CN113674450B - Anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence - Google Patents
Anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence Download PDFInfo
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
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- G—PHYSICS
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- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B13/00—Taximeters
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/40—Bus networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/40—Bus networks
- H04L2012/40208—Bus networks characterized by the use of a particular bus standard
- H04L2012/40215—Controller Area Network CAN
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2209/00—Additional information or applications relating to cryptographic mechanisms or cryptographic arrangements for secret or secure communication H04L9/00
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Abstract
The invention discloses an anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence, which comprises a multi-source data acquisition module, an artificial intelligence data processing module and a data encryption module; the multi-source data acquisition module is used for receiving GPS mileage and GPS time from a vehicle GPS receiving device, acquiring instrument mileage and running time of a vehicle from a vehicle CAN, calculating mileage from a vehicle meter device through a wheel speed pulse sensor and acquiring time from an RTC timing device; the artificial intelligence data processing module is used for carrying out data inspection and data state analysis on the data received from the multi-source data acquisition module; the data encryption module is used for carrying out encryption storage protection on the data acquisition and transmission process in the multi-source data acquisition module, and the intelligent terminal is provided with an artificial intelligence-based vehicle anti-cheating method for better realizing the function of the intelligent terminal.
Description
Technical Field
The invention relates to the technical field of artificial intelligence data processing, in particular to an anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence.
Background
The existing travel vehicles (network taxi, taxi for cruising and the like) are required to be provided with a price meter, a license plate, an ISU, a travel recorder, a display terminal, a plurality of cameras and the like for meeting relevant regulations and local regulations; and the vehicle is provided with 2 to 3 sets of equipment for 2 to 3 times after leaving the factory, which involves more links, complicated installation, high labor hour and high cost, and has potential safety hazards such as multiple broken lines, more space occupied by the original vehicle after the installation is completed, complicated wire harnesses, leakage and messy mutual interference of the equipment, and the attractive appearance of the vehicle is affected; meanwhile, the system has high complexity and more intermediate communication links, and brings more operable space for drivers to use various cheating tools and software.
The existing pricing system is more traditional, the tour vehicle mainly adopts a single pricing factor for generating a vehicle speed pulse signal by additionally installing a vehicle speed sensor, the pulse signal can be generated by a 555 chip and the like, and is easy to interfere and replace, and the confidence is low. The net-bound vehicle adopts GPS positioning of a mobile phone of a driver and pricing software to bill, and virtual tracks are easily generated by modifying software and hardware of system positioning so as to generate extra cost.
A GPS and a vehicle speed sensor are introduced into a few tour vehicle pricing systems to serve as factors for mutual correction and comparison, but pulse signals are easy to interfere and replace, GPS signals are greatly influenced by environmental factors and are easy to lose in shielding environments such as viaduct ground banks, errors existing in GPS integral calculation are added, so that mileage comparison results in two modes are large in fluctuation, and systematic errors are easy to cause.
Disclosure of Invention
The invention aims to provide an anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence comprises a multi-source data acquisition module, an artificial intelligence data processing module and a data encryption module;
the multi-source data acquisition module is used for receiving GPS mileage and GPS time from a vehicle GPS receiving device, acquiring instrument mileage and running time of a vehicle from a vehicle CAN, calculating mileage from a vehicle meter device through a wheel speed pulse sensor and acquiring time from an RTC timing device;
the artificial intelligence data processing module is used for carrying out data inspection and data state analysis on the data received from the multi-source data acquisition module;
the data encryption module is used for carrying out encryption storage protection on the data acquisition and transmission processes in the multi-source data acquisition module;
the intelligent terminal is designed into a vehicle-mounted terminal integrated machine on hardware; the license plate turning plate is arranged behind the integrated machine; the front of the all-in-one machine is provided with a liquid crystal display screen; the integrated machine comprises a price meter, a license plate, an ISU, a running recorder and a An Zhuoda screen; the ISU is a service terminal matched with the price meter for use; the intelligent terminal adopts a closed type deep customized android system, a relevant authority background in the system is remotely configurable, and the system adopts an own software store to detect all pre-installed software and third party software and then can authorize operation; and the lead seal and the hardware anti-disassembly switch are added to the integrated machine, so that the anti-disassembly alarm can be reported in real time when the shell is illegally disassembled and opened, and the number of local communication links is reduced and the data exposure possibility is reduced through the integrated hardware.
Further, the multi-source data acquisition module gathers various data source acquisition devices, mainly comprising a GPS receiving device, a vehicle CAN receiving unit, a price meter device and an RTC timing device;
the artificial intelligent data processing module comprises a metadata database unit, a data classifying unit, a primary data checking unit, an abnormal state analyzing unit and a pricing checking unit; the metadata base unit is used for collecting data when the vehicle is periodically started for pricing simulation in a non-order receiving state; the data classifying unit is used for classifying the data acquired in the metadata database unit; the primary data checking unit is used for checking the data in the metadata base unit based on the partial data acquired in the multi-source data acquisition module; the abnormal state analysis unit is used for carrying out abnormal state analysis on the related data in the primary data inspection unit based on the partial data acquired in the multi-source data acquisition module; the pricing verification unit is used for performing pricing verification based on the partial data acquired in the multi-source data acquisition module and the partial data transmitted in the abnormal state analysis unit;
the data in the vehicle order receiving state is checked and analyzed later through the data multi-source acquisition, and the data multi-source acquisition is used for realizing the data diversity and the data effective reference contrast in the process of obtaining the final data result.
Further, the intelligent terminal comprises a vehicle anti-cheating method based on artificial intelligence, and the method comprises the following steps:
step S100: the intelligent terminal periodically starts the vehicle pricing simulation under the condition that the vehicle is not checked, and randomly sets the destination mileage; acquiring and storing as a metadata base based on mileage calculated by a wheel speed pulse sensor in the vehicle pricing device, time acquired from an RTC timing device in the vehicle pricing device, and pricing data finally generated by the vehicle pricing device; the vehicle tariffs appearing herein are different from those conventionally used only in taxi vehicles, and in this patent, the vehicle tariffs collectively term the name of the tariff device of any commercial vehicle, whether a taxi or a net-bound vehicle, and the data transmitted from their tariff device are collectively referred to as tariff data;
step S200: confirming the classified number n of the mileage data according to the coverage range of the stored mileage data in a metadata base; starting to classify data of the metadata base based on the obtained classification number n, and generating a mileage classification threshold value in the classifying process;
step S300: the intelligent terminal obtains mileage calculated by a wheel speed pulse sensor in a vehicle pricing device in a bill receiving state of the vehicle, time obtained from an RTC timing device in the vehicle pricing device, vehicle driving speed displayed in corresponding mileage and time by a vehicle instrument panel and pricing data finally generated by the vehicle pricing device;
step S400: performing primary data inspection on data generated in a vehicle order receiving state; determining to perform abnormal state analysis or pricing verification on the single receipt state based on the primary data detection result and the data in the metadata base;
step S500: and (3) selecting overlay correction for the data or updating and supplementing the data to the metadata base based on the state analysis result or the pricing verification result obtained in the step S400.
Further, the step S200 of confirming the number n of classifications of mileage data includes:
step S201: for the storage liningThe distance data are statistically arranged to obtain a distance interval (P) max ，P min ) Wherein P is max Representing a highest mileage value; p (P) min Representing a lowest mileage value;
step S202: according to the formula
Judging the number of mileage thresholds; wherein y is a preset difference threshold value between the highest mileage value and the lowest mileage value, and n represents the mileage classification number;
the mileage data is classified by considering the way of price calculation of the mileage number of the vehicle in the market, for example, the mileage of the current vehicle is classified into short distance and long distance; the misjudgment on the subsequent pricing verification caused by different pricing modes can be effectively avoided by classifying the mileage data.
Further, classifying all mileage data in step S200 includes:
step S211: when n=2, there are 1 mileage classification threshold values, the remote mileage standard number set in the vehicle meter device is set as the mileage classification threshold value;
step S212: when n=3, there are 2 mileage classification thresholds, and the remote mileage standard number set in the vehicle meter device is set as a first mileage classification threshold; the second mileage classification threshold value is according to the formula
Wherein T represents a first mileage classification threshold; u represents the number of mileage data from which the mileage data smaller than the first mileage classification threshold value is removed;
step S213: according to the classification condition of mileage data, corresponding time data and pricing data of each mileage are correspondingly classified with mileage data;
the steps are used for classifying the mileage data, so that the accompanying time data and pricing data are correspondingly classified, and the data are favorable for subsequent data inspection and data state analysis.
Further, the process of performing the primary data verification in step S400 includes:
step S401: the intelligent terminal calculates the simulated mileage of the vehicle and simultaneously receives the GPS mileage of the vehicle from the GPS receiving device of the vehicle, and takes the GPS mileage as a first mileage; receiving the instrument mileage of the vehicle from the vehicle CAN, and taking the instrument mileage as a second mileage;
step S402: when the mileage difference between the first mileage and the second mileage is smaller than the mileage difference threshold value; taking the first mileage and the second mileage as mileage checking reference; if the mileage finally generated by the valuator is larger than the larger mileage value in the first mileage and the second mileage and the difference value is larger than the set difference value threshold value, carrying out abnormal state analysis on the receipt state; if the mileage value finally generated by the valuator is between the first mileage and the second mileage, performing valuation verification on the receipt status;
step S403: when the mileage difference between the first mileage and the second mileage is greater than the mileage difference threshold value; if the mileage number finally generated by the valuator is larger than the first mileage and the second mileage and the difference value between the mileage number and any one of the mileage values of the first mileage and the second mileage is larger than a difference value threshold value, carrying out abnormal state analysis on the trip receipt state; if the difference value between the mileage finally generated by the price meter and any one of the mileage values in the first mileage and the second mileage meets a difference value threshold, price checking is carried out on the receipt status;
the steps are used for checking the mileage data generated in the price meter in the vehicle order receiving state by taking the mileage data transmitted in the GPS receiving device and the vehicle CAN as a checking reference, so as to prevent the mileage from being cheated.
Further, the abnormal state analysis in step S400 includes:
step S411: collecting data of the mileage number generated in the price meter in the order receiving state along with the continuous time change of the order receiving state, calculating the vehicle speed V in a random time period, and obtaining the vehicle speed V according to a formula V=S/T;
step S412: correspondingly acquiring driving speed data displayed on a vehicle instrument panel in a random time period locked in the step S411, and performing deviation calculation on the driving speed data and the speed obtained in the step S411;
step S413: if the speed deviation value is greater than the deviation threshold value, determining that the data is abnormal; if the speed deviation value is smaller than the deviation threshold value, simultaneously sending feedback information to the client and the driver to confirm the vehicle anomaly, and judging that the vehicle anomaly is non-data anomaly when the vehicle anomaly is confirmed; when the non-anomaly is confirmed, judging that the data is abnormal; the abnormal state of the vehicle comprises that the original road section is blocked in the journey, and the customer has other requirements for suddenly changing the journey in the journey;
the step performs abnormality analysis on mileage data satisfying the primary data check in a travel speed dimension by taking a driving speed displayed on a vehicle instrument panel as reference data of the state analysis.
Further, the pricing verification in step S400 includes:
step S421: the intelligent terminal calculates the simulated mileage of the vehicle, and simultaneously receives the GPS time of the vehicle from the GPS receiving device of the vehicle, and takes the GPS mileage as the first time; receiving the meter time of the vehicle from the vehicle CAN, and taking the meter mileage as a second time;
step S422: the mileage generated in the price meter in the order receiving state is classified according to the mileage classification threshold value in the metadata base; the pricing information data in the mileage category to which the mileage generated in the price meter belongs in the receipt status is statistically arranged to obtain a price section (T) max ，T min ) Wherein T is max Representing the highest priced value that occurs within the mileage category; t (T) min Representing a lowest priced value occurring within the mileage category;
step S422: the price generated in the price counter in the receipt state is set as the state price, if the state price is not located in the price section (T max ，T min ) In the method, feedback information is sent to a client and a driver to confirm the vehicle abnormal state, and when the vehicle abnormal state is confirmed, the verification is judged to be normal; when confirming the non-anomaly, judging that the verification is abnormal; vehicle anomalies include, but are not limited to, the occurrence of original road blockages during travel, other sudden travel change requests by customers during travel;
step S423: if the status price is located in the price section (T max ，T min ) In, calling the first time and the second time; if the time difference between the first time and the second time is smaller than the system set time difference threshold, taking the time value in the first time and the second time as a verification reference, calling the time data with the smallest difference value in the metadata base, and searching the pricing data corresponding to the time data;
step S424: calculating deviation between the pricing data and the state price, and judging that the verification is abnormal when the deviation is larger than a system set price deviation threshold; when the deviation is smaller than a price deviation threshold value set by the system, judging that the verification is normal;
the GPS time of the vehicle received by the GPS receiving device of the vehicle is used as a time interval corresponding to the mileage data meeting the primary data inspection in the pricing verification process, and the similar price is searched in the metadata base for pricing verification based on the time interval.
Further, step S500 includes:
when the analysis result of the abnormal state shows that the data is abnormal, correcting the mileage number generated in the price meter in the single-trip state based on the first mileage and the second mileage, namely selecting a smaller mileage value from the first mileage and the second mileage to carry out coverage correction, and simultaneously generating a correction signal to feed back to the intelligent terminal for signal early warning; when the analysis result of the abnormal state shows that the data is abnormal, the receipt state is subjected to pricing verification;
when the pricing verification result shows that the verification is normal, storing the data in the order receiving state, namely storing the data into a metadata base to update and supplement the metadata base; when the pricing verification result shows that verification is abnormal, the corresponding pricing data in the metadata base is corrected to the pricing data in the receipt status.
Compared with the prior art, the invention has the following beneficial effects: the invention solves the problems of large error, easy invasion, easy tampering and the like of the existing trip pricing system and improves the safety and reliability of the trip pricing system; the cheating threshold is improved through integrated design and installation, the number of components is reduced through integrated attractive design, non-hidden installation and the addition of anti-disassembly trigger elements, and hidden troubles such as a large number of communication links among the components, unauthorized disassembly and easy cracking are avoided; meanwhile, an artificial intelligent algorithm is introduced, and autonomous inspection and state analysis are performed on system input data to ensure the validity and reliability of an output result.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic structural diagram of an anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence;
fig. 2 is a schematic flow chart of an anti-cheating method for an artificial intelligence-based vehicle in an artificial intelligence-based on-vehicle intelligent terminal for traveling.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: the anti-cheating travel vehicle-mounted intelligent terminal based on the artificial intelligence is characterized by comprising a multi-source data acquisition module, an artificial intelligence data processing module and a data encryption module;
the multi-source data acquisition module is used for receiving GPS mileage and GPS time from a vehicle GPS receiving device, acquiring instrument mileage and running time of a vehicle from a vehicle CAN, calculating mileage from a vehicle meter device through a wheel speed pulse sensor and acquiring time from an RTC timing device; the multi-source data acquisition module is used for collecting various data source acquisition devices and mainly comprises a GPS receiving device, a vehicle CAN receiving unit, a price meter device and an RTC timing device;
the artificial intelligence data processing module is used for carrying out data inspection and data state analysis on the data received from the multi-source data acquisition module; the artificial intelligent data processing module comprises a metadata database unit, a data classifying unit, a primary data checking unit, an abnormal state analyzing unit and a pricing checking unit; the metadata base unit is used for collecting data when the vehicle is periodically started for pricing simulation in a non-order receiving state; the data classifying unit is used for classifying the data acquired in the metadata database unit; the primary data checking unit is used for checking the data in the metadata base unit based on the partial data acquired in the multi-source data acquisition module; the abnormal state analysis unit is used for carrying out abnormal state analysis on the related data in the primary data inspection unit based on the partial data acquired in the multi-source data acquisition module; the pricing verification unit is used for performing pricing verification based on the partial data acquired in the multi-source data acquisition module and the partial data transmitted in the abnormal state analysis unit;
and the data encryption module is used for carrying out encryption storage protection on the data acquisition and transmission process in the multi-source data acquisition module.
The intelligent terminal is designed into a vehicle-mounted terminal integrated machine on hardware; the license plate turning plate is arranged behind the integrated machine; the front of the all-in-one machine is provided with a liquid crystal display screen; the integrated machine comprises a price meter, a license plate, an ISU, a running recorder and a An Zhuoda screen; the intelligent terminal adopts a closed type deep customized android system, is remotely configurable for relevant authority background in the system, adopts own software store, and can authorize operation after detection of all pre-installed software and third party software; the method comprises the steps of carrying out a first treatment on the surface of the The lead seal and the hardware anti-disassembly switch are added to the integrated machine, the anti-disassembly alarm can be reported in real time when the shell is illegally disassembled and opened, and the number of local communication links is reduced and the possibility of data exposure is reduced through the integrated integration of the hardware;
the intelligent terminal comprises a vehicle anti-cheating method based on artificial intelligence, and the method comprises the following steps:
step S100: the intelligent terminal periodically starts the vehicle pricing simulation under the condition that the vehicle is not checked, and randomly sets the destination mileage; acquiring and storing as a metadata base based on mileage calculated by a wheel speed pulse sensor in the vehicle pricing device, time acquired from an RTC timing device in the vehicle pricing device, and pricing data finally generated by the vehicle pricing device;
step S200: confirming the classified number n of the mileage data according to the coverage range of the stored mileage data in a metadata base; starting to classify data of the metadata base based on the obtained classification number n, and generating a mileage classification threshold value in the classifying process;
the step S200 of confirming the number n of classified mileage data includes:
step S201: the stored mileage data is statistically arranged to obtain mileage intervals (P max ，P min ) Wherein P is max Representing a highest mileage value; p (P) min Representing a lowest mileage value;
step S202: according to the formula
Judging the number of mileage thresholds; wherein y is a preset difference threshold value between the highest mileage value and the lowest mileage value, and n represents the mileage classification number;
wherein classifying all mileage data includes:
step S211: when n=2, there are 1 mileage classification threshold values, the remote mileage standard number set in the vehicle meter device is set as the mileage classification threshold value;
step S212: when n=3, there are 2 mileage classification thresholds, and the remote mileage standard number set in the vehicle meter device is set as a first mileage classification threshold; the second mileage classification threshold value is according to the formula
Wherein T represents a first mileage classification threshold; u represents the number of mileage data from which the mileage data smaller than the first mileage classification threshold value is removed;
step S213: according to the classification condition of mileage data, corresponding time data and pricing data of each mileage are correspondingly classified with mileage data;
step S300: the intelligent terminal obtains mileage calculated by a wheel speed pulse sensor in a vehicle pricing device in a bill receiving state of the vehicle, time obtained from an RTC timing device in the vehicle pricing device, vehicle driving speed displayed in corresponding mileage and time by a vehicle instrument panel and pricing data finally generated by the vehicle pricing device;
step S400: performing primary data inspection on data generated in a vehicle order receiving state; determining to perform abnormal state analysis or pricing verification on the single-trip state based on the data checking result and the data in the metadata base;
wherein, the process of primary data verification includes:
step S401: the intelligent terminal calculates the simulated mileage of the vehicle and simultaneously receives the GPS mileage of the vehicle from the GPS receiving device of the vehicle, and takes the GPS mileage as a first mileage; receiving the instrument mileage of the vehicle from the vehicle CAN, and taking the instrument mileage as a second mileage;
step S402: when the mileage difference between the first mileage and the second mileage is smaller than the mileage difference threshold value; taking the first mileage and the second mileage as mileage checking reference; if the mileage finally generated by the valuator is larger than the larger mileage value in the first mileage and the second mileage and the difference value is larger than the set difference value threshold value, carrying out abnormal state analysis on the receipt state; if the mileage value finally generated by the valuator is between the first mileage and the second mileage, performing valuation verification on the receipt status;
step S403: when the mileage difference between the first mileage and the second mileage is greater than the mileage difference threshold value; if the mileage number finally generated by the valuator is larger than the first mileage and the second mileage and the difference value between the mileage number and any one of the mileage values of the first mileage and the second mileage is larger than a difference value threshold value, carrying out abnormal state analysis on the trip receipt state; if the difference value between the mileage finally generated by the price meter and any one of the mileage values in the first mileage and the second mileage meets a difference value threshold, price checking is carried out on the receipt status;
wherein the abnormal state analysis includes:
step S411: collecting data of the mileage number generated in the price meter in the order receiving state along with the continuous time change of the order receiving state, calculating the vehicle speed V in a random time period, and obtaining the vehicle speed V according to a formula V=S/T;
step S412: correspondingly acquiring driving speed data displayed on a vehicle instrument panel in a random time period locked in the step S411, and performing deviation calculation on the driving speed data and the speed obtained in the step S411;
step S413: if the speed deviation value is greater than the deviation threshold value, determining that the data is abnormal; if the speed deviation value is smaller than the deviation threshold value, simultaneously sending feedback information to the client and the driver to confirm the vehicle anomaly, and judging that the vehicle anomaly is non-data anomaly when the vehicle anomaly is confirmed; when the non-anomaly is confirmed, judging that the data is abnormal; the abnormal state of the vehicle comprises that the original road section is blocked in the journey, and the customer has other requirements for suddenly changing the journey in the journey;
wherein, the pricing verification includes:
step S421: the intelligent terminal calculates the simulated mileage of the vehicle, and simultaneously receives the GPS time of the vehicle from the GPS receiving device of the vehicle, and takes the GPS mileage as the first time; receiving the meter time of the vehicle from the vehicle CAN, and taking the meter mileage as a second time;
step S422: the mileage generated in the price meter in the order receiving state is classified according to the mileage classification threshold value in the metadata base; the pricing information data in the mileage category to which the mileage generated in the price meter belongs in the receipt status is statistically arranged to obtain a price section (T) max ，T min ) Wherein T is max Representing the highest priced value that occurs within the mileage category; t (T) min Representing a lowest priced value occurring within the mileage category;
step S422: the price generated in the price counter in the receipt state is set as the state price, if the state price is not located in the price section (T max ，T min ) In the method, feedback information is sent to a client and a driver to confirm the vehicle abnormal state, and when the vehicle abnormal state is confirmed, the verification is judged to be normal; when it is confirmed that the state is not abnormal,judging that the verification is abnormal; vehicle anomalies include, but are not limited to, the occurrence of original road blockages during travel, other sudden travel change requests by customers during travel;
step S423: if the status price is located in the price section (T max ，T min ) In, calling the first time and the second time; if the time difference between the first time and the second time is smaller than the system set time difference threshold, taking the time value in the first time and the second time as a verification reference, calling the time data with the smallest difference value in the metadata base, and searching the pricing data corresponding to the time data;
step S424: calculating deviation between the pricing data and the state price, and judging that the verification is abnormal when the deviation is larger than a system set price deviation threshold; when the deviation is smaller than a price deviation threshold value set by the system, judging that the verification is normal;
step S500: and (3) selecting overlay correction for the data or updating and supplementing the data to the metadata base based on the state analysis result or the pricing verification result obtained in the step S400.
When the analysis result of the abnormal state shows that the data is abnormal, correcting the mileage number generated in the price meter under the single receipt state based on the first mileage and the second mileage, namely selecting a smaller mileage value from the first mileage and the second mileage to carry out coverage correction, and simultaneously generating a correction signal to feed back to the intelligent terminal for signal early warning; when the analysis result of the abnormal state shows that the data is abnormal, the receipt state is subjected to pricing verification;
when the pricing verification result shows that the verification is normal, storing the data in the order receiving state, namely storing the data into a metadata base to update and supplement the metadata base; when the pricing verification result shows that verification is abnormal, the corresponding pricing data in the metadata base is corrected to the pricing data in the receipt status.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence is characterized by comprising a multi-source data acquisition module, an artificial intelligence data processing module and a data encryption module;
the multisource data acquisition module is used for receiving GPS mileage and GPS time from a vehicle GPS receiving device, acquiring instrument mileage and running time of a vehicle from a vehicle CAN, calculating mileage from a vehicle price meter device through a wheel speed pulse sensor and acquiring time from an RTC timing device;
the artificial intelligence data processing module is used for carrying out data inspection and data state analysis on the data received from the multi-source data acquisition module;
the data encryption module is used for carrying out encryption storage protection on data acquisition and data transmission processes in the multi-source data acquisition module;
the intelligent terminal comprises a vehicle anti-cheating method based on artificial intelligence, and the method comprises the following steps:
step S100: the intelligent terminal periodically starts the vehicle pricing simulation under the state of not receiving the bill of the vehicle, and randomly sets the target mileage; acquiring and storing the mileage calculated by a wheel speed pulse sensor based on the vehicle price computing device, the time acquired from the RTC timing device in the vehicle price computing device and the price computing data finally generated by the vehicle price computing device as a metadata base;
step S200: confirming the classified number n of the mileage data according to the coverage range of the stored mileage data in the metadata base; starting to classify data of the metadata base based on the obtained classification number n, and generating a mileage classification threshold value in the classifying process;
step S300: the intelligent terminal acquires mileage calculated by a wheel speed pulse sensor in a vehicle pricing device in a bill receiving state of the vehicle, time acquired from an RTC timing device in the vehicle pricing device, vehicle driving speed displayed by a vehicle instrument panel in the corresponding mileage and time and pricing data finally generated by the vehicle pricing device;
step S400: performing primary data inspection on data generated in a vehicle order receiving state; determining to perform abnormal state analysis or pricing verification on the order receiving state by combining the result obtained by the primary data verification with the data in the metadata base;
the process of performing the primary data verification in the step S400 includes:
step S401: the intelligent terminal receives GPS mileage of a vehicle from a GPS receiving device of the vehicle in a vehicle order receiving state, and takes the GPS mileage as a first mileage; receiving an instrument mileage of a vehicle from a vehicle CAN, and taking the instrument mileage as a second mileage;
step S402: when the mileage difference between the first mileage and the second mileage is smaller than a mileage difference threshold value; taking the first mileage and the second mileage as mileage check references; if the mileage finally generated by the valuator is larger than the larger mileage value in the first mileage and the second mileage and the difference value is larger than a set difference value threshold value, carrying out abnormal state analysis on the order receiving state; if the mileage value finally generated by the valuator is between the first mileage and the second mileage, performing valuation verification on the order receiving state;
step S403: when the mileage difference between the first mileage and the second mileage is greater than a mileage difference threshold; if the mileage number finally generated by the valuator is larger than the first mileage and the second mileage and the difference value between any one mileage value of the first mileage and the second mileage is larger than a difference value threshold value, carrying out abnormal state analysis on the order receiving state; if the difference value between the mileage finally generated by the valuator and any one of the mileage values in the first mileage and the second mileage meets the difference value threshold, performing valuation verification on the order receiving state
Step S500: and (3) selecting overlay correction for the data or updating and supplementing the data for the metadata base based on the state analysis result or the pricing verification result obtained in the step S400.
2. The anti-cheating travel vehicle-mounted intelligent terminal based on artificial intelligence according to claim 1, wherein the multi-source data acquisition module is used for integrating a plurality of data source acquisition devices and mainly comprises a GPS receiving device, a vehicle CAN receiving unit, a price meter device and an RTC timing device;
the artificial intelligence data processing module comprises a metadata database unit, a data classifying unit, a primary data checking unit, an abnormal state analyzing unit and a pricing checking unit;
the metadata base unit is used for collecting data when the vehicle is periodically started for pricing simulation in a non-order receiving state; the data classifying unit is used for classifying the data acquired in the metadata database unit; the primary data checking unit is used for performing data checking on the data in the metadata base unit based on the partial data acquired in the multi-source data acquisition module; the abnormal state analysis unit is used for carrying out abnormal state analysis on the related data in the primary data inspection unit based on the partial data acquired in the multi-source data acquisition module; and the pricing verification unit is used for pricing verification of the partial data transmitted in the abnormal state analysis unit based on the partial data acquired in the multi-source data acquisition module.
3. The anti-cheating travel on-board intelligent terminal based on artificial intelligence according to claim 1, wherein the step S200 of confirming the number n of classifications of mileage data comprises:
step S201: the stored mileage data is statistically arranged to obtain mileage intervals (P max ，P min ) Wherein P is max Representing a highest mileage value; p (P) min Representing a lowest mileage value;
step S202: according to the formula
Judging the number of mileage thresholds; wherein y is a preset difference threshold value between the highest mileage value and the lowest mileage value, and n represents the mileage classification number.
4. The anti-cheating travel on-board intelligent terminal based on artificial intelligence of claim 1, wherein classifying all mileage data in step S200 comprises:
step S211: when n=2, there are 1 mileage classification threshold values, to which the remote mileage standard number set in the vehicle meter device is set;
step S212: when n=3, there are 2 mileage classification thresholds, and the remote mileage standard number set in the vehicle meter device is set as a first mileage classification threshold; the second mileage classification threshold value is according to the formula
Wherein T represents a first mileage classification threshold; u represents the number of mileage data greater than the first mileage classification threshold;
step S213: and according to the classification condition of the mileage data, correspondingly classifying the time data and the pricing data corresponding to each mileage with the mileage data.
5. The anti-cheating travel on-board intelligent terminal based on artificial intelligence according to claim 1, wherein the abnormal state analysis in step S400 comprises:
step S411: collecting data of the mileage generated in the price meter under the order receiving state along with the maintenance time change of the order receiving state, and calculating the vehicle speed V in a random time period, wherein the vehicle speed V is obtained according to a formula V=S/T;
step S412: correspondingly acquiring driving speed data displayed on a vehicle instrument panel in a random time period locked in the step S411, and performing deviation calculation on the driving speed data and the speed obtained in the step S411;
step S413: if the speed deviation value is greater than the deviation threshold value, determining that the data is abnormal; if the speed deviation value is smaller than the deviation threshold value, feedback information is sent to the client side and the driver side to confirm the vehicle abnormal state, and when the vehicle abnormal state is confirmed, the vehicle abnormal state is judged to be non-data abnormal; when the non-anomaly is confirmed, judging that the data is abnormal; the vehicle anomaly includes, but is not limited to, the occurrence of a blockage of the original road segment during the journey, and other sudden change of journey requirements of the customer during the journey.
6. The anti-cheating travel on-board intelligent terminal based on artificial intelligence according to claim 1, wherein the calculating and checking in step S400 includes:
step S421: the intelligent terminal receives GPS time of the vehicle from a GPS receiving device of the vehicle in a vehicle order receiving state, and takes the GPS mileage as first time; receiving the meter time of the vehicle from the vehicle CAN, and taking the meter mileage as a second time;
step S422: the mileage generated in the price meter in the order receiving state is classified according to the mileage classification threshold value in the metadata base; the price interval (T) is obtained by statistically arranging the price information data in the mileage category to which the mileage generated in the price meter belongs in the order receiving state max ，T min ) Wherein T is max Representing the highest priced value that occurs within the mileage category; t (T) min Is shown in the form ofThe lowest price in the mileage category;
step S422: the price generated in the price counter in the order receiving state is set as a state price, if the state price is not located in the price section (T max ，T min ) In the method, feedback information is sent to a client and a driver to confirm the vehicle abnormal state, and when the vehicle abnormal state is confirmed, the verification is judged to be normal; when confirming the non-anomaly, judging that the verification is abnormal; the vehicle abnormal state comprises, but is not limited to, that the original road section is blocked in the journey, and the customer has other requirements for suddenly changing the journey in the journey;
step S423: if the status price is located in the price range (T max ，T min ) In, calling the first time and the second time; if the time difference between the first time and the second time is smaller than a system-set time difference threshold, taking the time value in the first time and the second time as a time check reference, calling the time data with the smallest difference value in a metadata base, and simultaneously searching pricing data corresponding to the time data;
step S424: calculating deviation between the pricing data and the state price, and judging that verification is abnormal when the deviation is larger than a system set price deviation threshold value; and when the deviation is smaller than a system set price deviation threshold value, judging that the verification is normal.
7. The anti-cheating travel on-board intelligent terminal based on artificial intelligence of claim 1, wherein the step S500 comprises:
when the analysis result of the abnormal state shows that the data is abnormal, correcting the mileage generated in the price meter under the order receiving state based on the first mileage and the second mileage, namely selecting a smaller mileage value from the first mileage and the second mileage to carry out coverage correction, and simultaneously generating a correction signal to be fed back to the intelligent terminal for signal early warning; when the analysis result of the abnormal state shows that the data is abnormal, the order receiving state is subjected to price checking;
when the pricing verification result shows that the verification is normal, storing the data in the order receiving state, namely storing the data into the metadata base to update and supplement the data of the metadata base; when the pricing verification result shows that the verification is abnormal, the corresponding pricing data in the metadata base is corrected to the pricing data in the order receiving state.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1217587A1 (en) * | 2000-12-22 | 2002-06-26 | Automatisme et Techniques Avancees | Electronic taximeter |
| CN103035036A (en) * | 2012-12-05 | 2013-04-10 | 福建省计量科学研究院 | Method capable of achieving remote monitoring of taxi meter based on wireless network and satellite positioning |
| CN103810758A (en) * | 2013-12-19 | 2014-05-21 | 山东大学 | Working method and working device for credibly judging TAXI taximeter accuracy by combining GPS (global positioning system) and Beidou positioning system |
| CN108564074A (en) * | 2018-06-26 | 2018-09-21 | 杭州车厘子智能科技有限公司 | A kind of timesharing car rental method for managing security and system based on FACEID |
| CN209674405U (en) * | 2019-03-27 | 2019-11-22 | 广州八通电子实业有限公司 | A kind of calibrating device for taximeters |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101470012B (en) * | 2007-12-29 | 2011-03-30 | 厦门雅迅网络股份有限公司 | Method for real-time vehicle driving mileage statistics based on wireless network and GPS position information |
| CN201397143Y (en) * | 2009-05-11 | 2010-02-03 | 西安信唯信息科技有限公司 | Taximeter based on GPS and electronic geographic information |
| CN103778665B (en) * | 2013-11-08 | 2016-01-27 | 中国科学院深圳先进技术研究院 | A kind of method, system and computing machine detecting repacking fee register behavior of hiring a car |
| MA37421A1 (en) * | 2014-10-10 | 2016-06-30 | Univ Internationale De Rabat Privee Uir | Anti-fraud taxi counter |
| CN106997620A (en) * | 2016-01-26 | 2017-08-01 | 滴滴（中国）科技有限公司 | Charging method and device based on GPS data point |
| CN107784381A (en) * | 2016-08-31 | 2018-03-09 | 厦门雅迅网络股份有限公司 | It is a kind of to judge that taxi detours and valuate abnormal method and device |
| CN109697761A (en) * | 2017-10-20 | 2019-04-30 | 北京聚利科技股份有限公司 | Method of discrimination, device and the vehicle intelligent terminal of driving behavior |
| CN112381957A (en) * | 2020-11-16 | 2021-02-19 | 广州智体科技有限公司 | Vehicle-mounted terminal charging method and system for calculating car renting driving cost |
| CN113108808B (en) * | 2021-03-16 | 2023-02-10 | 北京理工大学 | Vehicle odometer online verification system and method |
-
2021
- 2021-10-08 CN CN202111172381.8A patent/CN113674450B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1217587A1 (en) * | 2000-12-22 | 2002-06-26 | Automatisme et Techniques Avancees | Electronic taximeter |
| CN103035036A (en) * | 2012-12-05 | 2013-04-10 | 福建省计量科学研究院 | Method capable of achieving remote monitoring of taxi meter based on wireless network and satellite positioning |
| CN103810758A (en) * | 2013-12-19 | 2014-05-21 | 山东大学 | Working method and working device for credibly judging TAXI taximeter accuracy by combining GPS (global positioning system) and Beidou positioning system |
| CN108564074A (en) * | 2018-06-26 | 2018-09-21 | 杭州车厘子智能科技有限公司 | A kind of timesharing car rental method for managing security and system based on FACEID |
| CN209674405U (en) * | 2019-03-27 | 2019-11-22 | 广州八通电子实业有限公司 | A kind of calibrating device for taximeters |
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