CN116561216B - Multi-dimensional space-time data visualization performance optimization method and system - Google Patents
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Abstract
The application discloses a multidimensional space-time data visualization performance optimization method and system, which relate to the technical field of space-time data optimization and solve the problems that an original data analysis optimization mode is too one-sided, the scene under consideration is not comprehensive, a certain data deviation exists because the scene where a corresponding variable is located is not considered, the variation of the corresponding variable under different three-dimensional space environments is analyzed according to the determined variable, the variable with the better variation is confirmed, then the influence factors in the three-dimensional space environments where the variable is located are eliminated, the selected three-dimensional space environments are re-analyzed, a group of optimal space environments is confirmed, an optimized data packet generated by the corresponding space environment is generated, the operator optimizes the subsequent space-time data, the original space environment is considered instead of the data optimization according to the difference between the data, and the considered factors are more comprehensive, so that the overall optimization effect of the corresponding space-time data is improved.
Description
Technical Field
The application relates to the technical field of space-time data optimization, in particular to a multidimensional space-time data visualization performance optimization method and system.
Background
Spatiotemporal data is data having both temporal and spatial dimensions, with more than 80% of the data in the real world being related to geographic location; the space-time big data comprises three-dimensional information of time, space and thematic attributes, and has the comprehensive characteristics of multisource, massive and rapid updating.
The application discloses a method and a system for optimizing the visualization performance of massive space-time data based on multidimensional indexes, which are disclosed by the application, wherein the method and the system are used for grasping the data characteristics and the data format of space-time data, organizing and managing the space-time data according to application scenes, providing a KD-H technology integrating KD-Tree and histogram to optimize the query performance respectively aiming at non-aggregated query and aggregated query, constructing multidimensional indexes and histograms after organizing and managing the space-time data, constructing proper KD-Tree and histograms aiming at different application scenes, taking a map geographic space visualization map as a base map, optimizing the performance of large-scale space-time data visualization on a high-performance WebGL rendering frame based on large-scale data visualization by utilizing an incremental visualization technology, gradually completing the visualization of a large-scale data set by increasing or updating the visualized details.
In the process of optimizing the multidimensional space-time data, a group of optimal parameter data is generally confirmed according to the difference existing between corresponding data in different scenes, the data existing in the scenes are optimized according to the confirmed parameter data, and the optimized data are displayed for external personnel to select.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides a multidimensional space-time data visualization performance optimization method and system, which solve the problems that an original data analysis optimization mode is too one-sided, the considered scene is not comprehensive, the scene where the corresponding variable is not taken into consideration, and certain data deviation exists.
In order to achieve the above purpose, the application is realized by the following technical scheme: a multi-dimensional spatiotemporal data visualization performance optimization system, comprising:
the space variable confirming unit confirms space variables from the confirmed multidimensional space-time data, marks a plurality of space variables belonging to the same space-time scene state as the same space-time variables, and transmits the plurality of space variables belonging to the same space-time variables into the variable object comparison unit;
the variable object comparison unit is used for confirming a plurality of space variables belonging to the same space-time variable, comparing the space variables with variable parameters preset in the storage unit, and confirming whether the corresponding space variables are dynamic variables or static variables, wherein the specific mode is as follows:
the storage unit stores a large number of variable parameters, and the corresponding variable parameters are preset parameters;
comparing the space variable with the variable parameter to confirm whether the space variable is a dynamic variable or a static variable, and if the space variable is a dynamic variable, transmitting a plurality of dynamic variables belonging to the same space-time scene into an active area confirming unit;
if the static variables are the static variables, diffusing a plurality of static variables belonging to the same space-time scene to the periphery by X1m to obtain space-time areas to be analyzed of the corresponding static variables, and transmitting the space-time areas to a change parameter confirmation unit;
the active region confirming unit confirms the active regions of different dynamic variables according to the confirmed dynamic variables, and judges the confirmed regions as space-time regions to be analyzed, and the specific mode is as follows:
confirming a plurality of dynamic variables belonging to the same space-time scene, and defining a group of monitoring periods T1, wherein T1 is a preset value, confirming a moving track area corresponding to the dynamic variables in the monitoring periods T1, and diffusing X1m to the periphery according to the confirmed moving track area to obtain a space-time area to be analyzed corresponding to the dynamic variables, wherein X1 is the preset value;
transmitting different space-time areas to be analyzed belonging to different dynamic variables into a change parameter confirmation unit;
the change parameter confirmation unit confirms another group of monitoring periods according to the received space-time areas to be analyzed, analyzes the change quantity generated by the corresponding variable of the space-time areas to be analyzed in the monitoring period, confirms the stage efficiency according to the change quantity, selects the stage efficiency, sorts the space-time areas to be analyzed in the first three groups, and transmits the space-time areas to be analyzed to the factor eliminating unit, and the concrete mode is as follows:
recording the variation generated between the initial time point and the end time point of the space-time region to be analyzed according to the confirmed monitoring period T2, and marking the variation as BH i Wherein i represents different spatiotemporal regions to be analyzed;
the variation BH of different space-time areas to be analyzed i Confirming, namely arranging different space-time areas to be analyzed according to the magnitude of the variable quantity value in a mode from large to small, integrating the space-time areas to be analyzed which are arranged in the first three groups to obtain a space-time area set, and transmitting the space-time area set to a factor eliminating unit;
the factor eliminating unit is used for receiving the time space region set, eliminating factors of three groups of time space regions to be analyzed existing in the time space region set, and transmitting the time space region set after factor elimination into the region environment analysis unit, wherein the specific mode is as follows:
constructing an area model belonging to the corresponding space-time area to be analyzed according to the confirmed space-time area to be analyzed, extracting a required used model part in the corresponding multidimensional space-time data from a storage unit, carrying out marking confirmation on the used model part in the area model according to the used model part, and eliminating the model part which is not marked;
transmitting the processed regional model to a regional environment analysis unit;
the regional environment analysis unit receives the three groups of treated regional models, confirms the variation and cost values existing in the regional models according to the three groups of received regional models, and selects the optimal regional model by the following specific modes:
the variation BH generated in different region models i Confirming, and simultaneously merging the model part finished product parameter values marked and confirmed in the region model to obtain a merging cost value CB of the corresponding region model i ；
By JC i =（BH i ×C1）÷（CB i X C2) obtaining the correction parameter JC of the corresponding region model i Wherein C1 and C2 are both preset fixed coefficient factors;
the parameter correction values JC generated by the three groups of regional models i And (3) confirming the maximum value, confirming the corresponding region model according to the confirmed maximum value, marking the region model as the optimal region model, and transmitting the optimal region model into the optimal data generating unit.
Preferably, the optimized data generating unit confirms the model data generated in the optimal region model according to the confirmed optimal region model, generates an optimized data packet from the confirmed model data, and transmits the generated optimized data packet to the display unit;
a multidimensional space-time data visualization performance optimization method comprises the following steps:
firstly, confirming space variables from the confirmed multidimensional space-time data, confirming a plurality of space variables belonging to the same space-time variable, comparing the space variables with preset variable parameters in a storage unit, and confirming whether the corresponding space variables are dynamic variables or static variables;
secondly, confirming the active areas of different dynamic variables according to the confirmed dynamic variables, judging the confirmed areas as space-time areas to be analyzed, confirming the variable quantity generated by the corresponding variables of the space-time areas to be analyzed according to the received space-time areas to be analyzed, confirming the stage efficiency according to the variable quantity, and selecting the stage efficiency to sequence the space-time areas to be analyzed in the first three groups;
thirdly, factor elimination is carried out on three groups of space-time areas to be analyzed existing in the space-time area set, marking and confirming are carried out on the used model pieces existing in the area model according to the used model pieces, and model pieces which are not marked are eliminated;
and fourthly, confirming the variation and cost value existing in the region model according to the confirmed three groups of region models, selecting the optimal region model, confirming model data from the optimal region model, generating an optimized data packet according to the model data, and transmitting the optimized data packet to the display unit.
Advantageous effects
The application provides a multidimensional space-time data visualization performance optimization method and system. Compared with the prior art, the method has the following beneficial effects:
according to the method, the variables in the three-dimensional space data are determined, the variable quantity of the corresponding variable under different three-dimensional space environments is analyzed according to the determined variables, the variable quantity with good variable quantity is confirmed, then influence factors in the three-dimensional space environments where the variable is located are removed, the selected three-dimensional space environments are re-analyzed after the influence factors are removed, a group of optimal space environments are confirmed, an optimized data packet generated according to the determined space environments is generated, then the operator optimizes the subsequent space-time data according to the optimized data packet, in the data optimization process, the original space environments are taken into consideration, data optimization is carried out according to differences among the data, the considered factors are more comprehensive, and therefore the overall optimization effect of the corresponding space-time data is improved.
Drawings
FIG. 1 is a schematic diagram of a principal frame of the present application;
FIG. 2 is a schematic flow chart of the method of the present application.
Detailed Description
The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
Referring to fig. 1, the application provides a multidimensional space-time data visualization performance optimization system, which comprises a space variable confirmation unit, a display unit and an optimization data generation center;
the space variable confirming unit is electrically connected with the input end of the optimized data generating center, the optimized data generating center comprises a variable object comparing unit, a storage unit, an active area confirming unit, a change parameter confirming unit, a factor eliminating unit, an area environment analyzing unit and an optimized data generating unit, the variable object comparing unit is connected with the storage unit in a bidirectional manner, the variable object comparing unit is electrically connected with the input end of the active area confirming unit, the active area confirming unit is electrically connected with the input end of the change parameter confirming unit, the change parameter confirming unit is electrically connected with the input end of the factor eliminating unit, the factor eliminating unit is electrically connected with the input end of the area environment analyzing unit, the area environment analyzing unit is electrically connected with the input end of the optimized data generating unit, and the optimized data generating unit is electrically connected with the input end of the display unit;
the space variable confirmation unit confirms space variables from the confirmed multidimensional space-time data, marks a plurality of space variables belonging to the same space-time scene state as the same space-time variables, and transmits the plurality of space variables belonging to the same space-time variables to the optimized data generation center, and specifically, for convenience of understanding: the same space-time scene can be understood as a working environment of a factory, the corresponding variable can be understood as a worker in the factory, or the same space-time scene can be understood as a plant cultivation environment, the corresponding variable can be understood as a corresponding cultivation variety, and specifically, the multi-dimensional space-time data comprise space scene data of different time points, the space scene data comprise monitoring pictures existing in the corresponding scene, different monitoring pictures exist in different time points, the multi-dimensional space-time data are obtained by fusing three-dimensional data with time parameters, the three-dimensional scene can be subjected to a series of changes according to the trend of time values, and the generated series of data are all multi-dimensional time data;
the variable object comparison unit is used for determining a plurality of space variables belonging to the same space-time variable, comparing the space variables with variable parameters preset in the storage unit, and determining whether the corresponding space variables are dynamic variables or static variables, wherein the specific comparison method is as follows:
the storage unit stores a large number of variable parameters, the corresponding variable parameters are preset parameters, and the specific parameters are empirically drawn by operators;
comparing the space variable with the variable parameter to confirm whether the space variable is a dynamic variable or a static variable, and if the space variable is a dynamic variable, transmitting a plurality of dynamic variables belonging to the same space-time scene into an active area confirming unit;
and if the static variables are the static variables, diffusing a plurality of static variables belonging to the same space-time scene to the periphery by X1m to obtain a space-time area to be analyzed of the corresponding static variables, and transmitting the space-time area to a change parameter confirmation unit.
The active region confirming unit confirms the active regions of different dynamic variables according to the confirmed dynamic variables and judges the confirmed regions as space-time regions to be analyzed, wherein the specific mode for confirming is as follows:
confirming a plurality of dynamic variables belonging to the same space-time scene, and defining a group of monitoring periods T1, wherein T1 is a preset value, the specific value of the monitoring periods is empirically drawn by operators, in the monitoring periods T1, confirming a moving track area corresponding to the dynamic variables, and according to the confirmed moving track area, diffusing X1m to the periphery to obtain a space-time area to be analyzed corresponding to the dynamic variables, wherein X1 is a preset value, the specific value of the monitoring periods T1 is empirically drawn by the operators, and the specific value of the monitoring periods is generally 1m;
and transmitting different space-time areas to be analyzed belonging to different dynamic variables into a change parameter confirmation unit.
The change parameter confirmation unit confirms another group of monitoring periods according to the received space-time areas to be analyzed, analyzes the change quantity generated by the corresponding variable of the space-time areas to be analyzed in the monitoring period, confirms the stage efficiency according to the change quantity, and then selects the stage efficiency to order the space-time areas to be analyzed in the first three groups and transmits the space-time areas to the factor elimination unit, wherein the specific mode for confirming the stage efficiency according to the change quantity is as follows:
recording the initial time of the space-time region to be analyzed according to the confirmed monitoring period T2The amount of change generated between the point and the end time point is marked as BH i Wherein i represents different space-time areas to be analyzed, and specifically, the variation can be understood as the variation generated by a certain variable, such as the growth amount or other variation, and also can be understood as the output amount of a corresponding worker in a corresponding station area;
the variation BH of different space-time areas to be analyzed i Confirming, namely arranging different space-time areas to be analyzed according to the magnitude of the variable quantity value in a mode from large to small, integrating the space-time areas to be analyzed which are arranged in the first three groups to obtain a space-time area set, and transmitting the space-time area set to a factor eliminating unit.
The factor removing unit is used for receiving the time-space region set, removing factors from three groups of to-be-analyzed time-space regions in the time-space region set, and transmitting the time-space region set after factor removal to the region environment analysis unit, wherein the specific mode for factor removal is as follows:
constructing an area model belonging to the corresponding space-time area to be analyzed according to the confirmed space-time area to be analyzed, extracting a required used model part in the corresponding multidimensional space-time data from a storage unit, carrying out marking confirmation on the used model part in the area model according to the used model part, and eliminating the model part which is not marked;
and transmitting the processed regional model to a regional environment analysis unit.
Example two
The regional environment analysis unit receives the three groups of processed regional models, confirms the variation and cost value existing in the regional models according to the three groups of received regional models, selects the optimal regional model, and transmits the selected optimal regional model to the optimized data generation unit, wherein the specific mode for confirming is as follows:
the variation BH generated in different region models i Confirming, and simultaneously merging the model part finished product parameter values marked and confirmed in the region model to obtain a corresponding regionMerge cost value CB of domain model i ；
By JC i =（BH i ×C1）÷（CB i X C2) obtaining the correction parameter JC of the corresponding region model i Wherein, C1 and C2 are both preset fixed coefficient factors, and the specific value is determined by an operator according to experience;
the parameter correction values JC generated by the three groups of regional models i And (3) confirming the maximum value, confirming the corresponding region model according to the confirmed maximum value, marking the region model as the optimal region model, and transmitting the optimal region model into the optimal data generating unit.
And the optimized data generating unit is used for confirming the model data generated in the optimal region model according to the confirmed optimal region model, generating an optimized data packet by the confirmed model data and transmitting the generated optimized data packet into the display unit.
And the display unit is used for receiving and displaying the optimized data packet for external personnel to check.
Example III
With reference to fig. 2, the application provides a multidimensional space-time data visualization performance optimization method, which comprises the following steps:
firstly, confirming space variables from the confirmed multidimensional space-time data, confirming a plurality of space variables belonging to the same space-time variable, comparing the space variables with preset variable parameters in a storage unit, and confirming whether the corresponding space variables are dynamic variables or static variables;
secondly, confirming the active areas of different dynamic variables according to the confirmed dynamic variables, judging the confirmed areas as space-time areas to be analyzed, confirming the variable quantity generated by the corresponding variables of the space-time areas to be analyzed according to the received space-time areas to be analyzed, confirming the stage efficiency according to the variable quantity, and selecting the stage efficiency to sequence the space-time areas to be analyzed in the first three groups;
thirdly, factor elimination is carried out on three groups of space-time areas to be analyzed existing in the space-time area set, marking and confirming are carried out on the used model pieces existing in the area model according to the used model pieces, and model pieces which are not marked are eliminated;
and fourthly, confirming the variation and cost value existing in the region model according to the confirmed three groups of region models, selecting the optimal region model, confirming model data from the optimal region model, generating an optimized data packet according to the model data, and transmitting the optimized data packet to the display unit.
Example IV
This embodiment includes all of the three embodiments described above in the specific implementation.
The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The above embodiments are only for illustrating the technical method of the present application and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present application may be modified or substituted without departing from the spirit and scope of the technical method of the present application.
Claims (4)
1. A multi-dimensional spatiotemporal data visualization performance optimization system, comprising:
the space variable confirming unit confirms space variables from the confirmed multidimensional space-time data, marks a plurality of space variables belonging to the same space-time scene state as the same space-time variables, and transmits the plurality of space variables belonging to the same space-time variables into the variable object comparison unit;
the variable object comparison unit is used for confirming a plurality of space variables belonging to the same space-time variable, comparing the space variables with variable parameters preset in the storage unit, and confirming whether the corresponding space variables are dynamic variables or static variables, wherein the specific mode is as follows:
the storage unit stores a large number of variable parameters, and the corresponding variable parameters are preset parameters;
comparing the space variable with the variable parameter to confirm whether the space variable is a dynamic variable or a static variable, and if the space variable is a dynamic variable, transmitting a plurality of dynamic variables belonging to the same space-time scene into an active area confirming unit;
if the static variables are the static variables, diffusing a plurality of static variables belonging to the same space-time scene to the periphery by X1m to obtain space-time areas to be analyzed of the corresponding static variables, and transmitting the space-time areas to a change parameter confirmation unit;
the active region confirming unit confirms active regions of different dynamic variables according to the confirmed dynamic variables and judges the confirmed regions as space-time regions to be analyzed;
the change parameter confirmation unit confirms another group of monitoring periods according to the received space-time areas to be analyzed, analyzes the change quantity generated by the corresponding variable of the space-time areas to be analyzed in the monitoring period, confirms the stage efficiency according to the change quantity, selects the stage efficiency, sorts the space-time areas to be analyzed in the first three groups, and transmits the time areas to the factor elimination unit;
the factor eliminating unit is used for receiving the time space region set, eliminating factors of three groups of time space regions to be analyzed existing in the time space region set, and transmitting the time space region set after factor elimination to the region environment analysis unit;
the regional environment analysis unit is used for receiving the three groups of processed regional models, confirming the variation and cost values existing in the regional models according to the three groups of received regional models, and selecting the optimal regional model;
the specific mode of the activity area confirming unit for confirming the activity areas of different dynamic variables is as follows:
confirming a plurality of dynamic variables belonging to the same space-time scene, and defining a group of monitoring periods T1, wherein T1 is a preset value, confirming a moving track area corresponding to the dynamic variables in the monitoring periods T1, and diffusing X1m to the periphery according to the confirmed moving track area to obtain a space-time area to be analyzed corresponding to the dynamic variables, wherein X1 is the preset value;
transmitting different space-time areas to be analyzed belonging to different dynamic variables into a change parameter confirmation unit;
the change parameter confirmation unit confirms the specific mode of stage efficiency according to the change amount:
recording the variation generated between the initial time point and the end time point of the space-time region to be analyzed according to the confirmed monitoring period T2, and marking the variation as BH i Wherein i represents different spatiotemporal regions to be analyzed;
the variation BH of different space-time areas to be analyzed i Confirming, namely arranging different space-time areas to be analyzed according to the magnitude of the variable quantity value in a mode from large to small, integrating the space-time areas to be analyzed which are arranged in the first three groups to obtain a space-time area set, and transmitting the space-time area set to a factor eliminating unit.
2. The multi-dimensional spatiotemporal data visualization performance optimization system of claim 1, wherein the factor elimination unit performs factor elimination on three groups of spatiotemporal regions to be analyzed in the following specific ways:
constructing an area model belonging to the corresponding space-time area to be analyzed according to the confirmed space-time area to be analyzed, extracting a required used model part in the corresponding multidimensional space-time data from a storage unit, carrying out marking confirmation on the used model part in the area model according to the used model part, and eliminating the model part which is not marked;
and transmitting the processed regional model to a regional environment analysis unit.
3. The multi-dimensional space-time data visualization performance optimization system according to claim 2, wherein the area environment analysis unit selects the optimal area model in the following specific ways:
the variation BH generated in different region models i Confirm and simultaneously to the regionCombining the model part finished product parameter values marked and confirmed in the model to obtain a combined cost value CB of the corresponding region model i ；
By JC i =（BH i ×C1）÷（CB i X C2) obtaining the correction parameter JC of the corresponding region model i Wherein C1 and C2 are both preset fixed coefficient factors;
the parameter correction values JC generated by the three groups of regional models i And (3) confirming the maximum value, confirming the corresponding region model according to the confirmed maximum value, marking the region model as the optimal region model, and transmitting the optimal region model into the optimal data generating unit.
4. A multi-dimensional spatiotemporal data visualization performance optimization system according to claim 3, wherein the optimization data generation unit confirms model data generated in the optimal region model based on the confirmed optimal region model, generates an optimization data packet from the confirmed model data, and transmits the generated optimization data packet to the presentation unit.
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