Calculation and application service optimization method based on two-dimensional space information and three-dimensional space information

Abstract

The invention relates to the technical field of spatial information, in particular to a calculation and application service optimization method based on two-dimensional spatial information, which comprises the following steps: step 1, collecting the two-dimensional spatial information of map application service to obtain various monitoring parameters, step 2, analyzing the various monitoring parameters collected in the step 1 to obtain a map application service model, and step 3, analyzing the map application service model to obtain a map application service model. The method comprises the following steps of: 1, calculating the two-dimensional space information to obtain each optimization index of the calculation of the two-dimensional space information and the map application service, 2, respectively comparing each optimization index obtained in the step 2 with a threshold value, optimizing according to a comparison result and generating an optimization strategy, and 4, evaluating an optimization coefficient with a comparison result greater than or equal to 1 obtained in the step 3, 5, an evaluation result obtained through evaluation and analysis in the step 4 is transmitted to an administrator information terminal for man-machine interaction; through the GIS technology, the map space precision is optimized, the map rendering effect is optimized, and accurate two-dimensional and three-dimensional map display is provided.

Description

Technical Field

[0001] The present invention relates to the field of spatial information technology, and in particular to a method for optimizing computing and application services based on two- and three-dimensional spatial information. Background Art

[0002] Two-dimensional and three-dimensional spatial information technology refers to the technology that uses two-dimensional and three-dimensional technologies to process and analyze spatial information. Through GIS, two-dimensional spatial data can be stored, queried, analyzed and visualized; three-dimensional data can provide richer information, such as terrain elevation, geological structure, etc., which helps researchers better understand the spatial pattern and improve the accuracy of prediction and decision-making.

[0003] With the rapid development of information technology, 2D and 3D spatial information is increasingly used in many fields such as 2D and 3D map applications, urban planning, architectural design, virtual reality, etc. In today's digital age, the demand for map applications is becoming increasingly complex and diverse, and the combined use of 2D and 3D spatial information has become the key to providing more accurate and comprehensive services.

[0004] However, when processing two-dimensional and three-dimensional spatial information, traditional computing methods and map application services often have problems such as low efficiency, insufficient precision, and poor user experience. For example, when processing large-scale data, the computing speed is slow; in the map display of complex scenes, the information is inaccurate and unclear; in terms of service response, the delay is high and cannot meet the user's real-time interaction needs, which seriously affects the performance of related applications and user experience. Therefore, with the widespread application and in-depth development of GIS technology, the demand for optimization of computing and map application services based on two-dimensional and three-dimensional spatial information is becoming increasingly urgent. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for optimizing computing and application services based on two- and three-dimensional spatial information to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information, comprising the following steps:

[0007] Step 1: Use big data technology to collect the two-dimensional and three-dimensional spatial information of map application services, and obtain various monitoring parameters for the calculation and optimization of the two-dimensional and three-dimensional spatial information and map application services, including map space calculation parameters and map application service parameters;

[0008] Step 2: Analyze the various monitoring parameters collected in step 1 through big data analysis technology to obtain various optimization indicators for the calculation of two- and three-dimensional spatial information and map application services, including a map space calculation optimization analysis unit and a map application service optimization analysis unit;

[0009] Step 3: Compare each optimization index obtained in step 2 with the threshold value, optimize according to the comparison results, and generate an optimization strategy, including a graph space calculation optimization unit and a map application service optimization unit;

[0010] Step 4: Evaluate the optimization coefficients of the comparison results ≥ 1 obtained in step 3 to obtain the optimization evaluation index of the calculation and application service of the two-dimensional and three-dimensional spatial information;

[0011] Step 5: The calculation and application service optimization evaluation indicators of the two- and three-dimensional spatial information obtained by the evaluation and analysis in step 4 are transmitted to the administrator information terminal for human-computer interaction.

[0012] Preferably, in step 2, step 2.1: map space calculation optimization analysis unit: obtain the map space through image recognition and three-dimensional reconstruction technology. Figure 2 The matching degree M between the 3D image and the 3D model, Where d(2D i ) and d(3D i ) represent the distances between corresponding points in two dimensions and three dimensions, respectively, and n represents the number of corresponding points. By comparing the actual geographic information with the map data, the spatial information deviation index E of the two-dimensional and three-dimensional maps is obtained. Where m represents the number of spatial information sampling points, (x map ,y map ) pixel j and (x real ,y real ) pixel j Respectively represent the map pixel coordinates and actual geographic pixel coordinates of the jth sampling point on the two-dimensional map, Co(x, y, z) j and Co(x,y,z)0 j They represent the three-dimensional coordinates and actual geographic coordinates of the jth sampling point on the three-dimensional map respectively; then, the geo Figure 2 The density increase rate of the three-dimensional conversion space point cloud is R, R = (R 3D -R 2D ) / R 2D , R 3D and R 2D They represent the point cloud density of two-dimensional and three-dimensional maps respectively. The map scanning area A and the total number of point clouds S are obtained through lidar scanning technology, and the map point cloud density D is obtained, D = S / A; finally, the two-dimensional and three-dimensional map spatial accuracy optimization index POI is obtained through big data analysis technology, POI = a1×M+a2×E+a3×R, a1, a2 and a3 represent the corresponding weights respectively.

[0013] Preferably, in step 2.1 of step 2: the map space calculation optimization analysis unit: obtains the vector data sets of the two object buffers on the two-dimensional map through the buffer analysis technology, displays them on the map to obtain the overlapping feature data sets, calculates the features of the overlapping parts through the big data analysis technology, and obtains the spatial proximity index PI of the two-dimensional map, CH(O1∩O2) represents the overlapping features of object O1 and object O2. CH(O1) and CH(O2) represent the features of object O1 and object O2 respectively. If the buffers do not overlap, the minimum distance between the boundaries of the two object buffers is calculated as the spatial proximity index PI of the two-dimensional map. PI=f[distance(O1,O2)], where f[] represents the distance algorithm. In the three-dimensional map space, the volume v(O3) of object O3, the volume v(O4) of object O4 and the overlapping volume v(O3,O4) are obtained to obtain the three-dimensional map space volume overlap index VOI. Finally, through big data analysis technology, we can get the ROI of spatial relationship optimization index of two-dimensional and three-dimensional maps. PI0 represents the corresponding reference value.

[0014] Preferably, in step 2, the map application service optimization analysis unit: obtains a feature data set Fd of k visual aspects of the three-dimensional map object through image recognition technology and GIS technology, where Fd = [F1, F2, ..., F l ,...,F k ],F l Represents the feature data of the lth aspect, and obtains the similarity function SI of the lth aspect l , SI l =F l / O l , O l The lth aspect feature data of the actual object O corresponding to the lth aspect feature data of the three-dimensional map object is represented, and the three-dimensional map object visual fidelity index VFI is obtained. b l Represents the weight of the lth aspect.

[0015] Preferably, in step 2, the map application service optimization analysis unit: obtains a response time data set Td when the user performs a map operation through user operation logs and timestamp technology, where Td = [t1, t2, ..., t I ,...,t N ], t IRepresents the response time of the I-th map interaction operation, N represents the total number of interaction operations, such as zooming, panning, clicking, etc., and the map interaction response efficiency index REI is obtained, REI = T(t)0 / T(t), μ(t)0 and μ(t) respectively represent the reference value and actual value of the total response time point of the interaction operation; based on the user's operation, through the performance testing tool, the two-dimensional and three-dimensional model is rendered in the map application. The number of frames Rr and the rendering time t_r are obtained, and the rendering speed Rv is obtained, Rv = Rr / Rt, and the rendering speed efficiency index VEI is obtained, VEI = Rv / Rv0, Rv0 represents the reference value of the rendering speed, and finally the two-dimensional and three-dimensional map space interaction response optimization index IOI is obtained, IOI = c1×REI+c2×VEI, c1 and c2 respectively represent the corresponding weights.

[0016] Preferably, generating the optimization strategy in step 3 comprises the following steps:

[0017] Step 3.1: Map space calculation optimization unit:

[0018] Step 3.1.1: Compare the two-dimensional and three-dimensional map spatial accuracy optimization index POI obtained in the map spatial accuracy optimization analysis with the threshold POI0 to obtain the optimization coefficient η(POI) of the two-dimensional and three-dimensional map spatial accuracy optimization index, η(POI) = exp(POI-POI0). If η(POI) ≥ 1, it means that the two-dimensional and three-dimensional map spatial accuracy calculation is qualified, otherwise it is optimized to generate an optimization strategy;

[0019] Step 3.1.2: Compare the two-dimensional and three-dimensional map spatial relationship optimization index ROI obtained in the map spatial relationship optimization analysis with the threshold ROI0 to obtain the optimization coefficient η(ROI) of the two-dimensional and three-dimensional map spatial relationship optimization index, η(ROI)=exp(ROI-ROI0). If η(ROI)≥1, it means that the map spatial relationship calculation is qualified, otherwise it is optimized to generate an optimization strategy.

[0020] Preferably, generating the optimization strategy in step 3 comprises the following steps:

[0021] Step 3.2: Map application service optimization unit:

[0022] Step 3.2.1: Compare the three-dimensional map object visual fidelity index VFI obtained in the map space data visualization optimization analysis with the threshold VFI0 to obtain the optimization coefficient η(VFI) of the three-dimensional map object visual fidelity index, η(VFI) = exp(VFI-VFI0), if η(VFI) ≥ 1, it means that the three-dimensional map object visual fidelity application service is qualified, otherwise it is optimized to generate an optimization strategy;

[0023] Step 3.2.2: Compare the two-dimensional and three-dimensional map space interaction response optimization index IOI obtained in the map space interaction response optimization analysis with the threshold IOI0 to obtain the optimization coefficient η(IOI) of the two-dimensional and three-dimensional map space interaction response optimization index IOI, η(IOI)=exp(IOI-IOI0), if η(IOI)≥1, it means that the two-dimensional and three-dimensional map space interaction response application service is qualified, otherwise it is optimized to generate an optimization strategy.

[0024] Preferably, the calculation and application service optimization evaluation index CSEI of the two-dimensional and three-dimensional spatial information in step 4, CSEI=w1×[η(POI)+η(ROI)]+w2×[η(VFI)+η(IOI)], wherein w1 and w2 respectively represent the weights of the calculation and application service optimization coefficients of the two-dimensional and three-dimensional spatial information, the optimization coefficient η(POI) of the two-dimensional and three-dimensional map spatial accuracy optimization index, the optimization coefficient η(ROI) of the two-dimensional and three-dimensional map spatial relationship optimization index, the optimization coefficient η(VFI) of the three-dimensional map object visual fidelity index, and the optimization coefficient η(IOI) of the two-dimensional and three-dimensional map spatial interactive response optimization index IOI.

[0025] Technical effects and advantages of the present invention:

[0026] 1. The present invention uses GIS technology and big data analysis technology to achieve query and analysis of rapidly growing spatial data volumes, as well as support storage and management of large-scale data, meet users' interactive needs for map applications, and achieve a more convenient and intuitive user interface and interaction method, thereby improving service quality and user experience;

[0027] 2. The present invention uses GIS technology to optimize map spatial accuracy and map rendering effects, provide clearer, more accurate and smoother two- and three-dimensional map displays, continuously improve map visualization effects, improve user experience and decision-making efficiency, reduce user waiting time, and improve user satisfaction, thereby improving service stability and scalability, and adapting to large-scale user concurrent access;

[0028] 3. The present invention optimizes the map space interactive response, utilizes multi-core CPU and GPU resources, improves rendering speed, optimizes service architecture and algorithms, and reduces the response time of switching from two-dimensional to three-dimensional views. The fast response time can improve user operating efficiency and satisfaction, thereby improving the service quality of map applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0030] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 As shown, the present invention provides a computing and application service optimization system based on two-dimensional and three-dimensional spatial information, including: a two-dimensional and three-dimensional spatial information acquisition module, a two-dimensional and three-dimensional spatial information analysis module, a two-dimensional and three-dimensional spatial information computing and application service optimization module, a two-dimensional and three-dimensional spatial information computing and application service optimization evaluation module and a two-dimensional and three-dimensional spatial information computing and application service optimization human-computer interaction module.

[0033] The two-dimensional and three-dimensional spatial information acquisition module is connected to the two-dimensional and three-dimensional spatial information analysis module, the two-dimensional and three-dimensional spatial information calculation and application service optimization module is respectively connected to the two-dimensional and three-dimensional spatial information calculation and application service optimization evaluation module and the two-dimensional and three-dimensional spatial information analysis module, and the two-dimensional and three-dimensional spatial information calculation and application service optimization human-computer interaction module is respectively connected to the two-dimensional and three-dimensional spatial information calculation and application service optimization evaluation module and the two-dimensional spatial information calculation and application service optimization module.

[0034] Two-dimensional and three-dimensional spatial information acquisition module: used to collect two-dimensional and three-dimensional spatial information of map application services, obtain and analyze the calculation of two-dimensional and three-dimensional spatial information and various monitoring parameters for map application service optimization, and transmit them to the two-dimensional and three-dimensional spatial information processing and optimization module;

[0035] What needs to be specifically explained in this embodiment is that the two- and three-dimensional spatial information of the map application service collected through GIS technology includes but is not limited to satellite remote sensing images, aerial photogrammetry data, geographic information system data, etc., and is integrated to ensure the consistency and integrity of the data.

[0036] Two-dimensional and three-dimensional spatial information analysis module: used to analyze the monitoring parameters obtained by the acquisition module, obtain the optimization indicators of the two-dimensional and three-dimensional spatial information calculation and map application services, and transmit them to the two-dimensional and three-dimensional spatial information calculation and application service optimization module;

[0037] The two-dimensional and three-dimensional spatial information computing and application service optimization module is used to compare the optimization indicators obtained by the analysis module with the thresholds, optimize according to the comparison results, generate optimization strategies, and transmit them to the two-dimensional and three-dimensional spatial information computing and application service optimization human-computer interaction module, and transmit the comparison results to the two-dimensional and three-dimensional spatial information computing and application service optimization evaluation module;

[0038] The two-dimensional and three-dimensional spatial information computing and application service optimization evaluation module is used to evaluate the comparison results of the optimization module, obtain the two-dimensional and three-dimensional spatial information computing and application service optimization evaluation indicators, and transmit them to the two-dimensional and three-dimensional spatial information computing and application service optimization human-computer interaction module;

[0039] The human-computer interaction module for optimizing the calculation and application services of two- and three-dimensional spatial information is used to output the evaluation indicators for optimizing the calculation and application services of two- and three-dimensional spatial information obtained by the evaluation module to the management personnel information terminal.

[0040] See also Figure 2 As shown, a method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information includes the following steps: Step 1: Using big data technology, the two-dimensional and three-dimensional spatial information of map application services is collected to obtain and analyze various monitoring parameters for the optimization of the computing and map application services of the two-dimensional and three-dimensional spatial information, including map space computing parameters and map application service parameters; Step 2: Using big data analysis technology, the various monitoring parameters collected in step 1 are analyzed to obtain various optimization indicators for the computing and map application services of the two-dimensional and three-dimensional spatial information, including a map space computing optimization analysis unit and a map application service optimization analysis unit; Step 3: Compare the various optimization indicators obtained in step 2 with thresholds respectively, optimize according to the comparison results, and generate an optimization strategy, including a map space computing optimization unit and a map application service optimization unit; Step 4: Evaluate the optimization coefficient of the comparison result ≥ 1 obtained in step 3 to obtain the optimization evaluation indicator of the computing and application services of the two-dimensional and three-dimensional spatial information; Step 5: Transmit the optimization evaluation indicator of the computing and application services of the two-dimensional and three-dimensional spatial information obtained by the evaluation and analysis in step 4 to the administrator information terminal for human-computer interaction.

[0041] Step 1: Through big data technology, the two-dimensional and three-dimensional spatial information of the map application service is collected to obtain various monitoring parameters for the calculation and optimization of the two-dimensional and three-dimensional spatial information and the map application service, including map space calculation parameters and map application service parameters; the map space calculation parameters include the distance between corresponding points in two-dimensional and three-dimensional, map pixel coordinates, actual geographic pixel coordinates, three-dimensional map coordinates and actual geographic coordinates, point cloud density of two-dimensional and three-dimensional maps, feature data of two object buffers on the two-dimensional map, and the volume of the three-dimensional map object; the map application service parameters include obtaining feature data of k aspects of the three-dimensional map object in vision, response time data when the user performs map operations, and the number of rendering frames and rendering time of the two-dimensional and three-dimensional models in the map application;

[0042] Step 2: Analyze the various monitoring parameters collected in step 1 through big data analysis technology to obtain various optimization indicators of two- and three-dimensional spatial information calculation and map application services, including map space calculation optimization analysis unit and map application service optimization analysis unit. The analysis includes the following steps:

[0043] Step 2.1: Map space calculation optimization analysis unit:

[0044] Step 2.1.1: Map spatial accuracy optimization analysis: Through image recognition and 3D reconstruction technology, the map Figure 2 The matching degree M between the 3D image and the 3D model, Where d(2D i ) and d(3D i ) represent the distances between corresponding points in two dimensions and three dimensions, respectively, and n represents the number of corresponding points. By comparing the actual geographic information with the map data, the spatial information deviation index E of the two-dimensional and three-dimensional maps is obtained. Where m represents the number of spatial information sampling points, (x map ,y map ) pixel j and (x real ,y real ) pixel j Respectively represent the map pixel coordinates and actual geographic pixel coordinates of the jth sampling point on the two-dimensional map, Co(x, y, z) j and Co(x,y,z)0 j The three-dimensional coordinates and actual geographic coordinates of the j-th sampling point on the three-dimensional map are respectively represented. Through GIS technology, the geographic coordinates are converted into map pixel coordinates. Then, through big data analysis technology, the geographic coordinates are obtained. Figure 2 The density increase rate of the three-dimensional conversion space point cloud is R, R = (R 3D -R 2D ) / R 2D , R 3D and R 2D Represent the point cloud density of the two-dimensional and three-dimensional maps respectively. The map scanning area A and the total number of point clouds S are obtained through laser radar scanning technology, and the map point cloud density D is obtained, D = S / A; finally, the two-dimensional and three-dimensional map spatial accuracy optimization index POI is obtained through big data analysis technology, POI = a1×M+a2×E+a3×R, a1, a2 and a3 represent the corresponding weights respectively, for example, a1 = 0.4, a2 = 0.3 and a3 = 0.3;

[0045] What needs to be specifically explained in this embodiment is that a higher point cloud density can provide richer spatial information, which helps to improve the accuracy and detail expression of three-dimensional modeling; a higher matching degree means that the information in the image can be more effectively mapped to the three-dimensional space, thereby achieving more accurate spatial information extraction and analysis; a smaller pixel deviation means that the two maps have a higher consistency in spatial information, which helps to achieve more accurate spatial positioning and navigation.

[0046] Step 2.1.2: Optimization analysis of map spatial relations: Through buffer analysis technology, obtain the vector data set of the two object buffers on the two-dimensional map, display them on the map to obtain the overlapping feature data set, and use big data analysis technology to calculate the features of the overlapping parts, including area, perimeter, etc., to obtain the spatial proximity index PI of the two-dimensional map. CH(O1∩O2) represents the overlapping features of object O1 and object O2. CH(O1) and CH(O2) represent the features of object O1 and object O2 respectively. If the buffers do not overlap, the minimum distance between the boundaries of the two object buffers is calculated as the spatial proximity index PI of the two-dimensional map. PI=f[distance(O1,O2)], where f[] represents the distance algorithm. The distance algorithm includes Euclidean distance, Manhattan distance, Chebyshev distance, etc. The two objects on the two-dimensional map include points, lines, and surfaces. In the three-dimensional map space, the volume v(O3) of object O3, the volume v(O4) of object O4, and the overlapping volume v(O3,O4) are obtained to obtain the three-dimensional map space volume overlap index VOI. Finally, through big data analysis technology, we can get the ROI of spatial relationship optimization index of two-dimensional and three-dimensional maps. PI0 represents the corresponding reference value;

[0047] Step 2.2: Map application service optimization analysis unit:

[0048] Step 2.2.1: Map spatial data visualization optimization analysis: Through image recognition technology and GIS technology, obtain the k-dimensional visual feature data set Fd of the 3D map object, Fd = [F1, F2, ..., F l ,...,F k ],F l Represents the feature data of the lth aspect, such as texture, color, shape, lighting effect, etc., and obtains the similarity function SI of the lth aspect l , SI l =F l / O l , O l The lth aspect feature data of the actual object O corresponding to the lth aspect feature data of the three-dimensional map object is represented, and the three-dimensional map object visual fidelity index VFI is obtained. bl Indicates the weight of the lth aspect, for example, bl = 0.2;

[0049] Step 2.2.2: Map space interactive response optimization analysis: Through user operation logs and timestamp technology, obtain the response time dataset Td when the user performs map operations, Td = [t1, t2, ..., t I ,...,t N ], t I Represents the response time of the I-th map interactive operation, N represents the total number of interactive operations, such as zooming, panning, clicking, etc., and the map interactive response efficiency index REI is obtained, REI = T(t)0 / T(t), μ(t)0 and μ(t) respectively represent the reference value and actual value of the total response time point of the interactive operation; based on the user's operation, through the performance testing tool, the two-dimensional and three-dimensional model is rendered in the map application. The number of frames Rr and the rendering time t_r are obtained, and the rendering speed Rv is obtained, Rv = Rr / Rt, and the rendering speed efficiency index VEI is obtained, VEI = Rv / Rv0, Rv0 represents the reference value of the rendering speed, and finally the two-dimensional and three-dimensional map space interactive response optimization index IOI is obtained, IOI = c1×REI+c2×VEI, c1 and c2 respectively represent the corresponding weights, such as c1 = 0.5 and c2 = 0.5;

[0050] Step 3: Compare each optimization index obtained in step 2 with the threshold value, optimize according to the comparison results, and generate an optimization strategy, including a graph space calculation optimization unit and a map application service optimization unit. Generating an optimization strategy includes the following steps:

[0051] Step 3.1: Map space calculation optimization unit:

[0052] Step 3.1.1: Compare the 2D and 3D map spatial accuracy optimization index POI obtained in the map spatial accuracy optimization analysis with the threshold POI0 to obtain the optimization coefficient η(POI) of the 2D and 3D map spatial accuracy optimization index, η(POI)=exp(POI-POI0). If η(POI)≥1, it means that the 2D and 3D map spatial accuracy calculation is qualified. Otherwise, optimization is performed to generate an optimization strategy. For example, the matching results are automatically corrected and optimized through a machine learning algorithm to improve the matching accuracy. The map data is refined, such as increasing the level of detail, optimizing the texture, etc., to improve the clarity and readability of the map.

[0053] Step 3.1.2: Compare the two-dimensional and three-dimensional map spatial relationship optimization index ROI obtained in the map spatial relationship optimization analysis with the threshold ROI0 to obtain the optimization coefficient η(ROI) of the two-dimensional and three-dimensional map spatial relationship optimization index, η(ROI) = exp(ROI-ROI0). If η(ROI)≥1, it means that the map spatial relationship calculation is qualified, otherwise it is optimized to generate an optimization strategy, such as using the proximity analysis (P_proximity) algorithm to optimize the proximity judgment of spatial objects, improve the accuracy of path planning and buffer zone analysis; optimize collision detection and spatial relationship analysis between spatial objects;

[0054] Step 3.2: Map application service optimization unit:

[0055] Step 3.2.1: Compare the three-dimensional map object visual fidelity index VFI obtained in the map space data visualization optimization analysis with the threshold VFI0 to obtain the optimization coefficient η(VFI) of the three-dimensional map object visual fidelity index, η(VFI) = exp(VFI-VFI0). If η(VFI) ≥ 1, it means that the three-dimensional map object visual fidelity application service is qualified. Otherwise, optimization is performed to generate an optimization strategy, such as optimizing the modeling technology of the dimensional model to improve the realism and clarity of the visual effect.

[0056] Step 3.2.2: Compare the two-dimensional and three-dimensional map space interaction response optimization index IOI obtained in the map space interaction response optimization analysis with the threshold IOI0 to obtain the optimization coefficient η(IOI) of the two-dimensional and three-dimensional map space interaction response optimization index IOI, η(IOI)=exp(IOI-IOI0), if η(IOI)≥1, it means that the two-dimensional and three-dimensional map space interaction response application service is qualified, otherwise it is optimized to generate an optimization strategy, for example, using hardware acceleration technology, such as GPU acceleration, to reduce the response time of switching from two-dimensional to three-dimensional view; parallelize the rendering process, use multi-core CPU and GPU resources to improve the rendering speed;

[0057] Step 4: Evaluate the optimization coefficients of the comparison results ≥ 1 obtained in step 3 to obtain the two-dimensional and three-dimensional spatial information calculation and application service optimization evaluation index CSEI, CSEI = w1×[η(POI)+η(ROI)]+w2×[η(VFI)+η(IOI)], where w1 and w2 represent the weights of the two-dimensional and three-dimensional spatial information calculation and application service optimization coefficients, the optimization coefficient η(POI) of the two-dimensional and three-dimensional map spatial accuracy optimization index, the optimization coefficient η(ROI) of the two-dimensional and three-dimensional map spatial relationship optimization index, the optimization coefficient η(VFI) of the three-dimensional map object visual fidelity index, and the optimization coefficient η(IOI) of the two-dimensional and three-dimensional map spatial interactive response optimization index IOI;

[0058] Step 5: The optimization evaluation indicators of the calculation and application services of the two- and three-dimensional spatial information obtained from the evaluation and analysis in step 4 are transmitted to the administrator information terminal for human-computer interaction. When the evaluation falls within the set allowable range, it means that the calculation and map application services of the two- and three-dimensional spatial information are good. Otherwise, the management personnel are prompted to take optimization strategies in time. According to the monitoring results, the optimization strategies are dynamically adjusted, such as adjusting rendering parameters, optimizing algorithm selection, updating data models, etc.

[0059] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0060] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information, characterized in that: The following steps are involved: Step 1: Use big data technology to collect the two-dimensional and three-dimensional spatial information of map application services, and obtain various monitoring parameters for the calculation and optimization of the two-dimensional and three-dimensional spatial information and map application services, including map space calculation parameters and map application service parameters; Step 2: Analyze the various monitoring parameters collected in step 1 through big data analysis technology to obtain various optimization indicators for the calculation of two- and three-dimensional spatial information and map application services, including a map space calculation optimization analysis unit and a map application service optimization analysis unit; Step 3: Compare each optimization index obtained in step 2 with the threshold value, optimize according to the comparison results, and generate an optimization strategy, including a graph space calculation optimization unit and a map application service optimization unit; Step 4: Evaluate the optimization coefficients of the comparison results ≥ 1 obtained in step 3 to obtain the optimization evaluation index of the calculation and application service of the two-dimensional and three-dimensional spatial information; Step 5: The calculation and application service optimization evaluation indicators of the two- and three-dimensional spatial information obtained by the evaluation and analysis in step 4 are transmitted to the administrator information terminal for human-computer interaction.

2. The method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information according to claim 1, characterized in that: Step 2.1 in the step 2: map space calculation optimization analysis unit: obtain the matching degree M between the two-dimensional image of the map and the three-dimensional model through image recognition and three-dimensional reconstruction technology, Where d(2D i ) and d(3D i ) represent the distances between corresponding points in two dimensions and three dimensions, respectively, and n represents the number of corresponding points. By comparing the actual geographic information with the map data, the spatial information deviation index E of the two-dimensional and three-dimensional maps is obtained. Where m represents the number of spatial information sampling points, (x map ,y map ) pixel j and (x real ,y real ) pixel j Respectively represent the map pixel coordinates and actual geographic pixel coordinates of the jth sampling point on the two-dimensional map, Co(x, y, z) j and Co(x,y,z)0 j Respectively represent the three-dimensional coordinates and actual geographic coordinates of the jth sampling point on the three-dimensional map; then, through big data analysis technology, the density improvement rate R of the point cloud in the two-dimensional and three-dimensional conversion space of the map is obtained, R = (R 3D -R 2D ) / R 2D , R 3D and R 2D They represent the point cloud density of two-dimensional and three-dimensional maps respectively. The map scanning area A and the total number of point clouds S are obtained through lidar scanning technology, and the map point cloud density D is obtained, D = S / A; finally, the two-dimensional and three-dimensional map spatial accuracy optimization index POI is obtained through big data analysis technology, POI = a1×M+a2×E+a3×R, a1, a2 and a3 represent the corresponding weights respectively.

3. The method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information according to claim 1, characterized in that: Step 2.1 in step 2: map space calculation optimization analysis unit: obtain the vector data set of two object buffers on the two-dimensional map through buffer analysis technology, display them on the map to obtain overlapping feature data sets, calculate the features of the overlapping parts through big data analysis technology, and obtain the spatial proximity index PI of the two-dimensional map. CH(O1∩O2) represents the overlapping features of object O1 and object O2. CH(O1) and CH(O2) represent the features of object O1 and object O2 respectively. If the buffers do not overlap, the minimum distance between the boundaries of the two object buffers is calculated as the spatial proximity index PI of the two-dimensional map. PI=f[distance(O1,O2)], where f[] represents the distance algorithm. In the three-dimensional map space, the volume v(O3) of object O3, the volume v(O4) of object O4 and the overlapping volume v(O3,O4) are obtained to obtain the three-dimensional map space volume overlap index VOI. Finally, through big data analysis technology, we can get the ROI of spatial relationship optimization index of two-dimensional and three-dimensional maps. PI0 represents the corresponding reference value.

4. The method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information according to claim 1, characterized in that: The map application service optimization analysis unit in step 2 obtains a feature data set Fd of k visual aspects of the three-dimensional map object through image recognition technology and GIS technology, where Fd = [F1, F2, ..., F l ,...,F k ], F l Represents the feature data of the lth aspect, and obtains the similarity function SI of the lth aspect l , SI l =F l / O l , O l The lth aspect feature data of the actual object O corresponding to the lth aspect feature data of the three-dimensional map object is represented, and the three-dimensional map object visual fidelity index VFI is obtained. b l Represents the weight of the lth aspect.

5. The method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information according to claim 1, characterized in that: In the step 2, the map application service optimization analysis unit obtains a response time data set Td when the user performs a map operation through the user operation log and timestamp technology, where Td = [t1, t2, ..., t I ,...,t N ], t I Represents the response time of the I-th map interaction operation, N represents the total number of interaction operations, such as zooming, panning, clicking, etc., and the map interaction response efficiency index REI is obtained, REI = T(t)0 / T(t), μ(t)0 and μ(t) respectively represent the reference value and actual value of the total response time point of the interaction operation; based on the user's operation, through the performance testing tool, the two-dimensional and three-dimensional model is rendered in the map application. The number of frames Rr and the rendering time t_r are obtained, and the rendering speed Rv is obtained, Rv = Rr / Rt, and the rendering speed efficiency index VEI is obtained, VEI = Rv / Rv0, Rv0 represents the reference value of the rendering speed, and finally the two-dimensional and three-dimensional map space interaction response optimization index IOI is obtained, IOI = c1×REI+c2×VEI, c1 and c2 respectively represent the corresponding weights.

6. The method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information according to claim 1, characterized in that: Generating the optimization strategy in step 3 includes the following steps: Step 3.1: Map space calculation optimization unit: Step 3.1.1: Compare the two-dimensional and three-dimensional map spatial accuracy optimization index POI obtained in the map spatial accuracy optimization analysis with the threshold POI0 to obtain the optimization coefficient η(POI) of the two-dimensional and three-dimensional map spatial accuracy optimization index, η(POI) = exp(POI-POI0). If η(POI) ≥ 1, it means that the two-dimensional and three-dimensional map spatial accuracy calculation is qualified, otherwise it is optimized to generate an optimization strategy; Step 3.1.2: Compare the two-dimensional and three-dimensional map spatial relationship optimization index ROI obtained in the map spatial relationship optimization analysis with the threshold ROI0 to obtain the optimization coefficient η(ROI) of the two-dimensional and three-dimensional map spatial relationship optimization index, η(ROI)=exp(ROI-ROI0). If η(ROI)≥1, it means that the map spatial relationship calculation is qualified, otherwise it is optimized to generate an optimization strategy.

7. The method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information according to claim 1, characterized in that: Generating the optimization strategy in step 3 includes the following steps: Step 3.2: Map application service optimization unit: Step 3.2.1: Compare the three-dimensional map object visual fidelity index VFI obtained in the map space data visualization optimization analysis with the threshold VFI0 to obtain the optimization coefficient η(VFI) of the three-dimensional map object visual fidelity index, η(VFI) = exp(VFI-VFI0), if η(VFI) ≥ 1, it means that the three-dimensional map object visual fidelity application service is qualified, otherwise it is optimized to generate an optimization strategy; Step 3.2.2: Compare the two-dimensional and three-dimensional map space interaction response optimization index IOI obtained in the map space interaction response optimization analysis with the threshold IOI0 to obtain the optimization coefficient η(IOI) of the two-dimensional and three-dimensional map space interaction response optimization index IOI, η(IOI)=exp(IOI-IOI0), if η(IOI)≥1, it means that the two-dimensional and three-dimensional map space interaction response application service is qualified, otherwise it is optimized to generate an optimization strategy.

8. The method for optimizing computing and application services based on two-dimensional and three-dimensional spatial information according to claim 1, characterized in that: The calculation and application service optimization evaluation index CSEI of the two-dimensional and three-dimensional spatial information in the step 4 is CSEI=w1×[η(POI)+η(ROI)]+w2×[η(VFI)+η(IOI)], wherein w1 and w2 respectively represent the weights of the calculation and application service optimization coefficients of the two-dimensional and three-dimensional spatial information, the optimization coefficient η(POI) of the two-dimensional and three-dimensional map spatial accuracy optimization index, the optimization coefficient η(ROI) of the two-dimensional and three-dimensional map spatial relationship optimization index, the optimization coefficient η(VFI) of the three-dimensional map object visual fidelity index, and the optimization coefficient η(IOI) of the two-dimensional and three-dimensional map spatial interactive response optimization index IOI.

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