CN202075864U - Abnormal traffic state automatic detection system - Google Patents
Abnormal traffic state automatic detection system Download PDFInfo
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- CN202075864U CN202075864U CN 201120131253 CN201120131253U CN202075864U CN 202075864 U CN202075864 U CN 202075864U CN 201120131253 CN201120131253 CN 201120131253 CN 201120131253 U CN201120131253 U CN 201120131253U CN 202075864 U CN202075864 U CN 202075864U
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Abstract
The utility model relates to an abnormal traffic state automatic detection system for judging whether a pedestrian traffic state at at least one position is abnormal by pedestrian traffic data, wherein the pedestrian traffic data comprises history pedestrian traffic data and real-time pedestrian traffic data. The system comprises a pedestrian traffic data sampling module, a history pedestrian traffic database, a threshold value database and an abnormal pedestrian traffic state detection module. The abnormal traffic state automatic detection system has good monitoring and controlling effects on crowd gathering risk of a crowd assembly occupancy by establishing a real-time judgment model of different pedestrian traffic states of crowds and is beneficial to the administration section to make the effective management strategy.
Description
Technical field
The utility model relates to the pedestrian traffic field, relates in particular to a kind of unusual traffic behavior automatic checkout system.
Background technology
Crowded place density of personnel early warning system, be to rely on the video equipment that is installed in the different location, gather the video image of monitored area in real time, by the real-time crowd's passenger flow that reflects in the video image is carried out data statistics and analysis, thereby realize the crowd density early warning prediction of crowded place.The detection method that needs the unusual traffic behavior of a kind of pedestrian in application to be setting up the real-time judge model of the different pedestrian's traffic behaviors of crowd, the crowd massing risk in the crowd is dense place reached monitor and control effect preferably.
The utility model content
The purpose of this utility model is to propose a kind of unusual traffic behavior automatic checkout system, is beneficial to administrative authority and formulates effective management game.
In order to achieve the above object, the utility model provides a kind of unusual traffic behavior automatic checkout system, wherein utilize the pedestrian traffic data to judge whether the pedestrian traffic state of at least one position takes place unusually, described pedestrian traffic data comprise historical pedestrian traffic data and real-time pedestrian traffic data, it is characterized in that, this system comprises: the pedestrian traffic data sampling module, in the real-time pedestrian traffic data of gathering described position of each sampling instant; Historical pedestrian traffic database, be connected with described real-time pedestrian traffic data sampling module, receive and store described pedestrian traffic data, the pedestrian traffic data of described storage comprise vertical time series, are the pedestrian traffic historical data sequence of the synchronization of arrangement in chronological order of same position certain day; The unusual pedestrian traffic state threshold data of described position are stored in the threshold data storehouse, and the threshold data of described storage comprises that the first threshold data are right, has one first upper limit threshold data and one first lower threshold data; Unusual pedestrian traffic state detection module, with described pedestrian traffic data sampling module, historical pedestrian traffic database is connected with the threshold data storehouse, obtain real-time pedestrian traffic data, vertically time series and first threshold data are right, and according to vertical predicted value of the real-time pedestrian traffic data of vertical time series forecasting, by described vertical time series, in real time the pedestrian traffic data and vertically predicted value make up a group traveling together's traffic behavior abnormal index, with described pedestrian traffic abnormal state index and described first threshold data to comparing and judging according to comparison result whether described position the pedestrian traffic abnormal state takes place.
Unusual traffic behavior automatic checkout system described in the utility model, wherein said pedestrian traffic data comprise pedestrian's flow, regional pedestrian's quantity, density and speed.
Unusual traffic behavior automatic checkout system described in the utility model wherein also comprises the pedestrian traffic data preprocessing module, the pedestrian traffic data that collect in order to pre-service pedestrian traffic data sampling module.
Unusual traffic behavior automatic checkout system described in the utility model, wherein said pedestrian traffic data preprocessing module comprises: obliterated data identification and reparation module, in order to discern and to repair the real-time pedestrian traffic data of losing; Misdata identification and reparation module are in order to identification and the real-time pedestrian traffic data of mis repair; The level and smooth module of time series is in order to carry out smothing filtering to the real-time pedestrian traffic data of gathering; And the time scale synthesis module, in order to adjust the time interval between adjacent pedestrian's traffic data.
Unusual traffic behavior automatic checkout system described in the utility model, also comprise an alarm module, be connected with described unusual pedestrian traffic state detection module, for a sampling time window, if the pedestrian traffic abnormal index surpasses a pre-determined number greater than the first upper limit threshold data or less than the number of times of the first lower threshold data continuously, unusual pedestrian traffic state for having taken place in then described alarm module judged result.
Unusual traffic behavior automatic checkout system described in the utility model, the pedestrian traffic data of wherein said storage also comprise horizontal time series, are arbitrary day the pedestrian traffic historical data sequence of arranging in chronological order of same position; The threshold data of described threshold data library storage comprises that also second threshold data is right, has one second upper limit threshold data and one second lower threshold data.
Unusual traffic behavior automatic checkout system described in the utility model, wherein if unusual pedestrian traffic state for having taken place in judged result, then described unusual pedestrian traffic state detection module obtains real-time pedestrian traffic data, laterally the time series and second threshold data are right, and according to the lateral prediction value of the real-time pedestrian traffic data of horizontal time series forecasting, by described horizontal time series, in real time pedestrian traffic data and lateral prediction value thereof make up a sudden change pedestrian traffic abnormal state index, with described sudden change pedestrian traffic abnormal state index and described second threshold data to comparing and judging the described position pedestrian traffic abnormal state of whether undergoing mutation according to comparison result.
Unusual traffic behavior automatic checkout system described in the utility model, also comprise an alarm module, be connected with described unusual pedestrian traffic state detection module, for a sampling time window, if pedestrian traffic sudden change abnormal index surpasses a predetermined probability greater than the second upper limit threshold data or less than the probability of the second lower threshold data, the unusual pedestrian traffic state that suddenlys change for having taken place in then described alarm module judged result.
Unusual traffic behavior automatic checkout system described in the utility model wherein also comprises information issuing module, is connected the unusual pedestrian traffic state of real-time release or the unusual pedestrian traffic status information of suddenling change with described unusual pedestrian traffic state detection module.
Set up the real-time judge model of the different pedestrian's traffic behaviors of crowd (unimpeded, gradual change unusual, unexpected abnormality, block up etc.) by the utility model, the crowd massing risk in the crowd is dense place is reached monitor and control effect preferably.
Description of drawings
Fig. 1 is the structured flowchart of the unusual traffic behavior automatic checkout system of the utility model pedestrian.
Embodiment
In conjunction with the embodiments the utility model is further elaborated with reference to the accompanying drawings.
A kind of structure and update method of pedestrian traffic data long-run development pattern at first are provided, described pedestrian traffic data have time scale and time scale, and comprise pedestrian traffic raw data and pedestrian traffic real time data, this method comprises: step S1 ': obtain many days pedestrian traffic raw data at least one position and storage; Step S2 ': described pedestrian traffic raw data is carried out time scale and time scale correction; Step S3 ': the pedestrian traffic original data sequence of the same time scale of arranging in chronological order of screening same position certain day and conduct be time series vertically; Step S4 ': sampling pedestrian traffic real time data is also carried out pre-service; Step S5 ': the pedestrian traffic raw data in described pretreated pedestrian traffic real time data and the described vertical time series is compared and upgrade vertical time series according to comparison result; Step S6 ': repeating step S4 ' and step S5 ', thus finish the structure and the renewal of pedestrian traffic data long-run development pattern
Described step S2 ' also comprises: arbitrary day pedestrian traffic original data sequence of arranging in chronological order of screening same position and conduct be time series laterally.Described pedestrian traffic data comprise flow, regional number, density and speed.Described step S2 ' comprising: with a time scale is benchmark, is step-length with a time yardstick, with not on the same day pedestrian traffic raw data time scale alignment of same position.The benchmark of described time scale is 00:00:00 every day, and described time scale is 5 minutes.Described step S3 ' comprising: the screening secular trend similar continuous a plurality of week the phase same date the pedestrian traffic raw data and as vertical time series.Continuous a plurality of weeks of described screening are 4-5 week.Described step S5 ' comprising: if the pedestrian traffic real time data is normal pedestrian's traffic data, then upgrade the long-run development pattern with real time data, reject data the earliest in the middle of the original long-run development pattern simultaneously; If the pedestrian traffic real time data for lose, mistake or the unusual traffic data of pedestrian, then keep original long-run development pattern constant.
According to time organizational form difference, the utility model is divided into horizontal time series and vertical time series two classes with the time series of pedestrian traffic data.Wherein, laterally time series is meant the data sequence of arranging by arbitrary day time sequencing; Vertically time series is meant in chronological sequence pedestrian traffic data sequence of same period of series arrangement certain day.
The pedestrian traffic data time sequence of particular spatial location has secular trend, the short-term trend of the times and random fluctuation three specific characters usually.(1) secular trend, the specific region generally has more stable socio-economic activity pattern, promptly go to work, go to school, activity such as shopping has certain rules in time and spatial distributions, causes different same date in week (Monday, Tuesdays ... Sunday) pedestrian traffic pattern has stronger similarity.The utility model is not with same monitoring position, the characteristics of the same supplemental characteristic time series of same date with similarity are called secular trend.(2) the short-term trend of the times, because the influence of factors such as pedestrian traffic incident, the phenomenon of secular trend may appear departing from pedestrian's rule of specific region in short-term, originally be referred to as the pedestrian traffic seasonal effect in time series short-term trend of the times.(3) random fluctuation, except secular trend, the short-term trend of the times, also there is tangible random fluctuation in the pedestrian traffic data, in order to eliminate random fluctuation to the influence that the pedestrian traffic management decision produces, it suitably should be carried out filtering.
For a certain date, if the evolution of pedestrian traffic data time sequence relatively meets the long-run development pattern, then think a kind of normal pedestrian's traffic behavior, otherwise, be called the unusual traffic behavior of pedestrian.The predictability of normal pedestrian's traffic behavior is stronger, can carry out early warning to it, and the unusual traffic behavior of pedestrian generally be difficult to prediction, can only carry out Realtime Alerts to it.
It is one of important function of administrative authority that the unusual traffic behavior of pedestrian is reported to the police, and therefore, the utility model designs automatic detection algorithm of the unusual traffic behavior of pedestrian and the unusual traffic behavior alarm mechanism of pedestrian respectively.
(1) the unusual traffic behavior automatic testing method of pedestrian
According to the definition of the unusual traffic behavior of pedestrian as can be known, the long-run development pattern that whether significantly departs from the pedestrian traffic data is to judge the standard of the unusual traffic behavior of pedestrian.Therefore, the utility model utilizes the vertical time series of pedestrian traffic data, adopts the standard deviation method to make up pedestrian traffic abnormal state index, and is concrete shown in formula (6-1).By itself and respective threshold are compared, judge whether to take place the unusual traffic behavior of pedestrian.At certain location, the detection threshold of the unusual traffic behavior of pedestrian need and be determined with the common negotiation of user.
In the formula: the pedestrian traffic abnormal state index of α (t)---current sampling time interval;
The pedestrian traffic data measured value of z (t)---current sampling time interval;
---the vertical predicted value of pedestrian traffic data of current sampling time interval;
z
K(t)---K the historical data in pedestrian traffic data long-run development pattern front on the contained same day;
N---the contained historical data quantity of pedestrian traffic data long-run development pattern;
σ
α(t)---the standard deviation of the contained historical data of pedestrian traffic data long-run development pattern.
For the unusual traffic behavior of burst pedestrian, the mutability of pedestrian traffic data time sequence is comparatively strong.Therefore, the utility model utilizes the horizontal time series of pedestrian traffic data, adopts the standard deviation method to make up the unusual traffic behavior mutation index of pedestrian, and is concrete shown in formula (6-3).By itself and respective threshold are compared, distinguish burst unusual traffic behavior of pedestrian and the unusual traffic behavior of gradual change pedestrian.At certain location, distinguish that threshold value need consult and determine with the user is common.
In the formula: the unusual traffic behavior mutation index of the pedestrian of β (t)---current sampling time interval;
The pedestrian traffic data measured value of z (t)---current sampling time interval;
Z (t-j ')---the measured value in the current sampling time interval individual time interval of front j ';
n
h---the unusual traffic behavior mutation index of pedestrian is calculated selected correlation time of interval quantity;
σ
β(t)---correlation time is the standard deviation of pedestrian's traffic data at interval.
The design's detection algorithm can detect the comparatively significantly unusual traffic behavior of pedestrian, but the testing result less stable is unfavorable for that administrative authority formulates effective management game, therefore, needs the unusual traffic behavior alarm mechanism of design pedestrian.
(2) the unusual traffic behavior alarm mechanism of pedestrian
At the different user's requests and the feature of data sequence, the alarm mechanism of two unusual traffic behaviors of pedestrian of the design.
Be meant for window sometime based on the alarm mechanism that continue to detect,, then carry out the unusual traffic behavior of pedestrian and report to the police if the pedestrian traffic abnormal index surpasses certain standard greater than the number of times of detection threshold continuously.The required time window of this kind alarm mechanism is less usually, generally adopts 2-3 sampling time interval to get final product.
Generally lower based on the alert rate of the unusual traffic behavior alarm mechanism mistake of the pedestrian who continues to detect, but verification and measurement ratio is also lower, therefore, relatively is suitable for detecting the unusual traffic behavior of pedestrian that causes pedestrian traffic data variation trend stable.
The lasting detection alarm mechanism of the design's the unusual traffic behavior of pedestrian is more effective, has significantly improved the stability of alarming result, helps administrative authority to formulate rational management decision.
Be meant for window sometime based on the alarm mechanism of probability estimate,, then carry out the unusual traffic behavior of pedestrian and report to the police if the pedestrian traffic abnormal index surpasses certain probability greater than the probability of threshold value.The required time window of this kind alarm mechanism is bigger usually, generally adopts 4-5 sampling time interval.
The unusual traffic behavior alarm mechanism of pedestrian verification and measurement ratio based on probability estimate is generally higher, and is also higher with the alert rate of mistiming, therefore, relatively is suitable for detecting and causes the unusual traffic behavior of the stronger pedestrian of pedestrian traffic data fluctuations.
As shown in Figure 1, be the structured flowchart of the unusual pedestrian traffic state of the utility model automatic checkout system, this system comprises: pedestrian traffic data sampling module M1, in the real-time pedestrian traffic data of gathering described position of each sampling instant; Historical pedestrian traffic database B1, be connected with described real-time pedestrian traffic data sampling module M1, receive and store described pedestrian traffic data, the pedestrian traffic data of described storage comprise vertical time series B11, are the pedestrian traffic historical data sequence of the synchronization of arrangement in chronological order of same position certain day; Threshold data storehouse B2 stores the unusual pedestrian traffic state threshold data of described position, and the threshold data of described storage comprises the first threshold data to B21, has one first upper limit threshold data and one first lower threshold data; Unusual pedestrian traffic state detection module M2, with described pedestrian traffic data sampling module M1, historical pedestrian traffic database B1 is connected with threshold data storehouse B2, obtain real-time pedestrian traffic data, vertically time series and first threshold data are right, and according to vertical predicted value of the real-time pedestrian traffic data of vertical time series forecasting, by described vertical time series, in real time the pedestrian traffic data and vertically predicted value make up a group traveling together's traffic behavior abnormal index, with described pedestrian traffic abnormal state index and described first threshold data to comparing and judging according to comparison result whether described position the pedestrian traffic abnormal state takes place.
Described pedestrian traffic data comprise regional one skilled in the art's flow, quantity, density and speed.Described unusual pedestrian traffic condition detecting system also comprises pedestrian traffic data preprocessing module M3, the pedestrian traffic data that collect in order to pre-service pedestrian traffic data sampling module.Described pedestrian traffic data preprocessing module comprises: obliterated data identification and reparation module M31, in order to discern and to repair the real-time pedestrian traffic data of losing; Misdata identification and reparation module M32 are in order to identification and the real-time pedestrian traffic data of mis repair; The level and smooth module M33 of time series is in order to carry out smothing filtering to the real-time pedestrian traffic data of gathering; And time scale synthesis module M34, in order to adjust the time interval between adjacent pedestrian's traffic data.
For a sampling time window, if the pedestrian traffic abnormal index surpasses a pre-determined number greater than the first upper limit threshold data or less than the number of times of the first lower threshold data continuously, then unusual pedestrian traffic state for having taken place in judged result.
Perhaps, for a sampling time window, if the pedestrian traffic abnormal index surpasses a predetermined probability greater than the second upper limit threshold data or less than the probability of the second lower threshold data, then unusual pedestrian traffic state for having taken place in judged result.
The pedestrian traffic data of described storage also comprise horizontal time series B12, are arbitrary day the pedestrian traffic historical data sequence of arranging in chronological order of same position; The threshold data of described threshold data library storage also comprises second threshold data to B22, has one second upper limit threshold data and one second lower threshold data.If unusual pedestrian traffic state for having taken place in judged result, then described unusual pedestrian traffic state detection module obtains real-time pedestrian traffic data, laterally the time series and second threshold data are right, and according to the lateral prediction value of the real-time pedestrian traffic data of horizontal time series forecasting, by described horizontal time series, in real time pedestrian traffic data and lateral prediction value thereof make up a sudden change pedestrian traffic abnormal state index, with described sudden change pedestrian traffic abnormal state index and described second threshold data to comparing and judging the described position pedestrian traffic abnormal state of whether undergoing mutation according to comparison result.
The unusual pedestrian traffic condition detecting system of the utility model can also comprise alarm module M4, is connected with described unusual pedestrian traffic state detection module, when unusual pedestrian traffic state has taken place in judgement or suddenly change unusual pedestrian traffic state, produces alerting signal.The unusual pedestrian traffic condition detecting system of utility model can also comprise information issuing module M5, is connected the unusual pedestrian traffic state of real-time release or the unusual pedestrian traffic status information of suddenling change with described unusual pedestrian traffic state detection module.
The probability estimate alarm mechanism of the design's the unusual traffic behavior of pedestrian is more effective, has improved the stability of alarming result with the probability form, helps administrative authority to formulate rational management decision.
The above only is preferred embodiment of the present utility model, non-limitation protection domain of the present utility model, and the equivalent structure that all utilization the utility model instructionss and accompanying drawing content are done changes, and all is contained in the protection domain of the present utility model.
Claims (9)
1. unusual traffic behavior automatic checkout system, it is characterized in that, utilize the pedestrian traffic data to judge whether the pedestrian traffic state of at least one position takes place unusually, described pedestrian traffic data comprise historical pedestrian traffic data and real-time pedestrian traffic data, it is characterized in that this system comprises:
The pedestrian traffic data sampling module is in the real-time pedestrian traffic data of gathering described position of each sampling instant;
Historical pedestrian traffic database, be connected with described real-time pedestrian traffic data sampling module, receive and store described pedestrian traffic data, the pedestrian traffic data of described storage comprise vertical time series, are the pedestrian traffic historical data sequence of the synchronization of arrangement in chronological order of same position certain day;
The unusual pedestrian traffic state threshold data of described position are stored in the threshold data storehouse, and the threshold data of described storage comprises that the first threshold data are right, has one first upper limit threshold data and one first lower threshold data;
Unusual pedestrian traffic state detection module, with described pedestrian traffic data sampling module, historical pedestrian traffic database is connected with the threshold data storehouse, obtain real-time pedestrian traffic data, vertically time series and first threshold data are right, and according to vertical predicted value of the real-time pedestrian traffic data of vertical time series forecasting, by described vertical time series, in real time the pedestrian traffic data and vertically predicted value make up a group traveling together's traffic behavior abnormal index, with described pedestrian traffic abnormal state index and described first threshold data to comparing and judging according to comparison result whether described position the pedestrian traffic abnormal state takes place.
2. unusual traffic behavior automatic checkout system as claimed in claim 1 is characterized in that, described pedestrian traffic data comprise pedestrian's flow, regional pedestrian's quantity, density and speed.
3. unusual traffic behavior automatic checkout system as claimed in claim 1 is characterized in that, also comprises the pedestrian traffic data preprocessing module, the pedestrian traffic data that collect in order to pre-service pedestrian traffic data sampling module.
4. unusual traffic behavior automatic checkout system as claimed in claim 3 is characterized in that, described pedestrian traffic data preprocessing module comprises:
Obliterated data identification and reparation module are in order to discern and to repair the real-time pedestrian traffic data of losing;
Misdata identification and reparation module are in order to identification and the real-time pedestrian traffic data of mis repair;
The level and smooth module of time series is in order to carry out smothing filtering to the real-time pedestrian traffic data of gathering; And
The time scale synthesis module is in order to adjust the time interval between adjacent pedestrian's traffic data.
5. unusual traffic behavior automatic checkout system as claimed in claim 1, it is characterized in that, also comprise an alarm module, be connected with described unusual pedestrian traffic state detection module, for a sampling time window, if the pedestrian traffic abnormal index surpasses a pre-determined number greater than the first upper limit threshold data or less than the number of times of the first lower threshold data continuously, unusual pedestrian traffic state for having taken place in then described alarm module judged result.
6. unusual traffic behavior automatic checkout system as claimed in claim 5 is characterized in that the pedestrian traffic data of described storage also comprise horizontal time series, is arbitrary day the pedestrian traffic historical data sequence of arranging in chronological order of same position; The threshold data of described threshold data library storage comprises that also second threshold data is right, has one second upper limit threshold data and one second lower threshold data.
7. unusual traffic behavior automatic checkout system as claimed in claim 6, it is characterized in that, if unusual pedestrian traffic state for having taken place in judged result, then described unusual pedestrian traffic state detection module obtains real-time pedestrian traffic data, laterally the time series and second threshold data are right, and according to the lateral prediction value of the real-time pedestrian traffic data of horizontal time series forecasting, by described horizontal time series, in real time pedestrian traffic data and lateral prediction value thereof make up a sudden change pedestrian traffic abnormal state index, with described sudden change pedestrian traffic abnormal state index and described second threshold data to comparing and judging the described position pedestrian traffic abnormal state of whether undergoing mutation according to comparison result.
8. unusual traffic behavior automatic checkout system as claimed in claim 7, it is characterized in that, also comprise an alarm module, be connected with described unusual pedestrian traffic state detection module, for a sampling time window, if pedestrian traffic sudden change abnormal index surpasses a predetermined probability greater than the second upper limit threshold data or less than the probability of the second lower threshold data, the unusual pedestrian traffic state that suddenlys change for having taken place in then described alarm module judged result.
9. as any described unusual traffic behavior automatic checkout system among the claim 1-8, it is characterized in that, also comprise information issuing module, be connected, the unusual pedestrian traffic state of real-time release or the unusual pedestrian traffic status information of suddenling change with described unusual pedestrian traffic state detection module.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103035123A (en) * | 2012-12-25 | 2013-04-10 | 中国科学院深圳先进技术研究院 | Abnormal data acquiring method and system in traffic track data |
| CN103514743A (en) * | 2013-09-28 | 2014-01-15 | 上海电科智能系统股份有限公司 | Method for recognizing abnormal traffic state characteristics of real-time index data matching memory range |
| CN106813669A (en) * | 2015-12-01 | 2017-06-09 | 骑记(厦门)科技有限公司 | The modification method and device of movable information |
| CN107194184A (en) * | 2017-05-31 | 2017-09-22 | 成都数联易康科技有限公司 | Based on Time Series Similarity analysis in institute person-time method for detecting abnormality and system |
| CN108665096A (en) * | 2018-04-28 | 2018-10-16 | 新华三大数据技术有限公司 | Flow of the people alarm method and device |
| CN109844832A (en) * | 2016-12-30 | 2019-06-04 | 同济大学 | A kind of multi-modal accident detection method based on journey time distribution |
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2011
- 2011-04-28 CN CN 201120131253 patent/CN202075864U/en not_active Expired - Fee Related
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103035123A (en) * | 2012-12-25 | 2013-04-10 | 中国科学院深圳先进技术研究院 | Abnormal data acquiring method and system in traffic track data |
| CN103035123B (en) * | 2012-12-25 | 2016-01-20 | 中国科学院深圳先进技术研究院 | Abnormal data acquisition methods and system in a kind of traffic track data |
| CN103514743A (en) * | 2013-09-28 | 2014-01-15 | 上海电科智能系统股份有限公司 | Method for recognizing abnormal traffic state characteristics of real-time index data matching memory range |
| CN103514743B (en) * | 2013-09-28 | 2016-01-06 | 上海电科智能系统股份有限公司 | A kind of abnormal traffic state characteristic recognition method of real-time index-matched memory range |
| CN106813669A (en) * | 2015-12-01 | 2017-06-09 | 骑记(厦门)科技有限公司 | The modification method and device of movable information |
| CN106813669B (en) * | 2015-12-01 | 2020-01-03 | 骑记(厦门)科技有限公司 | Motion information correction method and device |
| CN109844832A (en) * | 2016-12-30 | 2019-06-04 | 同济大学 | A kind of multi-modal accident detection method based on journey time distribution |
| CN109844832B (en) * | 2016-12-30 | 2021-06-15 | 同济大学 | Multi-mode traffic anomaly detection method based on travel time distribution |
| CN107194184A (en) * | 2017-05-31 | 2017-09-22 | 成都数联易康科技有限公司 | Based on Time Series Similarity analysis in institute person-time method for detecting abnormality and system |
| CN107194184B (en) * | 2017-05-31 | 2020-11-17 | 成都数联易康科技有限公司 | Method and system for detecting abnormality of people in hospital based on time sequence similarity analysis |
| CN108665096A (en) * | 2018-04-28 | 2018-10-16 | 新华三大数据技术有限公司 | Flow of the people alarm method and device |
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