CN111784830B - Rule-based three-dimensional geographic information model space analysis method and system - Google Patents
Rule-based three-dimensional geographic information model space analysis method and system Download PDFInfo
- Publication number
- CN111784830B CN111784830B CN202010549450.1A CN202010549450A CN111784830B CN 111784830 B CN111784830 B CN 111784830B CN 202010549450 A CN202010549450 A CN 202010549450A CN 111784830 B CN111784830 B CN 111784830B
- Authority
- CN
- China
- Prior art keywords
- tod
- rule
- dimensional
- monitoring
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Abstract
The invention discloses a rule-based three-dimensional geographic information model space analysis method, which comprises the following steps: the method comprises the steps of obtaining a TOD monitoring index and a data resource, and establishing an association relation between the TOD monitoring index and the data resource based on a TOD logical relation; the TOD monitoring index and the data resource are subjected to three-dimensional fitting to create a three-dimensional building model and generate a first rule; based on the first rule, according to the TOD current status assessment and the expected target, adjusting the first rule to generate a second rule, adjusting the three-dimensional building model according to the second rule, implementing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the intervention prognosis based on the second rule. The method can visually display the spatial information, enhance multi-dimensional spatial analysis and improve the analysis efficiency of TOD city big data.
Description
Technical Field
The invention relates to the field of spatial analysis, in particular to a rule-based three-dimensional geographic information model spatial analysis method and system.
Background
In the prior art, spatial information displayed by a three-dimensional geographic information system is too abstract, so that a user cannot judge quickly and accurately according to the spatial information, and the problem of low efficiency is caused.
In addition, the analysis process of the spatial information is often complex, dynamic and abstract, and in the presence of a large number, complex relationships and spatial information, the spatial analysis function of the two-dimensional geographic information system has certain limitations, such as high-level spatial analysis functions of flooding analysis, geological analysis, sunshine analysis, spatial diffusion analysis, visibility analysis and the like, the two-dimensional geographic information system cannot be realized, and the three-dimensional geographic information system lacks the spatial analysis function and cannot visually display the spatial information.
Therefore, how to intuitively display spatial information, enhance multi-dimensional spatial analysis, and improve analysis efficiency of TOD (transit-oriented distance) urban big data is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above problems, the present invention aims to solve the problems that in the prior art, the display of spatial information is too abstract, a three-dimensional geographic information system lacks a spatial analysis function, cannot analyze a multidimensional space, and reduces the analysis efficiency of TOD city big data, so as to realize the intuitive display of spatial information, enhance the multidimensional spatial analysis, and improve the monitoring and analysis efficiency of TOD city big data.
The embodiment of the invention provides a rule-based three-dimensional geographic information model space analysis method, which comprises the following steps:
the method comprises the steps of obtaining a TOD monitoring index and a data resource, and establishing an association relation between the TOD monitoring index and the data resource based on a TOD logical relation;
the TOD monitoring index and the data resource are subjected to three-dimensional fitting to create a three-dimensional building model and generate a first rule;
based on the first rule, according to the TOD current status assessment and the expected target, adjusting the first rule to generate a second rule, adjusting the three-dimensional building model according to the second rule, implementing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the intervention prognosis based on the second rule.
In one embodiment, the associating the TOD monitoring metrics with the data resources based on the TOD logical relationship includes:
based on the TOD logical relationship, establishing a mapping relationship and a topological connection relationship between two-dimensional plane data and attribute data corresponding to the urban space, and establishing a mapping relationship and a topological connection relationship between the TOD monitoring index and the two-dimensional plane data and the attribute data.
In one embodiment, the fitting the TOD monitoring metrics to the data resources through three-dimensionality, creating a three-dimensional building model, and generating a first rule includes:
storing the TOD monitoring index and the data resource according to a preset structure;
correlating the TOD monitoring index with geometric information and attribute values of the data resource and a driving modeling engine to generate the three-dimensional building model;
acquiring the TOD monitoring index and the data resource by utilizing ArcGIS, and extracting attribute values of the TOD monitoring index and the data resource;
and associating the TOD monitoring index with the attribute value of the data resource and the space entity in the three-dimensional building model to generate the first rule.
In one embodiment, the adjusting the first rule according to the TOD status assessment and expected target based on the first rule, generating a second rule, adjusting the three-dimensional building model according to the second rule, and performing actual intervention on the urban space according to the second rule, and monitoring TOD urban big data of the intervention platform based on the second rule includes:
judging whether the first rule is consistent with an expected target of the space entity or not according to the TOD monitoring index;
if the first rule is not in accordance with the expected target of the space entity, adjusting the attribute value of the space entity in the three-dimensional building model to generate a second rule;
adjusting other corresponding attribute values of the space entity according to the second rule, and updating the three-dimensional building model; the other attribute values include a remaining attribute value other than the adjusted attribute value of the space entity;
and simulating and evaluating the influence degree of a second rule on the TOD monitoring index, if the influence degree is within a preset range, implementing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the trunk prognosis according to the second rule.
In one embodiment, the TOD monitoring metrics include:
city scale index, corridor scale index and site scale index.
In a second aspect, the present invention further provides a rule-based three-dimensional geographic information model spatial analysis system, including:
the establishing module is used for acquiring a TOD monitoring index and a data resource and establishing an association relation between the TOD monitoring index and the data resource based on a TOD logical relation;
the three-dimensional fitting module is used for establishing a three-dimensional building model by three-dimensionally fitting the TOD monitoring index and the data resource and generating a first rule;
and the adjusting module is used for adjusting the first rule based on the first rule according to the TOD current situation assessment and the expected target, generating a second rule, adjusting the three-dimensional building model according to the second rule, implementing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the intervention prognosis based on the second rule.
In one embodiment, the establishing module establishes an association relationship between the TOD monitoring indicator and the data resource based on the TOD logical relationship, including:
based on the TOD logical relationship, establishing a mapping relationship and a topological connection relationship between two-dimensional plane data and attribute data corresponding to the urban space, and establishing a mapping relationship and a topological connection relationship between the TOD monitoring index and the two-dimensional plane data and the attribute data.
In one embodiment, the three-dimensional fitting module comprises:
the storage unit is used for storing the TOD monitoring index and the data resource according to a preset structure;
the modeling unit is used for associating the TOD monitoring index with the geometric information and attribute value of the data resource and a driving modeling engine to generate the three-dimensional building model;
the extraction unit is used for acquiring the TOD monitoring index and the data resource by utilizing ArcGIS and extracting the attribute values of the TOD monitoring index and the data resource;
and the rule generating unit is used for associating the TOD monitoring index with the attribute value of the data resource and the space entity in the three-dimensional building model to generate the first rule.
In one embodiment, the adjustment module includes:
the judging unit is used for judging whether the first rule is consistent with an expected target of the space entity or not according to the TOD monitoring index;
an adjusting unit, configured to adjust an attribute value of a space entity in the three-dimensional building model to generate the second rule if the first rule does not match an expected target of the space entity;
the updating unit is used for adjusting other corresponding attribute values of the space entity according to the second rule and updating the three-dimensional building model; the other attribute values include a remaining attribute value other than the adjusted attribute value of the space entity;
and the simulation monitoring unit is used for simulating and evaluating the influence degree of a second rule on the TOD monitoring index, implementing actual intervention on the urban space according to the second rule if the influence degree is within a preset range, and monitoring the TOD urban big data of the intervention result according to the second rule.
In one embodiment, the TOD monitoring metrics in the establishing module include:
city scale index, corridor scale index and site scale index.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
according to the rule-based three-dimensional geographic information model space analysis method provided by the embodiment of the invention, the incidence relation is established between the TOD monitoring index and the data resource based on the TOD logical relation, the TOD monitoring index and the data resource data are fitted into the three-dimensional building model, the consistency of the three-dimensional data precision, the spatial position and the attribute is ensured, the three-dimensional data is displayed in a three-dimensional scene, the spatial information is visualized, the three-dimensional building model has a space analysis function by generating the rule and realizing regulation, simulation, implementation and subsequent monitoring of the rule, the efficiency of multi-dimensional space analysis is improved, the rapid construction of the rule-based three-dimensional model and the integrated application of multi-source data are realized, and the TOD city big data can be rapidly and accurately monitored and analyzed.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a rule-based three-dimensional geographic information model spatial analysis method according to an embodiment of the present invention;
FIG. 2 is a flowchart of step S102 according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a macroscopic three-dimensional landscape provided by an embodiment of the present invention;
FIG. 4 is a schematic view of a three-dimensional landscape in a landscape format according to an embodiment of the present invention;
FIG. 5 is a schematic view of a microscopic three-dimensional landscape according to an embodiment of the present invention;
FIG. 6 is a flowchart of step S103 according to an embodiment of the present invention;
FIG. 7 is a global E-R diagram provided by an embodiment of the present invention;
FIG. 8 is a schematic three-dimensional analysis of an entrance to a utility facility from a site according to an embodiment of the present invention;
fig. 9 is a block diagram of a rule-based three-dimensional geographic information model spatial analysis system according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, an embodiment of the present invention provides a rule-based three-dimensional geographic information model spatial analysis method, where the method includes: s101 to S103;
s101, obtaining a TOD monitoring index and a data resource, and establishing an association relation between the TOD monitoring index and the data resource based on a TOD logical relation;
specifically, the TOD monitoring index includes: city scale index, corridor scale index and site scale index. The city scale indexes comprise TOD construction operation conditions, TOD driving capacity, TOD trip sharing rate (track trip proportion, ground bus trip proportion and the like), TOD planning and development strategy execution degree (planning execution implementation degree, planning execution consistency, construction project implementation degree and construction project consistency); the corridor scale indexes comprise service population (population scale, population density and population proportion), service facilities (education, medical treatment, convenience for people, business centers and cultural leisure), service quality (track connectivity, bus transfer indexes, land function mixedness and green vision rate); the station scale indexes comprise efficiency (population density, volume rate), centrality (centrality, centrality of intermediaries, centrality of nearness), quality (track connectivity, public transportation transfer index, land function mixity, green vision rate), service facilities (education, medical treatment, convenience, business center, cultural leisure).
Further, the data resources include, but are not limited to, the following: subway line data (subway lines, subway stations, subway entrances and exits), bus line data (bus lines, bus stations), population data (subway, bus operation passenger flow, population occupation, time-sharing population heating power), public service facilities (school, hospital, business), land utilization (different types of land space distribution), city buildings (plane space positions of city buildings, building height), image data (remote sensing images, street views).
From a type perspective, the data resources include, but are not limited to: various two-dimensional plane data of the urban space and attribute data thereof. The two-dimensional plane data includes, but is not limited to, a housing building floor, an office building floor, a public service building floor, a road centerline, a track line, a track platform plane, and the like. The attribute data includes, but is not limited to, floor height, lane width, building function (residential, office), and the like.
Furthermore, the data resource acquisition period is different due to application differences of various scales, various cities and various users, and can be specifically set according to application scenes, data cleaning and abnormal value processing are required in the big data acquisition process, data is reexamined and verified, and repeated information is deleted, so that the data consistency is ensured.
Further, based on the TOD logical relationship, establishing an association relationship between the TOD monitoring indicator and the data resource includes: establishing mapping relations and topological connection relations among various two-dimensional plane data and attribute data of the urban space; and establishing a mapping relation and a topological connection relation between the TOD monitoring index and various two-dimensional plane data and attribute data. For example, establishing that the Nth street view picture belongs to a point a on the road A; the track station closest to the B1 floor has a topological connection relationship such as a G3 station, and the B1 floor has office functions and can accommodate a mapping relationship such as 3500 employment population. The above incidence relations such as mapping relations and topological connection relations are established based on TOD logical relations, that is, the incidence relations are realized based on the requirements and targets of monitoring and analyzing TOD big data, which is beneficial to monitoring and analyzing the big data conforming to the TOD logical relations, for example, 3500 people in the field are mapped to the B1 building, and TOD monitoring indexes such as population density are mapped to the B1 building, which is beneficial to monitoring and analyzing the TOD big data of the population residence and flowing state of the B1 building.
S102, the TOD monitoring indexes and the data resources are subjected to three-dimensional fitting to create a three-dimensional building model, and a first rule is generated.
Specifically, the process of creating a three-dimensional building model through three-dimensional fitting includes, but is not limited to: three-dimensionally visualizing the two-dimensional plane data and the attribute data thereof by using a rule modeling tool to generate a three-dimensional building model, wherein the three-dimensional building model consists of a three-dimensional space entity formed by modeling; and substituting the corresponding TOD monitoring index into the three-dimensional building model based on the incidence relation, and correspondingly adjusting the three-dimensional building model.
Further, three-dimensional space entities of the building model are further elaborately carved by three-dimensionally visualizing the two-dimensional plane data and the attribute data thereof and substituting the TOD monitoring index into the three-dimensional building model, for example, unit division is performed in the three-dimensional building model of a residential building; dividing rooms and stations in a three-dimensional building model of an office building; the number of simulated cars (traffic flow) on the model of the lane, and the like.
Further, on the basis of fitting the TOD monitoring index and the data resource to the three-dimensional building model, a first rule is generated, for example, the number of workers per square meter or the number of residents, the power consumption per square meter, the traffic of each layer of building, the accessibility of various public service facilities, building function diversity rules, building energy consumption control quantity and the like, and therefore the first rule is to convert the current TOD monitoring index and the data resource into relevant rules meeting the monitoring and analysis requirements of TOD big data on the basis of the space of the three-dimensional model.
S103, based on the first rule, adjusting the first rule according to the TOD current situation assessment and the expected target, generating a second rule, adjusting the three-dimensional building model according to the second rule, performing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the intervention prognosis based on the second rule.
Specifically, the first rule is adjusted based on TOD current status evaluation and an expected target, and a second rule is generated; for example, if we want to increase the number of employment posts around a certain station, we adjust the original first rule that 1 square meter corresponds to 1 station to a new second rule that 1 square meter corresponds to 2 stations; furthermore, the three-dimensional building model in the digital space is adjusted according to a second rule, and at the moment, the three-dimensional graph of the space is changed, for example, working partitions and tables and chairs between grids are increased; meanwhile, the influence degree of the adjusted second rule on each relevant TOD monitoring index is simulated and evaluated, for example, a newly added post is confirmed through models such as traffic travel simulation and the like, the traffic of surrounding roads is not influenced, and the simulation process can be displayed in a three-dimensional dynamic mode; if no negative influence or the negative influence is confirmed to be within an acceptable range, updating and modifying the real entity space according to the second rule, such as modifying by decoration, rearranging the stations of the business office building, and expecting to accommodate more employment population; then, the employment number around the site is monitored through city TOD big data such as mobile phone signaling and Baidu LBS, so as to confirm whether the actual employment number is improved or not, and timely feedback adjustment of the second rule is facilitated. It can be seen that the second rule is a rule that can reflect the expected target after being adjusted based on the current situation evaluation of the first rule and taking the expected target as a guide.
In this embodiment, an association relationship is established between the TOD monitoring index and the data resource based on the TOD logical relationship, and then the TOD monitoring index and the data resource data are fitted into the three-dimensional building model, thereby ensuring consistency of three-dimensional data precision, spatial position and attribute, and displaying in a three-dimensional scene, so that spatial information is visualized, and by generating rules and implementing adjustment, simulation, implementation and subsequent monitoring of the rules, the three-dimensional building model has a spatial analysis function, so as to improve efficiency of multidimensional spatial analysis, so as to implement rapid establishment of the rule-based three-dimensional model and integrated application of multi-source data, and can rapidly and accurately monitor and analyze large data in the TOD city.
In one embodiment, referring to fig. 2, the creating a three-dimensional building model based on the TOD monitoring metrics and the data resources in step S102 includes:
s1021, storing the TOD monitoring index and the data resource according to a preset structure.
Specifically, the TOD monitoring index and the Data resource are organized by a Geodatabase model and stored in a Data Store.
And S1022, associating the TOD monitoring index with the geometric information and attribute value of the data resource and a driving modeling engine to generate the three-dimensional building model.
Specifically, the CityEngine is used as a driving modeling engine for driving modeling, and a model generated by the CityEngine can directly generate a data format supporting the I3S standard: and the SLPK can directly upload the format to Portal and distribute the service, so that the cloud integrated modeling is realized.
Further, the appropriate visual expression mode can be selected according to three scales of macro, meso and micro by adopting the CityEngine for driving modeling. As shown in FIG. 3, in a macro scene, the data size is large, the detail requirement is not high, the city building three-dimensional models are generated in batches by using the City Engine rule for modeling, and the rapid construction and smooth expression of the three-dimensional models in the large scene are realized. As shown in fig. 4, in a mesoscopic scene, the above-ground and underground integrated display of the corridor needs to be realized, the spatial position relationship between the corridor and the three-dimensional building model in a certain buffer area along the corridor is analyzed, and the mutual relationship between indexes is further analyzed. And monitoring indexes around the corridor are visually expressed by three-dimensional roaming along the corridor. As shown in fig. 5, in a micro scene, association relationships between sites and various public service facilities (for example, a passage path from a site to a primary school) need to be displayed, a BIM model can be introduced into a site to be monitored in a future, and information such as internal details and a hierarchical structure can be expressed.
And S1023, acquiring the TOD monitoring index and the data resource by utilizing ArcGIS, and extracting the TOD monitoring index and the data resource attribute value.
Specifically, the ArcGIS stores TOD monitoring indexes and data resources such as real-time population data and public service facility data.
And S1024, associating the TOD monitoring index with the attribute value of the data resource and the space entity in the three-dimensional building model to generate the first rule.
In the embodiment, the citreenine is used for realizing the rapid construction of the three-dimensional building model, the construction of the three-dimensional building model based on the TOD monitoring index and the data resource can enable the related data of the three-dimensional building model to be more accurate, and the accuracy and precision of the three-dimensional building model data are improved by using the association of the geometric information and the attribute value. And moreover, the consistency of three-dimensional data precision, spatial position and attribute information is ensured by deeply integrating the CityEngine and the ArcGIS. Meanwhile, an updating mechanism which is the same as that of the two-dimensional data is provided, the three-dimensional model data and the attribute can be updated rapidly, and the operability and the efficiency are improved.
In one embodiment, referring to fig. 6, the adjusting step S103 adjusts the first rule based on the first rule according to the TOD status quoting and the expected target,
generating a second rule, adjusting the three-dimensional building model according to the second rule, implementing actual intervention on the urban space according to the second rule, and monitoring TOD urban big data of the intervention platform based on the second rule, wherein the monitoring comprises the following steps:
and S1031, judging whether the first rule is consistent with an expected target of the space entity according to the TOD monitoring index.
S1032, if the first rule is not in accordance with the expected target of the space entity, adjusting the attribute value of the space entity corresponding to the three-dimensional building model, and generating the second rule.
S1033, adjusting other corresponding attribute values of the space entity according to a second rule, and updating the three-dimensional building model; the other attribute values include remaining attribute values except the adjusted attribute value of the space entity.
S1034, simulating and evaluating the influence degree of a second rule on the TOD monitoring index, if the influence degree is in a preset range, implementing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the intervention result according to the second rule.
Specifically, referring to fig. 7, based on the second rule, an entity-relationship graph and a three-dimensional entity-relationship graph of each scale are established, and a global entity-relationship logic is established to form thematic data for TOD city big data monitoring.
Further, the space in the three-dimensional environment is analyzed according to a second rule, for example, as shown in fig. 8, a guidance route from the subway station entrance a to the bus station B is calculated according to the subway station entrance, the bus station point and the personnel flow amount, and on the basis, three-dimensional analysis functions such as sunlight intensity, landscape visual analysis, plot volume ratio, function mixing degree and the like can be developed and added through an installer micro key.
In the implementation, the TOD monitoring indexes such as population data, public service facility data and the like, the data resources and the three-dimensional building model are fused and displayed in the three-dimensional scene, so that the distribution positions of population and facilities around the urban subway station can be displayed more intuitively. The subway line, the subway state, the entrance and exit and the building model with population and facility attributes are displayed in a superimposed mode in the three-dimensional scene, the urban TOD construction current situation is restored in the three-dimensional scene, the platform user operation experience is improved, and the method is a breakthrough in comparison with the traditional two-dimensional map display method.
The method for the rule-based three-dimensional geographic information model spatial analysis is described below by a specific embodiment.
Example 1:
existing track station L4-6 (No. 4 line station 6), station periphery serves residential building A, office building B, market C, park D. Based on the collected traditional two-dimensional plane data and big data of TOD monitoring indexes, 2273 residents belonging to a residential building A are established, wherein 10% (227) residents go out through a rail station L4-6; 3000 employment personnel belong to office building B, wherein 30% (900) employment personnel go out through rail station L4-6 and other series of mapping relations and topological connection relations.
Based on the TOD logical relationship, fitting and modeling by using a three-dimensional rule, and generating a refined three-dimensional city model in a large batch, wherein each digitalized spatial position in the three-dimensional model corresponds to multi-dimensional attribute information and a first rule, for example, 8 residents and 26 residents in the 12 th floor of a residential building A; the 10 floors of the office building B can accommodate 30 employees; staff above 10 floors of the office building B can see park landscape through sight analysis; track station L4-6 has 1127 passengers from residential building A, office building B, and has passenger loading rate of 45% (which can be presented using behavioral individual models), etc.
Now, according to the expected goal of TOD development, the serving capacity of the track station L4-6 needs to be improved, and the number of persons served by the track station L4-6 should be increased by 50% to 1690 (1127 by 1+ 50%). Therefore, in the digital space, the correspondence between the three-dimensional space and the multi-dimensional attributes, which meets the TOD development goal, i.e., the second rule, is obtained by operations such as adding the work stations of the office building B, dividing a part of the large dwelling size of the residential building a into the small dwelling size, or adding the residential apartment E. For example, a residential apartment E is built; office building B has 10 levels that can accommodate up to 40 employees.
Based on the second rule, a series of city influence simulations such as a traffic flow influence analysis model, a TOD site people flow simulation model, a landscape view analysis model, a public service matching reachable analysis model and the like are developed in the digital space of the model, and the change is found to cause no congestion of peripheral roads, no influence on the view of the park D by original residents and employment personnel and no overload of peripheral hospitals; while track station L4-6 traffic is rising, station traffic simulation based on singles is normal.
Therefore, the second rule is applied to actual city reconstruction, and after the reconstruction is completed, the TOD city big data monitoring system is used for feeding back the running condition of the city, for example, whether the actual employment personnel achieve the expected target after the post supply is increased or not is performed, and the monitoring result is used as an important fact basis for next step of adjusting and optimizing the TOD development target and changing or maintaining the second rule.
Based on the same inventive concept, the embodiment of the invention also provides a rule-based three-dimensional geographic information model spatial analysis system, and as the principle of the problem solved by the device is similar to the rule-based three-dimensional geographic information model spatial analysis method, the implementation of the device can refer to the implementation of the method, and repeated parts are not repeated.
The three-dimensional geographic information model space analysis system based on the rule provided by the embodiment of the invention, as shown in fig. 9, includes:
the establishing module 101 is configured to obtain a TOD monitoring index and a data resource, and establish an association relationship between the TOD monitoring index and the data resource based on a TOD logical relationship.
Specifically, the TOD monitoring index includes: city scale index, corridor scale index and site scale index.
Further, the data resources include, but are not limited to, the following: subway line data (subway lines, subway stations, subway entrances and exits), bus line data (bus lines, bus stations), population data (subway, bus operation passenger flow, population occupation, time-sharing population heating power), public service facilities (school, hospital, business), land utilization (different types of land space distribution), city buildings (plane space positions of city buildings, building height), image data (remote sensing images, street views).
Further, from a type perspective, the data resources include, but are not limited to: various two-dimensional plane data of the urban space and attribute data thereof. The two-dimensional plane data includes, but is not limited to, a housing building floor, an office building floor, a public service building floor, a road centerline, a track line, a track platform plane, and the like. The attribute data includes, but is not limited to, floor height, lane width, building function (residential, office), and the like.
Furthermore, the data resource acquisition period is different due to application differences of various scales, various cities and various users, and can be specifically set according to application scenes, data cleaning and abnormal value processing are required in the big data acquisition process, data is reexamined and verified, and repeated information is deleted, so that the data consistency is ensured.
Further, based on the TOD logical relationship, establishing an association relationship between the TOD monitoring indicator and the data resource specifically includes: establishing mapping relations and topological connection relations among various two-dimensional plane data and attribute data of the urban space; and establishing a mapping relation and a topological connection relation between the TOD monitoring index and various two-dimensional plane data and attribute data. For example, establishing that the Nth street view picture belongs to a point a on the road A; the track station closest to the B1 floor has a topological connection relationship such as a G3 station, and the B1 floor has office functions and can accommodate a mapping relationship such as 3500 employment population. The above incidence relations such as mapping relations and topological connection relations are established based on TOD logical relations, that is, the incidence relations are realized based on the requirements and targets of monitoring and analyzing TOD big data, which is beneficial to monitoring and analyzing the big data conforming to the TOD logical relations, for example, 3500 people in the field are mapped to the B1 building, and TOD monitoring indexes such as population density are mapped to the B1 building, which is beneficial to monitoring and analyzing the TOD big data of the population residence and flowing state of the B1 building.
A three-dimensional fitting module 102, configured to perform three-dimensional fitting on the TOD monitoring indicator and the data resource, create a three-dimensional building model, and generate a first rule; .
Specifically, the process of creating a three-dimensional building model through three-dimensional fitting includes, but is not limited to: three-dimensionally visualizing the two-dimensional plane data and the attribute data thereof by using a rule modeling tool to generate a three-dimensional building model, wherein the three-dimensional building model consists of a three-dimensional space entity formed by modeling; based on the incidence relation, substituting the corresponding TOD monitoring index into the three-dimensional building model, and correspondingly adjusting the three-dimensional building model
Further, three-dimensional space entities of the building model are further elaborately carved by three-dimensionally visualizing the two-dimensional plane data and the attribute data thereof and substituting the TOD monitoring index into the three-dimensional building model, for example, unit division is performed in the three-dimensional building model of a residential building; dividing rooms and stations in a three-dimensional building model of an office building; the number of simulated cars (traffic flow) on the model of the lane, and the like.
Further, on the basis of fitting the TOD monitoring index and the data resource to the three-dimensional building model, a first rule is generated, for example, the number of workers or residents per square meter, the electricity consumption per square meter, the going quantity of each layer of building, the accessibility of various public service facilities, building function diversity rules, building energy consumption control quantity and the like are generated, and the first rule is to convert the TOD monitoring index and the data resource into relevant rules which accord with TOD big data monitoring and analyzing requirements and targets on the basis of the space of the three-dimensional model.
The adjusting module 103 is configured to adjust the first rule based on the first rule according to the TOD current status assessment and the expected target, generate a second rule, adjust the three-dimensional building model according to the second rule, perform actual intervention on an urban space according to the second rule, and monitor the TOD urban big data of the intervention result based on the second rule.
In one embodiment, the establishing module 101 establishes an association relationship between the TOD monitoring indicator and the data resource based on the TOD logical relationship, including:
based on the TOD logical relationship, establishing a mapping relationship and a topological connection relationship between two-dimensional plane data and attribute data corresponding to the urban space, and establishing a mapping relationship and a topological connection relationship between the TOD monitoring index and the two-dimensional plane data and the attribute data.
In one embodiment, referring to fig. 9, the three-dimensional fitting module 102 includes:
a storage unit 1021, configured to store the TOD monitoring indicator and the data resource according to a predetermined structure.
Specifically, the TOD monitoring index and the Data resource are organized by a Geodatabase model and stored in a Data Store.
A modeling unit 1022, configured to associate the TOD monitoring indicator with the geometric information and attribute value of the data resource and drive a modeling engine, so as to generate the three-dimensional building model.
Specifically, the CityEngine is used as a driving modeling engine for driving modeling, and a model generated by the CityEngine can directly generate a data format supporting the I3S standard: and the SLPK can directly upload the format to Portal and distribute the service, so that the cloud integrated modeling is realized.
Further, the appropriate visual expression mode can be selected according to three scales of macro, meso and micro by adopting the CityEngine for driving modeling.
The extracting unit 1023 is configured to acquire the TOD monitoring index and the data resource by using the ArcGIS, and extract attribute values of the TOD monitoring index and the data resource.
Specifically, the ArcGIS stores TOD monitoring indexes and data resources such as real-time population data and public service facility data.
A rule generating unit 1024, configured to associate the TOD monitoring indicator and the attribute value of the data resource with a space entity in the three-dimensional building model, and generate the first rule.
In one embodiment, referring to fig. 9, the adjusting module 103 includes:
a determining unit 1031, configured to determine whether the first rule matches an expected target of the space entity according to the TOD monitoring index;
an adjusting unit 1032, configured to adjust an attribute value of a corresponding space entity of the three-dimensional space model to generate a second rule if the first rule does not match the expected target of the space entity;
an updating unit 1033, configured to adjust other attribute values corresponding to the space entity according to the second rule, and update the three-dimensional building model; the other attribute values include a remaining attribute value other than the adjusted attribute value of the space entity;
and a simulation monitoring unit 1034 for simulating and evaluating an influence degree of a second rule on the TOD monitoring index, if the influence degree is within a preset range, implementing actual intervention on an urban space according to the second rule, and monitoring TOD urban big data of the intervention result according to the second rule.
Specifically, based on the second rule, an entity-relationship graph and a three-dimensional entity-relationship graph of each scale are established, and a global entity-relationship logic is established to form thematic data for TOD city big data monitoring.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A three-dimensional geographic information model space analysis method based on rules is characterized by comprising the following steps:
the method comprises the steps of obtaining a TOD monitoring index and a data resource, and establishing an association relation between the TOD monitoring index and the data resource based on a TOD logical relation;
the TOD monitoring index and the data resource are subjected to three-dimensional fitting to create a three-dimensional building model and generate a first rule;
based on the first rule, according to the TOD current state assessment and the expected target, adjusting the first rule to generate a second rule, adjusting the three-dimensional building model according to the second rule, implementing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the intervention prognosis based on the second rule;
the adjusting the first rule based on the first rule according to the TOD current status assessment and the expected target, generating a second rule, adjusting the three-dimensional building model according to the second rule, implementing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the intervention prognosis based on the second rule, including:
judging whether the first rule is consistent with an expected target of the space entity or not according to the TOD monitoring index;
if the first rule is not in accordance with the expected target of the space entity, adjusting the attribute value of the space entity in the three-dimensional building model to generate a second rule;
adjusting other corresponding attribute values of the space entity according to the second rule, and updating the three-dimensional building model; the other attribute values include a remaining attribute value other than the adjusted attribute value of the space entity;
through a traffic flow influence analysis model, a TOD site people flow simulation model, a landscape visual field analysis model and a public service matching reachable analysis model, the influence degree of a second rule on the TOD index and the whole urban operation condition is simulated and evaluated, if the influence degree improves the TOD service level and the influence degree of the whole urban operation condition is in a preset range, actual intervention is implemented on an urban space according to the second rule, the TOD urban big data of the trunk prognosis is monitored according to the second rule, feedback information is generated, and the second rule is adjusted and optimized based on the feedback information.
2. The method according to claim 1, wherein the associating the TOD monitoring indicator with the data resource based on the TOD logical relationship comprises:
based on the TOD logical relationship, establishing a mapping relationship and a topological connection relationship between two-dimensional plane data and attribute data corresponding to the urban space, and establishing a mapping relationship and a topological connection relationship between the TOD monitoring index and the two-dimensional plane data and the attribute data.
3. The method of claim 1, wherein the step of fitting the TOD monitoring index to the data resource in a three-dimensional manner to create a three-dimensional building model and generate the first rule comprises:
storing the TOD monitoring index and the data resource according to a preset structure;
correlating the TOD monitoring index with geometric information and attribute values of the data resource and a driving modeling engine to generate the three-dimensional building model;
acquiring the TOD monitoring index and the data resource by utilizing ArcGIS, and extracting attribute values of the TOD monitoring index and the data resource;
and associating the TOD monitoring index with the attribute value of the data resource and the space entity in the three-dimensional building model to generate the first rule.
4. The method of claim 1, wherein the TOD monitoring criteria comprises:
city scale index, corridor scale index and site scale index.
5. A rule-based three-dimensional geographic information model spatial analysis system, comprising:
the establishing module is used for acquiring a TOD monitoring index and a data resource and establishing an association relation between the TOD monitoring index and the data resource based on a TOD logical relation;
the three-dimensional fitting module is used for establishing a three-dimensional building model by three-dimensionally fitting the TOD monitoring index and the data resource and generating a first rule;
the adjusting module is used for adjusting the first rule based on the first rule according to the TOD current situation assessment and the expected target, generating a second rule, adjusting the three-dimensional building model according to the second rule, implementing actual intervention on the urban space according to the second rule, and monitoring the TOD urban big data of the intervention result based on the second rule;
the adjustment module includes:
the judging unit is used for judging whether the first rule is consistent with an expected target of the space entity or not according to the TOD monitoring index;
an adjusting unit, configured to adjust an attribute value of a space entity in the three-dimensional building model to generate the second rule if the first rule does not match an expected target of the space entity;
the updating unit is used for adjusting other corresponding attribute values of the space entity according to the second rule and updating the three-dimensional building model; the other attribute values include a remaining attribute value other than the adjusted attribute value of the space entity;
and the simulation monitoring unit is used for simulating and evaluating the influence degree of a second rule on the TOD monitoring index, implementing actual intervention on the urban space according to the second rule if the influence degree is within a preset range, and monitoring the TOD urban big data of the intervention result according to the second rule.
6. The system of claim 5, wherein the establishing module establishes an association relationship between the TOD monitoring indicator and the data resource based on the TOD logical relationship, and comprises:
based on the TOD logical relationship, establishing a mapping relationship and a topological connection relationship between two-dimensional plane data and attribute data corresponding to the urban space, and establishing a mapping relationship and a topological connection relationship between the TOD monitoring index and the two-dimensional plane data and the attribute data.
7. The system of claim 5, wherein the three-dimensional fitting module comprises:
the storage unit is used for storing the TOD monitoring index and the data resource according to a preset structure;
the modeling unit is used for associating the TOD monitoring index with the geometric information and attribute value of the data resource and a driving modeling engine to generate the three-dimensional building model;
the extraction unit is used for acquiring the TOD monitoring index and the data resource by utilizing ArcGIS and extracting the attribute values of the TOD monitoring index and the data resource;
and the rule generating unit is used for associating the TOD monitoring index with the attribute value of the data resource and the space entity in the three-dimensional building model to generate the first rule.
8. The system of claim 5, wherein the TOD monitoring criteria in the building module comprises:
city scale index, corridor scale index and site scale index.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010549450.1A CN111784830B (en) | 2020-06-16 | 2020-06-16 | Rule-based three-dimensional geographic information model space analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010549450.1A CN111784830B (en) | 2020-06-16 | 2020-06-16 | Rule-based three-dimensional geographic information model space analysis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111784830A CN111784830A (en) | 2020-10-16 |
| CN111784830B true CN111784830B (en) | 2021-05-14 |
Family
ID=72756616
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010549450.1A Active CN111784830B (en) | 2020-06-16 | 2020-06-16 | Rule-based three-dimensional geographic information model space analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111784830B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115017748B (en) * | 2022-08-09 | 2022-11-18 | 武汉市规划编制研究和展示中心 | Improved vision field segmentation method based method for simulating people flow in large public building |
| CN115796633B (en) * | 2022-09-30 | 2023-08-08 | 北京大学深圳研究生院 | Urban village land utilization comprehensive renovation performance evaluation method and system |
| CN117634826A (en) * | 2023-12-06 | 2024-03-01 | 青岛诚晟达精密机械有限公司 | Intelligent building state monitoring management system based on cloud computing and multidimensional data |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201114135Y (en) * | 2007-08-21 | 2008-09-10 | 上海鸣啸实业发展有限公司 | Traffic guide information alternating issue device for public space |
| CN101630419A (en) * | 2009-08-13 | 2010-01-20 | 苏州市数字城市工程研究中心有限公司 | Structuring method for three-dimensional visualizing system of urban synthesis pipeline network |
| CN103942388A (en) * | 2014-04-18 | 2014-07-23 | 重庆市勘测院 | Large-scale building information model and three-dimensional digital city integration method |
| CN105243837A (en) * | 2015-11-03 | 2016-01-13 | 东南大学 | Bus passenger flow corridor discriminating method |
| CN109446553A (en) * | 2018-09-14 | 2019-03-08 | 清华大学 | A kind of aerial Dynamic IR Scene analogue system based on Unity3D |
| CN109903234A (en) * | 2019-01-18 | 2019-06-18 | 重庆邮电大学 | A kind of city thermal landscape quantitative description and Analysis On Multi-scale Features analysis method |
| US10620921B2 (en) * | 2017-06-05 | 2020-04-14 | Umajin Inc. | Application system for generating 3D applications |
| CN111178761A (en) * | 2019-12-30 | 2020-05-19 | 成都工业学院 | Urban underground space model management method and system based on GIS technology |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10430961B2 (en) * | 2015-12-16 | 2019-10-01 | Objectvideo Labs, Llc | Using satellite imagery to enhance a 3D surface model of a real world cityscape |
| CN108022051A (en) * | 2017-12-13 | 2018-05-11 | 吉林大学 | A kind of urban track traffic scheduling decision method based on information fusion |
-
2020
- 2020-06-16 CN CN202010549450.1A patent/CN111784830B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201114135Y (en) * | 2007-08-21 | 2008-09-10 | 上海鸣啸实业发展有限公司 | Traffic guide information alternating issue device for public space |
| CN101630419A (en) * | 2009-08-13 | 2010-01-20 | 苏州市数字城市工程研究中心有限公司 | Structuring method for three-dimensional visualizing system of urban synthesis pipeline network |
| CN103942388A (en) * | 2014-04-18 | 2014-07-23 | 重庆市勘测院 | Large-scale building information model and three-dimensional digital city integration method |
| CN105243837A (en) * | 2015-11-03 | 2016-01-13 | 东南大学 | Bus passenger flow corridor discriminating method |
| US10620921B2 (en) * | 2017-06-05 | 2020-04-14 | Umajin Inc. | Application system for generating 3D applications |
| CN109446553A (en) * | 2018-09-14 | 2019-03-08 | 清华大学 | A kind of aerial Dynamic IR Scene analogue system based on Unity3D |
| CN109903234A (en) * | 2019-01-18 | 2019-06-18 | 重庆邮电大学 | A kind of city thermal landscape quantitative description and Analysis On Multi-scale Features analysis method |
| CN111178761A (en) * | 2019-12-30 | 2020-05-19 | 成都工业学院 | Urban underground space model management method and system based on GIS technology |
Non-Patent Citations (2)
| Title |
|---|
| ＴＯ Ｄ 城市大数据监测分析平台前期研究;李克鲁;《城市规划通讯》;20191223;第15-16页 * |
| 基于可持续宜居城市发展的TOD城市空间设计策略研究;吴放;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20150815(第8期);第201、278-299页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111784830A (en) | 2020-10-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101937096B1 (en) | 3D monitoring server using 3D BIM object model and 3D monitoring system comprising it | |
| CN111784830B (en) | Rule-based three-dimensional geographic information model space analysis method and system | |
| WO2021103323A1 (en) | Historical city protection and development cooperative control scheme aided design system | |
| CN109214653A (en) | B, C class region medium voltage network planning and designing platform based on three-dimensional live | |
| CN110726411B (en) | Indoor emergency path finding method of building information model based on subdivision grids | |
| CN109900276A (en) | The real-time emergency route planing method in station based on point-line-surface barrier model construction | |
| Luo et al. | A rule-based city modeling method for supporting district protective planning | |
| KR101876114B1 (en) | Terminal, server, system for 3d modeling and 3d modeling method using the same | |
| CN111221867B (en) | Protective building information management system | |
| CN115758522A (en) | Digital twin city management system and method | |
| CN110705904A (en) | Online traffic bearing capacity evaluation system | |
| Dong et al. | Spatial autocorrelation and spatial heterogeneity of underground parking space development in Chinese megacities based on multisource open data | |
| CN110288125B (en) | Commuting model establishing method based on mobile phone signaling data and application | |
| CN106875091A (en) | A kind of Management System of Urban Dangers based on address cloud service | |
| CN115049215A (en) | Method for constructing urban traffic flow spatial distribution model | |
| Huang et al. | Livability assessment of 101,630 communities in China’s major cities: A remote sensing perspective | |
| Xuan et al. | Research on Correlation between Spatial Quality of Urban Streets and Pedestrian Walking Characteristics in China Based on Street View Big Data | |
| Lu et al. | Exploring spatial and environmental heterogeneity affecting energy consumption in commercial buildings using machine learning | |
| CN117114210B (en) | Barrier-free public facility layout optimization method, device, equipment and storage medium | |
| Song et al. | Effects of planning variables on urban traffic noise at different scales | |
| CN111400877B (en) | Intelligent city simulation system and method based on GIS data | |
| CN114331232B (en) | Street space quality monitoring, evaluating and early warning method | |
| CN113763216A (en) | WebGIS-based smart campus system | |
| Chmielewski et al. | Hexagonal Zones in Transport Demand Models | |
| Srikanth et al. | Complexity science-based spatial performance analyses of UNStudio/DP Architects’ SUTD Campus and WOHA's Kampung Admiralty |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |