CN113190634A - Intelligent and efficient space-time big data analysis method - Google Patents
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Abstract
The invention discloses an intelligent and efficient space-time big data analysis method, relates to the technical field of space-time big data analysis, and discloses an intelligent and efficient space-time big data analysis method. Identification of spatio-temporal large datasets: aiming at statistical analysis conditions, a large number of space-time big data sets are identified by a space-time big data identification module, the identification data is stored, and a space-time big data system is analyzed: and aiming at the statistical analysis conditions, reading data of the large space-time database by using an active acquisition module and completing acquisition of corresponding service data. According to the invention, the large space-time data is identified through the large space-time data identification module, so that the centralized analysis of a large amount of data is avoided, and the analysis efficiency of the large space-time data is improved; the analysis method can be applied to complex association of multi-granularity space-time objects, visual inference of a relational network and simulation prediction of a complex space-time process, so that the analysis method is wider in application range.
Description
Technical Field
The invention relates to the technical field of space-time big data analysis, in particular to an intelligent and efficient space-time big data analysis method.
Background
With the continuous development and progress of internet technology and social media platforms, human activities generate a large amount of time-space data at every moment, and the time-space data have position coordinates and time labels, and specifically comprise movement track data, social media data, shopping order data, mobile phone signaling data and the like. These data record human daily life, imply the underlying laws of human activities, and their unprecedented growth and accumulation in speed and scale are urgently needed to be reasonably, efficiently and fully exploited. In recent years, large spatiotemporal data facing human activities are gradually mined, utilized and generated into various intelligent services, and permeate into various aspects of life of people. In the aspect of intelligent economy, enterprises acquire the consumption habits of people from the space-time big data consumed by customers by using a data mining technology and divide the consumption habits into different consumer groups, so that products are put in a targeted manner, and accurate marketing is realized; in the aspect of intelligent traffic, by analyzing the space-time big data of the pedestrian flow and the vehicle moving track, the pedestrian flow density and the traffic condition of a road section can be predicted, so that the traffic jam phenomenon is effectively improved; in the aspect of intelligent medical treatment, the space-time distribution rule of the diseases of people can be known by analyzing and modeling massive medical record data, so that the diseases are prevented and controlled in time, and the existing space-time big data often have the following problems in the analysis process: 1. the existing large spatiotemporal data is not high in analysis efficiency in the analysis process, and a large amount of large spatiotemporal data needs to be analyzed in a centralized manner, so that the analysis data volume is too large, and the analysis efficiency is influenced; 2. the analysis and amplification of the existing space-time big data has narrow application range and is not beneficial to large-area popularization and use.
Disclosure of Invention
The invention mainly aims to provide an intelligent and efficient space-time big data analysis method, which can effectively solve the problems in the background technology.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent and efficient space-time big data analysis method comprises the following steps:
step one, obtaining a space-time big data set: collecting and accessing space-time big data through a sensor network and fusing the data to build a space-time big data set;
step two, identifying a space-time big data set: aiming at the statistical analysis conditions, a large number of space-time big data sets are identified by a space-time big data identification module;
step three, storage of identification data: when the space-time big data identification module obtains the sensor semaphore, the identified space-time big data set information is stored in the semaphore data, and the semaphore data is uploaded to a space-time big database;
step four, analyzing a space-time big data system: aiming at the statistical analysis conditions, an active acquisition module is utilized to read data of a large time-space database and finish the acquisition of corresponding service data;
storing the corresponding service data by utilizing a data storage module;
step six, data application: and counting the data stored in the data storage module by using a counting module according to the service requirement to obtain corresponding service model data and applying the corresponding service model data.
Preferably, the space-time big data in the first step include space-time reference data, GNSS and position trajectory data, geodetic and gravity-magnetic survey data, remote sensing image data, map data and spatial media data.
Preferably, the spatiotemporal reference data includes time reference data and spatial reference data, the geodetic and gravity and magnetic measurement data includes geodetic control data, gravity field data and magnetic force data, the map data is various types of map and map set data, and the spatial media data is time-varying digital text, graphics, images, sounds, videos, images and animation media data with spatial position characteristics.
Preferably, the GNSS and position trajectory data includes GNSS reference station data and position trajectory data, and the position trajectory data includes personal trajectory data, group trajectory data, traffic trajectory data, information flow trajectory data, logistics trajectory data, and fund trajectory data.
Preferably, the remote sensing image data comprises satellite remote sensing image data, aerial remote sensing image data, ground remote sensing image data and underground sensing data, and the satellite remote sensing image data comprises visible light image data, microwave remote sensing image data, infrared image data and laser radar scanning image data.
Preferably, the sensor network comprises a ground sensor network with spatial position characteristics and an underground pipeline intelligent sensing device.
Preferably, the space-time big data identification module in the second step includes a signal feature library and a signal category determination module, the signal feature library includes a large number of sensor sampling signals and signal features corresponding to the sampling signals, and the signal features include digital quantity features and analog quantity features, and the signal category determination module is configured to establish a sensor model for signal quantity feature data obtained in real time in a sensor signal quantity transmission process, extract the digital quantity features or the analog quantity features from the sensor model, and find the sampling signals matched with the digital quantity features or the analog quantity features in the signal feature library.
Preferably, the active acquisition module in the fourth step is used for receiving input data in the large spatio-temporal database or actively reading and acquiring service data corresponding to the large spatio-temporal database.
Preferably, the data storage module in the fifth step is configured to store service data corresponding to data read from the large spatio-temporal database according to the statistical analysis conditions.
Preferably, the active acquisition module in the fourth step includes an interactive driving unit, a model driving unit, a data driving unit and a condition analysis unit.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the large space-time data is identified through the large space-time data identification module, and the large space-time data of the sensor semaphore is identified and stored, so that the invalidity of subsequent analysis is avoided, the centralized analysis of a large amount of data is avoided, and the analysis efficiency of the large space-time data is improved; the analyzed space-time big data comprise space-time reference data, GNSS (global navigation satellite system) and position track data, geodetic surveying and gravity-magnetic surveying data, remote sensing image data, map data and space media data, and the space-time big data are acquired and accessed by a sensor network, so that the subsequent data can be comprehensively analyzed, and the accuracy of analysis is ensured.
2. In the invention, the active acquisition module is used for receiving input data in the large space-time database or actively reading service data corresponding to the large space-time database, and the interactive drive unit, the model drive unit, the data drive unit and the condition analysis unit in the active acquisition module are used for completing analysis work.
Drawings
FIG. 1 is a flow chart of an intelligent and efficient spatio-temporal big data analysis method of the present invention;
FIG. 2 is a flow chart of the spatio-temporal big data analysis in the intelligent and efficient spatio-temporal big data analysis method of the present invention;
FIG. 3 is a schematic diagram of the structure of numerical control big data of an intelligent and efficient space-time big data analysis method of the present invention;
FIG. 4 is a schematic structural diagram of spatio-temporal reference data of an intelligent and efficient spatio-temporal big data analysis method according to the present invention;
FIG. 5 is a schematic diagram of the GNSS and position trajectory data of an intelligent and efficient space-time big data analysis method of the present invention;
FIG. 6 is a schematic structural diagram of remote sensing image data of an intelligent and efficient space-time big data analysis method of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "connected," and the like are to be construed broadly, such as "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
As shown in fig. 1-6, an intelligent and efficient space-time big data analysis method is characterized in that: the method comprises the following steps:
step one, obtaining a space-time big data set: collecting and accessing space-time big data through a sensor network and fusing the data to build a space-time big data set;
step two, identifying a space-time big data set: aiming at the statistical analysis conditions, a large number of space-time big data sets are identified by a space-time big data identification module;
step three, storage of identification data: when the space-time big data identification module obtains the sensor semaphore, the identified space-time big data set information is stored in the semaphore data, and the semaphore data is uploaded to a space-time big database;
step four, analyzing a space-time big data system: aiming at the statistical analysis conditions, an active acquisition module is utilized to read data of a large time-space database and finish the acquisition of corresponding service data;
storing the corresponding service data by utilizing a data storage module;
step six, data application: and counting the data stored in the data storage module by using a counting module according to the service requirement to obtain corresponding service model data and applying the corresponding service model data.
The large space-time data in the first step comprise space-time reference data, GNSS and position track data, geodetic surveying and gravity-magnetic surveying data, remote sensing image data, map data and space media data, and the multi-type space-time data is convenient for analyzing subsequent data, so that the analysis accuracy is ensured; the time-space reference data comprises time reference data and space reference data, the geodetic measurement and gravity-magnetic measurement data comprises geodetic control data, gravity field data and magnetic force data, the map data are various maps and map set data, and the space media data are time-varying digital text, graphics, images, sounds, videos, images and animation media data with space position characteristics, such as communication data, social network data, search engine data, online e-commerce data, city monitoring camera data and the like; the GNSS and position track data comprise GNSS reference station data and position track data, the position track data comprise personal track data, group track data, traffic track data, information flow track data, logistics track data and fund track data, and user activity data obtained by methods such as GNSS measurement and mobile phones can be used for reflecting the position of a user, social preference of the user, related traffic conditions and the like; the remote sensing image data comprises satellite remote sensing image data, aerial remote sensing image data, ground remote sensing image data and underground sensing data, and the satellite remote sensing image data comprises visible light image data, microwave remote sensing image data, infrared image data and laser radar scanning image data; the sensor network comprises a ground sensor network with spatial position characteristics and an underground pipeline intelligent sensing device, and information is conveniently conveyed; the space-time big data identification module in the second step comprises a signal feature library and a signal category judgment module, the signal feature library comprises a large number of sensor sampling signals and signal features corresponding to the sampling signals, the signal features comprise digital quantity features and analog quantity features, the signal category judgment module is used for establishing a sensor model for signal quantity feature data obtained in real time in the transmission process of sensor signal quantity, extracting the digital quantity features or the analog quantity features from the sensor model, finding out the sampling signals matched with the digital quantity features or the analog quantity features from the signal feature library, and identifying and storing the space-time big data of the obtained sensor signal quantity, so that subsequent invalidity is avoided, and the efficiency of space-time analysis is improved; the active acquisition module in the fourth step is used for receiving input data in the large space-time database or actively reading and acquiring service data corresponding to the large space-time database; the data storage module in the fifth step is used for storing service data corresponding to the data read in the space-time big database according to the statistical analysis conditions; the active acquisition module in the fourth step comprises an interactive driving unit, a model driving unit, a data driving unit and a condition analysis unit, and the interactive driving unit, the model driving unit, the data driving unit and the condition analysis unit can be applied to the application of complex association of multi-granularity space-time objects, relational network visual reasoning and complex space-time process simulation prediction, so that the analysis method has wider application range.
The invention relates to an intelligent and efficient space-time big data analysis method, which comprises the steps of firstly collecting and accessing space-time big data through a sensor network and fusing the data to build a space-time big data set; aiming at the statistical analysis conditions, a large number of space-time big data sets are identified by a space-time big data identification module; when the space-time big data identification module is used for obtaining the sensor semaphore, the identified space-time big data set information is stored in the semaphore data, and the semaphore data is uploaded to a space-time big database; aiming at the statistical analysis conditions, an active acquisition module is utilized to read data of a large time-space database and finish the acquisition of corresponding service data; storing the corresponding service data by using a data storage module; the data stored in the data storage module is counted by the counting module according to the service requirement to obtain corresponding service model data and applied, the corresponding service model data is applied according to the application under different scenes, the analyzed space-time big data comprises space-time reference data, GNSS and position track data, geodetic survey and gravity magnetic survey data, remote sensing image data, map data and space media data, the space-time big data is acquired and accessed by a sensing network, the subsequent data is analyzed conveniently, the analysis accuracy is ensured, the space-time big data is identified by the space-time big data identification module before being analyzed, the space-time big data of the obtained sensor signal amount is identified and stored, the subsequent invalidity is avoided, the space-time analysis efficiency is improved, and the active acquisition module is utilized to receive input data in a space-time big data base or actively read and acquire corresponding service data of the space-time big data base in the analysis process of the space-time big data, the analysis method is matched with an interactive driving unit, a model driving unit, a data driving unit and a condition analysis unit in an active acquisition module to complete analysis work, and is applicable to the application of complex association of multi-granularity space-time objects, relational network visual reasoning and complex space-time process simulation prediction, so that the analysis method is wider in application range.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. An intelligent and efficient space-time big data analysis method is characterized by comprising the following steps: the method comprises the following steps:
step one, obtaining a space-time big data set: collecting and accessing space-time big data through a sensor network and fusing the data to build a space-time big data set;
step two, identifying a space-time big data set: aiming at the statistical analysis conditions, a large number of space-time big data sets are identified by a space-time big data identification module;
step three, storage of identification data: when the space-time big data identification module obtains the sensor semaphore, the identified space-time big data set information is stored in the semaphore data, and the semaphore data is uploaded to a space-time big database;
step four, analyzing a space-time big data system: aiming at the statistical analysis conditions, an active acquisition module is utilized to read data of a large time-space database and finish the acquisition of corresponding service data;
storing the corresponding service data by utilizing a data storage module;
step six, data application: and counting the data stored in the data storage module by using a counting module according to the service requirement to obtain corresponding service model data and applying the corresponding service model data.
2. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 1, wherein: the space-time big data in the first step comprise space-time reference data, GNSS and position track data, geodetic surveying and gravity-magnetic surveying data, remote sensing image data, map data and space media data.
3. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 2, wherein: the time-space reference data comprises time reference data and space reference data, the geodetic and gravity-magnetic measurement data comprises geodetic control data, gravity field data and magnetic force data, the map data are various maps and map set data, and the space media data are time-varying digital characters, graphs, images, sounds, videos, images and animation media data with space position characteristics.
4. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 2, wherein: the GNSS and position trajectory data comprise GNSS reference station data and position trajectory data, and the position trajectory data comprise personal trajectory data, group trajectory data, traffic trajectory data, information flow trajectory data, logistics trajectory data and fund trajectory data.
5. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 2, wherein: the remote sensing image data comprises satellite remote sensing image data, aerial remote sensing image data, ground remote sensing image data and underground sensing data, and the satellite remote sensing image data comprises visible light image data, microwave remote sensing image data, infrared image data and laser radar scanning image data.
6. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 1, wherein: the sensor network comprises a ground sensor network with spatial position characteristics and an underground pipeline intelligent sensing device.
7. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 1, wherein: and in the second step, the space-time big data identification module comprises a signal feature library and a signal category judgment module, the signal feature library comprises a large number of sensor sampling signals and signal features corresponding to the sampling signals, the signal features comprise digital quantity features and analog quantity features, and the signal category judgment module is used for establishing a sensor model for signal quantity feature data acquired in real time in the transmission process of the sensor signal quantity, extracting the digital quantity features or the analog quantity features from the sensor model, and finding out the sampling signals matched with the digital quantity features or the analog quantity features in the signal feature library.
8. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 1, wherein: and the active acquisition module in the fourth step is used for receiving input data in the space-time large database or actively reading and acquiring service data corresponding to the space-time large database.
9. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 1, wherein: and the data storage module in the fifth step is used for storing the service data corresponding to the data read from the space-time big database according to the statistical analysis conditions.
10. The intelligent and efficient spatio-temporal big data analysis method as claimed in claim 1, wherein: and the active acquisition module in the fourth step comprises an interactive driving unit, a model driving unit, a data driving unit and a condition analysis unit.
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| CN109672986A (en) * | 2018-12-18 | 2019-04-23 | 成都方未科技有限公司 | A kind of space-time big data analysis system |
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