CN111930868A - Big data behavior trajectory analysis method based on multi-dimensional data acquisition - Google Patents
Big data behavior trajectory analysis method based on multi-dimensional data acquisition Download PDFInfo
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Abstract
The invention relates to the technical field of trajectory analysis, and provides a big data behavior trajectory analysis method based on multi-dimensional data acquisition, which comprises the following steps: step 100, establishing an association relation between acquisition equipment and a physical position; step 200, acquiring dynamic data of personnel information and vehicle information in real time through acquisition equipment to form a database of the personnel information and the vehicle information; step 300, analyzing a personnel behavior track or a vehicle behavior track according to a database of personnel information and vehicle information; step 400, analyzing the extracted target space-time trajectory data to obtain liveness analysis of the target within a certain time range; and 500, dynamically and visually displaying the target space-time behavior trajectory through a GIS map. According to the invention, historical activity records can be inquired according to the activity records of community personnel, the association relation between the personnel, the vehicle and the dynamic data is established, and the track analysis and display can be carried out on the personnel and the vehicle to form continuous tracking analysis effectiveness.
Description
Technical Field
The invention relates to the technical field of trajectory analysis, in particular to a big data behavior trajectory analysis method based on multi-dimensional data acquisition.
Background
At present, the intelligent community as the crystal of informatization and urbanization integration will become an important power engine for promoting the transformation and upgrading of the economic society of China. Essentially, the smart community is a new city development form with optimized economic and social activities, extends the intelligence of people on the basis of new generation information technologies such as internet of things, cloud computing and mobile internet, and promotes innovation of system mechanisms and operation modes in modes such as perceptionalization, interconnection, intellectualization and the like, so that the optimal allocation of resources and the lowest city operation cost, higher efficiency, the highest value and the strongest happiness of people are realized. The security management is the basis of intelligent community construction, the information of people is used as the basis, the files of people and derivatives of the people are constructed, the activity data of the people in the community is acquired through terminal acquisition equipment, the historical activity records of the people can be inquired in an information processing mode, and the situation can be well documented.
For "Big data" (Big data), the research institute Gartner gives such a definition: the big data is information assets which need a new processing mode and have stronger decision-making power, insight discovery power and flow optimization capability to adapt to mass, high growth rate and diversification. The strategic significance of big data technology is not to grasp huge data information, but to specialize the data containing significance. In other words, if big data is compared to an industry, the key to realizing profitability in the industry is to improve the "processing ability" of the data and realize the "value-added" of the data through the "processing".
The existing intelligent community management technology can only collect the activity records of community personnel and can inquire historical activity records, the collected mass data cannot be associated, dynamic space-time behavior tracks of the community personnel and vehicles cannot be obtained through analysis, continuous tracking analysis effectiveness cannot be formed on the behavior tracks of the personnel, only point position records of a plurality of time positions exist, and faults exist in space and time.
Disclosure of Invention
The invention mainly solves the technical problems that correlation cannot be established for collected data in the prior art, dynamic space-time behavior tracks of community personnel and vehicles cannot be obtained through analysis, and the like, and provides a big data behavior track analysis method based on multi-dimensional data collection.
The invention provides a big data behavior trajectory analysis method based on multi-dimensional data acquisition, which comprises the following steps:
the personnel behavior trajectory analysis process is as follows:
1) inquiring basic information of personnel in a static database, extracting dynamic data of the personnel information in an ElasticSearch database by taking the basic information of the personnel as a condition, wherein the dynamic data of the personnel information comprises face snapshot identification information, vehicle identification information, entrance guard passing information and mobile phone code information, and establishing an association relationship between the personnel and the dynamic data;
2) taking the basic information of the personnel, the liveness of the personnel, the dynamic data acquisition place, the dynamic data acquisition time and the dynamic data related to the personnel as a combination, establishing a personnel space-time trajectory data model, and storing the personnel space-time trajectory data model into an ElasticSearch database;
3) analyzing the behavior track of the target person: extracting a personnel data list from a database; extracting personnel space-time trajectory data from an elastic search database of a big data search engine; analyzing data, namely serially connecting relative personnel information, face snapshot identification information, vehicle identification information, entrance guard traffic information and mobile phone code information of the personnel in a time axis sequence, and displaying the information in a google map through map mapping to form personnel behavior track analysis;
the vehicle behavior track analysis process is as follows:
1) inquiring basic information of the vehicle in a static database, extracting dynamic data of the vehicle information in an ElasticSearch database by taking the vehicle information as a condition, wherein the dynamic data of the vehicle information comprises vehicle identification information, and establishing an association relationship between the vehicle and the dynamic data;
2) taking basic information, liveness, dynamic data acquisition place information, acquisition time and dynamic data related to the vehicle as a combination, establishing a vehicle space-time trajectory data model, and storing the model into an ElasticSearch database;
3) analyzing the behavior track of the target vehicle: extracting a vehicle data list from a database; extracting vehicle space-time trajectory data from a big data search engine ElasticSearch database; and analyzing data, namely serially connecting the vehicle tracks in a time axis sequence, and displaying the vehicle tracks in a google map through map mapping to form vehicle behavior track analysis.
Further, after step 300, the method further includes:
and (3) analyzing the activity of the personnel:
1) extracting person space-time trajectory data from an elastic search database of a big data search engine according to date and time;
2) counting the activity of the personnel according to the space-time trajectory data of the personnel, and displaying the statistical information in an activity time chart through echart;
vehicle activity analysis:
1) extracting vehicle space-time trajectory data from a big data search engine ElasticSearch database according to date and time;
2) and (3) counting the vehicle liveness according to the vehicle space-time trajectory data, and displaying the statistical information in an liveness time chart through echart.
Further, after step 400, the method further includes:
step 501, dynamically acquiring a URL address of a GIS map;
step 502, drawing a map;
step 503, extracting a cell information data list from the database;
step 504, drawing community graphs in a map by using a leaflet library;
step 505, drawing a person clustering point in a map by using a leaflet library;
step 506, drawing a person track in a map by using a leaflet library;
step 507, drawing a dynamic moving graph in a map by using a leaflet library;
and step 508, adding a click event in the map, and clicking pop-up prompt information.
Compared with the prior art, the big data behavior trajectory analysis method based on multi-dimensional data acquisition has the following advantages:
1. the dynamic data acquisition method comprises the steps that the acquisition equipment is linked with position information, so that the acquired dynamic data are marked with time and physical positions, the dynamic data are acquired data describing targets such as personnel or vehicles, and can be associated and bound with the personnel or the vehicles during acquisition, and the data comprise face snapshot, license plate snapshot, entrance guard passage, electronic fence and the like.
2. Through a big data technology, an incidence relation model of personnel and vehicles and community activity data is established, and space-time tracks representing target behavior characteristics can be extracted by taking the personnel or the vehicles as initial nodes, so that the continuous tracking analysis capability of the target behavior is achieved, and the behavior tracks of the concerned target can be efficiently restored.
3. The space-time behavior trajectory of the target is dynamically and visually displayed through a GIS map, and the space-time movement trajectory of the target is displayed on the map in an animation mode, so that the information acquisition is more visual and complete compared with the simple query of a single dynamic data record.
4. The method can be applied to the field of security and protection, can quickly position the positions of targets such as personnel, vehicles, mobile phones and the like through behavior track analysis, can quickly determine the moving track of the targets in a period of time in the field of epidemic prevention, finds potential risk areas and risk personnel, controls epidemic spread in time, and has a good application prospect.
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FIG. 1 is a flow chart of an implementation of a big data behavior trajectory analysis method based on multi-dimensional data acquisition according to the present invention;
FIG. 2 is a flow chart of an implementation of a human behavior trajectory analysis process provided by the present invention;
fig. 3 is a diagram of the effect of the application of the present invention.
Detailed Description
In order to make the technical problems solved, technical solutions adopted and technical effects achieved by the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings.
Fig. 1 is a flowchart of an implementation of a big data behavior trajectory analysis method based on multi-dimensional data acquisition according to the present invention. As shown in fig. 1, the method for analyzing big data behavior trajectory based on multi-dimensional data acquisition according to the embodiment of the present invention includes the following steps:
Marking the physical position of deployed acquisition equipment on a GIS map through a map marking module, wherein the acquisition equipment comprises human information such as a face recognition camera, a vehicle access, an intelligent access control machine, an electronic fence and the like and acquisition equipment of vehicle information, establishing binding association relations between different types of acquisition equipment and the geographic position, and establishing an information data structure of the acquisition equipment, wherein the information data structure contains map coordinate information; and preparing for subsequent dynamic data acquisition.
And 200, acquiring dynamic data of the personnel information and the vehicle information in real time through acquisition equipment to form a database of the personnel information and the vehicle information.
The data acquisition module acquires dynamic data acquired on the equipment in real time, establishes a data structure according to the dynamic data, and stores the data structure into an elastic search database to form a database of personnel information and vehicle information. The dynamic data comprises face snapshot identification information, vehicle identification information, entrance guard traffic information, mobile phone code information and the like.
The data acquisition module can acquire the data acquired by the equipment at regular time, establish dynamic acquisition data containing coordinate information by associating the coordinate information with the equipment, and store the dynamic acquisition data in a classified manner.
And 300, analyzing the personnel behavior track or the vehicle behavior track according to the database of the personnel information and the vehicle information.
FIG. 2 is a flow chart of an implementation of a human behavior trajectory analysis process provided by the present invention. As shown in fig. 2, the human behavior trajectory analysis process is as follows:
1) the method comprises the steps of inquiring basic information of personnel in a static database, extracting dynamic data of the personnel information in an ElasticSearch database by taking the basic information of the personnel as a condition, wherein the dynamic data is collected data for describing objects such as personnel or vehicles, the dynamic data of the personnel information comprises face snapshot identification information (identifying the personnel), vehicle identification information (being associated with owner information), access control passing information (passing the personnel), mobile phone code information (being associated with owner information), and establishing an association relationship between the personnel and the dynamic data.
2) The method comprises the steps of establishing a person space-time trajectory data model by taking basic information of a person, the liveness of the person, a dynamic data acquisition place, dynamic data acquisition time and dynamic data related to the person as a combination, namely establishing a database table by taking the data as data fields, and storing the database table into an ElasticSearch database.
3) Analyzing the behavior track of the target person, wherein the specific process comprises the following steps: extracting a personnel data list from a database; extracting personnel space-time trajectory data from an elastic search database of a big data search engine; analyzing data, and serially connecting information such as relative personnel information, face snapshot identification information, vehicle identification information, entrance guard traffic information, mobile phone code information and the like of the personnel in a time axis sequence, and displaying the information in a google map through map mapping to form personnel behavior track analysis.
The vehicle behavior track analysis process is as follows:
1) inquiring basic information of the vehicle in a static database, extracting dynamic data of the vehicle information in an elastic search database under the condition of the vehicle information, wherein the vehicle information takes a license plate number as a unique identifier, the dynamic data of the vehicle information comprises vehicle identification information, the license plate can be identified, and the vehicle and the dynamic data are in an association relation;
2) the method comprises the steps of taking basic information, liveness, dynamic data acquisition place information, acquisition time and dynamic data related to a vehicle as a combination, establishing a vehicle space-time trajectory data model, namely establishing a database table by taking the data as data fields, and storing the database table into an ElasticSearch database.
The model building step is to build a database by taking basic information, liveness, dynamic data acquisition place information, acquisition time and dynamic data associated with the vehicle as a table.
3) Analyzing the behavior track of the target vehicle, wherein the specific process comprises the following steps: extracting a vehicle data list from a database; extracting vehicle space-time trajectory data from a big data search engine ElasticSearch database; and analyzing data, namely serially connecting the vehicle tracks in a time axis sequence, and displaying the vehicle tracks in a google map through map mapping to form vehicle behavior track analysis.
And 400, analyzing the extracted target space-time trajectory data to obtain the activity analysis of the target within a certain time range.
And (3) analyzing the activity of the personnel:
1) extracting person space-time trajectory data from an elastic search database of a big data search engine according to date and time;
2) and counting the activity of the personnel according to the personnel space-time trajectory data, and displaying the statistical information in an activity time chart through echart.
Vehicle activity analysis:
1) extracting vehicle space-time trajectory data from a big data search engine ElasticSearch database according to date and time;
2) and (3) counting the vehicle liveness according to the vehicle space-time trajectory data, and displaying the statistical information in an liveness time chart through echart.
And 500, dynamically and visually displaying the space-time behavior track of the personnel or the vehicle through a GIS map.
Step 501, dynamically acquiring a GIS map url address.
Step 502, drawing a map.
Step 503, extract the cell information data list from the database.
At step 504, a community graph is rendered in the map using the leaflet library.
And 505, drawing the clustering point of the people or the vehicles in the map by using a LEAFLET library.
At step 506, the person or vehicle trajectory is mapped using the leaflet library.
In step 507, the dynamic moving graph is drawn in the map by using the leaflet library.
And step 508, adding a click event in the map, and clicking pop-up prompt information.
Fig. 3 is a diagram of the effect of the application of the present invention. As shown in fig. 3, through the processing of steps 501-508, a community graph, a person or vehicle cluster point, a person or vehicle track, a dynamic moving graph may be displayed, and a click event may be added to the map.
The invention is further illustrated below by way of example:
the method comprises the steps of establishing an association relation between acquisition equipment and a physical position, wherein the acquisition equipment comprises a face recognition camera, a vehicle access port, an intelligent access control machine and an electronic fence, for example, taking the face recognition camera as an example, acquiring coordinate information of the acquisition equipment at a deployment position of the face recognition camera, associating a map coordinate with the acquisition equipment through a map equipment marking module, and establishing an information data structure of the acquisition equipment, which comprises the map coordinate information.
And dynamic data are acquired, wherein the dynamic data comprise face snapshot identification information, vehicle identification information, entrance guard passing information and mobile phone code information. The data acquisition module can acquire the data acquired by the equipment at regular time, establish dynamic acquisition data containing coordinate information by associating the coordinate information with the equipment, and store the dynamic acquisition data in a classified manner according to human faces, vehicles, door controls and mobile phones.
Behavior trajectory analysis is divided into two processes of personnel and vehicles. Taking Zhang III as an example, acquiring basic information of Zhang III, extracting dynamic data related to Zhang III including face snapshot identification information, vehicle identification information, entrance guard traffic information and mobile phone code information in an ElasticSearch database under the condition of Zhang III, and establishing an incidence relation between Zhang III and the dynamic data in the ElasticSearch. When the behavior track of Zhang III is to be analyzed, the associated dynamic data of Zhang III in a certain time range are extracted and are connected in series in a time shaft sequence to form the behavior track of the personnel. The dynamic data are classified and comprise face snapshot identification information, vehicle identification information, entrance guard traffic information and mobile phone code information, and categories can be added or deleted according to needs.
And (4) liveness analysis, taking Zhang III as an example, acquiring basic information of Zhang III, extracting dynamic data related to Zhang III from an ElasticSearch database by taking Zhang III and time as conditions, analyzing the data, counting the liveness of Zhang III, and displaying the statistical information in a liveness time chart by echart.
And (4) visualizing the behavior track, drawing the position of the third place on the google map through a leaf let on the basis of the behavior track data of the third place, connecting the positions according to a time axis sequence to draw the track, moving the icon of the third place through the leaf let, and playing back the track of the third place according to a time-space sequence.
The method of the invention collects real-time dynamic data by marking equipment on the map, analyzes the behavior track of personnel or vehicles, and obtains liveness data by analysis on the basis of the behavior track analysis. The personnel behavior track can be played back in space and time on a GIS map.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: modifications of the technical solutions described in the embodiments or equivalent replacements of some or all technical features may be made without departing from the scope of the technical solutions of the embodiments of the present invention.
Claims (3)
1. A big data behavior trajectory analysis method based on multi-dimensional data acquisition is characterized by comprising the following processes:
step 100, establishing an association relation between acquisition equipment and a physical position;
step 200, acquiring dynamic data of personnel information and vehicle information in real time through acquisition equipment to form a database of the personnel information and the vehicle information;
step 300, analyzing a personnel behavior track or a vehicle behavior track according to a database of personnel information and vehicle information;
the personnel behavior trajectory analysis process is as follows:
1) inquiring basic information of personnel in a static database, extracting dynamic data of the personnel information in an ElasticSearch database by taking the basic information of the personnel as a condition, wherein the dynamic data of the personnel information comprises face snapshot identification information, vehicle identification information, entrance guard passing information and mobile phone code information, and establishing an association relationship between the personnel and the dynamic data;
2) taking the basic information of the personnel, the liveness of the personnel, the dynamic data acquisition place, the dynamic data acquisition time and the dynamic data related to the personnel as a combination, establishing a personnel space-time trajectory data model, and storing the personnel space-time trajectory data model into an ElasticSearch database;
3) analyzing the behavior track of the target person: extracting a personnel data list from a database; extracting personnel space-time trajectory data from an elastic search database of a big data search engine; analyzing data, namely serially connecting relative personnel information, face snapshot identification information, vehicle identification information, entrance guard traffic information and mobile phone code information of the personnel in a time axis sequence, and displaying the information in a google map through map mapping to form personnel behavior track analysis;
the vehicle behavior track analysis process is as follows:
1) inquiring basic information of the vehicle in a static database, extracting dynamic data of the vehicle information in an ElasticSearch database by taking the vehicle information as a condition, wherein the dynamic data of the vehicle information comprises vehicle identification information, and establishing an association relationship between the vehicle and the dynamic data;
2) taking basic information, liveness, dynamic data acquisition place information, acquisition time and dynamic data related to the vehicle as a combination, establishing a vehicle space-time trajectory data model, and storing the model into an ElasticSearch database;
3) analyzing the behavior track of the target vehicle: extracting a vehicle data list from a database; extracting vehicle space-time trajectory data from a big data search engine ElasticSearch database; and analyzing data, namely serially connecting the vehicle tracks in a time axis sequence, and displaying the vehicle tracks in a google map through map mapping to form vehicle behavior track analysis.
2. The big-data behavior trajectory analysis method based on multi-dimensional data acquisition as claimed in claim 1, further comprising, after step 300:
step 400, analyzing the extracted target space-time trajectory data to obtain liveness analysis of the target within a certain time range;
and (3) analyzing the activity of the personnel:
1) extracting person space-time trajectory data from an elastic search database of a big data search engine according to date and time;
2) counting the activity of the personnel according to the space-time trajectory data of the personnel, and displaying the statistical information in an activity time chart through echart;
vehicle activity analysis:
1) extracting vehicle space-time trajectory data from a big data search engine ElasticSearch database according to date and time;
2) and (3) counting the vehicle liveness according to the vehicle space-time trajectory data, and displaying the statistical information in an liveness time chart through echart.
3. The big data behavior trajectory analysis method based on multi-dimensional data acquisition according to claim 2, further comprising, after the step 400:
step 500, dynamically and visually displaying the target space-time behavior trajectory through a GIS map;
step 501, dynamically acquiring a URL address of a GIS map;
step 502, drawing a map;
step 503, extracting a cell information data list from the database;
step 504, drawing community graphs in a map by using a leaflet library;
step 505, drawing a person clustering point in a map by using a leaflet library;
step 506, drawing a person track in a map by using a leaflet library;
step 507, drawing a dynamic moving graph in a map by using a leaflet library;
and step 508, adding a click event in the map, and clicking pop-up prompt information.
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| CN112380405A (en) * | 2020-12-10 | 2021-02-19 | 中国人民解放军战略支援部队信息工程大学 | Travel event multi-dimensional integrated visualization method based on map |
| CN112528099A (en) * | 2020-12-17 | 2021-03-19 | 青岛以萨数据技术有限公司 | Personnel peer-to-peer analysis method, system, equipment and medium based on big data |
| CN113177453A (en) * | 2021-04-22 | 2021-07-27 | 巢湖学院 | Intelligent monitoring system for intelligent community based on image recognition |
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| CN112380405A (en) * | 2020-12-10 | 2021-02-19 | 中国人民解放军战略支援部队信息工程大学 | Travel event multi-dimensional integrated visualization method based on map |
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| CN113392115B (en) * | 2021-08-16 | 2021-10-29 | 成都数联铭品科技有限公司 | Time axis-based map association relation data dynamic displacement change method and device |
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| CN115147911A (en) * | 2022-08-22 | 2022-10-04 | 山东海博科技信息系统股份有限公司 | Smart city file information integration method, system and terminal |
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