CN113434483A - Visual modeling method and system based on space-time big data - Google Patents
Visual modeling method and system based on space-time big data Download PDFInfo
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Abstract
The invention discloses a visual modeling method and a visual modeling system based on space-time big data, wherein the method comprises the following steps: performing labeling classification on the spatial feature data set, the attribute feature data set and the time scale feature data set to obtain a first service feature data set; acquiring a corresponding visual image data set according to the first service characteristic data set; inputting the service space-time database and the service management index into a deep convolution neural model for data training to obtain a first service space-time data model; performing incremental learning on the first service spatio-temporal data model according to the first service periodic variation characteristics to obtain a second service spatio-temporal data model; and carrying out support management on the first construction project information according to the second service spatio-temporal data model. The technical problems that in the prior art, service models are numerous and are frequently changed and adjusted, and rapid data extraction is not facilitated to meet service requirements are solved.
Description
Technical Field
The invention relates to the field of model establishment, in particular to a visual modeling method and system based on space-time big data.
Background
In the construction and promotion process of smart cities, space-time big data are accumulated more and more, and the space-time data refer to space data which have time elements and change along with time change and are an expression mode for describing feature element information in the earth environment. The space-time data not only has obvious space distribution characteristics, but also has the characteristics of huge data volume, nonlinearity, time variation and the like, and has the comprehensive characteristics of multiple sources, large quantity and quick updating.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the prior art has the technical problems that the service models are numerous and are frequently changed and adjusted, and the data are not convenient to rapidly extract to meet the service requirements.
Disclosure of Invention
The embodiment of the application provides a visual modeling method and system based on space-time big data, solves the technical problems that in the prior art, business models are numerous and frequently changed and adjusted, and data cannot be extracted quickly to meet business requirements, achieves visual self-defined modeling in a space-time big data mode, can adjust the business models at any time according to business changes, efficiently and quickly excavates valuable information from the space-time big data, and further meets the technical effects of leadership decision support and business management requirements.
In view of the above, the present invention has been developed to provide a solution to, or at least partially solve, the above problems.
In a first aspect, an embodiment of the present application provides a visualization modeling method based on spatiotemporal big data, where the method includes: obtaining first construction project information; obtaining a first information analysis instruction, and performing service analysis on the first construction project information according to the first information analysis instruction to obtain a first analysis service result; constructing a service retrieval feature decision tree; searching and classifying the first analysis service result according to the service searching characteristic decision tree to obtain each service characteristic information; determining a spatial characteristic data set, an attribute characteristic data set and a time scale characteristic data set according to the service characteristic information; performing labeling classification on the spatial feature data set, the attribute feature data set and the time scale feature data set to obtain a first service feature data set; acquiring a corresponding visual image data set according to the first service characteristic data set; constructing a business space-time database through a space-time big data platform, wherein the business space-time database comprises the first business feature data set and the visual image data set; acquiring a service management index of a preset association degree according to the application requirement of a first user; inputting the service space-time database and the service management index into a deep convolution neural model for data training to obtain a first service space-time data model; obtaining a first service period change characteristic; performing incremental learning on the first service spatio-temporal data model according to the first service periodic variation characteristics to obtain a second service spatio-temporal data model; and carrying out support management on the first construction project information according to the second service spatio-temporal data model.
In another aspect, the present application further provides a visualization modeling system based on spatiotemporal big data, the system including: a first obtaining unit configured to obtain first construction project information; the second obtaining unit is used for obtaining a first information analysis instruction, and performing service analysis on the first construction project information according to the first information analysis instruction to obtain a first analysis service result; the first construction unit is used for constructing a service retrieval feature decision tree; a third obtaining unit, configured to perform retrieval and classification on the first analysis service result according to the service retrieval feature decision tree, so as to obtain each service feature information; the first determining unit is used for determining a spatial feature data set, an attribute feature data set and a time scale feature data set according to the service feature information; a fourth obtaining unit, configured to perform labeling classification on the spatial feature data set, the attribute feature data set, and the time scale feature data set to obtain a first service feature data set; a fifth obtaining unit, configured to obtain a corresponding visual image data set according to the first service feature data set; a second construction unit, configured to construct a business space-time database through a space-time big data platform, where the business space-time database includes the first business feature data set and the visual image data set; a sixth obtaining unit, configured to obtain a service management index of a predetermined association degree according to a first user application requirement; a seventh obtaining unit, configured to input the service space-time database and the service management index into a deep convolution neural model for data training, so as to obtain a first service space-time data model; an eighth obtaining unit, configured to obtain a first service period change characteristic; a ninth obtaining unit, configured to perform incremental learning on the first service spatio-temporal data model according to the first service periodic variation feature to obtain a second service spatio-temporal data model; and the first management unit is used for carrying out support management on the first construction project information according to the second service spatio-temporal data model.
In a third aspect, an embodiment of the present invention provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the method for controlling output data includes any one of the steps described above.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for controlling output data according to any one of the above.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of determining a spatial characteristic data set, an attribute characteristic data set and a time scale characteristic data set according to the service characteristic information; performing labeling classification on the spatial feature data set, the attribute feature data set and the time scale feature data set to obtain a first service feature data set; acquiring a corresponding visual image data set according to the first service characteristic data set; constructing a service time-space database through a time-space big data platform; inputting the service space-time database and the service management index into a deep convolution neural model for data training to obtain a first service space-time data model; obtaining a first service period change characteristic; performing incremental learning on the first service spatio-temporal data model according to the first service periodic variation characteristics to obtain a second service spatio-temporal data model; and carrying out support management on the first construction project information according to the second service spatio-temporal data model. And further, the technical effects of visual self-defined modeling in a space-time big data mode, business models can be adjusted at any time according to business changes, valuable information can be efficiently and quickly mined from the space-time big data, and leadership decision support and business management requirements are further met.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a visualization modeling method based on spatiotemporal big data according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a process of obtaining a service characteristic data set in a visualization modeling method based on spatiotemporal big data according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating the process of obtaining various business feature data in a visual modeling method based on spatio-temporal big data according to an embodiment of the present application;
FIG. 4 is a schematic flow chart of a spatial feature data set obtained in a visualization modeling method based on spatiotemporal big data according to an embodiment of the present application;
FIG. 5 is a schematic flow chart illustrating a second business spatio-temporal data model obtained in a spatio-temporal big data-based visual modeling method according to an embodiment of the present application;
FIG. 6 is a schematic flow chart illustrating a process of obtaining a project progress state in a visualization modeling method based on spatiotemporal big data according to an embodiment of the present application;
FIG. 7 is a schematic flow chart illustrating a business retrieval feature decision tree constructed in a visualization modeling method based on spatiotemporal big data according to an embodiment of the present application;
FIG. 8 is a schematic structural diagram of a visualization modeling system based on spatiotemporal big data according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device for executing a method of controlling output data according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a first constructing unit 13, a third obtaining unit 14, a first determining unit 15, a fourth obtaining unit 16, a fifth obtaining unit 17, a second constructing unit 18, a sixth obtaining unit 19, a seventh obtaining unit 20, an eighth obtaining unit 21, a ninth obtaining unit 22, a first managing unit 23, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
Detailed Description
In the description of the embodiments of the present invention, it should be apparent to those skilled in the art that the embodiments of the present invention can be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, embodiments of the invention may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied in the medium.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, an optical fiber, a compact disc read-only memory, an optical storage device, a magnetic storage device, or any combination thereof. In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or apparatus.
Summary of the application
The method, the device and the electronic equipment are described through the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The embodiments of the present invention will be described below with reference to the drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a visualization modeling method based on spatiotemporal big data, where the method includes:
step S100: obtaining first construction project information;
step S200: obtaining a first information analysis instruction, and performing service analysis on the first construction project information according to the first information analysis instruction to obtain a first analysis service result;
specifically, the first construction project information is a construction project object that requires spatio-temporal data modeling, and the data service model changes with the change of the project. And performing service analysis on the first construction project information according to the first information analysis instruction to obtain an analysis service result required by the construction project.
Step S300: constructing a service retrieval feature decision tree;
as shown in fig. 7, further, in the building of the service retrieval feature decision tree, step S300 in the embodiment of the present application further includes:
step S310: acquiring a corresponding service characteristic information set according to a historical analysis service result set;
step S320: performing principal component analysis on the data characteristics of the service characteristic information set to obtain a first dimension reduction data characteristic set, wherein the first dimension reduction data characteristic set comprises a first characteristic, a second characteristic and a third characteristic;
step S330: respectively carrying out information theory coding operation on the first feature, the second feature and the third feature to obtain node feature information of a decision tree;
step S340: and constructing a service retrieval characteristic decision tree according to the node characteristic information.
Specifically, a Decision Tree (Decision Tree) is a Decision analysis method for obtaining a probability that an expected value of a net present value is equal to or greater than zero by constructing a Decision Tree on the basis of known occurrence probabilities of various situations, evaluating a project risk, and judging feasibility thereof, and is a graphical method for intuitively using probability analysis. The service retrieval characteristics can be used as internal nodes of the service retrieval characteristic decision tree, the characteristics with the minimum entropy value can be classified preferentially by calculating the information entropy of the service retrieval characteristics, the service retrieval characteristic decision tree is constructed recursively by the method until the final characteristic leaf node can not be subdivided, and the classification is finished, so that the service retrieval characteristic decision tree is formed.
Further, a corresponding service characteristic information set is obtained according to the historical analysis service result set, principal component analysis is performed on the data characteristics of the service characteristic information set, the principal component analysis is the most common linear dimension reduction method, the objective of the principal component analysis is to map high-dimensional data into a low-dimensional space through certain linear projection, and the maximum information amount (maximum variance) of the data on the projected dimension is expected, so that fewer data dimensions are used, and the characteristics of more original data points are retained. Obtaining a first dimension reduction data feature set after the principal component analysis dimension reduction, wherein the first dimension reduction data feature set comprises a first feature, a second feature and a third feature. The purpose of dimension reduction is to reduce the dimension of the original features under the condition of ensuring that the information content is not lost as much as possible, namely, the original features are projected to the dimension with the maximum projection information content as much as possible, and the original features are projected to the dimensions, so that the loss of the information content after dimension reduction is minimum.
In order to specifically construct the service retrieval feature decision tree, information entropy calculation can be performed on the first feature, the second feature and the third feature respectively, that is, the information entropy is specifically calculated through a shannon formula in information theory coding, so that corresponding feature information entropy is obtained, further, the information entropy represents uncertainty of information, when the uncertainty is larger, the contained information amount is larger, the information entropy is higher, and the purity is lower, and when all samples in a set are uniformly mixed, the information entropy is maximum, and the purity is lowest. Therefore, the characteristic information entropy is compared with the size value thereof based on the data size comparison model, then the characteristic with the minimum entropy value, namely the first root node characteristic information, is obtained, the characteristic with the minimum entropy value is preferentially classified, then the node characteristics are sequentially classified according to the order of the entropy values from small to large, and finally the service retrieval characteristic decision tree is constructed. Each analysis service result is matched with the appropriate service characteristic information, and the technical effect of specifically constructing the service retrieval characteristic decision tree is further realized.
Step S400: searching and classifying the first analysis service result according to the service searching characteristic decision tree to obtain each service characteristic information;
step S500: determining a spatial characteristic data set, an attribute characteristic data set and a time scale characteristic data set according to the service characteristic information;
specifically, according to the service retrieval feature decision tree, the first analysis service result is subjected to retrieval classification to obtain corresponding service feature information. And determining a spatial characteristic data set, an attribute characteristic data set and a time scale characteristic data set according to the service characteristic information. The spatial feature data set comprises a position relation and a spatial relation corresponding to the business feature, the attribute feature data set is attribute information such as an object, a quantity, a shape and an internal contact rule of elements corresponding to the business feature, and the time scale feature data set is information of different time points or time periods corresponding to the business feature.
As shown in fig. 4, further, in the spatial feature data set, step S500 in this embodiment of the present application further includes:
step S510: acquiring a first service application scene set according to the service characteristic information;
step S520: constructing a plane position coordinate system, and carrying out position normalization processing on the first service application scene set to obtain a first plane position coordinate;
step S530: obtaining a first identification instruction, wherein the first identification instruction is used for identifying scene relative positions in the first service application scene set to obtain a scene spatial position distribution list;
step S540: constructing a spatial three-dimensional coordinate system according to the first plane position coordinate and the scene space position distribution list;
step S550: and obtaining a corresponding spatial characteristic data set according to the spatial three-dimensional coordinate system.
Specifically, according to the service characteristic information, an application scene data set corresponding to each service is obtained, a plane position coordinate system of the application scene is constructed, position coordinate normalization processing is carried out on the service application scene set, and the accuracy of the coordinate position is ensured. And identifying scene relative positions in the first service application scene set according to the first identification instruction to obtain the scene space position distribution list, which comprises scene position distribution and relative position relation. And constructing a space three-dimensional coordinate system of the application scene according to the first plane position coordinate and the scene space position distribution list, and acquiring a corresponding space characteristic data set according to the space three-dimensional coordinate system. The technical effects that the three-dimensional coordinate system is constructed to accurately restore the spatial position of the service application scene and accurate basis is provided for obtaining subsequent service characteristic data are achieved.
Step S600: performing labeling classification on the spatial feature data set, the attribute feature data set and the time scale feature data set to obtain a first service feature data set;
as shown in fig. 2, further, in the step S600 of the embodiment of the present application, the performing labeling classification on the spatial feature data set, the attribute feature data set, and the time scale feature data set to obtain a first service feature data set further includes:
step S610: constructing a service characteristic coordinate system, wherein the service characteristic coordinate system is a multi-dimensional coordinate system;
step S620: performing regional labeling classification on the service characteristic coordinate system to obtain a first label classification result;
step S630: respectively inputting the feature data in the spatial feature data set, the attribute feature data set and the time scale feature data set into the service feature coordinate system to obtain corresponding service feature vectors;
step S640: performing mapping matching according to the first label classification result and the characteristic vectors of the services to obtain characteristic data of the services;
step S650: and performing collection processing on the service characteristic data to obtain a first service characteristic data set.
Specifically, a business feature coordinate system is constructed, wherein the business feature coordinate system is a multidimensional coordinate system and comprises attribute features such as space features, objects and quantity of businesses and time scale features. And performing area labeling classification on the service characteristic coordinate system, wherein different areas correspond to different label classification results, for example, different areas correspond to different service characteristics. And respectively inputting the characteristic data in the spatial characteristic data set, the attribute characteristic data set and the time scale characteristic data set into the service characteristic coordinate system to obtain corresponding service characteristic vectors. And mapping and matching are carried out according to the first label classification result and the service characteristic vectors to obtain matched service characteristic data, and the service characteristic data are subjected to set processing to obtain an integrated service characteristic data set. The method achieves the technical effect of enabling the classification result of the service characteristic data to be more accurate and effective by constructing the service characteristic coordinate system to carry out vector mapping.
Step S700: acquiring a corresponding visual image data set according to the first service characteristic data set;
step S800: constructing a business space-time database through a space-time big data platform, wherein the business space-time database comprises the first business feature data set and the visual image data set;
specifically, according to the first service characteristic data set, a corresponding visual image data set is obtained, wherein the visual image data set comprises a scene image, a visual map picture, a special effect combination picture, an application service picture and the like, and the subsequent model can be ensured to be visualized through data. The space-time big data platform is a data platform which converges various dispersed (point data) and segmented (strip data) big data to a specific platform (space-time data or geographic frame data platform) and enables the platform to generate a continuous aggregation effect, and has the comprehensive characteristics of multiple sources, mass and quick updating. And constructing a business space-time database through the space-time big data platform, wherein the business space-time database comprises the first business feature data set and the visual image data set.
Step S900: acquiring a service management index of a preset association degree according to the application requirement of a first user;
specifically, according to the application requirements of the model using users, the business management indexes with the preset relevance are obtained, namely the business management indexes with the relevance to the user using requirements are obtained, the corresponding indexes are applied to the model, the model is ensured to support diversified definition indexes, and further the requirements of customer management indexes are met.
Step S1000: inputting the service space-time database and the service management index into a deep convolution neural model for data training to obtain a first service space-time data model;
step S1100: obtaining a first service period change characteristic;
specifically, the service space-time database and the service management index are input into a deep convolutional neural model for data training to obtain a first service space-time data model, the first service space-time data model is a model of a multi-element network structure comprising a convolutional neural network and a long and short time memory network, time and space attribute information of data can be extracted, and compared with a traditional space-time data model, prediction errors are reduced to a great extent, and the training speed of the model is improved. The first service periodic variation characteristic is a periodic variation characteristic of a service along with the progress of a project, and model data is corrected by adding the periodic variation characteristic, so that the accuracy of model construction is higher.
Step S1200: performing incremental learning on the first service spatio-temporal data model according to the first service periodic variation characteristics to obtain a second service spatio-temporal data model;
step S1300: and carrying out support management on the first construction project information according to the second service spatio-temporal data model.
As shown in fig. 5, further, in which the incremental learning is performed on the first service spatio-temporal data model according to the first service period variation feature to obtain a second service spatio-temporal data model, step S1200 in this embodiment of the present application further includes:
step 1210: inputting the first service periodic variation characteristic into the first service spatio-temporal data model to obtain first predicted service data;
step S1220: obtaining first loss data by performing data loss analysis on the first prediction service data;
step S1230: and inputting the first loss data into the first business space-time data model for training to obtain the second business space-time data model.
Specifically, the first construction project information is supported and managed, such as analysis report and modification processing, based on the second service spatiotemporal data model obtained after incremental learning is performed on the first service spatiotemporal data model according to the first service period change feature. The first prediction service data is corresponding prediction service data obtained by training in the first service space-time data model based on the first service period variation characteristic, and the first service space-time data is obtained by data training based on the service space-time database and the service management index. Therefore, the loss function is introduced to complete the analysis of data loss and further obtain the first loss data, where the first loss data is the related data knowledge loss data representing the first business spatio-temporal data model for the first business period variation feature, and then the incremental learning of the first business spatio-temporal data model is completed based on the first loss data, and since the first business spatio-temporal data model is obtained by forming a neural network by connecting a plurality of neurons with each other, the second business spatio-temporal data model retains the basic function of the first business spatio-temporal data model through the training of the loss data, and maintains the model updating performance, thereby improving the updating performance of the spatio-temporal data and ensuring the technical effect of accuracy of the model updating result.
As shown in fig. 3, further, in the step S640 according to the embodiment of the present invention, performing mapping matching according to the first label classification result and the service feature vectors to obtain the service feature data, further includes:
step S641: respectively carrying out distance calculation on the characteristic vectors of each service to obtain Euclidean distance data sets;
step S642: obtaining corresponding business feature classification data sets according to the Euclidean distance data sets, wherein each business feature classification data set is the shortest k distances in the Euclidean distance data sets;
step S643: performing mapping matching according to the service feature classification data sets and the first label classification result to obtain a first classification result;
step S644: and obtaining the characteristic data of each service according to the first classification result.
Specifically, the euclidean distance dataset is an euclidean metric distance dataset, that is, a linear distance between two points in a coordinate system, and the euclidean distance dataset is obtained by performing distance calculation on the feature vectors of each service. And each service characteristic classification data set is the shortest k distances in the Euclidean distance data set, and the k value is a part of the Euclidean distance data set and can be set by self. And performing mapping matching according to the service characteristic classification data sets and the label classification results to obtain classification labels corresponding to the vectors, and determining service characteristic data corresponding to the vectors according to the classification results. The classification method for calculating the vector distance is used for classifying and determining the service characteristic data, and the technical effect of ensuring more accurate establishment of a subsequent model is achieved.
As shown in fig. 6, further, the embodiment of the present application further includes:
step S1410: obtaining a first query instruction, wherein the first query instruction is used for querying and calling the second service spatiotemporal data model at a first time point to obtain a first query result of the first construction project information;
step S1420: obtaining a second query instruction, where the first query instruction is used to query and call the second service spatiotemporal data model at a second time point, and obtain a second query result of the first construction project information, and the second time point is later than the first time point;
step S1430: analyzing the progress of the first construction project information according to the first query result and the second query result to obtain a first progress state;
step S1440: and according to the first progress state, performing management analysis on the first construction project.
Specifically, the second service spatiotemporal data model is queried and called at a first time point according to the first query instruction, and a first query result of the first construction project information is obtained. And inquiring and calling the second service spatiotemporal data model at a second time point according to the second inquiry instruction to obtain a second inquiry result of the first construction project information, wherein the second time point is later than the first time point, and progress inquiry is carried out on the construction project at different time points. And analyzing the progress of the first construction project information according to the first query result and the second query result to obtain the progress state of the project, and managing and analyzing the first construction project according to the first progress state, such as modifying and adjusting. The technical effect of carrying out progress query on the construction project through different time points so as to correspondingly adjust and manage the spatio-temporal model according to the progress is achieved.
To sum up, the visual modeling method and system based on the spatio-temporal big data provided by the embodiment of the application have the following technical effects:
the method comprises the steps of determining a spatial characteristic data set, an attribute characteristic data set and a time scale characteristic data set according to the service characteristic information; performing labeling classification on the spatial feature data set, the attribute feature data set and the time scale feature data set to obtain a first service feature data set; acquiring a corresponding visual image data set according to the first service characteristic data set; constructing a service time-space database through a time-space big data platform; inputting the service space-time database and the service management index into a deep convolution neural model for data training to obtain a first service space-time data model; obtaining a first service period change characteristic; performing incremental learning on the first service spatio-temporal data model according to the first service periodic variation characteristics to obtain a second service spatio-temporal data model; and carrying out support management on the first construction project information according to the second service spatio-temporal data model. And further, the technical effects of visual self-defined modeling in a space-time big data mode, business models can be adjusted at any time according to business changes, valuable information can be efficiently and quickly mined from the space-time big data, and leadership decision support and business management requirements are further met.
Example two
Based on the same inventive concept as the visual modeling method based on the spatio-temporal big data in the foregoing embodiment, the present invention further provides a visual modeling system based on the spatio-temporal big data, as shown in fig. 8, the system includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining first construction project information;
the second obtaining unit 12, where the second obtaining unit 12 is configured to obtain a first information analysis instruction, perform service analysis on the first construction project information according to the first information analysis instruction, and obtain a first analysis service result;
a first constructing unit 13, where the first constructing unit 13 is used to construct a service retrieval feature decision tree;
a third obtaining unit 14, where the third obtaining unit 14 is configured to perform retrieval and classification on the first analysis service result according to the service retrieval feature decision tree, so as to obtain feature information of each service;
a first determining unit 15, where the first determining unit 15 is configured to determine a spatial feature data set, an attribute feature data set, and a time scale feature data set according to the service feature information;
a fourth obtaining unit 16, where the fourth obtaining unit 16 is configured to perform tagging classification on the spatial feature data set, the attribute feature data set, and the time scale feature data set, so as to obtain a first service feature data set;
a fifth obtaining unit 17, where the fifth obtaining unit 17 is configured to obtain a corresponding visual image data set according to the first service feature data set;
a second construction unit 18, wherein the second construction unit 18 is configured to construct a business spatio-temporal database through a spatio-temporal big data platform, and the business spatio-temporal database includes the first business feature data set and the visual image data set;
a sixth obtaining unit 19, where the sixth obtaining unit 19 is configured to obtain a service management index of a predetermined association degree according to the first user application requirement;
a seventh obtaining unit 20, where the seventh obtaining unit 20 is configured to input the service space-time database and the service management index into a deep convolutional neural model for data training, so as to obtain a first service space-time data model;
an eighth obtaining unit 21, where the eighth obtaining unit 21 is configured to obtain a first service period variation characteristic;
a ninth obtaining unit 22, where the ninth obtaining unit 22 is configured to perform incremental learning on the first service spatio-temporal data model according to the first service period variation feature to obtain a second service spatio-temporal data model;
a first management unit 23, where the first management unit 23 is configured to support and manage the first construction project information according to the second service spatio-temporal data model.
Further, the system further comprises:
the third construction unit is used for constructing a service characteristic coordinate system, and the service characteristic coordinate system is a multi-dimensional coordinate system;
a tenth obtaining unit, configured to perform area labeling classification on the service feature coordinate system to obtain a first label classification result;
an eleventh obtaining unit, configured to input feature data in the spatial feature data set, the attribute feature data set, and the time scale feature data set into the service feature coordinate system, respectively, to obtain corresponding service feature vectors;
a twelfth obtaining unit, configured to perform mapping matching according to the first label classification result and the service feature vectors to obtain service feature data;
a thirteenth obtaining unit, configured to perform set processing on the service feature data to obtain a first service feature data set.
Further, the system further comprises:
a fourteenth obtaining unit, configured to perform distance calculation on the service feature vectors respectively to obtain an euclidean distance data set;
a fifteenth obtaining unit, configured to obtain, according to the euclidean distance data set, each corresponding service feature classification data set, where each service feature classification data set is the shortest k distances in the euclidean distance data set;
a sixteenth obtaining unit, configured to perform mapping matching according to the service feature classification data sets and the first label classification result to obtain a first classification result;
a seventeenth obtaining unit, configured to obtain the service characteristic data according to the first classification result.
Further, the system further comprises:
an eighteenth obtaining unit, configured to obtain a first service application scene set according to the service feature information;
a nineteenth obtaining unit, configured to construct a plane position coordinate system, perform position normalization processing on the first service application scene set, and obtain a first plane position coordinate;
a twentieth obtaining unit, configured to obtain a first identification instruction, where the first identification instruction is used to identify a scene relative position in the first service application scene set, and obtain a scene spatial position distribution list;
a fourth construction unit, configured to construct a spatial three-dimensional coordinate system according to the first plane position coordinate and the scene spatial position distribution list;
a twenty-first obtaining unit, configured to obtain a corresponding spatial feature data set according to the spatial three-dimensional coordinate system.
Further, the system further comprises:
a twenty-second obtaining unit, configured to input the first service period variation feature into the first service spatio-temporal data model, so as to obtain first predicted service data;
a twenty-third obtaining unit, configured to obtain first loss data by performing data loss analysis on the first predicted service data;
a twenty-fourth obtaining unit, configured to input the first loss data into the first service spatio-temporal data model for training, and obtain the second service spatio-temporal data model.
Further, the system further comprises:
a twenty-fifth obtaining unit, configured to obtain a first query instruction, where the first query instruction is used to query and call the second service spatiotemporal data model at a first time point, and obtain a first query result of the first construction project information;
a twenty-sixth obtaining unit, configured to obtain a second query instruction, where the first query instruction is used to query and call the second service spatiotemporal data model at a second time point, and obtain a second query result of the first construction project information, and the second time point is later than the first time point;
a twenty-seventh obtaining unit, configured to analyze the progress of the first construction project information according to the first query result and the second query result, and obtain a first progress state;
and the second management unit is used for managing and analyzing the first construction project according to the first progress state.
Further, the system further comprises:
a twenty-eighth obtaining unit, configured to obtain a corresponding service feature information set according to a historical analysis service result set;
a twenty-ninth obtaining unit, configured to perform principal component analysis on the data features of the service feature information set to obtain a first dimension reduction data feature set, where the first dimension reduction data feature set includes a first feature, a second feature, and a third feature;
a thirtieth obtaining unit, configured to perform information theory encoding operations on the first feature, the second feature, and the third feature, respectively, to obtain node feature information of a decision tree;
and the fifth construction unit is used for constructing a service retrieval feature decision tree according to the node feature information.
Various changes and specific examples of the visualization modeling method based on the spatio-temporal big data in the first embodiment of fig. 1 are also applicable to the visualization modeling system based on the spatio-temporal big data in the present embodiment, and through the foregoing detailed description of the visualization modeling method based on the spatio-temporal big data, those skilled in the art can clearly know the implementation method of the visualization modeling system based on the spatio-temporal big data in the present embodiment, so for the brevity of the description, detailed descriptions are not repeated here.
In addition, an embodiment of the present invention further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the processes of the method for controlling output data are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Exemplary electronic device
Specifically, referring to fig. 9, an embodiment of the present invention further provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of the method embodiments of controlling output data described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In embodiments of the invention in which a bus architecture (represented by bus 1110) is used, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in embodiments of the invention, the memory 1150 may further include memory located remotely with respect to the processor 1120, which may be coupled to a server via a network. One or more portions of the above-described network may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and a combination of two or more of the above. For example, the cellular telephone network and the wireless network may be a global system for mobile communications, code division multiple access, global microwave interconnect access, general packet radio service, wideband code division multiple access, long term evolution, LTE frequency division duplex, LTE time division duplex, long term evolution-advanced, universal mobile communications, enhanced mobile broadband, mass machine type communications, ultra-reliable low latency communications, etc.
It is to be understood that the memory 1150 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the embodiments of the invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the present invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various system programs such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media player, browser, used to realize various application services. A program implementing a method of an embodiment of the invention may be included in application program 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements each process of the above method for controlling output data, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The above description is only a specific implementation of the embodiments of the present invention, but the scope of the embodiments of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present invention, and all such changes or substitutions should be covered by the scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A visual modeling method based on space-time big data, wherein the method is applied to a visual modeling system based on space-time big data, and the method comprises the following steps:
obtaining first construction project information;
obtaining a first information analysis instruction, and performing service analysis on the first construction project information according to the first information analysis instruction to obtain a first analysis service result;
constructing a service retrieval feature decision tree;
searching and classifying the first analysis service result according to the service searching characteristic decision tree to obtain each service characteristic information;
determining a spatial characteristic data set, an attribute characteristic data set and a time scale characteristic data set according to the service characteristic information;
performing labeling classification on the spatial feature data set, the attribute feature data set and the time scale feature data set to obtain a first service feature data set;
acquiring a corresponding visual image data set according to the first service characteristic data set;
constructing a business space-time database through a space-time big data platform, wherein the business space-time database comprises the first business feature data set and the visual image data set;
acquiring a service management index of a preset association degree according to the application requirement of a first user;
inputting the service space-time database and the service management index into a deep convolution neural model for data training to obtain a first service space-time data model;
obtaining a first service period change characteristic;
performing incremental learning on the first service spatio-temporal data model according to the first service periodic variation characteristics to obtain a second service spatio-temporal data model;
and carrying out support management on the first construction project information according to the second service spatio-temporal data model.
2. The method of claim 1, wherein the tag classifying the spatial feature dataset, the attribute feature dataset, and the time scale feature dataset to obtain a first business feature dataset comprises:
constructing a service characteristic coordinate system, wherein the service characteristic coordinate system is a multi-dimensional coordinate system;
performing regional labeling classification on the service characteristic coordinate system to obtain a first label classification result;
respectively inputting the feature data in the spatial feature data set, the attribute feature data set and the time scale feature data set into the service feature coordinate system to obtain corresponding service feature vectors;
performing mapping matching according to the first label classification result and the characteristic vectors of the services to obtain characteristic data of the services;
and performing collection processing on the service characteristic data to obtain a first service characteristic data set.
3. The method of claim 2, wherein the performing mapping matching according to the first label classification result and the service feature vectors to obtain service feature data comprises:
respectively carrying out distance calculation on the characteristic vectors of each service to obtain Euclidean distance data sets;
obtaining corresponding business feature classification data sets according to the Euclidean distance data sets, wherein each business feature classification data set is the shortest k distances in the Euclidean distance data sets;
performing mapping matching according to the service feature classification data sets and the first label classification result to obtain a first classification result;
and obtaining the characteristic data of each service according to the first classification result.
4. The method of claim 1, wherein the spatial feature data set comprises:
acquiring a first service application scene set according to the service characteristic information;
constructing a plane position coordinate system, and carrying out position normalization processing on the first service application scene set to obtain a first plane position coordinate;
obtaining a first identification instruction, wherein the first identification instruction is used for identifying scene relative positions in the first service application scene set to obtain a scene spatial position distribution list;
constructing a spatial three-dimensional coordinate system according to the first plane position coordinate and the scene space position distribution list;
and obtaining a corresponding spatial characteristic data set according to the spatial three-dimensional coordinate system.
5. The method of claim 1, wherein the incrementally learning the first business spatio-temporal data model as a function of the first business period variation feature to obtain a second business spatio-temporal data model comprises:
inputting the first service periodic variation characteristic into the first service spatio-temporal data model to obtain first predicted service data;
obtaining first loss data by performing data loss analysis on the first prediction service data;
and inputting the first loss data into the first business space-time data model for training to obtain the second business space-time data model.
6. The method of claim 1, wherein the method comprises:
obtaining a first query instruction, wherein the first query instruction is used for querying and calling the second service spatiotemporal data model at a first time point to obtain a first query result of the first construction project information;
obtaining a second query instruction, where the first query instruction is used to query and call the second service spatiotemporal data model at a second time point, and obtain a second query result of the first construction project information, and the second time point is later than the first time point;
analyzing the progress of the first construction project information according to the first query result and the second query result to obtain a first progress state;
and according to the first progress state, performing management analysis on the first construction project.
7. The method of claim 1, wherein the constructing a business retrieval feature decision tree comprises:
acquiring a corresponding service characteristic information set according to a historical analysis service result set;
performing principal component analysis on the data characteristics of the service characteristic information set to obtain a first dimension reduction data characteristic set, wherein the first dimension reduction data characteristic set comprises a first characteristic, a second characteristic and a third characteristic;
respectively carrying out information theory coding operation on the first feature, the second feature and the third feature to obtain node feature information of a decision tree;
and constructing a service retrieval characteristic decision tree according to the node characteristic information.
8. A spatiotemporal big data-based visualization modeling system, wherein the system comprises:
a first obtaining unit configured to obtain first construction project information;
the second obtaining unit is used for obtaining a first information analysis instruction, and performing service analysis on the first construction project information according to the first information analysis instruction to obtain a first analysis service result;
the first construction unit is used for constructing a service retrieval feature decision tree;
a third obtaining unit, configured to perform retrieval and classification on the first analysis service result according to the service retrieval feature decision tree, so as to obtain each service feature information;
the first determining unit is used for determining a spatial feature data set, an attribute feature data set and a time scale feature data set according to the service feature information;
a fourth obtaining unit, configured to perform labeling classification on the spatial feature data set, the attribute feature data set, and the time scale feature data set to obtain a first service feature data set;
a fifth obtaining unit, configured to obtain a corresponding visual image data set according to the first service feature data set;
a second construction unit, configured to construct a business space-time database through a space-time big data platform, where the business space-time database includes the first business feature data set and the visual image data set;
a sixth obtaining unit, configured to obtain a service management index of a predetermined association degree according to a first user application requirement;
a seventh obtaining unit, configured to input the service space-time database and the service management index into a deep convolution neural model for data training, so as to obtain a first service space-time data model;
an eighth obtaining unit, configured to obtain a first service period change characteristic;
a ninth obtaining unit, configured to perform incremental learning on the first service spatio-temporal data model according to the first service periodic variation feature to obtain a second service spatio-temporal data model;
and the first management unit is used for carrying out support management on the first construction project information according to the second service spatio-temporal data model.
9. A visualization modeling system based on spatiotemporal big data, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program realizes the steps in the method of controlling output data according to any of claims 1-7 when executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of controlling output data according to any one of claims 1-7.
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