CN111599165A - Multi-source big data-based electric vehicle robbery real-time warning method and system - Google Patents
Multi-source big data-based electric vehicle robbery real-time warning method and system Download PDFInfo
- Publication number
- CN111599165A CN111599165A CN202010070409.6A CN202010070409A CN111599165A CN 111599165 A CN111599165 A CN 111599165A CN 202010070409 A CN202010070409 A CN 202010070409A CN 111599165 A CN111599165 A CN 111599165A
- Authority
- CN
- China
- Prior art keywords
- electric vehicle
- alarm
- robbery
- time
- deviation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
Abstract
The invention discloses an automatic real-time warning method and system for electric vehicle robbery based on multi-source big data, which is based on real-time data of an electric vehicle and a mobile phone and realizes automatic real-time warning of electric vehicle robbery by introducing data mining algorithms such as track deviation, separation of people and vehicles, synchronization of target people and the like. According to the invention, through analysis of mass data and combination of a data mining algorithm, traditional after-the-fact tracking is changed into active early warning. In terms of reliability, the real-time performance and accuracy of the alarm result pushing are improved based on the historical track analysis of the electric vehicle and the combination of a big data technology and a data mining algorithm. Functionally, the invention provides functions of vehicle activity range analysis, vehicle riding personnel analysis, vehicle abnormal alarm and the like.
Description
Technical Field
The invention relates to the technical field of data mining, in particular to a multisource big data-based electric vehicle robbery and robbery real-time warning method and system.
Background
Conversation is the most frequently used communication method for people. The abundance of network systems also leads to the development of Chinese electric vehicles with the complexity of mobile communication signaling over a decade, and the Chinese electric vehicles become the main transportation means of Chinese people. The electric vehicle has simple anti-theft facilities, is easy to steal and robbe, is convenient to transfer and disassemble and has low recovery probability.
With the development of the technology of the internet of things, the internet of things chip of the electric vehicle is installed in a large scale in a plurality of provinces in the country. Taking a certain city as an example, the installation rate of the chip of the Internet of things of the electric vehicle reaches 50%, the number of the chip signal receiving points of the Internet of things exceeds 2000, and the chip basically covers all levels of roads in the city.
Based on the background, an electric vehicle robbery and robbery warning system is urgently needed, and the electric vehicle robbery and robbery active warning can be realized through big data analysis based on real-time data such as a combination networking chip, a mobile phone, an electric vehicle and the like.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the prior art, the invention provides the electric vehicle robbery real-time warning method based on the multi-source big data, the method can solve the problems of time consumption and low efficiency in the event of the electric vehicle robbery, and the invention also provides the electric vehicle robbery real-time warning system based on the multi-source big data.
The technical scheme is as follows: the invention relates to a multisource big data-based electric vehicle robbery real-time warning method, which comprises the following steps:
(1) acquiring running data of the electric vehicle and mobile phone data of a user within a certain range in real time; analyzing the historical track of the electric vehicle for a period of time, and determining the position track of the movement of the electric vehicle;
(2) analyzing mobile phone data of a user and electric vehicle driving data, calculating an incidence relation between a person and a vehicle, and forming an electric vehicle driver library so as to determine a common driver of the electric vehicle;
(3) if the time interval between the current electric vehicle number uploading point and the last number uploading point is greater than S seconds, clearing cache information of the current electric vehicle, otherwise, judging whether the distance from the last number uploading point is not greater than T meters, if so, clearing the cache information of the current electric vehicle, otherwise, confirming whether current driving accords with an alarm type, wherein the alarm type comprises deviation from the position track of the electric vehicle, separation of a common driver from the electric vehicle and co-operation of a target person and the electric vehicle;
(4) if any one or more of deviation of the position track of the electric vehicle, separation of a common driver from the electric vehicle and simultaneous warning of a target person and the electric vehicle exist, and the electric vehicle passes through a key base station, key base station warning is carried out;
(5) respectively giving scores to the alarms, calculating the total alarm score, marking the alarm level according to the total alarm score, recording the alarm type, and pushing alarm information to a display terminal;
(6) and (5) circulating the steps (3) to (5) until all the electric vehicles are determined and judged, and displaying the alarm information of the electric vehicles with the alarm on a display terminal.
Further, comprising:
in the step (4), the method for judging the warning of the deviation of the position track of the electric vehicle comprises the following steps: if the electric vehicle is on the moving position track of the electric vehicle, updating the number of times of non-deviation, if the number of times of non-deviation after updating is larger than G, clearing the number of times of non-deviation and deviation, otherwise, if the electric vehicle is not on the moving position track of the electric vehicle, generating track deviation, updating the number of times of deviation, and marking the alarm type as track deviation until the number of times of deviation after updating is larger than H, and performing track deviation alarm on the electric vehicle.
Further, comprising:
the number of times G ranges from 3 to 7, and the number of times H ranges from 4 to 8.
Further, comprising:
in the step (4), the method for determining that the common driver and the electric vehicle are separated for warning comprises the following steps: if a common driver is inquired, reading mobile phone data of the first common driver in nearly J seconds, calculating the speed according to the distance difference and the time difference between the mobile phone data and the electric vehicle data, if the distance is larger than K kilometer and the time is smaller than L minutes or the time is larger than L minutes and the speed is larger than M meters per second, updating the separation times, if the updated separation times are larger than N, judging that human-vehicle separation occurs, circularly traversing all the common drivers, and if all the common drivers judge that human-vehicle separation occurs, performing human-vehicle separation early warning.
Further, comprising:
the range of J is 400-700 seconds, the range of K is 1-3 kilometers, the range of L is 1-2 minutes, the range of M is 50-70 meters per second, and the range of N is 1-2 times.
Further, comprising:
in the step (4), the method for judging the peer-to-peer alarm of the target person and the electric vehicle comprises the following steps: reading the latest mobile phone data of the target person, and if the distance between the target person and the electric vehicle is less than P kilometers and the time difference is less than Q minutes, considering that the target person and the electric vehicle are in the same line, and recording the position information of the target person; and if the times that the target person continuously passes through different positions are more than R times and the same-row conditions are met, judging that the electric vehicle and the target person are in the same row, and performing the target person same-row warning on the electric vehicle.
Further, comprising:
the range of P is 1-3 kilometers, the range of Q is 1-2 minutes, and the range of R is 2-4 times.
Further, comprising:
the range of S is 1500-2000 seconds, and the range of T is 160-240 meters.
On the other hand, the invention also provides a multisource big data-based electric vehicle robbery real-time warning system, which comprises:
the acquisition module is used for acquiring the driving data of the electric vehicle and the mobile phone data of a user within a certain range in real time;
the position track determining module is used for analyzing the historical track of the electric vehicle for a period of time and determining the moving position track of the electric vehicle;
the driver determining module is used for analyzing mobile phone data of a user and electric vehicle driving data, calculating the association relation between a person and a vehicle, and forming an electric vehicle driver library so as to determine a common driver of the electric vehicle;
the alarm type judging module is used for judging whether the current electric vehicle has an alarm type, and specifically comprises the following steps: if the time interval between the current electric vehicle number uploading point and the last number uploading point is greater than S seconds, clearing cache information of the current electric vehicle, otherwise, judging whether the distance from the last number uploading point is not greater than T meters, if so, clearing the cache information of the current electric vehicle, otherwise, confirming whether current driving accords with an alarm type, wherein the alarm type comprises deviation from the position track of the electric vehicle, separation of a common driver from the electric vehicle and co-operation of a target person and the electric vehicle;
if any one or more of deviation of the position track of the electric vehicle, separation of a common driver from the electric vehicle and simultaneous warning of a target person and the electric vehicle exist, and the electric vehicle passes through a key base station, key base station warning is carried out;
the display module is used for giving scores to the alarms respectively, calculating the total score of the alarms, marking the alarm level according to the total score of the alarms, recording the alarm type and pushing the alarm information to the display terminal;
and (5) a circulating module for circulating the steps (3) to (5) until all the electric vehicles are determined and judged, and displaying the alarm information of the electric vehicles with the alarm on a display terminal.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: according to the invention, based on the Internet of things data of the electric vehicle, the mobile phone and the like, a big data analysis technology is adopted, and the tradition tracking after the stealing and robbing of the electric vehicle is changed into active early warning. From the reliability, the real-time performance and the accuracy of the early warning result are improved based on the historical track analysis of the electric vehicle and by combining a big data technology and a data mining algorithm. Functionally, the invention provides the functions of vehicle riding personnel analysis, vehicle activity range analysis, vehicle abnormity early warning and the like, reduces the rate of accident, shortens the tracking time and improves the recovery efficiency.
Drawings
FIG. 1 is a schematic diagram of an alarm result model;
FIG. 2 is a schematic diagram of a flow implementation of a trajectory deviation warning;
FIG. 3 is a schematic view of a flow implementation of a man-vehicle separation alarm;
fig. 4 is a schematic diagram of a flow implementation of the target person peer alarm.
Detailed Description
In order to clearly and clearly explain the objects, technical solutions and advantages of the present invention, the present invention is further described below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are intended to be illustrative only and are not intended to be limiting.
As shown in FIG. 1, the invention discloses an automatic real-time alarming method for electric vehicle robbery based on multi-source big data, which comprises the following steps:
step 1, acquiring running data of an electric vehicle and mobile phone data of a user within a certain range in real time; analyzing the historical track of the electric vehicle for a period of time, and determining the position track of the movement of the electric vehicle;
and 2, analyzing the mobile phone data of the user and the electric vehicle driving data, and calculating the association relation between the person and the vehicle to form an electric vehicle driver library so as to determine the common driver of the electric vehicle.
The running track of the electric vehicle in the recent period of time can be analyzed through the big data distributed computing engine to obtain the common activity range of the electric vehicle, the mobile phone data and the electric vehicle data are analyzed, and the incidence relation between a person and a vehicle is computed, so that the common driver of the electric vehicle is obtained.
Step 3, if the time interval between the current electric vehicle number uploading point and the last number uploading point is greater than S seconds, clearing the cache information of the current electric vehicle, otherwise, judging whether the distance between the current electric vehicle number uploading point and the last number uploading point is not greater than T meters, if so, clearing the cache information of the current electric vehicle, otherwise, confirming whether the current running accords with an alarm type, wherein the alarm type comprises deviation from the position track of the electric vehicle, separation of a common driver from the electric vehicle and co-operation of a target person and the electric vehicle;
in this embodiment, S ranges from 1500 to 2000 seconds, and T ranges from 160 to 240 meters.
Step 4, if any one or more of deviation of the position track of the electric vehicle, separation of a common driver from the electric vehicle, and simultaneous warning of a target person and the electric vehicle exists, and the electric vehicle passes through a key base station, key base station warning is carried out;
the judging and analyzing process of the deviation electric vehicle position track alarm is shown in fig. 2, whether the electric vehicle is on the historical track or not is judged, if yes, the number of times of deviation does not need to be updated, and if the number of times of deviation does not exceed G, the number of times of deviation and the number of times of deviation are cleared. If the trajectory is not on the historical trajectory, the trajectory deviation occurs, and the deviation times are updated. If the deviation times are larger than H, the mark alarm type is track deviation, and track deviation alarm is carried out on the electric vehicle. The threshold G, H can be configured according to practical situations, in this embodiment, the number G ranges from 3 to 7 times, and the number H ranges from 4 to 8 times.
The analysis flow of the judgment method for the separation warning of the common driver and the electric vehicle is shown in fig. 3, and the common driver of the electric vehicle is obtained from an electric vehicle driver base. And if the common driver is found, reading the mobile phone data of the common driver for nearly J seconds. And calculating the speed according to the distance difference and the time difference between the mobile phone data and the electric vehicle data, and if the distance is more than K kilometers and the time is less than L minutes or the time is more than L minutes and the speed is more than M meters per second, updating the separation times, wherein the separation times are more than N, and the occurrence of human-vehicle separation is judged. And when all the drivers in common use judge that the separation of the driver and the vehicle occurs, the early warning of the separation of the driver and the vehicle is carried out. The threshold J, K, L, M, N may be configured according to the circumstances. In this embodiment, J ranges from 400 to 700 seconds, K ranges from 1 to 3 kilometers, L ranges from 1 to 2 minutes, M ranges from 50 to 70 meters per second, and N ranges from 1 to 2 times.
Judging and analyzing that the target person and the electric vehicle are in the same-row warning is as shown in fig. 4, the latest mobile phone data of the target person is read, if the distance between the target person and the electric vehicle is less than P kilometers and the time difference is less than Q minutes, the target person is considered to be in the same row with the electric vehicle at the position, and position information is recorded. And if the times that the target person continuously passes through different positions are more than R times and the same-row conditions are met, judging that the electric vehicle and the target person are in the same row, and giving an alarm that the target person is in the same row to the electric vehicle. The threshold P, Q, R may be configured according to the circumstances. In this embodiment, P ranges from 1 to 3 kilometers, Q ranges from 1 to 2 minutes, and R ranges from 2 to 4 times.
Step 5, respectively giving scores to the alarms, calculating the total score of the alarms, marking the alarm level according to the total score of the alarms, recording the alarm type, and pushing the alarm information to a display terminal;
and for any alarm type, giving a score according to a scoring mechanism, and calculating the total score of the alarm. And marking the alarm level according to the total alarm value and recording the alarm type, pushing the alarm information to the front end, wherein the page can display the alarm information of the electric vehicle in real time, including information such as the number, time, place, alarm type and level of the electric vehicle.
The scoring mechanism is as follows, the score of the track deviation is A, the score of the people-vehicle separation is B, the score of the target person-co-movement is C, the score of the key base station is D, if the track deviation, the people-vehicle separation and the target person-co-movement are simultaneously met, the scores are accumulated, and A, B, C, D is between [0 and 1 ]. And scoring the electric vehicles passing through the key base station, wherein the electric vehicles need to meet any one condition of track deviation, separation of people and vehicles and target person co-walking early warning conditions, and the early warning score is D. And in some special time periods, the high-incidence period of the robbery case is achieved, and the target person usually does not have a mobile phone, so that the score of the peer of the target person needs to be reduced, and the score is reduced to E. The early warning result of other types of early warnings in a special time period needs to be added with a score F on the basis of the original score, the track deviation score of the special time period is A + F, the separation score of people and vehicles is B + F, the peer score of a target person is C-E, and the score of a key base station is D + F. The threshold A, B, C, D, E, F may be configured according to the circumstances. The alarm level can be classified into high level alarm, middle level alarm and low level alarm, and the specific score range can be determined according to the actual situation.
And 6, circulating the steps 3-5 until all the electric vehicles are determined, and displaying the alarm information of the electric vehicles with the alarm on a display terminal.
The invention discloses an automatic real-time warning system for electric vehicle robbery based on multi-source big data, which is based on real-time data of an electric vehicle and a mobile phone and realizes automatic real-time warning of electric vehicle robbery by introducing data mining algorithms such as track deviation, separation of people and vehicles, synchronization of target people and the like. Through the analysis of mass data and the combination of a data mining algorithm, the traditional after-the-fact tracking is changed into active early warning. In terms of reliability, the real-time performance and accuracy of the alarm result pushing are improved based on the historical track analysis of the electric vehicle and the combination of a big data technology and a data mining algorithm. Functionally, the invention provides functions of vehicle activity range analysis, vehicle riding personnel analysis, vehicle abnormal alarm and the like.
Claims (9)
1. A multisource big data-based electric vehicle robbery real-time warning method is characterized by comprising the following steps:
(1) acquiring running data of the electric vehicle and mobile phone data of a user within a certain range in real time; analyzing the historical track of the electric vehicle for a period of time, and determining the position track of the movement of the electric vehicle;
(2) analyzing mobile phone data of a user and electric vehicle driving data, calculating an incidence relation between a person and a vehicle, and forming an electric vehicle driver library so as to determine a common driver of the electric vehicle;
(3) if the time interval between the current electric vehicle number uploading point and the last number uploading point is greater than S seconds, clearing cache information of the current electric vehicle, otherwise, judging whether the distance from the last number uploading point is not greater than T meters, if so, clearing the cache information of the current electric vehicle, otherwise, confirming whether current driving accords with an alarm type, wherein the alarm type comprises deviation from the position track of the electric vehicle, separation of a common driver from the electric vehicle and co-operation of a target person and the electric vehicle;
(4) if any one or more of deviation of the position track of the electric vehicle, separation of a common driver from the electric vehicle and simultaneous warning of a target person and the electric vehicle exist, and the electric vehicle passes through a key base station, key base station warning is carried out;
(5) respectively giving scores to the alarms, calculating the total alarm score, marking the alarm level according to the total alarm score, recording the alarm type, and pushing alarm information to a display terminal;
(6) and (5) circulating the steps (3) to (5) until all the electric vehicles are determined and judged, and displaying the alarm information of the electric vehicles with the alarm on a display terminal.
2. The multisource big data-based electric vehicle robbery and robbery real-time warning method according to claim 1, wherein in the step (4), the method for judging the warning of the deviation of the position track of the electric vehicle comprises the following steps: if the electric vehicle is on the moving position track of the electric vehicle, updating the number of times of non-deviation, if the number of times of non-deviation after updating is larger than G, clearing the number of times of non-deviation and deviation, otherwise, if the electric vehicle is not on the moving position track of the electric vehicle, generating track deviation, updating the number of times of deviation, and marking the alarm type as track deviation until the number of times of deviation after updating is larger than H, and performing track deviation alarm on the electric vehicle.
3. The multisource big data-based electric vehicle robbery and robbery real-time warning method according to claim 2, wherein the number of times G ranges from 3 to 7 times, and the number of times H ranges from 4 to 8 times.
4. The multisource big data-based electric vehicle robbery and robbery real-time warning method according to claim 1, wherein in the step (4), the judgment method for the separation warning of the common driver and the electric vehicle comprises the following steps: if a common driver is inquired, reading mobile phone data of the first common driver in nearly J seconds, calculating the speed according to the distance difference and the time difference between the mobile phone data and the electric vehicle data, if the distance is larger than K kilometer and the time is smaller than L minutes or the time is larger than L minutes and the speed is larger than M meters per second, updating the separation times, if the updated separation times are larger than N, judging that human-vehicle separation occurs, circularly traversing all the common drivers, and if all the common drivers judge that human-vehicle separation occurs, performing human-vehicle separation early warning.
5. The multisource big data-based electric vehicle robbery and robbery real-time warning method according to claim 4, wherein J ranges from 400 to 700 seconds, K ranges from 1 to 3 kilometers, L ranges from 1 to 2 minutes, M ranges from 50 to 70 meters per second, and N ranges from 1 to 2 times.
6. The multisource big data-based electric vehicle robbery and robbery real-time warning method according to claim 1, wherein in the step (4), the warning judgment method for the target person and the electric vehicle in the same row is as follows: reading the latest mobile phone data of the target person, and if the distance between the target person and the electric vehicle is less than P kilometers and the time difference is less than Q minutes, considering that the target person and the electric vehicle are in the same line, and recording the position information of the target person; and if the times that the target person continuously passes through different positions are more than R times and the same-row conditions are met, judging that the electric vehicle and the target person are in the same row, and performing the target person same-row warning on the electric vehicle.
7. The multisource big data-based electric vehicle robbery and robbery real-time warning method according to claim 6, wherein the range of P is 1-3 kilometers, the range of Q is 1-2 minutes, and the range of R is 2-4 times.
8. The multisource big data-based electric vehicle robbery and robbery real-time warning method according to claim 1, wherein the range of S is 1500-2000 seconds, and the range of T is 160-240 meters.
9. The utility model provides a real-time alarm system is robbed in electric motor car robbery based on multisource big data which characterized in that includes:
the acquisition module is used for acquiring the driving data of the electric vehicle and the mobile phone data of a user within a certain range in real time;
the position track determining module is used for analyzing the historical track of the electric vehicle for a period of time and determining the moving position track of the electric vehicle;
the driver determining module is used for analyzing mobile phone data of a user and electric vehicle driving data, calculating the association relation between a person and a vehicle, and forming an electric vehicle driver library so as to determine a common driver of the electric vehicle;
the alarm type judging module is used for judging whether the current electric vehicle has an alarm type, and specifically comprises the following steps: if the time interval between the current electric vehicle number uploading point and the last number uploading point is greater than S seconds, clearing cache information of the current electric vehicle, otherwise, judging whether the distance from the last number uploading point is not greater than T meters, if so, clearing the cache information of the current electric vehicle, otherwise, confirming whether current driving accords with an alarm type, wherein the alarm type comprises deviation from the position track of the electric vehicle, separation of a common driver from the electric vehicle and co-operation of a target person and the electric vehicle;
if any one or more of deviation of the position track of the electric vehicle, separation of a common driver from the electric vehicle and simultaneous warning of a target person and the electric vehicle exist, and the electric vehicle passes through a key base station, key base station warning is carried out;
the display module is used for giving scores to the alarms respectively, calculating the total score of the alarms, marking the alarm level according to the total score of the alarms, recording the alarm type and pushing the alarm information to the display terminal;
and (5) a circulating module for circulating the steps (3) to (5) until all the electric vehicles are determined and judged, and displaying the alarm information of the electric vehicles with the alarm on a display terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010070409.6A CN111599165A (en) | 2020-01-21 | 2020-01-21 | Multi-source big data-based electric vehicle robbery real-time warning method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010070409.6A CN111599165A (en) | 2020-01-21 | 2020-01-21 | Multi-source big data-based electric vehicle robbery real-time warning method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111599165A true CN111599165A (en) | 2020-08-28 |
Family
ID=72187022
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010070409.6A Pending CN111599165A (en) | 2020-01-21 | 2020-01-21 | Multi-source big data-based electric vehicle robbery real-time warning method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111599165A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11755592B2 (en) | 2021-08-25 | 2023-09-12 | International Business Machines Corporation | Data search with automated selection of artificial intelligence inference models and inference label indexing |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203946197U (en) * | 2014-05-19 | 2014-11-19 | 北京玛施德利科技有限公司 | Anti-theft monitoring device and Electrical Bicycle antitheft tracing system |
| CN105989062A (en) * | 2015-02-04 | 2016-10-05 | 南京乐行天下智能科技有限公司 | Defining method based on electric vehicle travelling track characteristics and data mining technology |
| CN107798510A (en) * | 2017-09-27 | 2018-03-13 | 北京市振隆科技股份有限公司 | Electric bicycle, motorcycle, motor tricycle monitoring management system and its method |
| CN109558431A (en) * | 2018-12-27 | 2019-04-02 | 福建小电科技有限公司 | A kind of electric bicycle theftproof system based on big data |
| CN109785595A (en) * | 2019-02-26 | 2019-05-21 | 成都古河云科技有限公司 | A kind of vehicle abnormality track real-time identification method based on machine learning |
| CN109842850A (en) * | 2018-12-28 | 2019-06-04 | 武汉依迅电子信息技术有限公司 | Electric vehicle monitoring method and device |
| CN109886204A (en) * | 2019-02-25 | 2019-06-14 | 武汉烽火众智数字技术有限责任公司 | A kind of Multidimensional Awareness system based on the application of big data police service |
| CN209265697U (en) * | 2018-09-29 | 2019-08-16 | 贵州省广播电视信息网络股份有限公司 | A kind of cart management system based on Internet of Things |
| US10449967B1 (en) * | 2016-03-01 | 2019-10-22 | Allstate Insurance Company | Vehicle to vehicle telematics |
-
2020
- 2020-01-21 CN CN202010070409.6A patent/CN111599165A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203946197U (en) * | 2014-05-19 | 2014-11-19 | 北京玛施德利科技有限公司 | Anti-theft monitoring device and Electrical Bicycle antitheft tracing system |
| CN105989062A (en) * | 2015-02-04 | 2016-10-05 | 南京乐行天下智能科技有限公司 | Defining method based on electric vehicle travelling track characteristics and data mining technology |
| US10449967B1 (en) * | 2016-03-01 | 2019-10-22 | Allstate Insurance Company | Vehicle to vehicle telematics |
| CN107798510A (en) * | 2017-09-27 | 2018-03-13 | 北京市振隆科技股份有限公司 | Electric bicycle, motorcycle, motor tricycle monitoring management system and its method |
| CN209265697U (en) * | 2018-09-29 | 2019-08-16 | 贵州省广播电视信息网络股份有限公司 | A kind of cart management system based on Internet of Things |
| CN109558431A (en) * | 2018-12-27 | 2019-04-02 | 福建小电科技有限公司 | A kind of electric bicycle theftproof system based on big data |
| CN109842850A (en) * | 2018-12-28 | 2019-06-04 | 武汉依迅电子信息技术有限公司 | Electric vehicle monitoring method and device |
| CN109886204A (en) * | 2019-02-25 | 2019-06-14 | 武汉烽火众智数字技术有限责任公司 | A kind of Multidimensional Awareness system based on the application of big data police service |
| CN109785595A (en) * | 2019-02-26 | 2019-05-21 | 成都古河云科技有限公司 | A kind of vehicle abnormality track real-time identification method based on machine learning |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11755592B2 (en) | 2021-08-25 | 2023-09-12 | International Business Machines Corporation | Data search with automated selection of artificial intelligence inference models and inference label indexing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110176139B (en) | DBSCAN + -based road congestion identification visualization method | |
| CN108550262B (en) | Urban traffic sensing system based on millimeter wave radar | |
| CN110400478A (en) | A kind of road condition notification method and device | |
| CN108074396A (en) | The evaluation method that drives safely and system | |
| CN110428604B (en) | Taxi illegal parking monitoring and early warning method based on track and map data | |
| KR101601034B1 (en) | System for processing and analysing big data obtaining from digital tachograph | |
| CN108460968A (en) | A kind of method and device obtaining traffic information based on car networking | |
| CN100589143C (en) | Method and appaatus for judging the traveling state of a floating vehicle | |
| WO2015081335A2 (en) | Advanced context-based driver scoring | |
| US10369995B2 (en) | Information processing device, information processing method, control device for vehicle, and control method for vehicle | |
| CN114435138B (en) | Vehicle energy consumption prediction method and device, vehicle and storage medium | |
| CN104318327A (en) | Predictive parsing method for track of vehicle | |
| CN111341120A (en) | Vehicle reverse running detection method and system and vehicle | |
| CN110428621A (en) | A kind of monitoring of Floating Car dangerous driving behavior and method for early warning based on track data | |
| TW201719541A (en) | Method and system of analyzing and predicting high-risk road section by means of driving behavior utilizing an on-board unit to collect a driver's driving trajectory for analyzing bad driving behaviors and therefore learn a high-risk road section | |
| CN111599165A (en) | Multi-source big data-based electric vehicle robbery real-time warning method and system | |
| CN116279500B (en) | Vehicle collision recognition method | |
| CN115909752B (en) | Method for identifying and counting sharp turns based on historical data of vehicle users | |
| KR101601031B1 (en) | Method of processing and analysing driving record big data obtaining from digital tachograph | |
| CN115019532A (en) | Automatic identification and early warning system for potential safety hazards of roads based on passenger traffic data | |
| CN110544378B (en) | Method for judging traffic jam condition of mobile phone user | |
| El-Tawab et al. | Real-time weather notification system using intelligent vehicles and smart sensors | |
| CN108648479B (en) | Device and method for reminding night foggy road section in real time by using electronic map | |
| CN112261581A (en) | Tourism bus supervision system based on GIS map and multiple positioning | |
| CN111127055A (en) | User portrait construction method and system based on vehicle body collection bus signals |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200828 |