspending the driver's operating authority, and triggering a manual review mechanism to further verify the cheating behavior.

5. The cheating identification method according to claim 4, characterized in that: The method further comprises: S80, based on the monitoring module, a detailed log of the driver's operating behavior is recorded, including each connection / disconnection of the Bluetooth key, door switch data, GPS data, and abnormal monitoring results. The log provides a basis for subsequent audits, investigations, and problem tracking.

6. The cheating identification method according to claim 5, characterized in that: The method further comprises: S90: regularly back up and archive the data set based on the monitoring module. The backed up data will be stored in the server.

7. A Bluetooth key-based online car-hailing driver cheating identification system, characterized in that: The cheating identification system includes the online car-hailing driver cheating identification method described in any one of claims 1-6 when performing identification.

8. The online car-hailing driver cheating identification system according to claim 7, characterized in that: The system comprises: An identity authentication module, which is used to bind the unique identifier of the driver's device to the Bluetooth key, wherein the unique identifier includes the driver's device ID and mobile phone number; A data acquisition module, the data acquisition module is used to collect a data set of a vehicle to be identified through a data sensor and transmit the data set to the Bluetooth key, the data sensor includes a vehicle sensor and a Bluetooth key sensor, the vehicle sensor includes a vehicle GPS module and a door sensor module, the data set includes GPS positioning data of the vehicle to be identified, signal strength data of the Bluetooth key, connection status data between the Bluetooth key and the vehicle to be identified, and door sensor data; An order information collection module, which is used to obtain the driver's order information, including the starting point location, the passing point location and the end point location information in the order; A monitoring module, the monitoring module is used to collect the data set of the driver and vehicle to be identified sent by the Bluetooth key and determine whether the driver has cheated; Wherein, the monitoring module includes a cheating identification unit, The cheating identification unit is used to compare the collected GPS positioning data of the vehicle of the driver to be identified with the order information. When the GPS positioning data is the same as the order information, it is determined that the driver has not cheated; when the GPS positioning data is different from the order information, it is determined that the driver has cheated.

9. The online car-hailing driver cheating identification system according to claim 8, characterized in that: The system comprises: A marking alarm module, the marking alarm module is used to mark the cheating means and driver information and trigger an alarm; The monitoring module includes an abnormal trajectory determination unit, which is used to compare the vehicle GPS trajectory with the GPS data provided by the Bluetooth key, and trigger an abnormal alarm when an abnormal situation occurs in the vehicle GPS trajectory.

10. The online car-hailing driver cheating identification system according to claim 7, characterized in that: The system comprises: Anti-cheating module: When the monitoring module determines that the driver has cheated, the anti-cheating module automatically takes measures to deal with the cheating behavior based on preset rules. The measures to deal with the cheating behavior include disabling the cheating driver's account, freezing related orders, suspending the driver's operating authority, and triggering a manual review mechanism to further verify the cheating behavior.

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