Intelligent updating and optimizing method for three-dimensional form of old block based on rigid-flexible rule driving

Abstract

The invention discloses an old block three-dimensional form intelligent updating optimization method based on rigid-flexible rule driving. The method comprises the steps of collecting vector data of a target old block and design specifications and standard text data of a city where the target old block is located; establishing a rule map of urban design rigid industry specification requirements; a Mask R-CNN coding technology is used to automatically label vector data of a space structure, a function layout, a road system and a blue-green system of the design, a knowledge graph of a classic city design result rule is formed, and a rule graph oriented by city design flexible professional knowledge is established; establishing multi-layer network association of an old block three-dimensional morphological graph network, a rigid rule graph and a flexible knowledge graph; and outputting the optimized three-dimensional form scheme of the target old block to a space holographic sand table, and finally outputting an optimized three-dimensional form graph network and a visualization scheme of the old block. According to the method, the three-dimensional form of the old block can be optimized based on rigid rule and flexible knowledge hybrid driving.

Description

Technical Field

[0001] The present invention relates to the technical field of urban planning, and specifically to a method for intelligently updating and optimizing the three-dimensional form of old blocks based on rigid-flexible rule driving. Background Art

[0002] The renewal and optimization of the morphology of old blocks is an important part of urban design work and is of great significance in the sustainable development of cities and the shaping of urban image. The reasonable renewal and optimization of the morphology of old blocks will take into account both rigid rules and flexible knowledge, which can not only ensure that the urban renewal plan meets the requirements of norms and policies, but also meet the design experience, local culture and aesthetic standards, and help balance the interests of the government, developers, designers and residents. The traditional renewal and optimization of the morphology of old blocks still has a lot of room for improvement in terms of taking into account both rigid and flexible rules, and lacks the support of rule maps, and it is impossible to iteratively optimize the plan based on intelligent algorithms and network associations. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the shortcomings of the prior art, the present invention provides an intelligent renewal and optimization method for the three-dimensional morphology of old blocks based on rigid-flexible rule driving, which solves the problems that the traditional renewal and optimization of the morphology of old blocks lacks the influence of rigid rule control and flexible design knowledge, lacks scientific rationality, and cannot dynamically interact to iteratively optimize the three-dimensional morphology of old blocks.

[0005] (II) Technical solution

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] A method for intelligently updating and optimizing the three-dimensional morphology of old blocks based on rigid-flexible rule driving, comprising:

[0008] (1) Collect vector data of the target old block, text data of the design specifications and standards of the city where it is located, and image data of design plans for other blocks in the city.

[0009] (2) Extract control terms and specific control contents from the design specifications and standard text data of the city, and use the extensible primitive model method to perform the basic structured representation of "object element-matter element-relation element"; from the association relationship of the sentence, classify and store the primitive model according to the conjugate relationship-complementary relationship-constraint relationship-suggestion relationship, and establish a rule map of rigid industry specifications for urban design.

[0010] (3) Use topological graph structure and three-dimensional space to perform correlation calculations and extract professional knowledge rules in urban design space aesthetics. Use Mask R-CNN encoding technology to automatically annotate the spatial structure, functional layout, road system, and blue-green system vector data of classic urban design results to form a knowledge graph of classic urban design results rules and establish a rule graph guided by flexible professional knowledge of urban design.

[0011] (4) Establish a multi-layer network association between the three-dimensional morphological map network of old blocks, the rigid rule map, and the flexible knowledge map, so that the three-dimensional morphological map network of old blocks is gradually iterated until the constraints of the rigid rule map and the flexible knowledge map are met, and then output a solution.

[0012] (5) The optimized three-dimensional morphological scheme of the target old block is output to the spatial holographic sandbox, and the three-dimensional multi-scheme display and interactive optimization of the block are carried out, and finally the optimized three-dimensional morphological map network and visualization scheme of the old block are output.

[0013] Preferably, the step (1) collects vector data of the target old block, text data of the design specifications and standards of the city where the target old block is located, and image data of design plans of other blocks in the city where the target old block is located, and specifically includes:

[0014] According to the scope of the determined target old block, the block vector data of the city where the target old block is located is obtained through the OpenStreetMap open data platform, and the design specifications and standard text data of the city and the image data of other block design plans in the city are obtained through the Baidu open data platform;

[0015] The block vector data of the city where the target old block is located includes plot data and building data in shapefile format; the plot data shp file should include land use type information, geographic coordinate information, plot area information and plot shape information; the building data shp file should include building function information, building number of floors information, building height information, building shape information and building location information;

[0016] The design specifications and standard text data of the city in question, including urban land classification and planning and construction land standards, urban public facilities planning specifications, urban road traffic planning and design specifications, urban environmental sanitation facilities planning specifications, urban residential area planning and design specifications, urban engineering pipeline comprehensive planning specifications, urban environmental planning standards, urban water supply engineering design specifications, urban drainage engineering design specifications, urban road greening planning and design specifications, and target city planning technical standards and criteria;

[0017] Image data of design plans for other blocks in the city, including design drawings for block stock renewal, environmental improvement of old blocks, optimization of public facilities and green space, and protection and renewal of the urban landscape.

[0018] Preferably, in step (2), control terms and specific control contents are extracted from the design specifications and standard text data of the city, and a basic structured representation of "matter element-event element-relation element" is performed using an extensible primitive model method, specifically including:

[0019] Use the collected design specifications and standard text data of the cities where the old blocks are located to extract control terms and specific control content, including road network control rules, open space control rules, height morphology control rules, and environment and facility control rules;

[0020] The extensible primitive model method refers to the use of formal language to extract control terms and specific control contents from the design specifications and standard text data of the city, and its logical unit is the basic element, including object element, event element and relationship element;

[0021] The matter-element M that describes things is expressed as:

[0022] M=(O m ,c m ,v m )

[0023] Among them, O m Represents an object, c m Indicates the name of the feature, v m Indicates O m About c m the value of the quantity taken;

[0024] The expression of event A describing the interaction between objects is:

[0025] A=(O a ,c a ,v a )

[0026] Among them, O a Represents an object, c a Indicates the name of the feature, v a Indicates O a About c a the value of the quantity taken;

[0027] The relational element R that describes the relationship between features is expressed as:

[0028]

[0029] Among them, O rIndicates the relationship, c r1 , c r2 , ...c rn represents n features, v r1 , v r2 ,...v rn Indicates the corresponding value.

[0030] Preferably, in step (2), from the association relationship of the statements, the primitive models are classified and stored according to the conjugate relationship, the complementary relationship, the constraint relationship, and the suggestion relationship, and a rule map of the rigid industry specification requirements for urban design is established, which specifically includes:

[0031] Analyze the relationship between the object elements, primitive elements and relational elements that have been basically structured, and enter them into the NEO4J platform according to the four categories of conjugate relationship, complementary relationship, constraint relationship and suggestion relationship; use the NEO4J platform to digitally encode the rigid industry specifications of urban design that have been structured, use algorithms to visualize the primitive relationship and generate a rule map;

[0032] The language constraint model is fine-tuned using large language models, including LLama-7B, chat-GLM, and Bloom.

[0033] Preferably, in step (3), the topological graph structure is used to perform correlation calculation with the three-dimensional space to extract professional knowledge rules in the urban design space aesthetics, specifically including:

[0034] Aerial photos of urban space in classic urban charm areas around the world are collected as a sample library. The aerial photos are segmented and recognized through semantic segmentation and recognition technology, and the image elements are identified as five categories of elements: buildings, roads, squares, green spaces, and water systems, and abstracted into a topological graph structure composed of nodes and edges. The topological graph structure is processed using a deep learning model of a multi-level graph neural network, and the patterns and information in the graph are captured by learning the relationship between nodes, and the capture effect is improved through a hierarchical learning structure. The spatial agglomeration, openness, and staggeredness of the case blocks are calculated through spatial self-learning technology, and the quantitative indicators are associated with the topological graph structure to extract professional knowledge rules in urban design space aesthetics and form a knowledge graph of spatial aesthetic rules.

[0035] Preferably, in step (3), the Mask R-CNN coding technology is used to automatically annotate the spatial structure, functional layout, road system, and blue-green system vector data of the design to form a knowledge graph of classic urban design results rules and establish a rule graph required by flexible industry specifications for urban design, specifically including:

[0036] Collect classic urban design achievements, use convolutional neural networks to identify the design scheme image data of the classic urban design achievements, extract the two-dimensional vector graphic data of the urban design plan, and use Mask R-CNN encoding technology to annotate the spatial structure, functional layout, road system, and blue-green system vector data of the design scheme. Among them, the spatial structure vector data contains the geometric center of each functional core, such as the main core and the secondary core; the functional layout vector data contains the functional layout data of commerce, office, residence, industry, green space, public service and transportation; the road system vector data contains the three-level vector information of the main road, secondary road and branch road; the blue-green system vector data contains the vector data information of the green corridor, secondary green corridor and water area. Further, the master style of urban design in each classic work is reversely analyzed through the knowledge graph technology to form a knowledge graph of classic design rules. Further, combined with the knowledge graph of spatial aesthetic rules, a rule graph required by flexible industry specifications for urban design is formed.

[0037] Preferably, in step (4), a multi-layer network association of the three-dimensional morphological graph network of the old block, the rigid rule graph and the flexible knowledge graph is established, which specifically includes:

[0038] Based on the three-dimensional morphological graph network of old blocks, the rule graph required by the rigid industry specifications of urban design, and the rule graph required by the flexible industry specifications of urban design, the three-dimensional morphological graph network of old blocks is gradually iterated according to the location of the site of the design scheme until the constraints of the rigid rule graph and the flexible knowledge graph are met, and the rules and knowledge parameter data of the potential urban design scheme are output. On this basis, the vector data associated with the potential scheme and the sample library are screened out, thereby establishing an automatic association process between knowledge rules and physical space elements. Among them, the screening process adopts the gradient descent method to observe the fluctuation of the loss function of the convolution pooling layer of the corresponding model of each parameter during training, and compare the training time and the generated results to determine the optimal learning rate and the number of iterations until the optimal, and finally establish a multi-layer network association of the three-dimensional morphological graph network of old blocks, the rigid rule graph and the flexible knowledge graph.

[0039] Preferably, the step (5) of outputting the optimized three-dimensional morphological map network and visualization scheme of the old block specifically includes:

[0040] The three-dimensional morphological schemes of the old blocks generated by the simulation iteration layer by layer and satisfying both the rigid rule graph and the flexible knowledge graph are output to the spatial holographic sandbox for display; the three-dimensional morphological schemes of the old blocks are displayed, compared, selected and modified with the assistance of VR\AR equipment and combined with gesture commands, and the local spatial structure, functional layout, road system and blue-green system are adjusted and interactively optimized before output in the form of a three-dimensional model file of SketchUp; the VR\AR equipment includes VRTRIX data gloves, VR glasses and VR 3D display stand.

[0041] (III) Beneficial effects

[0042] (1) The present invention is based on the intelligent optimization method for updating the three-dimensional form of old blocks driven by rigid and flexible rules. By performing basic structural representation on the collected vector data of old blocks, design specifications and standard text data, and design scheme image data, the standardized extraction of target urban area information is ensured, the logical relationship between design elements is clearly expressed, and design decisions based on complex urban systems are assisted;

[0043] (2) The intelligent renewal optimization method for the three-dimensional morphology of old blocks driven by rigid and flexible rules in this invention overcomes the problem that the traditional morphology renewal optimization of old blocks still has a large room for improvement in terms of taking into account both rigid and flexible rules, lacks rule graph support, and cannot perform iterative optimization of solutions based on intelligent algorithms and network associations. The rule graph is constructed based on rigid rules and flexible knowledge;

[0044] (3) The present invention is based on the intelligent optimization method for the three-dimensional form of old blocks driven by rigid and flexible rules. The MaskR-CNN coding technology is used to automatically annotate the design vector data to form a flexible knowledge graph. By automatically associating the knowledge rules with the physical space elements, the design standards in terms of spatial structure, functional layout, road system, and blue-green system are established to reduce the arbitrariness and uncertainty based on flexible knowledge in the design process.

[0045] (4) The present invention is based on a three-dimensional morphological intelligent optimization method for old blocks driven by rigid and flexible rules. It uses a gradient descent method to screen out the rules and knowledge parameter data of the optimal potential urban design solutions, reducing the time and effort of manually adjusting parameters, automatically executing the iterative process, avoiding the problems of low design efficiency and the inability to ensure the scientificity and rationality of design decisions, and ensuring that the design solutions can better adapt to the old urban renewal optimization process under different design scenarios and needs.

[0046] (5) The present invention is based on a method for intelligently updating and optimizing the three-dimensional form of old blocks driven by rigid-flexible rules. The present invention allows users to output the three-dimensional model of the block through the Hololens 2 holographic computer and display the corresponding image on VR glasses. With the help of VRTRIX data gloves and VR glasses, users can wear full-angle devices and freely select and operate any local spatial structure, functional layout, road system, and blue-green system to make push-pull adjustments. By clicking on the indicator calculation function, the impact of the adjusted block on the overall three-dimensional morphological characteristic parameters of the block can be displayed in real time, thereby enhancing the interactivity, display, and convenience of the scheme decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of the method of the present invention; DETAILED DESCRIPTION

[0048] The following will be combined with the accompanying drawings 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.

[0049] Example

[0050] like Figure 1 As shown, the technical solution of the present invention will be described in detail below by taking the urban design of an old block in a certain city as an example.

[0051] (1) Collect vector data of the target old block, text data of the design specifications and standards of the city where it is located, and image data of design plans for other blocks in the city, including:

[0052] (1.1) Based on the scope of the determined target old block, obtain the block vector data of the city where the target old block is located through the OpenStreetMap open data platform, including plot data and building data, and the data format is shapefile format; the plot data shp file should include land use type information, geographic coordinate information, plot area information and plot shape information; the building data shp file should include building function information, building number of floors information, building height information, building shape information and building location information;

[0053] (1.2) Use Baidu Open Data Platform to obtain text data on design specifications and standards for the city where the applicant is located and image data on design plans for other blocks in the city where the applicant is located; the text data on design specifications and standards for the city where the applicant is located includes urban land classification and planning and construction land standards, urban public facilities planning specifications, urban road traffic planning and design specifications, urban environmental sanitation facilities planning specifications, urban residential area planning and design specifications, urban engineering pipeline comprehensive planning specifications, urban environmental planning standards, urban water supply engineering design specifications, urban drainage engineering design specifications, urban road greening planning and design specifications, and target city planning technical standards and guidelines; image data on design plans for other blocks in the city where the applicant is located includes design plan drawings for block stock renewal, environmental improvement of old blocks, optimization of public facilities and green space, and protection and renewal of urban features.

[0054] (2) Extract control terms and specific control contents from the design specifications and standard text data of the city, and use the extension primitive model method to perform the basic structured representation of "matter element-event element-relation element"; from the association relationship of the sentences, classify and store the primitive models according to the conjugate relationship-complementary relationship-constraint relationship-suggestion relationship, and establish a rule map of rigid industry specifications for urban design, including:

[0055] (2.1) Using the design specifications and standard text data of the city where the old blocks are located collected in step (1), extract the control terms and specific control contents, including road network control rules, open space control rules, height morphology control rules, and environment and facility control rules;

[0056] The extensible primitive model method refers to the use of formal language to extract control terms and specific control contents from the design specifications and standard text data of the city, and its logical unit is the basic element, including object element, event element and relationship element;

[0057] The matter-element M that describes things is expressed as:

[0058] M=(O m ,c m ,v m )

[0059] Among them, O m Represents an object, c m Indicates the name of the feature, v m Indicates O m About c m the value of the quantity taken;

[0060] The expression of event A describing the interaction between objects is:

[0061] A=(O a ,ca ,v a )

[0062] Among them, O a Represents an object, c a Indicates the name of the feature, v a Indicates O a About c a the value of the quantity taken;

[0063] The relational element R that describes the relationship between features is expressed as:

[0064]

[0065] Among them, O r Indicates the relationship, c r1 , c r2 , ...c rn represents n features, v r1 , v r2 ,...v rn Indicates the corresponding value.

[0066] (2.2) Analyze the relationship between the object elements, primitive elements and relation elements that have been basically structured in step (2.1), and enter them into the NEO4J platform according to the four categories of conjugate relationship, complementary relationship, constraint relationship and suggestion relationship; use the NEO4J platform to digitally encode the rigid industry specifications of urban design that have been structured, use algorithms to visualize the primitive element relationships, and generate rule maps;

[0067] (2.3) Using large language models to fine-tune the language constraint model; the large language models include LLama-7B, chat-GLM and Bloom.

[0068] (3) Use the topological graph structure and three-dimensional space to perform correlation calculations and extract professional knowledge rules in urban design space aesthetics; use Mask R-CNN encoding technology to automatically annotate the spatial structure, functional layout, road system, and blue-green system vector data of the design to form a knowledge map of classic urban design results and rules, and establish a rule map guided by flexible professional knowledge of urban design.

[0069] Specifically include:

[0070] (3.1) Collect aerial photos of urban space in classic urban charm areas around the world as a sample library, segment and identify the aerial photos through semantic segmentation and recognition technology, identify image elements into five categories of elements: buildings, roads, squares, green spaces, and water systems, and abstract them into a topological graph structure composed of nodes and edges. Use a multi-level graph neural network deep learning model to process the topological graph structure, capture patterns and information in the graph by learning the relationship between nodes, and improve the capture effect through a hierarchical learning structure. Calculate quantitative indicators such as spatial agglomeration, openness, and staggeredness of the case blocks through spatial self-learning technology, and perform correlation calculations with the topological graph structure to extract professional knowledge rules in urban design space aesthetics and form a knowledge graph of spatial aesthetic rules.

[0071] (3.2) Collect classic urban design achievements, use convolutional neural networks to identify the design scheme image data of the classic urban design achievements, extract the two-dimensional vector graphic data of the urban design plan, and use Mask R-CNN encoding technology to annotate the spatial structure, functional layout, road system, and blue-green system vector data of the design scheme. Among them, the spatial structure vector data includes the geometric center of each functional core, such as the main core and the secondary core; the functional layout vector data includes the functional layout data of commerce, office, residence, industry, green space, public service and transportation; the road system vector data includes the three-level vector information of the main road, secondary road and branch road; the blue-green system vector data includes the vector data information of the green corridor, secondary green corridor and water area. Further, the master style of urban design in each classic work is reversely analyzed through the knowledge graph technology to form a knowledge graph of classic design rules. Further, combined with the knowledge graph of the spatial aesthetic rules described in step (3.1), a rule graph required by the flexible industry specification of urban design is formed.

[0072] (4) Establish a multi-layer network association between the three-dimensional morphological graph network of old blocks, the rigid rule graph, and the flexible knowledge graph, so that the three-dimensional morphological graph network of old blocks is gradually iterated until the constraints of the rigid rule graph and the flexible knowledge graph are met, and the output plan is output, which specifically includes:

[0073] (4.1) Based on the target old block vector data described in step (1), a three-dimensional morphological map network of the old block is established. Combined with the rule map required by the rigid industry specification for urban design described in step (2) and the rule map required by the flexible industry specification for urban design described in step (3), the three-dimensional morphological map network of the old block is gradually iterated according to the site location of the design scheme until the constraints of the rigid rule map and the flexible knowledge map are met, and the rule and knowledge parameter data of the potential urban design scheme are output.

[0074] (4.2) On this basis, the potential solutions are screened out from the vector data associated with the sample library, thereby establishing an automatic association process between knowledge rules and entity space elements. The screening process uses the gradient descent method to observe the fluctuation of the loss function of the convolutional pooling layer of each parameter corresponding to the model during training, and compare the training time and generation results to determine the optimal learning rate and number of iterations until the optimal one is found.

[0075] (4.3) Finally, a multi-layer network association of the three-dimensional morphological graph network of old neighborhoods, the rigid rule graph and the flexible knowledge graph is established.

[0076] (5) Output the optimized three-dimensional morphological scheme of the target old block to the spatial holographic sand table, display and interactively optimize the three-dimensional multi-scheme of the block, and finally output the optimized three-dimensional morphological map network and visualization scheme of the old block, including:

[0077] (5.1) Output multiple three-dimensional morphological schemes of old blocks generated by layer-by-layer iteration of simulations that satisfy both rigid rule graphs and flexible knowledge graphs to the spatial holographic sandbox for display;

[0078] (5.2) Using VR\AR equipment to assist, combined with gesture commands, the three-dimensional morphological schemes of old blocks are displayed, compared, selected, and modified, and the local spatial structure, functional layout, road system, and blue-green system are adjusted and interactively optimized before output in the form of SketchUp three-dimensional model files; the VR\AR equipment includes VRTRIX data gloves, VR glasses, and VR 3D display stands.

[0079] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An intelligent optimization method for updating the three-dimensional morphology of old blocks based on rigid-flexible rule driving, characterized in that: The following steps are involved: (1) Collect vector data of the target old block, text data of the design specifications and standards of the city where it is located, and image data of design plans for other blocks in the city; (2) Extract control terms and specific control contents from the design specifications and standard text data of the city, and use the extension primitive model method to perform the basic structured representation of "object element-event element-relation element"; From the association relationship of the statements, the primitive models are classified and stored according to the conjugate relationship, complementary relationship, constraint relationship and suggestion relationship, and a rule map of rigid industry norms for urban design is established; (3) Use topological graph structure and three-dimensional space to perform correlation calculations and extract professional knowledge rules in urban design space aesthetics; use Mask R-CNN encoding technology to automatically annotate the spatial structure, functional layout, road system, and blue-green system vector data of classic urban design results, form a knowledge map of classic urban design results rules, and establish a rule map guided by flexible professional knowledge of urban design; (4) Establish a multi-layer network association between the three-dimensional morphological graph network of old blocks, the rigid rule graph, and the flexible knowledge graph, so that the three-dimensional morphological graph network of old blocks is gradually iterated until the constraints of the rigid rule graph and the flexible knowledge graph are met, and the solution is output; (5) The optimized three-dimensional morphological scheme of the target old block is output to the spatial holographic sandbox, and the three-dimensional multi-scheme display and interactive optimization of the block are carried out, and finally the optimized three-dimensional morphological map network and visualization scheme of the old block are output.

2. The method for intelligently updating and optimizing the three-dimensional form of old blocks based on rigid-flexible rule driving according to claim 1 is characterized in that: The step (1) collects vector data of the target old block, text data of the design specifications and standards of the city where the target old block is located, and image data of design plans of other blocks in the city where the target old block is located, and specifically includes: According to the scope of the determined target old block, the block vector data of the city where the target old block is located is obtained through the OpenStreetMap open data platform, and the design specifications and standard text data of the city and the image data of other block design plans in the city are obtained through the Baidu open data platform; The block vector data of the city where the target old block is located includes plot data and building data in shapefile format; the plot data shp file should include land use type information, geographic coordinate information, plot area information and plot shape information; the building data shp file should include building function information, building number of floors information, building height information, building shape information and building location information; The design specifications and standard text data of the city in question, including urban land classification and planning and construction land standards, urban public facilities planning specifications, urban road traffic planning and design specifications, urban environmental sanitation facilities planning specifications, urban residential area planning and design specifications, urban engineering pipeline comprehensive planning specifications, urban environmental planning standards, urban water supply engineering design specifications, urban drainage engineering design specifications, urban road greening planning and design specifications, and target city planning technical standards and criteria; Image data of design plans for other blocks in the city, including design drawings for block stock renewal, environmental improvement of old blocks, optimization of public facilities and green space, and protection and renewal of the urban landscape.

3. The method for intelligently updating and optimizing the three-dimensional form of old blocks based on rigid-flexible rule driving according to claim 2 is characterized in that: In the step (2), control terms and specific control contents are extracted from the design specifications and standard text data of the city, and a basic structural representation of "matter element-event element-relation element" is performed using the extension primitive model method, which specifically includes: Use the collected design specifications and standard text data of the cities where the old blocks are located to extract control terms and specific control content, including road network control rules, open space control rules, height morphology control rules, and environment and facility control rules; The extensible primitive model method refers to the use of formal language to extract control terms and specific control contents from the design specifications and standard text data of the city, and its logical unit is the basic element, including object element, event element and relationship element; The matter-element M that describes things is expressed as: M=(O m ,c m ,in m ) Among them, O m Represents an object, c m Indicates the name of the feature, v m Indicates O m About c m the value of the quantity taken; The expression of event A describing the interaction between objects is: A=(O a ,c a ,in a ) Among them, O a Represents an object, c a Indicates the name of the feature, v a Indicates O a About c a the value of the quantity taken; The relational element R that describes the relationship between features is expressed as: Among them, O r Indicates the relationship, c r1 , c r2 , ...c rn represents n features, v r1 , v r2 ,...v rn Indicates the corresponding value.

4. The method for intelligently updating and optimizing the three-dimensional form of old blocks based on rigid-flexible rule driving according to claim 3 is characterized in that: In the step (2), the primitive models are classified and stored according to the conjugate relationship, complementary relationship, constraint relationship and suggestion relationship from the association relationship of the statements, and a rule map of rigid industry specifications for urban design is established, which specifically includes: Analyze the relationship between the object elements, primitive elements and relational elements that have been basically structured, and enter them into the NEO4J platform according to the four categories of conjugate relationship, complementary relationship, constraint relationship and suggestion relationship; use the NEO4J platform to digitally encode the rigid industry specifications of urban design that have been structured, use algorithms to visualize the primitive relationship and generate a rule map; The language constraint model is fine-tuned using large language models, including LLama-7B, chat-GLM, and Bloom.

5. The method for intelligently updating and optimizing the three-dimensional form of old blocks based on rigid-flexible rule driving according to claim 4 is characterized in that: In the step (3), the topological graph structure is used to perform correlation calculation with the three-dimensional space to extract professional knowledge rules in the urban design space aesthetics, which specifically includes: Aerial photos of urban space plans of classic urban charm areas around the world are collected as a sample library, and the aerial photos are segmented and recognized through semantic segmentation and recognition technology. The image elements are identified as five categories of elements: buildings, roads, squares, green spaces, and water systems, and abstracted into a topological graph structure composed of nodes and edges; the topological graph structure is processed using a deep learning model of a multi-level graph neural network, and the patterns and information in the graph are captured by learning the relationship between nodes, and the capture effect is improved through a hierarchical learning structure; the spatial agglomeration, openness, staggeredness and other quantitative indicators of the case blocks are calculated through spatial self-learning technology, and associated calculations are performed with the topological graph structure to extract professional knowledge rules in urban design space aesthetics and form a knowledge graph of spatial aesthetic rules.

6. The method for intelligently updating and optimizing the three-dimensional form of old blocks based on rigid-flexible rule driving according to claim 5 is characterized in that: In the step (3), the spatial structure, functional layout, road system, and blue-green system vector data of the design are automatically annotated using the Mask R-CNN encoding technology to form a knowledge graph of classic urban design results rules and establish a rule graph of flexible industry specifications for urban design, specifically including: Collect classic urban design achievements, use convolutional neural networks to identify design plan image data of the classic urban design achievements, extract them as two-dimensional vector graphic data of urban design plan, and use Mask R-CNN encoding technology to annotate the spatial structure, functional layout, road system, and blue-green system vector data of the design plan; wherein, the spatial structure vector data includes the geometric center of each functional core; the functional layout vector data includes the functional layout data of commerce, office, residence, industry, green space, public service and transportation; the road system vector data includes three-level vector information of main roads, secondary roads and branches; the blue-green system vector data includes vector data information of green corridors, secondary green corridors and water areas; reverse parse the master style of urban design in each classic work through knowledge graph technology to form a knowledge graph of classic design rules; combine the knowledge graph of spatial aesthetic rules to form a rule graph required by flexible industry specifications for urban design.

7. The method for intelligently updating and optimizing the three-dimensional form of old blocks based on rigid-flexible rule driving according to claim 6 is characterized in that: The step (4) of establishing a multi-layer network association of the old block three-dimensional morphological graph network, the rigid rule graph and the flexible knowledge graph specifically includes: Based on the vector data of the target old blocks, a three-dimensional morphological map network of the old blocks is established. Combined with the rule map required by the rigid industry specifications of urban design and the rule map required by the flexible industry specifications of urban design, the three-dimensional morphological map network of the old blocks is gradually iterated according to the site location of the design plan until the constraints of the rigid rule map and the flexible knowledge map are met, and the rule and knowledge parameter data of the potential urban design plan are output; on this basis, the vector data associated with the potential plan and the sample library are screened out, so as to establish an automatic association process between knowledge rules and physical space elements; wherein, the screening process adopts the gradient descent method, observes the fluctuation of the loss function of the convolution pooling layer of the model corresponding to each parameter during training, compares the training time and the generated results to determine the optimal learning rate and the number of iterations until the optimal, and finally establishes a multi-layer network association of the three-dimensional morphological map network of the old blocks, the rigid rule map and the flexible knowledge map.

8. The method for intelligently updating and optimizing the three-dimensional form of old blocks based on rigid-flexible rule driving according to claim 7 is characterized in that: The step (5) of outputting the optimized three-dimensional morphological network and visualization scheme of the old block specifically includes: The three-dimensional morphological schemes of the old blocks generated by the simulation iteration layer by layer and satisfying both the rigid rule graph and the flexible knowledge graph are output to the spatial holographic sandbox for display; the three-dimensional morphological schemes of the old blocks are displayed, compared, selected and modified with the assistance of VR\AR equipment and combined with gesture commands, and the local spatial structure, functional layout, road system and blue-green system are adjusted and interactively optimized before output in the form of a three-dimensional model file of SketchUp; the VR\AR equipment includes VRTRIX data gloves, VR glasses and VR 3D display stand.

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